Front Matter

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Wiley Series on Technologies for the Pharmaceutical Industry Sean Ekins, Series Editor

Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals Edited by Sean Ekins

Pharmaceutical Applications of Raman Spectroscopy Edited by Slobodan Šašic´

Pathway Analysis for Drug Discovery: Computational Infrastructure and Applications Edited by Anton Yuryev

Drug Eﬃcacy, Safety, and Biologics Discovery: Enmerging Technologies and Tools Edited by Sean Ekins and Jinghai J. Xu

The Engines of Hippocrates: From the Dawn of Medicine to Medical and Pharmaceutical Informatics Barry Robson and O.K. Baek

Pharmaceutical Data Mining: Applications for Drug Discovery Edited by Konstantin V. Balakin

The Agile Approach to Adaptive research: Optimizing Eﬃciency in Clinical Development Michael J. Rosenberg

Pharmaceutical and Biomedical Project Management in a Changing Global Environment, Edited by Scott D. Babler k Systems Biology in Drug Discovery and Development k Edited by Daniel L. Young and Seth Michelson

Collaborative Computational Technologies for Biomedical Research Edited by Sean Ekins, Maggie A.Z. Hupcey and Antony J. William

Predictive Approaches in Drug Discovery and Development: Biomarkers and In Vitro/ In Vivo correlations Edited by J. Andrew Williams, Richard Lalonde, Jeﬀrey Koup and David D. Christ

Collaborative Innovation in Drug Discovery, Strategies for Public and Private Partnerships Edited by Rathnam Chaguturu

Computational Toxicology: Risk Assessment for Chemicals Edited by Sean Ekins

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Computational Toxicology

Risk Assessment for Chemicals

Edited by

Sean Ekins k Collaborations Pharmaceuticals, Inc. Raleigh, USA k

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This edition first published 2018 © 2018 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Sean Ekins to be identified as the Editor in this work has been asserted in accordance with law. Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by k sales representatives, written sales materials or promotional statements for this work. The fact that an k organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data:

Names: Ekins, Sean, editor. Title: Computational toxicology : risk assessment for chemicals / edited by Sean Ekins. Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2018. | Series: Wiley series on technologies for the pharmaceutical industry | Includes bibliographical references and index. | Identiﬁers: LCCN 2017037714 (print) | LCCN 2017046458 (ebook) | ISBN 9781119282570 (pdf) | ISBN 9781119282587 (epub) | ISBN 9781119282563 (cloth) Subjects: LCSH: Toxicology–Mathematical models. | Toxicology–Computer simulation. | QSAR (Biochemistry) Classiﬁcation: LCC RA1199.4.M37 (ebook) | LCC RA1199.4.M37 C66 2018 (print) | DDC 615.90285–dc23 LC record available at https://lccn.loc.gov/2017037714 Cover Design: Wiley Cover Images: (Front cover) Courtesy of Daniela Schuster; (Author photo) Courtesy of Sean Ekins Set in 10/12pt WarnockPro by SPi Global, Chennai, India

10987654321

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I should have no objection to go over the same life from its beginning to the end: requesting only the advantage authors have, of correcting in a second edition the faults of the ﬁrst.

Benjamin Franklin

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To my family and collaborators.

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Contents

List of Contributors xvii Preface xxi Acknowledgments xxiii

Part I Computational Methods 1

1 Accessible Machine Learning Approaches for Toxicology 3 k Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, k Alexandru Korotcov, and Valery Tkachenko 1.1 Introduction 3 1.2 Bayesian Models 5 1.2.1 CDD Models 7 1.3 Deep Learning Models 13 1.4 Comparison of Diﬀerent Machine Learning Methods 16 1.4.1 Classic Machine Learning Methods 17 1.4.1.1 Bernoulli Naive Bayes 17 1.4.1.2 Linear Logistic Regression with Regularization 18 1.4.1.3 AdaBoost Decision Tree 18 1.4.1.4 Random Forest 18 1.4.1.5 Support Vector Machine 19 1.4.2 Deep Neural Networks 19 1.4.3 Comparing Models 20 1.5 Future Work 21 Acknowledgments 21 References 21

2 Quantum Mechanics Approaches in Computational Toxicology 31 Jakub Kostal 2.1 Translating Computational Chemistry to Predictive Toxicology 31

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2.2 Levels of Theory in Quantum Mechanical Calculations 33 2.3 Representing Molecular Orbitals 38 2.4 Hybrid Quantum and Molecular Mechanical Calculations 39 2.5 Representing System Dynamics 40 2.6 Developing QM Descriptors 42 2.6.1 Global Electronic Parameters 42 2.6.1.1 Electrostatic Potential, Dipole, and Polarizability 43 2.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 45 2.6.2 Local (Atom-Based) Electronic Parameters 47 2.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 48 2.6.2.2 Partial Atomic Charges 51 2.6.2.3 Hydrogen-Bonding Interactions 51 2.6.2.4 Bond Enthalpies 53 2.6.3 Modeling Chemical Reactions 53 2.6.4 QM/MM Calculations of Covalent Host-Guest Interactions 56 2.6.5 Medium Eﬀects and Hydration Models 59 2.7 Rational Design of Safer Chemicals 61 References 64 k k Part II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 69

3 Computational Approaches for Predicting hERG Activity 71 Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade 3.1 Introduction 71 3.2 Computational Approaches 73 3.3 Ligand-Based Approaches 73 3.4 Structure-Based Approaches 77 3.5 Applications to Predict hERG Blockage 77 3.5.1 Pred-hERG Web App 79 3.6 Other Computational Approaches Related to hERG Liability 82 3.7 Final Remarks 83 References 83

4 Computational Toxicology for Traditional Chinese Medicine 93 Ni Ai and Xiaohui Fan 4.1 Background, Current Status, and Challenges 93 4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 99

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4.2.1 Introduction to OAT1 and TCM 99 4.2.2 Construction of TCM Compound Database 101 4.2.3 OAT1 Inhibitor Pharmacophore Development 101 4.2.4 External Test Set Evaluation 102 4.2.5 Database Searching 102 4.2.6 Results: OAT1 Inhibitor Pharmacophore 103 4.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 104 4.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 104 4.2.9 Discussion 110 4.3 Conclusion 114 Acknowledgment 114 References 114

5 Pharmacophore Models for Toxicology Prediction 121 Daniela Schuster 5.1 Introduction 121 5.2 Antitarget Screening 125 5.3 Prediction of Liver Toxicity 125 5.4 Prediction of Cardiovascular Toxicity 127 k 5.5 Prediction of Central Nervous System (CNS) Toxicity 128 k 5.6 Prediction of Endocrine Disruption 130 5.7 Prediction of ADME 135 5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136 References 137

6 Transporters in Hepatotoxicity 145 Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker 6.1 Introduction 145 6.2 Basolateral Transporters 146 6.3 Canalicular Transporters 148 6.4 Data Sources for Transporters in Hepatotoxicity 148 6.5 In Silico Transporters Models 150 6.6 Ligand-Based Approaches 150 6.7 OATP1B1 and OATP1B3 150 6.8 NTCP 154 6.9 OCT1 154 6.10 OCT2 154 6.11 MRP1, MRP3, and MRP4 155 6.12 BSEP 155 6.13 MRP2 156 6.14 MDR1/P-gp 156

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6.15 MDR3 157 6.16 BCRP 157 6.17 MATE1 158 6.18 ASBT 159 6.19 Structure-Based Approaches 159 6.20 Complex Models Incorporating Transporter Information 160 6.21 In Vitro Models 160 6.22 Multiscale Models 161 6.23 Outlook 162 Acknowledgments 164 References 164

7 Cheminformatics in a Clinical Setting 175 Matthew D. Krasowski and Sean Ekins 7.1 Introduction 175 7.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 177 7.3 Similarity Analysis Applied to Therapeutic Drug Monitoring Immunoassays 187 7.4 Similarity Analysis Applied to Steroid Hormone k Immunoassays 191 k 7.5 Cheminformatics Applied to "Designer Drugs" 195 7.6 Relevance to Antibody-Ligand Interactions 202 7.7 Conclusions and Future Directions 202 Acknowledgment 203 References 204

Part III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 211

8 Computational Tools for ADMET Profiling 213 Denis Fourches, Antony J. Williams, Grace Patlewicz, Imran Shah, Chris Grulke, John Wambaugh, Ann Richard, and Alexander Tropsha 8.1 Introduction 213 8.2 Cheminformatics Approaches for ADMET Profiling 214 8.2.1 Chemical Data Curation Prior to ADMET Modeling 215 8.2.2 QSAR Modelability Index 217 8.2.3 Predictive QSAR Model Development Workflow 218 8.2.4 Hybrid QSAR Modeling 220 8.2.4.1 Simple Consensus 223 8.2.4.2 Mixed Chemical and Biological Features 223

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8.2.4.3 Two-Step Hierarchical Workflow 224 8.2.5 Chemical Biological Read-Across 226 8.2.6 Public Chemotype Approach to Data-Mining 229 8.3 Unsolved Challenges in Structure Based Profiling 230 8.3.1 Biological Data Curation 231 8.3.2 Identification and Treatment of Activity and Toxicity Cliffs 233 8.3.3 In Vitro to In Vivo Continuum in the Context of AOP 233 8.4 Perspectives 234 8.4.1 Profilers on the Go with Mobile Devices 235 8.4.2 Structure–Exposure–Activity Relationships 236 8.5 Conclusions 237 Acknowledgments 237 Disclaimer 237 References 238

9 Computational Toxicology and Reach 245 Emilio Benfenati, Anna Lombardo, and Alessandra Roncaglioni 9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 245 9.2 Reach and the Other Legislations 247 k 9.3 AnnexXIofReachforQSARModels 248 k 9.3.1 The First Condition of Annex XI and QMRF 249 9.3.2 The Second Condition and the Applicability Domain 251 9.3.3 The Third Condition of Annex XI, and the Use of the QSAR Models 252 9.3.4 Adequate and Reliable Documentation of the Applied Method 254 9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 255 9.4.1 Example of Bioconcentration Factor (BCF) 255 9.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction 260 9.5 Conclusions 266 References 266

10 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269 Jim E. Riviere and Jason Chittenden 10.1 Introduction 269 10.2 Principles of Dermal Absorption 270 10.3 Dermal Mixtures 274 10.4 Model Systems 275 10.5 Local Skin Versus Systemic Endpoints 277 10.6 QSAR Approaches to Model Dermal Absorption 278

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10.7 Pharmacokinetic Models 281 10.8 Conclusions 284 References 285

Part IV New Technologies for Toxicology, Future Perspectives 291

11 Big Data in Computational Toxicology: Challenges and Opportunities 293 Linlin Zhao and Hao Zhu 11.1 Big Data Scenario of Computational Toxicology 293 11.2 Fast-Growing Chemical Toxicity Data 295 11.3 The Use of Big Data Approaches in Modern Computational Toxicology 299 11.3.1 Proﬁling the Toxicants with Massive Biological Data 299 11.3.2 Read-Across Study to Fill Data Gap 301 11.3.3 Unstructured Data Curation 302 11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303 k References 304 k

12 HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling 313 George van Den Driessche and Denis Fourches 12.1 Introduction 313 12.2 Human Leukocyte Antigens 314 12.2.1 HLA Proteins 314 12.2.2 ADR–HLA Associations 316 12.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms 321 12.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 322 12.3.1 Structure-Based Docking 324 12.3.2 Case Study: Abacavir with B*57:01 326 12.3.3 Limitations 332 12.4 Perspectives 334 References 335

13 Open Science Data Repository for Toxicology 341 Valery Tkachenko, Richard Zakharov, and Sean Ekins 13.1 Introduction 341 13.2 Open Science Data Repository 342 13.3 Beneﬁts of OSDR 344

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13.3.1 Chemically and Semantically Enabled Scientific Data Repository 344 13.3.2 Chemical Validation and Standardization Platform 346 13.3.3 Format Adapters 347 13.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 350 13.3.5 Dataledger 350 13.4 Technical Details 351 13.5 Future Work 353 13.5.1 Implementation of Ontology-Based Properties 356 13.5.2 Implementation of an Advanced Search System 357 13.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357 References 358

14 Developing Next Generation Tools for Computational Toxicology 363 Alex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins 14.1 Introduction 363 14.2 Developing Apps for Chemistry 364 k 14.3 Green Chemistry 364 k 14.3.1 Green Solvents and Lab Solvents 367 14.3.2 Green Lab Notebook 370 14.4 Polypharma and Assay Central 374 14.4.1 Future Eﬀorts with Assay Central 380 14.5 Conclusion 382 Acknowledgments 383 References 383

Index 389

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List of Contributors

Ni Ai Jason Chittenden Pharmaceutical Informatics Institute Center for Chemical Toxicology College of Pharmaceutical Sciences Research and Pharmacokinetics Zhejiang University Biomathematics Program Hangzhou North Carolina State University Zhejiang, PR Raleigh, NC China USA

Vinicius M. Alves Alex M. Clark k k LabMol – Laboratory for Molecular Molecular Materials Informatics, Inc. Modeling and Design, Faculty of Montreal, Quebec Pharmacy Canada Federal University of Goias Goiania, GO Daniela Digles Brazil Department of Pharmaceutical Chemistry Carolina Horta Andrade University of Vienna LabMol – Laboratory for Molecular Wien Modeling and Design, Faculty of Austria Pharmacy Federal University of Goias George van Den Driessche Goiania, GO Department of Chemistry Brazil Bioinformatics Research Center North Carolina State University Rodolpho C. Braga Raleigh, NC LabMol – Laboratory for Molecular USA Modeling and Design, Faculty of Pharmacy Federal University of Goias Goiania, GO Brazil

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Gerhard F. Ecker and Department of Pharmaceutical Chemistry Division of Infectious Disease University of Vienna Department of Medicine and the Ruy Wien V. Lourenço Center for the Study of Austria Emerging and Re-emerging Pathogens Sean Ekins New Jersey Medical School, Rutgers Collaborations Pharmaceuticals, Inc. University Raleigh, NC Newark, NJ USA USA

Emilio Benfenati Chris Grulke IRCCS – Istituto di Ricerche National Center for Computational Farmacologiche “Mario Negri” Toxicology, Oﬃce of Research and Laboratory of Environmental Development Chemistry and Toxicology U.S. Environmental Protection Milan Agency Italy Research Triangle Park Durham, NC k Xiaohui Fan USA k Pharmaceutical Informatics Institute College of Pharmaceutical Sciences Sankalp Jain Zhejiang University Department of Pharmaceutical Hangzhou Chemistry Zhejiang, PR University of Vienna China Wien Austria Denis Fourches Department of Chemistry Alexandru Korotcov Bioinformatics Research Center Gaithersburg, MD North Carolina State University USA Raleigh, NC USA Jakub Kostal Chemistry Department Joel S. Freundlich The George Washington University Department of Pharmacology & Washington DC Physiology USA New Jersey Medical School Rutgers University Newark, NJ USA

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List of Contributors xix

Eleni Kotsampasakou Ann Richard Department of Pharmaceutical National Center for Computational Chemistry Toxicology, Oﬃce of Research and University of Vienna Development Wien U.S. Environmental Protection Austria Agency Research Triangle Park Matthew D. Krasowski Durham, NC Department of Pathology USA University of Iowa Hospitals and Clinics Jim E. Riviere Iowa City, IA Center for Chemical Toxicology USA Research and Pharmacokinetics Biomathematics Program Mary A. Lingerfelt North Carolina State University Collaborations Pharmaceuticals, Inc. Raleigh, NC Raleigh, NC USA USA

Anna Lombardo Alessandra Roncaglioni IRCCS – Istituto di Ricerche IRCCS – Istituto di Ricerche k Farmacologiche “Mario Negri” Farmacologiche “Mario Negri” k Laboratory of Environmental Laboratory of Environmental Chemistry and Toxicology Chemistry and Toxicology Milan Milan Italy Italy

Grace Patlewicz Daniela Schuster National Center for Computational Institute of Toxicology, Oﬃce of Research and Pharmacy/Pharmaceutical Development Chemistry U.S. Environmental Protection University of Innsbruck Agency Innsbruck Research Triangle Park Austria Durham, NC USA Imran Shah National Center for Computational Alexander L. Perryman Toxicology, Oﬃce of Research and Department of Pharmacology & Development Physiology U.S. Environmental Protection New Jersey Medical School Agency Rutgers University Research Triangle Park Newark, NJ Durham, NC USA USA

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Valery Tkachenko Richard Zakharov Rockville, MD Rockville, MD USA USA

Alexander Tropsha Linlin Zhao UNC Eshelman School of Pharmacy Center for Computational and University of North Carolina at Integrative Biology Chapel Hill Rutgers University Chapel Hill, NC Camden, NJ USA USA

John Wambaugh Hao Zhu National Center for Computational Center for Computational and Toxicology, Oﬃce of Research and Integrative Biology Development Rutgers University U.S. Environmental Protection Camden, NJ Agency USA Research Triangle Park Durham, NC and USA k k Antony J. Williams Department of Chemistry National Center for Computational Rutgers University Camden, NJ Toxicology, Oﬃce of Research and USA Development U.S. Environmental Protection Agency Kimberley M. Zorn Research Triangle Park Collaborations Pharmaceuticals, Inc. Durham, NC Raleigh, NC USA USA

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Preface

Since the publication of Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals in 2007 a lot has happened both in the career of the editor and in science in general. For one, my focus has expanded towards many computational applications to drug discovery rather than solely focused on ADME/Tox. I have also garnered new collaborators some of whom have very graciously agreed to contribute to this volume. Science is changing. Publishing may be adjusting slowly too. This book will likely be read as much on mobile devices or computers as in k physical hard copies. Computational toxicology has also evolved in the past k decade with the dramatic increase in public data availability. There have also been a number of more collaborative projects in Europe around toxicology (e.g. e-Tox and OpenTox), in addition we have seen a growth in open computational tools and model sharing (QSAR toolbox, Chembench, CDD, Bioclipse etc.). Groups like the EPA have developed and expanded ToxCast which represents a valuable resource for toxicology modeling. We are now therefore in the age of truly Big Data compared with a decade ago and there have been several efforts to combine different types of data for toxicology. To round this off, the growth in nanotechnology has seen the emergence of computational nanotoxicology which would not have been predicted my earlier book. This book is therefore aimed at this next generation of computational toxicology scientist, comprehensively discussing the state-of-the-art of currently available molecular-modelling tools and the role of these in testing strategies for different types of toxicity. The overall role of these computational approaches in addressing environmental and occupational toxicity is also covered. These chapters before you aim to describe topics in an accessible manner especially for those who are not experts in the field. My goal with this book was to not cover too much of the same ground as the earlier book because much of what we published then is still generally valid, but to make the book focused on newer topics. I hope this book also serves to introduce some of the younger scientists from around the world who will likely drive this next generation of computational toxicology for many years to come. Finally, I hope this book inspires

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scientists to pursue computational toxicology so that it continues to expand across diﬀerent industries from pharmaceutical to consumer products and its importance increases, as it has over the past decade.

Sean Ekins November 12, 2017 Fuquay Varina, NC, USA

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xxiii

Acknowledgments

I am extremely grateful to Jonathan Rose and colleagues at Wiley for their assis- tance and considerable patience. My proposal reviewers are gratefully acknowledged for their many suggestions which helped shape this. I would like to acknowledge my many collaborators over the years whoseworkinsomecaseshasbeenmentionedhere.Inparticular,DrJoel S. Freundlich, Dr Antony J. Williams, Dr Alex M. Clark, Dr Matthew D. Krasowski, Dr Carolina H. Andrade, and many others. I am also grateful for the support of SC Johnson who have kept me challenged and engaged with k new applications for computational toxicology over the years. I would also like k to acknowledge Dr Daniela Schuster for the kind use of her graphic for the book cover. This book would not have been possible without the support of Dr Maggie A.Z. Hupcey and my family who have tolerated late nights, and frequent disap- pearances to the library to write over the holidays.

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Part I

Computational Methods

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Accessible Machine Learning Approaches for Toxicology Sean Ekins1, Alex M. Clark2, Alexander L. Perryman3, Joel S. Freundlich3,4, Alexandru Korotcov5, and Valery Tkachenko6

1Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA 2Molecular Materials Informatics, Inc., Montreal, Quebec, Canada 3Department of Pharmacology & Physiology, New Jersey Medical School, Rutgers University, Newark, NJ, USA 4Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, New Jersey Medical School, Rutgers University, Newark, NJ, USA 5 Gaithersburg, MD, USA 6 Rockville, MD, USA k k CHAPTER MENU

Introduction, 3 Bayesian Models, 5 Deep Learning Models, 13 Comparison of Diﬀerent Machine Learning Methods, 16 Future Work, 21

1.1 Introduction

Computational approaches have in recent years played an increasingly important role in the drug discovery process within large pharmaceutical ﬁrms. Virtual screening of compounds using ligand-based and structure-based methods to predict potency enables more eﬃcient utilization of high throughput screening (HTS) resources, by enriching the set of compounds physically screened with those more likely to yield hits [1–4]. Computation of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties exploiting statistical techniques greatly reduces the number of expensive assays that must be performed, now making it practical to consider these factors very early in the discovery process to minimize late-stage failures of potent lead compounds that are not drug-like [5–11]. Large pharma have successfully

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integrated these in silico methods into operational practice, validated them, and then realized their benefits, because these firms have (i) expensive commercial software to build models, (ii) large, diverse proprietary datasets based on consistent experimental protocols to train and test the models, and (iii) staff with extensive computational and medicinal chemistry expertise to run the models and interpret the results. Drug discovery efforts centered in universities, foundations, government laboratories, and small biotechnology companies, however, generally lack these three critical resources and, as a result, have yet to exploit the full benefits of in silico methods. For close to a decade, we have aimed to used machine learning approaches and have evaluated how we could circumvent these limitations so that others can benefit from current and emerging best industry practices. The current practice in pharma is to integrate in silico predictions into a combined workflow together with in vitro assays to find “hits” that can then be reconfirmed and optimized [12]. The incremental cost of a virtual screen is minimal, and the savings compared with a physical screen are magnified if the compound would also need to be synthesized rather than purchased from a vendor. Imagine if the blind hit rate against some library is 1%, and the in silico model can pre-filter the library to give an experimental hit rate of 2%, then significant resources are freed up to focus on other promising regions of chem- k ical property space [13]. Our past pharmaceuticals collaborations [14, 15] have k suggested that computational approaches are critical to making drug discovery more efficient. The relatively high cost of in vivo and in vitro screening of ADME and toxicity properties of molecules has motivated our efforts to develop in silico methods to filter and select a subset of compounds for testing. By relying on very large, internally consistent datasets, large pharma has succeeded in developing highly predictive proprietary models [5–8]. At Pfizer (and probably other companies), for example, many of these models (e.g., those that predict the volume of distribution, aqueous kinetic solubility, acid dissociation constant, and distribution coefficient) [5–8, 16] are believed (according to discussions with scientists) to be so accurate that they have essentially put experimental assays out of business. In most other cases, large pharma perform experimental assays for a small fraction of compounds of interest to augment or validate their computational models. Efforts by smaller pharma and academia have not been as successful, largely because they have, by necessity, drawn upon much smaller datasets and, in a few cases, tried to combine them [11, 17–22]. However, this is changing rapidly, and public datasets in PubChem, ChEMBL, Collaborative Drug Discovery (CDD) and elsewhere are becoming available for ADME/Tox properties. For example, the CDD public database has >100 public datasets that can be used to generate community-based models, including extensive neglected infectious disease structure–activity relationship (SAR) datasets (malaria, tuberculosis, Chagas disease, etc.), and ADMEdata.com

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Accessible Machine Learning Approaches for Toxicology 5

datasets that are broadly applicable to many projects. Recent efforts with them have led to a platform that enables drug discovery projects to benefit from open source machine learning algorithms and descriptors in a secure environment, which allows models to be shared with collaborators or made accessible to the community. In the area of pharmaceutical research and development and specifically that of cheminformatics, there are many machine learning methods, such as support vector machines (SVM), k-nearest neighbors, naïve Bayesian, and decision trees, [23] which have seen increasing use as our datasets, have grown to become “big data” [24–27]. These methods [23] can be used for binary classification, multiple classes, or continuous data. In more recent years, the biological data amassed from HTS and high content screens has called for different tools to be used that can account for some of the issues with this bigger data [26]. Many of these resulting machine learning models can also be implemented on a mobile phone [28, 29].

1.2 Bayesian Models

Our machine learning experience over a decade [14, 30–46] has focused on k Bayesian approaches (Figure 1.1). Bayesian models classify data as active or k inactive on the basis of user-defined thresholds using a simple probabilistic classification model based on Bayes’ theorem. We initially used the Bayesian modeling software within the Pipeline Pilot and Discovery Studio (BIOVIA) with many ADME/Tox and drug discovery datasets. Most of these models have used molecular function class fingerprints of maximum diameter 6 and several other simple descriptors [47, 48]. The models were internally validated through the generation of receiver operator characteristic (ROC) plots. We have also compared single- and dual-event Bayesian models utilizing published screening data [49, 50]. As an example, the single-event models use only whole-cell antitubercular activity, either at a single compound concentration

or as a dose–response IC50 or IC90 (amount of compound inhibiting 50% or 90% of growth, respectively), while the dual-event models also use a selectivity

index (SI = CC50/IC90,whereCC50 is the compound concentration that is cytotoxic and inhibits 50% of the growth of Vero cells). While single-event models [13, 51, 52] are widely published, dual-event models [53] attempt to predict active compounds with acceptable relative activity against the pathogen (in this case, Mtb), versus the model mammalian cell line (e.g., Vero cells). Our models identiﬁed 4–10 times more active compounds than random screening did and the models also had relatively high hit rates, for example, 14% [54], 71% (Figure 1.1) [53], or intermediate [55] for Mtb. Recent machine learning work on Chagas disease has identiﬁed in vivo active compounds [56], one of which is an approved antimalarial in Europe. Most recently, we

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Figure 1.1 Summary of machine learning models generated for Mycobacterium tuberculosis in vitro data. This approach has also been applied to ADME/Tox datasets.

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Accessible Machine Learning Approaches for Toxicology 7

have been actively constructing Bayesian models for ADME properties such as aqueous solubility, mouse liver microsomal stability [57], and Caco-2 cell permeability [30], which complement our earlier ADME/Tox machine learning work [13, 52, 58–64]. We have also summarized the application of these methods to toxicology datasets [58] and transporters [34, 59, 62, 63, 65–67]. This has led to models with generally good to acceptable ROC scores > 0.7 [30]. Open source implementation of the ECFP6/FCFP6 ﬁngerprints [28] and Bayesian model building module [25, 30] has also enabled their use in new software implementations (see later). We are keen to explore machine learning algorithms and make them accessible for seeding drug discovery projects, as we have demonstrated.

1.2.1 CDD Models ADME properties have been modeled by us with collaborators [30] and others using an array of machine learning algorithms, such as SVMs [68], Bayesian modeling [69], Gaussian processes [70], or others [71]. A major challenge remains the ability to share such models. CDD has developed and marketed a robust, innovative commercial software platform that enables scientists to archive, mine, and (optionally) share SAR, ADME/Tox, and other types of k preclinical research data [72]. CDD hosts the software and customers’ data k vaults on its secure servers. CDD collaborated with computational chemists at Pfizer in a proof of concept study. This demonstrated that models constructed with open descriptors and keys (chemical development kit, CDK + SMARTS) using open software (C5.0 - once built, models can be made open) performed essentially identically to expensive proprietary descriptors and models (MOE2D + SMARTS + Rulequest’s Cubist) across all metrics of performance when evaluated on multiple Pfizer-proprietary ADME datasets: human liver microsomal (HLM) stability, RRCK passive permeability, P-gp efflux, and aqueous solubility [14]. Pfizer’s HLM dataset, for example, contained more than 230,000 compounds and covered a diverse range of chemistry, as well as many therapeutic areas. The HLM dataset was split into a training set (80%) and a test set (20%) using the venetian blind splitting method; in addition, a newly screened set of 2310 compounds was evaluated as a blind dataset. All the key metrics of model performance - for example, R2, root-mean-square error (RMSE), kappa, sensitivity, specificity, positive predictive value (PPV) - were nearly identical for the open source approach versus the proprietary software (e.g., PPV of 0.80 vs 0.82). The open source approach even computed slightly faster (0.2 vs 0.3 s/compound). All the datasets studied yielded the same conclusion, that is, models built with open descriptors and models are as predictive as the commercial tools [14]. This result is an important prerequisite for a goal of creating a machine learning model exchange platform that can be deployed without requiring

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licenses for other software or algorithms, which would otherwise make it too expensive to achieve widespread adoption [73, 74]. This preliminary study did not directly address the issue of whether the descriptors mask the underlying data sufficiently well that structure identities cannot be reverse-engineered, but others have begun to assess this question with respect to an array of molecular descriptor types [75] and open source descriptors and models could be used in any other software (GLP license). Compared to the large datasets available in pharma, there are few that are freely available. Jean Claude Bradley, Andrew Lang, and Antony Williams have, however, provided a curated dataset of melting points for the community using several open data sources, which was then used for modeling. A training set comprising 2205 compounds and a test set of 500 compounds with doubly validated melting points were used with 132 Open CDK [76] descriptors and the RandomForest package (v4.5-34) in R. The resulting RandomForest model had an RMSE of 40.9 ∘CandanR2 value of 0.82 when used to predict the test set. We then compared these results to what could be obtained in the commercial SAS JMP (v8.0.1, SAS, Cary, NC) and Discovery Studio (v2.5.5. San Diego, CA). A neural network model in SAS had an RMSE of 48.5 ∘CandanR2 value of 0.75. In comparison, a backpropagation neural network model in Discov- ery Studio had an RMSE of 40.8 ∘CandanR2 valueof0.83forthesametestset. k These melting point models are all superior to 17 models identified in 10 papers k between 2003 and 2011 using commercial and other tools [77]. The results also suggested that open descriptors and algorithms can produce models that are comparable to those generated with commercial tools. Similarly, we have curated PubChem BioAssay data on mouse liver microsomal (MLM) stability. Our curated training set with MLM half-life values on 894 compounds (from a compilation of 99 different sets of assay results), our external test set with MLM half-life values on 30 antitubercular compounds, and our independent, external validation set with percentage that compounds the remaining data on 571 compounds (from combining 78 different sets of assay results) are all freely available as sdf files in the supplementary material [57]. We hypothesized that when constructing a binary classifier model, the moderately stable/moderately unstable compounds might generate confusion or even disinformation during the machine learning process. Consequently, we proposed that a novel data “pruning” strategy should be investigated: the conventional, or “full,” model was constructed using a training set in which stable ≥ compounds were defined as having a t1/2 60 min and unstable compounds < had a t1/2 60 min, while the new “pruned” model had a training set that used ≥ the same stable compounds with a t1/2 60 min, but only the compounds with < a t1/2 30 min were used as unstable compounds. Compounds with a half-life between 30 and 59.4 min were simply deleted from the full training set in order to create the pruned training set. The pruned MLM Bayesian model displayed superior predictive power versus the full model (in terms of internal

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Accessible Machine Learning Approaches for Toxicology 9

and external statistics, as well as histogram-based analyses), even though less information was used to train the pruned model [57]. Since then, we have continued to explore our novel data pruning strategy when constructing Bayesian models to predict other types of properties: in some cases, the pruned models are significantly more accurate, while in one case, the pruning process did not improve predictive power (but it did not substantially degrade performance, either). Pruning is a simple protocol but perhaps a counterintuitive notion (i.e., the machine can learn more by teaching it with less data). Our results thus far indicate that this pruning strategy merits further investigation. We have recently integrated validated computational models for ADME/Tox and physicochemical properties, for example, human metabolic stability, Caco-2 permeability, protein binding, solubility, melting point, hERG, pregnane X receptor (PXR), cytotoxicity, CYP3A4 inhibition, CYP2D6 inhibition, CYP2C9 inhibition, drug induced liver injury (DILI) [52], and P-gp (and other transporters) [34, 63, 66, 67]. NCGC and others have generated large, open or published datasets for Cytochrome P450’s, PXR, hERG [78], aggregation, [79] and so on, which can also be used for modeling, although the structures used may need additional curation based on our recent findings that lead us to question the structure quality [80, 81]. Molecule quality could adversely affect computational models, so it will be important to run these through new k tools for structure assessment, such as those available in ChemSpider, among k others [82]. One of the key reasons for using open source tool kits is that this will allow big pharma companies to share their models with outside groups more readily, whereas different vendor tools for building models are generally incompatible. We will now provide some additional detail to justify why we think it is important to put considerable effort into building this model-sharing capability and community. In this case, we considered how models could be shared and the outputs visualized. In general, the quality of model scales with leave-one-out or fivefold cross-validation ROC (values > 0.7 to 0.8 would be ideal). Using models with ROC > 0.7, we have demonstrated that these models can reliably rank molecules such that the users can either take the top N% of compounds or use medicinal chemistry intuition to filter them, with essentially the same hit rates observed [53, 54, 56, 83]. A number of modeling projects in recent years have successfully made use of the extended connectivity fingerprints, commonly referred to as ECFP_n or FCFP_n (n = 2, 4, or 6, etc.). For example, we have amassed experience in applying the FCFP_6 descriptors to modeling phenotypic HTS data for Mtb and other datasets. These fingerprints are created by enumerating a collection of substructures using breadth-first expansion from a starting atom. The fingerprint method was originally made available as part of the Pipeline Pilot project and similar methods have been made available from ChemAxon’s proprietary JChem and RDKit. The Accelrys fingerprint methodology used by us in all our

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10 Computational Toxicology

previous modeling work was published in detail, but the disclosure omitted a number of trade secrets, which means that while it is now straightforward to implement an algorithm that generates fingerprints that are similarly effective, it is not possible to produce results that can be directly comparable between the two different implementations. We therefore created a drop-in replacement for the ECFP_6 fingerprints that can be readily ported between multiple toolkits and programming languages. We have thus built and validated an algorithm that follows the published references for ECFP and FCFP fingerprints as closely as possible, and we made theresultingcodeavailabletothepublicasafeatureintheCDKprojectunder an open source license. We have evaluated the ROC of models built previously in the literature and with our own Bayesian and open source descriptors and found them to be near identical. While this is in itself a valuable addition to the popular Java-based toolkit, we have taken care to implement the algorithm in a concise manner with few external dependencies. Avoiding toolkit-specific supporting algorithms has allowed us to port the ECFP_6 algorithm to other platforms. As part of the model building software, we have initially opted for the Bayesian algorithm, as we found little difference between the Bayesian, SVM, and recursive partitioning algorithms when tested on external datasets or using internal cross-validation. k We have coded the software and implemented a version of CDD mod- k els. The source code for the Bayes model is open source (MIT license), https://github.com/cdd/modified-bayes. Creating a model requires two sets of molecules to train the model: the “good or active” molecules and a previously screened training set. CDD Vault uses the FCFP_6 structural fingerprints to build a Bayesian statistical model. The model then generates a score that can be used to rank compounds that have not yet been screened. The model is stored as a special type of protocol (category = quantitative structure–activity relationship (QSAR) model), and it provides an ROC plot, so its effectiveness can be gauged. ROC curves are graphic representations of the relationship existing between the sensitivity (i.e., the true positive rate on the y-axis) and the specificity (i.e., the false positive rate on the x-axis) of a statistical test. It is generated by plotting the fraction of true positives out of the total number of actual positives (sensitivity) versus the fraction of false positives out of the total actual negatives (1 − specificity). Each molecule receives a relative score, applicability number, and maximum similarity number. The model will automatically score all compounds in the project that is selected, while creating it. It can subsequently be shared with other projects to score more molecules. A naïve Bayesian model is optimized for sparse datasets. The learned models are created with a straightforward learn-by-example paradigm: give it a set of hit compounds (the “good” samples), and the system learns to distinguish them from other baseline data. The learning process generates a large set of Boolean

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Accessible Machine Learning Approaches for Toxicology 11

features from the input FCFP_6 fingerprints, then collects the frequency of occurrence of each feature in the “good” subset and in all data samples. To apply the model to a particular compound, the features of the compound are generated and a weight is calculated for each feature using a Laplacian-adjusted probability estimate. The model reports a score, which is calculated by normal- izing the probability, taking the natural log, and summing the results. This score is a relative predictor of the likelihood of that sample being from the “good” subset: the higher the score, the higher the likelihood. Once trained, the model can be applied to a set of compounds whose activity is unknown, and it provides a score whose value gives a prediction of the likelihood that the molecule will be a hit in the modeled protocol. To get an idea of the range of scores, the user can sort the score column by clicking on the header in the search results table. By clicking again one can sort from the highest number to the lowest. Now that the user has an idea of the range of possible scores, the molecules can be filtered to show only high values. The Applicability score is the fraction of structural features that a particular compound shared with the entire training set of molecules. Maximum Tanimoto/Jaccard similarity to any of the “good” molecules in the training set is also calculated. This value is independent of the Bayesian model, and it provides a way to perform a similarity search that compares it to all of the active k compounds at once. It is also a way to identify whether a compound was in the k training set for the model, in which case, the similarity value is equal to 1. We have described the testing of this software using datasets for malaria, tuberculosis, cholera, Ames mutagenicity, mouse intrinsic clearance, human intrinsic clearance, Caco-2 cell permeability, 5-HT2B, solubility, PXR activation, maximum recommended therapeutic dose, and blood-brain barrier permeability. In most cases, the threefold cross-validation ROC values are greater than 0.75. The ROC values were comparable to models previously published by us using the commercial descriptors and Bayesian algorithm. In addition tomakingthetechnologiesopensource,wehavealsodescribedhowthe models can be built and implemented in a mobile app called mobile molecular datasheet (MMDS) (Figure 1.2). Models for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas disease, tuberculosis, and malaria were created and also made open source (http://molsync.com/bayesian1). As a follow-up to this work, (and not using the CDD platform), we have now undertaken a large-scale validation study [25] in order to ensure that the Bayesian modeling technique generalizes to a broad variety of drug discovery datasets and the open source software can be used in different scenarios. Most recently, we have been involved in developing semiquantitative Bayesian models and making these open source, as well [84]. These efforts would suggest that a modeling ecosystem can be created, with multiple software being able to use the open source descriptors and algorithms, so that a consistent model format is achieved.

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k k

Figure 1.2 Example of Bayesian models implemented in MMDS. (See color plate section for the color representation of this ﬁgure.)

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Accessible Machine Learning Approaches for Toxicology 13

1.3 Deep Learning Models

In recent years, there has been increasing use of an approach called deep learning (DL), which builds on many years of artificial neural network research [85] and which has shown powerful advantages in learning from images and languages [86]. This may represent the next era of cheminformatics and pharmaceutical research in general, which is focused on mining the heterogeneous big data that is accumulating, using more sophisticated algorithms such as DL. Widely described artificial neural networks (ANN) approaches use an input layer, hidden layer, and output layer (Figure 1.3a), where each connection has a weight, and these vary during training in order to connect input to output data.Thismethodhasbeenusedextensively,butitsuffersfromoverfittingof data and a poor ability to generalize with an external dataset [23], although more recent versions such as Bayesian regularized artificial neural networks are less prone to being overtrained [87]. DL or deep neural networks (DNNs) [23] are in many ways similar to ANN in that they mimic how the brain works and take information via an input layer. But unlike ANN, DL has many hidden layers [88] to combine signals with different weights, passing the results succes- sively deeper in the network until reaching an output layer (Figure 1.3b). The DL model is trained with a dataset by adjusting the weights to give the response k expected for a certain input (e.g., whether a compound is active or inactive k or the level of activity/inactivity). The ability to have multiple learnable stages makes this approach more useful for tackling more complex problems. DL can be used for unsupervised learning and appears to work well with noisy data. However, it still suffers from the potential to overfit data, besides displaying higher computational cost than ANN or other methods [89]. To date, there has been relatively limited application of DL to pharmaceutical problems and very few studies in the area of cheminformatics, as compared with other machine

Output layer Output layer Input layer Input layer Hidden layer Hidden layer 1 Hidden layer 2 (a) (b)

Figure 1.3 (a) A two-layer neural network (one hidden layer of four neurons (or units) and one output layer with two neurons), and three inputs. (b) A three-layer neural network with three inputs, two hidden layers of four neurons each and one output layer. In both cases, there are connections (synapses) between neurons across layers, but not within a layer. Source: Adapted from http://cs231n.github.io/neural-networks-1/.

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learning methods [85]. DL tools are available in popular open source statistical software, such as R [90]. In addition, we have TensorFlow [91], Deeplearning4j [92] and Facebook, who made their DL software (Torch) open source [93, 94], followed a year later by Microsoft (CNTK) [95]. Some of these methods have been summarized in a recent review [96]. While these are open source, they need some considerable expertise to utilize, or they require the employment of a specialist that is skilled in integrating these with cheminformatics data such as molecular descriptors. We are currently developing an open science data repository (OSDR) [97] for connecting scientists and sharing data for many types of projects relevant to drug discovery (see also Chapter 13). OSDR represents a general platform for acquisition, curation, semantic enrichment, and management of various scientific data related to chemistry, bioinformatics, and pharmacology. OSDR also provides a powerful and extensible framework for hosting not just data but also various prediction algorithms, as well as previously generated models. We have integrated DL into OSDR to provide a user-friendly implementation of the technology. There is increasing interest from big pharma companies working on new methods for QSAR [98, 99]. While such experts have ready access to a wide variety of in-house and commercial software, smaller companies may be at a disadvantage as these skills and software may be less accessible. k It is our goal to make DL for cheminformatics accessible to non-experts in k academia and industry. In addition, while there are many proponents of DL and other machine learning techniques, they do not have the advantage of drug discovery expertise; consequently, they frequently oversell the utility of such technology or misuse public datasets. It is therefore important to access and test DL. Adding machine learning methods and DL to OSDR would clearly differentiate it from capabilities found elsewhere (e.g., Figshare, Mendeley, CDD, and many other systems, both commercial and open source) for depositing data. It would enable the ability to learn from data, to build and share models, as well as make predictions that could enable many uses in drug discovery and similar areas where it is important to learn from molecular structures. It should be noted that the open source DL toolkits described earlier are far from “plug and play” type software tools for the average scientist, in which their molecules and data are input to train a model (or for that matter in any training or test datasets) and then generate predictions. Significant expertise in using these software toolkits is needed and integrating them with molecular descriptor software is a problem in itself, requiring deep knowledge of cheminformatics toolkit(s) and their capabilities. It is more likely that a specialized program- mer/statistician/cheminformatician with knowledge of the software tools will be needed to generate the models, which can then be made available for others to use. Conversely, our approaches described herein could facilitate making DL more accessible to non-expert users by developing easy to use, fully integrated

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Accessible Machine Learning Approaches for Toxicology 15

tools, which can be applied with any dataset in OSDR or used as standalone software to produce models. There have been very few discussions of the potential for using DL in pharmaceutical research [88, 89]. The results obtained thus far have admittedly focused on internal validation with little prospective testing, as seen with other machine learning methods [53, 100]. DL appears promising and will likely see greater application in the years ahead. So how long will it be before DL is widespread in pharmaceutical research [88] and what can we expect? It is possible that DL could be the source of more predictive models, but hurdles remain in the implementation and accessibility of these models. In addition, there is also the healthy skepticism of any new computational technology that has to be addressed before it is able to be used widely in the industry. What is clearly needed is software that is tightly integrated with the data to be modeled. This data would most frequently reside in private or public databases and could represent many diﬀerent endpoints, both quantitative and qualitative. Therefore, any eﬀorts to bring the molecules, sources of data, and DL algorithms together would greatly streamline model generation and make it more accessible to other scientists. However, as with other computational modeling approaches, we may also want to consider the applicability domain [101] and various critical factors, such as the quality of the underlying data k [80, 102], which may determine the utility and relevance of a DL model for k making a prospective prediction [103]. Already, comparisons of DL with other machine learning algorithms have shown that it frequently improves upon the state of the art, when using predominantly internal cross-validation as the form of evaluation. At the time of this writing, there are over 100 DL start-up companies globally, but few are focused on pharmaceutical applications alone [104, 105]. Presently, there are a variety of open source libraries implementing DL algorithms. There is also a set of mature and well-recognized open source cheminformatics toolkits which are able to generate feature sets for chemical structures that, when combined with labeling information on properties or descriptors, can be used to train machine learning algorithms to generate predictive models. Unfortunately, these two areas usually have to be manually connected to support the overall pipeline of drug discovery. DL algorithms need to be accessible to readily scour libraries of compounds for the property of interest. OSDR provides a powerful and extensible framework for hosting not just data but also various prediction algorithms as well as previously generated models. We have built a Jupyter Notebook directly into OSDR to seamlessly integrate chemical operations, datasets manipulation, and machine learning models (DL, as well as Bayesian, trees, etc.) within one framework. As DL methods have not been widely assessed using prospective validation, we can use our approach to take previously published and novel data input in

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OSDR, build models, and evaluate them for internal quality, before validating them using prospective predictions on vendor libraries.

1.4 Comparison of Diﬀerent Machine Learning Methods

We have been interested in comparing DNNs with classic machine learning (CML) methods with different datasets of toxicological relevance for future embedding into the OSDR [97]. Diverse publicly available datasets for different types of ADME/Tox activities were used to develop prediction pipelines [30, 106] (Table 1.1). The ECFP6 fingerprints, consisting of 1024-bin datasets, were computed from sdf files using RDKit (http://www.rdkit.org/). A typical frequency of fingerprints occurrence in the 1024 bin compound representation in a dataset is shown in

Table 1.1 Comparison of machine learning methods using FCFP6 1024 bit descriptors on ADME/Tox properties using ﬁvefold cross-validation ROC values.

Active/ inactive k Models BNB LLR ABDT RF SVM DNN-2 DNN-3 and ratio k

Solubility 0.9594 0.9911 0.9963 0.9336 0.9833 0.9996 0.9996 1144/155, 7.38 train Solubility 0.8621 0.9375 0.9323 0.8738 0.9267 0.9349 0.9332 test

hERG Train 0.9302 0.9162 0.9916 0.9219 0.9600 1.0000 1.0000 373/433, 0.86 hERG Test 0.8424 0.8529 0.8436 0.8343 0.8637 0.8400 0.8409

KCNQ 0.7951 0.8637 0.8087 0.7644 0.8638 1.0000 1.0000 301, 737/3878, Train 77.81 KCNQ Test 0.7855 0.8256 0.8012 0.7321 0.8318 0.8608 0.8559

ERα agonist 0.9320 0.9820 0.9730 0.9300 0.9920 0.9986 0.9986 966/1178, 0.82 train ERα agonist 0.9120 0.9340 0.9370 0.9120 0.9280 0.9360 0.9364 test

The test set consists of 20-25% of the original records, separated before training and used for validation. BNB, Bernoulli naive Bayes; LLR, logistic linear regression; ABDT, AdaBoost decision trees; RF, random forest; SVM, support vector machines; DNN-N,DNNwithtwoorthreehidden layers. The solubility dataset consisted of 1299 molecules, hERG had 806 molecules, KCNQ1 had 305,615 molecules, and the ERα agonist dataset had 2144 molecules. Note: The active/inactive ratios for hERG and KCNQ1 are reversed as we are trying to obtain compounds that are more desirable (active = noninhibitors).

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Accessible Machine Learning Approaches for Toxicology 17

Frequency of fingerprints occurence in the bins for entire dataset

300

250

200

150 Frequency 100

0 0 200 400 600 800 1000 Bin number

Figure 1.4 Typical frequency of ﬁngerprints occurrence in the 1024-bin compounds in a dataset.

Figure 1.4. Two general prediction pipelines were developed. The first pipeline k k used only CML methods, such as Bernoulli naive Bayes (BNB), linear logistic regression, AdaBoost decision tree, Random Forest (RF), and SVM. The open source Scikit-learn (http://scikit-learn.org/stable/) ML python library was used for building, tuning, and validating all these CML models. The second pipeline used DNN learning models using Keras (https://keras.io/), a DL library, and Tensorflow (www.tensorflow.org) as a backend. The developed pipeline consists of stratified splitting of the input dataset into train (80%) and test (20%) datasets. Hence tuning of all the models and the search for hyper parameters were conducted solely on the training dataset for better model generalization. The ROC curve and the area under the curve (AUC) were computed for each model.

1.4.1 Classic Machine Learning Methods The following details the classic machine learning methods used in the ﬁrst pipeline.

1.4.1.1 Bernoulli Naive Bayes Naive Bayes method is a supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of independence between every pair of features. BNB implements the naive Bayes training and classiﬁcation algorithms for data that are distributed according to multivariate Bernoulli distributions; that is, there may be multiple features but each one is assumed

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18 Computational Toxicology

to be a binary-valued (Bernoulli, Boolean) variable. Naive Bayes learners and classifiers can be extremely fast compared to more sophisticated methods. The decoupling of the class conditional feature distributions means that each distribution can be independently estimated as a one-dimensional distribution. On the other hand, although naive Bayes is known as a decent classifier, it is known to be not a very good estimator, so the class probability outputs are not very accurate. The BNB model was tuned and trained using the BernoulliNB() method from Naïve Bayes module of Scikit-learn. The fourfold stratified cross-validation with a nonparametric approach based on isotonic regression for balancing classes (most of datasets are heavily imbalanced) was used. The cross-validation generator estimates the model parameter on the training portions of the cross-validation split for each split, and the calibration is done on the test cross-validation split of the training dataset, the probabilities predicted for the folds are then averaged. AUC was computed using those probabilities.

1.4.1.2 Linear Logistic Regression with Regularization Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution, thus pre- k dicting the probability of particular outcomes. The L2 binominal regularized k logistic regression method was used to classify the activities. A stochastic average gradient optimizer was used in the LogisticRegressionCV() method from the linear module of Scikit-learn. A fourfold stratified cross-validation method was used in a grid search of the best regularization parameter (L2 penalties were in logarithmic scale between 1e−5and1e−1). The AUC of ROC was used for scoring the classification (maximizing AUC) performance for each fold of balanced classes’ classification task.

1.4.1.3 AdaBoost Decision Tree AdaBoost is a type of “ensemble learning” where multiple learners are employed to build a stronger learning algorithm by conjugating many weak classifiers. The decision tree (DT) was chosen as a base algorithm in our implementation of the AdaBoost method (ABDT). The AdaBoostClassifier() method with 100 estimators and 0.9 learning rate from Scikit-learn ensemble methods was used. Similarly to naïve Bayes, the ABDT model was tuned using isotonic calibration for the imbalanced classes with the fourfold stratified cross-validation method.

1.4.1.4 Random Forest The RF method is another ensemble method, which fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control overfitting. The RandomForest- Classifier() method with maximum depth of tree 5 and balanced classes weights

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was used to build the model. The fourfold stratiﬁed cross-validation grid search was done using 5, 10, 25, and 50 estimators with the AUC of ROC as a scoring function of the estimator.

1.4.1.5 Support Vector Machine SVM is one of the most popular supervised machine learning algorithms used mostly in classification problems and it is quite effective in high-dimensional spaces. The learning of the hyperplane in SVM algorithm can be done using different kernel functions for the decision function. The C SVM classification with libsvm implementation method from Scikit-learn was also used (svm.SVC()). The fourfold stratified cross-validation grid search using weighted classes was done for two kernels (linear, rbf), C (1, 10, 100), and gamma values (1e−2, 1e−3, 1e−4). The parameter C, common to all SVM kernels, trades off misclassifica- tion of training examples against simplicity of the decision surface. A low C makes the decision surface smooth, while a high C aims at classifying all training examples correctly. Gamma defines how much influence a single training example has. The larger gamma is, the closer other examples must be to be affected. The implementation of SVM automatically finds the best parameters and saves the best SVM model for activity predictions. k 1.4.2 Deep Neural Networks k N-layer neural networks are shown in Figure 1.2. It is worth noting that a single-layer neural network describes a network with no hidden layers where the input is directly mapped to the output layer. In that sense, the logistic regression or SVM methods are simply a special case of single-layer neural networks. In this work, for simplification of the DNN representation, we will be counting hidden layers only. Neural networks with 1–2 hidden layers are often called shallow neural networks and those with three or more hidden layers are known as the DNNs. Two basic approaches to avoid DNN moel overfitting used in training are the L2 norm and dropout regularizaton for all hidden layers. The following hyperparameter optimization was performed using a DNN with three hidden layers: Keras with Tensorflow backend and the grid-search method from Scikit-learn. The following parameters were optimized prior to final model training: • optimization algorithm: SGD, Adam, Nadam • learning rate: 0.05, 0.025, 0.01, 0.001 • network weight initialization: uniform, lecun_uniform, normal, glorot_ normal, he_normal, he_normal • hidden layers activation function: relu, tanh, LeakyReLU, SReLU • output function: softmax, softplus, sigmoid • L2 regularization: 0.05, 0.01, 0.005, 0.001, 0.0001 • dropout regularization: 0.2, 0.3, 0.5, 0.8

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• the number of nodes in a hidden layer (all hidden layers): 512, 1024, 2048, 4096 • The following hyperparameters were used for further DNN training: SGD, learning rate 0.01 (automatically 10% reduced on plateau of 50 epochs), weight initialization he_normal, hidden layers activation SReLU, output layer function sigmoid, L2 regularization 0.001, dropout 0.5. The binary crossentropy was used as a loss function. In order to save training time, an early training termination was implemented by stopping the training if no change in loss was observed after 200 epochs. The number of hidden nodes in all hidden layers was set equal to the number of input features (number of bins in the ﬁngerprints).

1.4.3 Comparing Models The AUC values of the all trained models for compounds represented as ECFP6 in 1024-bin fingerprints are summarized in Table 1.1 and the F1 scores [107] are summarized in Table 1.2. The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst at 0. In all cases, the SVM models were better than any other CLM models including naïve Bayes for the test set ROC values. The DNN models k were also better than the SVM for three out of four datasets based on ROC k values. Using the F1 scores, DNN outperformed all methods for the solubility using KCNQ data and ARα agonist datasets, while the Bayesian method performed well for the hERG data (Table 1.2). The 1024-bin fingerprints may not however be sufficient for maximizing DNN performance, thus other 2D or 3D

Table 1.2 Comparison of machine learning methods using FCFP6 1024-bit descriptors on ADME/Tox properties using ﬁvefold cross-validation F1 values at p = 0.5.

Models BNB LLR ABDT RF SVM DNN-2 DNN-3

Solubility train 0.9417 0.9626 0.9595 0.9559 0.9539 0.9917 0.9917 Solubility test 0.9087 0.9446 0.9457 0.9451 0.9396 0.9586 0.9609 hERG Train 0.8536 0.8406 0.9562 0.8249 0.8852 1.0000 1.0000 hERG Test 0.7976 0.7975 0.7152 0.7799 0.7843 0.7763 0.7843 KCNQ train 0.7962 0.8646 0.8193 0.8332 0.8558 0.9991 0.9999 KCNQ test 0.7938 0.8578 0.8157 0.8251 0.8508 0.9911 0.9923 ERα agonist train 0.8355 0.9173 0.8927 0.8304 0.9705 0.9697 0.9697 ERα agonist test 0.8017 0.8201 0.8330 0.7881 0.8535 0.8542 0.8542

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Accessible Machine Learning Approaches for Toxicology 21

ﬁngerprints may need to be tried in future with this method. In addition, a farlargernumberofdatasetsneedtobeassessedacrossthemultiplemachine learning methods. This work suggests DNN and SVM generally outperform all other machine learning methods when dealing with this selection of four small to very large toxicology datasets and does not depend on whether the datasets are balanced or not.

1.5 Future Work

Doubtless there will be new machine learning algorithms developed in the coming decade. The key for computational toxicology will be to integrate these into cheminformatics workflows and tools that are used in decision making. Our efforts have lead us to providing as open source some of the software tools we have previously taken for granted. Sustaining software companies and the very developers of these tools will require some intelligent choices of how to monetize this work as services such as training and customization of the tools. As scientists, we are driven to solve problems and having the best software available as we deal with different datasets for toxicology will enable us to come up with solutions and hypotheses which we can test experimentally. Clearly, k trying out more machine learning approaches in parallel may lead to the k selection of the best model per endpoint. Readily accessible machine learning models are likely to be an increasingly important tool for drug discovery in general and these may fuse public and private data. Such models will still require some expertize to use and interpret, thus creating new opportunities for cheminformaticians.

Acknowledgments

S.E. acknowledges support from NIH Grants 9R44TR000942-02 (while at CDD). S.E. also acknowledges many fruitful discussions with Dr Barry Bunin and Dr Antony Williams. Kimberley Zorn is acknowledged for providing the ERα agonist dataset.

References

1 Oprea, T.I. and Matter, H. (2004) Integrating virtual screening in lead discovery. Curr. Opin. Chem. Biol., 8, 349–358. 2 Ekins, S., Mestres, J., and Testa, B. (2007) In silico pharmacology for drug discovery: applications to targets and beyond. Br.J.Pharmacol., 152, 21–37.

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3 Ekins, S., Mestres, J., and Testa, B. (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol., 152, 9–20. 4 McGaughey, G.B., Sheridan, R.P., Bayly, C.I. et al. (2007) Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model., 47, 1504–1519. 5 Lombardo, F., Obach, R.S., Dicapua, F.M. et al. (2006) A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. J. Med. Chem., 49, 2262–2267. 6 Lombardo, F., Obach, R.S., Shalaeva, M.Y., and Gao, F. (2004) Predic- tion of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics. J. Med. Chem., 47, 1242–1250. 7 Lombardo, F., Obach, R.S., Shalaeva, M.Y., and Gao, F. (2002) Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding. J. Med. Chem., 45, 2867–2876. 8 Lombardo, F., Shalaeva, M.Y., Tupper, K.A., and Gao, F. (2001) ElogDoct: a k tool for lipophilicity determination in drug discovery. 2 Basic and neutral k compounds. J. Med. Chem., 44, 2490–2497. 9 Lombardo, F., Blake, J.F., and Curatolo, W.J. (1996) Computation of brain–blood partitioning of organic solutes via free energy calculations. J. Med. Chem., 39, 4750–4755. 10 Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Rev., 23, 3–25. 11 Ekins, S., Ring, B.J., Grace, J. et al. (2000) Present and future in vitro approaches for drug metabolism. J. Pharm. Tox. Meth., 44, 313–324. 12 Tanrikulu, Y., Kruger, B., and Proschak, E. (2013) The holistic integration of virtual screening in drug discovery. Drug Discov. Today, 18, 358–364. 13 Zientek, M., Stoner, C., Ayscue, R. et al. (2010) Integrated in silico-in vitro strategy for addressing cytochrome P450 3A4 time-dependent inhibition. Chem. Res. Toxicol., 23, 664–676. 14 Gupta, R.R., Gifford, E.M., Liston, T. et al. (2010) Using open source computational tools for predicting human metabolic stability and additional ADME/TOX properties. Drug Metab. Dispos., 38, 2083–2090. 15 Ekins, S., Gupta, R.R., Gifford, E. et al. (2010) Chemical space: missing pieces in cheminformatics. Pharm. Res., 27, 2035–2039.

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16 Lombardo, F., Shalaeva, M.Y., Tupper, K.A. et al. (2000) ElogPoct a tool for lipophilicity determination in drug discovery. J. Med. Chem., 43, 2922–2928. 17 Lagorce, D., Sperandio, O., Galons, H. et al. (2008) FAF-Drugs2: free ADME/tox ﬁltering tool to assist drug discovery and chemical biology projects. BMC Bioinformatics, 9, 396. 18 Villoutreix, B.O., Renault, N., Lagorce, D. et al. (2007) Free resources to assist structure-based virtual ligand screening experiments. Curr. Protein Pept. Sci., 8, 381–411. 19 Ekins, S. (2007) Computational Toxicology: Risk Assessment for Pharmaceu- tical and Environmental Chemicals, John Wiley & Sons, Hoboken, NJ. 20 Balani, S.K., Miwa, G.T., Gan, L.S. et al. (2005) Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection. Curr.Top.Med.Chem., 5, 1033–1038. 21 van De Waterbeemd, H., Smith, D.A., Beaumont, K., and Walker, D.K. (2001) Property-based design: optimization of drug absorption and pharmacokinetics. J. Med. Chem., 44, 1313–1333. 22 Walters, W.P. and Murcko, M.A. (2002) Prediction of ’drug-likeness’. Adv. Drug Del. Rev., 54, 255–271. 23 Mitchell, J.B. (2014) Machine learning methods in chemoinformatics. k Wiley Interdiscip. Rev. Comput. Mol. Sci., 4, 468–481. k 24 Zhu, H., Zhang, J., Kim, M.T. et al. (2014) Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants. Chem. Res. Toxicol., 27, 1643–1651. 25 Clark, A.M. and Ekins, S. (2015) Open source Bayesian models: 2. Mining a ‘big dataset’ to create and validate models with ChEMBL. J. Chem. Inf. Model., 55, 1246–1260. 26 Ekins, S., Clark, A.M., Swamidass, S.J. et al. (2014) Bigger data, collaborative tools and the future of predictive drug discovery. J. Comput. Aided Mol. Des., 28, 997–1008. 27 Ekins, S., Freundlich, J.S., and Reynolds, R.C. (2014) Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis. J.Chem.Inf.Model., 54, 2157–2165. 28 Clark, A.M., Sarker, M., and Ekins, S. (2014) New target predictions and visualization tools incorporating open source molecular ﬁngerprints for TB Mobile 2.0. J. Cheminform., 6, 38. 29 Ekins, S., Clark, A.M., and Wright, S.H. (2015) Making transporter models for drug–drug interaction prediction mobile. Drug Metab. Dispos., 43, 1642–1645. 30 Clark, A.M., Dole, K., Coulon-Spector, A. et al. (2015) Open source bayesian models: 1. Application to ADME/Tox and drug discovery

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datasets. J. Chem. Inf. Model., 55, 1231–1245. doi: 10.1021/acs.jcim. 5b00143. Epub 2015 Jun 3. 31 Kortagere, S. and Ekins, S. (2010) Troubleshooting computational methods in drug discovery. J. Pharmacol. Toxicol. Methods, 61, 67–75. 32 Ekins, S. and Williams, A.J. (2010) Precompetitive preclinical ADME/Tox data: set it free on the web to facilitate computational model building to assist drug development. Lab Chip, 10, 13–22. 33 Ekins, S., Honeycutt, J.D., and Metz, J.T. (2010) Evolving molecules using multi-objective optimization: applying to ADME. Drug Discov. Today, 15, 451–460. 34 Bahadduri, P.M., Polli, J.E., Swaan, P.W., and Ekins, S. (2010) Targeting drug transporters – combining in silico and in vitro approaches to predict in vivo. Methods Mol. Biol., 637, 65–103. 35 Ekins, S., Bugrim, A., Brovold, L. et al. (2006) Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms. Xenobiotica; the fate of foreign compounds in biological systems, 36, 877–901. 36 Ekins, S., Andreyev, S., Ryabov, A. et al. (2006) A combined approach to drug metabolism and toxicity assessment. Drug Metab. Dispos., 34, k 495–503. k 37 Ekins, S. (2006) Systems-ADME/Tox: resources and network approaches. J. Pharmacol. Toxicol. Methods, 53, 38–66. 38 Chang, C. and Ekins, S. (2006) Pharmacophores for human ADME/Tox-related proteins, in Pharmacophores and Pharmacophore Searches (eds T. Langer and R.D. Hoﬀman), Wiley-VCH, Weinheim, pp. 299–324. 39 Ekins, S., Nikolsky, Y., and Nikolskaya, T. (2005) Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends Pharmacol. Sci., 26, 202–209. 40 Ekins, S., Andreyev, S., Ryabov, A. et al. (2005) Computational prediction of human drug metabolism. Expert Opin. Drug Metab. Toxicol., 1, 303–324. 41 Balakin, K.V., Ivanenkov, Y.A., Savchuk, N.P. et al. (2005) Comprehensive computational assessment of ADME properties using mapping techniques. Curr. Drug Discov. Technol., 2, 99–113. 42 Ekins, S. and Swaan, P.W. (2004) Computational models for enzymes, transporters, channels and receptors relevant to ADME/TOX. Rev. Com- put. Chem., 20, 333–415. 43 Ekins, S., Boulanger, B., Swaan, P.W., and Hupcey, M.A. (2002) Towards a new age of virtual ADME/TOX and multidimensional drug discovery. Mol. Divers., 5, 255–275.

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44 Ekins, S. and Wrighton, S.A. (2001) Application of in silico approaches to predicting drug–drug interactions. J. Pharmacol. Toxicol. Methods, 45, 65–69. 45 Ekins, S., Waller, C.L., Swaan, P.W. et al. (2000) Progress in predicting human ADME parameters in silico. J. Pharmacol. Toxicol. Methods, 44, 251–272. 46 Ekins, S., Ring, B.J., Grace, J. et al. (2000) Present and future in vitro approaches for drug metabolism. J. Pharmacol. Toxicol. Methods, 44, 313–324. 47 Ekins, S., Bradford, J., Dole, K. et al. (2010) A collaborative database and computational models for tuberculosis drug discovery. Mol. Biosyst., 6, 840–851. 48 Ekins, S., Kaneko, T., Lipinksi, C.A. et al. (2010) Analysis and hit ﬁltering of a very large library of compounds screened against Mycobacterium tuberculosis. Mol. Biosyst., 6, 2316–2324. 49 Ananthan, S., Faaleolea, E.R., Goldman, R.C. et al. (2009) High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. Tuberculo- sis, 89, 334–353. 50 Maddry, J.A., Ananthan, S., Goldman, R.C. et al. (2009) Antituberculo- sis activity of the molecular libraries screening center network library. k Tuberculosis, 89, 354–363. k 51 Langdon, S.R., Mulgrew, J., Paolini, G.V., and van Hoorn, W.P. (2010) Predicting cytotoxicity from heterogeneous data sources with Bayesian learning. J. Cheminform., 2, 11. 52 Ekins, S., Williams, A.J., and Xu, J.J. (2010) A predictive ligand-based Bayesian model for human drug-induced liver injury. Drug Metab. Dispos., 38, 2302–2308. 53 Ekins,S.,Reynolds,R.,Kim,H.et al. (2013) Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery. Chem. Biol., 20, 370–378. 54 Ekins S, Reynolds RC, Franzblau SG, Wan B, Freundlich JS, Bunin BA. Enhancing hit identiﬁcation in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models PLOS ONE. 2013;8:e63240. 55 Ekins, S., Casey, A.C., Roberts, D. et al. (2014) Bayesian models for screening and TB mobile for target inference with Mycobacterium tuberculosis. Tuberculosis, 94, 162–169. 56 Ekins, S., Lage de Siqueira-Neto, J., McCall, L.-I. et al. (2015) Machine learning models and pathway genome data base for Trypanosoma cruzi drug discovery. PLoS neglected tropical diseases, 9, e0003878. 57 Perryman, A.L., Stratton, T.P., Ekins, S., and Freundlich, J.S. (2015) Predict- ing mouse liver microsomal stability with ‘pruned’ machine learning models and public data. Pharm. Res., 33, 433–449.

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58 Ekins, S. (2014) Progress in computational toxicology. J. Pharmacol. Toxi- col. Methods, 69, 115–40. 59 Dong, Z., Ekins, S., and Polli, J.E. (2013) Structure-activity relationship for FDA approved drugs as inhibitors of the human sodium taurocholate cotransporting polypeptide (NTCP). Mol. Pharm., 10, 1008–1019. 60 Astorga, B., Ekins, S., Morales, M., and Wright, S.H. (2012) Molecular determinants of ligand selectivity for the human multidrug and toxin extrusion proteins, MATE1 and MATE-2K. J. Pharmacol. Exp. Ther., 341, 743–755. 61 Pan, Y., Li, L., Kim, G. et al. (2011) Identiﬁcation and validation of novel hPXR activators amongst prescribed drugs via ligand-based virtual screening. Drug Metab. Dispos., 39, 337–344. 62 Diao, L., Ekins, S., and Polli, J.E. (2010) Quantitative structure activity relationship for inhibition of human organic cation/carnitine transporter. Mol. Pharm., 7, 2120–2130. 63 Zheng, X., Ekins, S., Raufman, J.P., and Polli, J.E. (2009) Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter. Mol. Pharm., 6, 1591–1603. 64 Ekins, S., Kortagere, S., Iyer, M. et al. (2009) Challenges predicting ligand–receptor interactions of promiscuous proteins: the nuclear receptor k PXR. PLoS Comput. Biol., 5, e1000594. k 65 Dong, Z., Ekins, S., and Polli, J.E. (2014) Quantitative NTCP pharmacophore and lack of association between DILI and NTCP inhibition. Eur. J. Pharm. Sci., 66C,1–9. 66 Ekins, S., Diao, L., and Polli, J.E. (2012) A substrate pharmacophore for the human organic cation/carnitine transporter identiﬁes compounds associated with rhabdomyolysis. Mol. Pharm., 9, 905–913. 67 Diao, L., Ekins, S., and Polli, J.E. (2009) Novel inhibitors of human organic cation/carnitine transporter (hOCTN2) via computational modeling and in vitro testing. Pharm. Res., 26, 1890–1900. 68 Kortagere, S., Chekmarev, D.S., Welsh, W.J., and Ekins, S. (2008) New predictive models for blood brain barrier permeability of drug-like molecules. Pharm. Res., 25, 1836–1845. 69 Klon, A.E., Lowrie, J.F., and Diller, D.J. (2006) Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J.Chem.Inf.Model., 46, 1945–1956. 70 Obrezanova, O., Csanyi, G., Gola, J.M., and Segall, M.D. (2007) Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem.Inf.Model., 47, 1847–1857. 71 Zhang, L., Zhu, H., Oprea, T.I. et al. (2008) QSAR modeling of the blood–brain barrier permeability for diverse organic compounds. Pharm. Res., 25, 1902–1914.

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Quantum Mechanics Approaches in Computational Toxicology Jakub Kostal

Chemistry Department, The George Washington University, Washington DC, USA

CHAPTER MENU

Translating Computational Chemistry to Predictive Toxicology, 31 Levels of Theory in Quantum Mechanical Calculations, 33 Representing Molecular Orbitals, 38 Hybrid Quantum and Molecular Mechanical Calculations, 39 Representing System Dynamics, 40 k Developing QM Descriptors, 42 k Rational Design of Safer Chemicals, 61

2.1 Translating Computational Chemistry to Predictive Toxicology

Computational chemistry has made impressive strides over the past two decades. We can now use it to study organic and enzymatic reactions; assess viability of molecular mechanisms; propose novel inhibitors of biological targets in computerized drug discovery; and even attempt de novo design of macromolecules with novel functionalities. To this end, substantial research efforts have been devoted in recent years to adapting and applying methodology from computational chemistry to predictive toxicology. These efforts have yielded powerful new descriptors of molecular events that can be applied to many types of predictive statistical models. The descriptors we obtain from computational models are molecular properties and the changes that result in the course of a (bio)chemical event. They are influenced by model size (the extent of system representation), level of theory (the rigor of the method), and by system dynamics (evolution of the system to mimic real-life conditions). How sophisticated our description of the system can be is limited by model size; as size grows, the computational

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method must become more simplistic to allow for completing calculations within reasonable time frames. The growth of computing resources, their decreasing costs, and parallelization of computing algorithms have allowed the application of quite rigorous quantum dynamics approaches to biochemical systems, but in principle, the need for compromise always remains. Thus, judicious selection of suitable descriptors and computational methods is key to developing predictive models for challenging endpoints. The specific strategy will differ across toxicity pathways and will be guided by (i) the complexity of the system under study; (ii) the depth of mechanistic insights; (iii) targeted accuracy of the model (including acceptable level of specificity to separate conflicting molecular events); and (iv) external factors, such as client’s needs, cost, and volume of work requests. Modelers working on well-characterized systems have the advantage of being able to apply mechanistic insights to preselect mechanistically relevant descriptors, and benchmark the performance against a complete system, which can be analyzed experimentally or computationally. To this end, they can choose the appropriate mix of variables to yield the desired outcome. For example, consider the haptenation of skin proteins in the sensitization pathway, which involves binding of electrophilic toxicants to surface residues. One can carry out binding assays or calculate binding affinities using an in sil- k ico model. While a benchmark model may consider full protein dynamics in k solution, such calculation may be too expensive for screening large datasets of compounds in hazard assessments. Thus, the final tool needs to be optimized to be as simplistic as possible, while preserving acceptable level of accuracy. A simplification could mean modeling the covalent modifications of a single residue in vacuum, rather than considering the entire protein. If the molecular event is not well understood, for example, we may not know which residues are haptenated, then carefully designed computations might be able to suggest or support mechanistic hypotheses. If the system itself is not well understood, say we lack the X-ray structure of the skin protein, the modeler can develop a hypothetical framework based on known characteristics of the system. For example, general trends in acid–base reactivity may be captured with frontier orbital energies and other electronic and structural descriptors. In such a case, the level of theory chosen can be low, as the largest source of error originates from insufficient mechanistic knowledge. The affordability of QM calculations in describing even relatively large biochemical systems and processes, such as skin-protein haptenation, is nowadays made entirely possible by access to inexpensive, massive-scale computing resources. To this end, the purpose of this chapter is to outline the potential of quantum mechanics (QM) to study and predict toxicodynamics phenomena, that is, covalent and noncovalent molecular interactions between toxicants and their biological targets. Specifically, the following sections focus on the descriptors that can be derived from QM calculations, and the calculation/descriptor

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Quantum Mechanics Approaches in Computational Toxicology 33

pairings that are practical for diﬀerent system representations. The importance of dynamics and solvent eﬀects on system properties are discussed as well. Simple examples are used throughout the chapter to demonstrate the utility of the outlined approaches. Since software packages for QM calculations have become increasingly more user-friendly (to the great dismay of computational chemists), we begin by outlining the basics of quantum chemistry, as is relevant to the non-experts in computational chemistry. The intent is to promote informed and responsible usage of available tools. In a broader scheme, the point of this chapter is to not only outline recent approaches but also to expose novel areas of overlap between computational chemistry and toxicology that have not yet been realized and to propose strategies for safer chemical design using methods adopted from computational chemistry.

2.2 Levels of Theory in Quantum Mechanical Calculations

QM provides the means to determine molecular structure in which electrons are treated as moving under the inﬂuence of the nuclei in the whole molecule in accordance with molecular orbital theory. QM calculations are uniquely suited k to describe covalent interactions, that is, bond breaking/forming processes. k Additionally, partitioning of the molecular electron density into atomic contributions can be used to determine partial atomic charges, which are important to noncovalent interactions. Covalent and noncovalent interactions can describe all toxicodynamic interactions;thus having means of assessing these interactions accurately is critical to predicting toxic phenomena. Among quantum mechanical methods, we distinguish between ab initio, density functional and semiempirical approaches. Ab initio methods rely on a few fundamental laws of physics – notably Schrödinger’s equation and Coulomb’s law – and, with relatively few approximations, attempt to ﬁnd the molecular wave function (or electron density), and from it calculate the energy and other molecular properties of the system. For multi-electron systems, this equation cannot be solved analytically; thus, a variational theorem is used, which states that the energy predicted from any trial wave function, 𝜙, will never be lower than the true ground-state energy:

𝜙 ̂ 𝜙 𝜏 ≥ ∫ ∗ H d E0 (2.1) Therefore, solving Schrödinger’s equation is analogous to an energy minimization problem. The trial wave functions are deﬁned as a linear combination of basis functions, which are usually atomic orbitals. In the Hartree–Fock (HF) approximation, one assumes that each electron is in its own spin-orbital, and interacts with other electrons only via their average

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electron densitydistribution. This results in the neglect of dynamic electron correlation, which is one of the major limitations of the HF method. In practice, the HF equations are cast into a matrix representation known as the Roothaan–Hall equations, which are then solved iteratively through the self-consistent-field (SCF) method. More sophisticated ab initio methods start from the HF approximation and correct for the missing electron correlation by using perturbation theory (as in the Møller–Plesset perturbation theory (MPn method)). The highest-level ab initio methods can be very accurate. For example, the Gaussian-2 (G2), Gaussian-3 (G3), or complete basis set (CBS) methods, which are composites of several ab initio methods, can be used to predict the heats of formation of small molecules within 1 kcal/mol of the experimental value. However, because of their accuracy, these ab initio methods come at a price: they are relatively slow and do not scale well with system size. Thus, for the purposes of computational toxicology, density functional and semiempirical methods (discussed below) often provide greater utility. Density functional theory (DFT) was first introduced by Hohenberg and Kohn [1]. Similarly to an ab initio approach, density-functional methods allow computation of the system’s ground-state energy by iteratively solving Schrödinger’s equation. In contrast to ab initio methods, however, they do k not attempt to calculate the molecular wave function but rather calculate k the molecular electronic probability density 𝜌 and calculate the ground-state molecular electronic energy from 𝜌.Theuseof𝜌 in place of a wave function underlines the significance of the Hohenberg–Kohn theorem. While a wave function for an N-electron system contains 4N variables, three spatial and one spin coordinate for each electron, the electron density is the square of the wave function integrated over N − 1 electron coordinates, and each spin density only depends on three spatial coordinates, independent of the number of electrons. Therefore, while the complexity of a wave function increases exponentially with the number of electrons, electron density retains the same number of variables, independent of the system size. Early attempts at designing DFT models by expressing all the energy components as a function of the electron density suffered from poor performance owing to inadequate description of the kinetic energy. The widespread success of modern DFT methods is based on the Kohn–Sham theory, whereby the electron kinetic energy is calculated from an auxiliary set of orbitals used for representing the electron density [2]. The reintroduction of orbitals increases the complexity from 3 to 3N variables, and brings modern DFT methods conceptually and computationally closer to the HF method, sharing identical formulas for kinetic, electron–nuclear, and Coulomb electron–electron energies. A significant improvement over the HF method is in the exchange–correlation energy, which can be calculated from the electron density assuming that the density is a slowly varying function (in the local

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Quantum Mechanics Approaches in Computational Toxicology 35

density approximation (LDA)). A further improvement in accuracy can be obtained by making the exchange–correlation functional dependent also on the first and second derivatives of the density and mix HF exchange into the functional. The popular B3LYP method is a good example, where the exchange energy, calculated from Becke’s exchange functional, is combined with the exact HF exchange. Ab initio and DFT methods are available to the user via commercial software packages such as Gaussian (www.gaussian.com) or Q-Chem (www.q-chem .com) and open-source programs such as GAMESS (www.msg.chem.iastate .edu) or NWChem (www.nwchem-sw.org), among many others. Modern DFT methods are the de facto go-to approaches for calculating electronic structure for medium-large systems. If greater computational speed is required and/or very large systems need to be evaluated, semiempirical methods (discussed below) can provide a suitable alternative. Because many density functionals exist, all of which have different strengths and weaknesses resulting from different approximations and parameterizations. The user is always recommended to survey the benchmarking studies available in the literature, and carries out his or her own if possible, prior to commencing calculations. Furthermore, close inspection of the results of any DFT calculation is necessary to obtaining reliable results. k Performance of popular hybrid density functionals and ab initio methods k is compared in Figure 2.1, which presents results adopted from a recent benchmarking study by Goerigk and Grimme [3]. Figure 2.1 outlines weighted total mean absolute deviations for basic molecular properties, reaction energetics, and noncovalent interactions obtained from the GMTKN30 database. All values were corrected for dispersion and are based on the (aug-)def2-QZVP basis set, which is comparable in performance to the popular 6-31G*. It should be noted that Figure 2.1 is merely a crude guide to accurate, robust, and broadly applicable functionals; the reader is highly encouraged to review the literature in detail to appreciate the full scope of the results as well as the rationale behind the generated metrics. Briefly, a few broad remarks can be made that are relevant to computational toxicologists. First, the best hybrid functionals for computing physicochemical properties, which are often used as descriptors in predictive models, are PW6B95, BMK, and M062X. Second, the widely popular B3LYP method has been an average performer overall, while it has shown the worst performance in computing reaction energies among all tested density functionals. Finally, performance aside, the user should always consider computational cost, which varies across density functionals. Double-hybrid functionals (Figure 2.1, middle group), although more accurate, are noted to be more costly than general hybrid functionals; increase in predictivity may not warrant the increase in computational cost for many applications.

k k

36 Computational Toxicology

20 18.5 18

8 MAE (kcal/mol) 6 5.3 4.4 3.7 4 3.9 3.8 3.6 3.6 4 3.3 3.4 3.1 2.9 2.5 2.6 2.6 2.6 2.5 2.7 2.2 2 1. 9 2 1. 7 1. 6 1. 5

0 HF M05 M06 MP2 PBE0 BMK B3LYP BHLYP PBE38 B1B95 XYG3 TPSShTPSS0 PWB6K M052X M062XM06HF B2PLYP B3PW91 PW6B95 MPWB1K PWPB95 MPW1895 B2GPPLYPDSD-BLYP

Figure 2.1 Total mean absolute errors (MAEs) recorded for a selection of popular hybrid density functionals (B3LYP–M06HF), double hybrid functionals (B2PLYP–DSD-BLYP) and two ab initio methods (HF and MP2), reﬂecting basic physicochemical properties, reaction energetics, and noncovalent interactions from the GMTKN30 database. Results were k adopted from a study by Goerigk and Grimme [3]. k

Semiempirical molecular orbital (SMO) methods are related to HF and DFT methods in attempting to solve the Schrödinger equation with some approximations. Notably, the Hamiltonian operator from Equation 2.1 is much simpler than in the case of HF and DFT methods and contains empirical parameters which are obtained by fitting to experimental or ab initio results. For the SCF calculation, only valence electrons are taken into account and the core-core repulsion is treated using a parametric formula instead of Coulomb’s law. The most significant approximation comes from the zero differential overlap (ZDO) assumption, which states that the overlap between two different basis functions is zero for all volume elements: 𝜙 𝜙 𝜏 𝜇 vd = 0 (2.2) All modern SMO formalisms stem from a neglect of diatomic differential overlap (NDDO), which is a variation of the ZDO approximation wherein all one-center, two-electron integrals are included, in addition to the two-center integrals when 𝜇 is on the same atom as v and 𝜆 isonthesameatomas𝜎. These approximations result in a major improvement in scaling and considerably reduce computing time. In most instances, SMO methods trail behind ab initio and DFT methods in terms of accuracy, particularly for systems that deviate from those used in the training set. The training set for parameter optimization is constrained by

k k

Quantum Mechanics Approaches in Computational Toxicology 37

the lack of reliable reference data, which poses a significant problem for many elements and functional groups. Furthermore, typically only the ground state of the most stable conformer is included, which may contribute to poor estimation of reaction kinetics. To this end, benchmarking against experimental or higher level of theory data that is part of or similar to the data in the intended study is essential for obtaining meaningful results. The performance of several SMO methods is compared in Figure 2.2. These results were obtained from a benchmarking study by Korth and Thiel [4], who tested five SMO methods (AM1, PM6, and OM1-3) against a reduced GMTKN24 database, which included compounds with HCNO elements only. The overall performance of SMO methods is considerably worse than that of selected DFT methods, except for the OM2-3 methods (Figure 2.2). However, absolute errors are often less relevant in predictive toxicology than relative errors, which arise from comparisons of different molecules. Thus, the user should not discard SMO methods; rather she should carry out benchmarking study on her specific system before developing predictive models. Furthermore, for several subsets of the database, even an inexpensive and common SMO method such as AM1 can offer accuracy comparable to DFT methods. From Figure 2.2, these subsets include radical stabilization energies, conformation energetics, and energies of noncovalent interactions. k k 35

15 MAE (kcal/mol)

0 B3LYP PBE OM3 OM2 OM1 PM6 AM1 Total MAE Reaction energetics Conformational energies Noncovalent interactions Radical stabilization energies Electron affinity/lonization potential

Figure 2.2 Mean absolute errors (MAEs) recorded for a selection of ﬁve semiempirical methods (AM1, PM6, and OM1-3) and two DFT methods (B3LYP and PBE) from the reduced (HCNO elements only) GMTKN24 database [4]. Performance across relevant subsets of the GMTKN24 database is provided in pattern ﬁll next to the total MAEs.

k k

38 Computational Toxicology

The most widely used SMO methods are available through software packages such as Gaussian and GAMESS, among others. Additionally, smaller-footprint programs exist that are dedicated to SMO calculations, including MOPAC2009 (Molecular Orbital PACkage, www.openmopac.net), MNDO99, or BOSS (Biochemical and Organic Simulation System, www.cemcomco.com). QikProp (www.schrodinger.com) may be of special interest to the reader; this program uses the PM3 (Parameterized Model number 3) method to compute electronic structure and from it a wide array of molecular properties relevant to bioavailability and reactivity.

2.3 Representing Molecular Orbitals

Basis functions are mathematical constructs that are used to represent atomic orbitals in the system’s total wavefunction. In the HF theory, each molecular orbital is expressed as a linear combination of atomic orbitals, the coefficients for which are determined iteratively. Ideally, one would use an infinite set of functions, which permits an optimal description of the electron probability density. However, in practice, we rely on approximate mathematical functions that allow wave functions to approach this ideal scenario as closely and effi- k ciently as possible. Computational efficiency is important in studying toxico- k dynamics, particularly when non-equilibrium events involving large systems are modeled. Keeping the number of basis functions to a minimum and choosing functional forms that permit efficient solution of the Schrödinger equation in a chemical sense are key considerations. The most frequently used basis functions take the form of contracted Gaussian functions. From a chemical standpoint, it is advantageous to have valence orbitals, which can vary widely as a function of chemical bonding, represented differently than core orbitals, which are only weakly affected by chemical bonding. The ubiquitous 3-21G and 6-31G are examples of such split-valence basis sets that treat valence and core electrons differently. Polarization functions are usually added when describing polar bonds, for example, in describing O—H, N—H, or C—O bonds in biomolecules. Diffusion functions are needed for the highest-energy molecular orbitals of anions, highly excited electronic states, and loose supermolecu- lar complexes, which are spatially diffuse. When a basis set does not have the flexibility necessary to allow a weakly bound electron to localize far from the remaining density, significant errors in energies and other molecular properties can occur. In practice, using large basis sets augmented with polarization and diffusion functions can be challenging, and the often recommended approach is to first obtain a wavefunction from a minimal basis set of functions, and then use the result as an input for increasingly larger split-valence basis sets. An example of how significant a role basis sets play in predictive toxicology can

k k

Quantum Mechanics Approaches in Computational Toxicology 39

be made for orbital energies. Indices of reactivity (discussed in greater detail in subsequent sections) are often derived from energies of the HOMO, highest occupied molecular orbital, and LUMO, lowest unoccupied molecular orbital. A poorly converged calculation that results from an improperly chosen basis set can generate a molecular structure characterized by very small separation between HOMO and LUMO orbitals. Such a calculation may give a prediction of a highly reactive compound, where in fact it is not. Zhan et al.showed that the 6-31 + G(d) basis set is generally suﬃcient for calculating HOMO and LUMO energies (if the latter are negative), but may not be economical for screening large datasets and/or large systems [5]. Lynch and Truhlar evaluated a host of small basis sets in combination with several density functionals and assessed their applicability for calculating accurate reaction energetics and molecular/ properties [6]. Several methods noted in their study, such as mPW1PW91 MIDIX+ referenced throughout this chapter, are both economical and reasonably accurate.

2.4 Hybrid Quantum and Molecular Mechanical Calculations k Purely quantum-mechanical calculations can be too costly to describe complex k events in large biochemical systems. However, if the key molecular interaction under study is localized, the quantum-mechanical calculation can be reduced to describe that particular region only, while the rest of the system that acts as a spectator can be represented with inexpensive classical force ﬁelds. Purely classical (molecular) mechanics (MM) can provide accurate structural and energetic descriptions of equilibrium systems; therefore, they are adequate at describing the non-reacting parts of the biomolecules and/or nearby water molecules. Mixed quantum and molecular mechanics (QM/MM) methods are often applied to the study of medium eﬀects on chemical or enzymatic reactions. In QM/MM calculations, the energy and charges of the reacting system are described by a quantum-mechanical Hamiltonian, while the environment surrounding the QM region, that is, the rest of the macromolecule and/or hundreds of explicitly simulated solvent molecules, is represented by classical potential energy functions. The total energy of the system comprises of three terms:

E = EQM + EMM + EQM∕MM (2.3) A computationally inexpensive semiempirical method is typically used for the QM region, although the rapid growth of computational power and resources has made it feasible to use higher-level ab initio and density func-

tional methods in certain applications as well. The EQM/MM term in Equation 2.3

k k

40 Computational Toxicology

represents the interaction energy between the QM and MM regions, which in the case of noncovalent interactions includes the intermolecular van der Waals and Coulombic contributions. A more complicated QM/MM scenario involves a covalent bond between the QM and MM regions.

2.5 Representing System Dynamics

Including system dynamics can considerably increase the accuracy of system representation. Whether we need to treat the system as a dynamic entity will depend on the system and allocated computing time. If the molecule is small and is characterized by rigid molecular coordinates, its potential energy surface will have a global minimum represented by narrow and deep well; in such a case, it is reasonable to assume the molecule is a static entity (i.e., remaining at its minimum-energy state). However, for larger molecules with many flexible molecular coordinates (i.e., internal degrees of freedom with small force constants), the probability distribution of possible structures is not localized and the very concept of structure as a time-independent property is dubious. System dynamics are often completely disregarded (or substantially reduced) in predictive models; however, the vast majority of experimental techniques k that are used to train predictive models measure molecular properties as time k or ensemble averages or, most typically, both. Thus, we require computational methods that simulate real-life conditions of experimental measurements. In the simplest approach, we recognize that a system is a set of geometrical conformations and the lowest-energy conformation, that is, the ground state, is the most populated state. We try to locate the ground state by rotating σ bonds. This is the preferred approach for small, mostly rigid molecules. Conformational analysis is an important technique because the structure of a molecule can have a significant influence on molecular properties, including dictating the outcome of a reaction. For example, even a small molecule/ like amino-2-propanol has 17 different conformers. Evaluated at the B3LYP 6-31+ G(d) level of theory in aqueous solution using a polarizable continuum model IEF-PCM (integral equation formalism-polarizable continuum models), these conformers span about 3.7 kcal/mol in free energy and 10.5 kcal/mol (0.5 eV) in frontier orbital energies. How these quantities are relevant to computational predictions of toxicity will become clear in following sections. To consider dynamics of larger systems, Monte Carlo (MC) or molecular dynamics (MD) simulations can be employed. Both techniques are used to study equilibrium properties of a system, unless “steered” or “constrained” in an exact way to investigate non-equilibrium processes. The MC procedure is a technique used to generate an ensemble of states by producing random configurations of the system weighted by their Boltzmann probabilities. Since this

k k

Quantum Mechanics Approaches in Computational Toxicology 41

process would yield an unrealistically large ensemble, importance sampling is typically used in the Metropolis method, which produces conﬁgurations with a probability proportional to their Boltzmann factors. This approach biases the sampling toward low-energy structures. Molecular properties are

calculated as ensemble averages according to Equation 2.4, where Ai is the instantaneous value of a property A weighted by its probability, pi.Theformof pi is determined by the macroscopic properties that are common to all states of the ensemble. Thus, unlike a conformational search, MC (and MD) yield observables that include information from all populated states. ∑ ⟨ ⟩ A = Ai pi (2.4) i The MC method is a large calculation, requiring millions to tens-of-millions of configurations for larger biochemical systems to obtain meaningful property averages. To this end, it is almost always used in conjunction with fast molecular mechanics calculations, although hybrid QM/MM/MC approaches using semiempirical methods have been shown to be effective in describing organic or enzymatic reactions in aqueous solutions [7, 8]. The relevance of QM/MM/MC simulations to predictive toxicology is demonstrated in Table 2.1, where a clinical prodrug (CAS 623152-11-4) is assessed for basic physicochemical properties in the gas phase. The results k k from a simple geometry minimization are contrasted with those calculated as ensemble averages from MC simulations. The AM1 semiempirical method was employed in both cases, and the TIP4P hydration model (MM) was used in MC simulations to provide realistic equilibrium structures. Table 2.1 shows that significant differences in computed physicochemical properties can emerge between single-point and sampling methods. Notably, the magnitude

Table 2.1 Determining selected physicochemical properties for a clinical prodrug (CAS 623152-11-4) from an MC simulation versus a simple geometry optimization using the AM1 semiempirical method.

H N HN SASA O O 2 N (Å ): total/ N O N Dipole IP/EA hydrophilic/ (D) (eV) hydrophobic Cost (s)

AM1/MC simulation 4.3 9.0/0.6 754/121/428 13,266 AM1 minimization 2.9 8.9/0.9 735/127/415 1,670

All values were calculated in the gas phase: the dipole moment was assessed using CM1A charges; ionization potential (IP), and electron aﬃnity (EA) were determined from energies of frontier molecular orbitals; and the solvent-accessible surface area (SASA) was partitioned into hydrophilic and hydrophobic components.

k k

42 Computational Toxicology

of the dipole moment and electron affinities for this molecule show differences that could readily lead to different predictions of toxicity (see later discussions of electronic parameters). An increase in accuracy comes at a cost, however, withtheAM/MCsimulationbeingabouteighttimesmoreexpensivethana simple AM1 geometry minimization. The MD method provides an alternative strategy for generating large number of configurations necessary for simulations of condensed phases. Instead of randomly generating structures as in the MC method, it takes advantage of forces as the first derivatives of the force field equations in molecular mechanics. In the MD procedure, system energy is calculated, typically with a molecular mechanics method, and Newton’s classical equations of motion are applied to allow the system to accelerate along the trajectories established by the forces. After a set amount of time, the procedure is stopped, the new structure generated is considered as a new configuration to be averaged, and its energy is computed. This process is repeated until an ensemble of structures is produced that is comparable to one generated by the MC method. Each time-step typically takes about 1–2 fs; the overall dynamics trajectory can be as long as several nanoseconds. However, even longer trajectories (microsecond range) are often needed to encounter low-probability events associated with structural changes; thus, MD simulations are best suited for k computing thermodynamic data near the system’s ground state. k

2.6 Developing QM Descriptors

Now that we have outlined the basic levels of theory within quantum chemistry as well as approaches to study system dynamics, we can focus on how to use these methods to describe various biochemical systems in order to develop descriptors for predictive models. We start with the simplest and computationally least-demanding strategies and end with rigorous modeling of reaction coordinates.

2.6.1 Global Electronic Parameters In the simplest approach, we can develop predictive models using descriptors that consider the 3D structures of toxicants as a whole. This is a sensible approach if the biological activity/property is unknown and/or if we want to develop a generalized model of biological activity. There is a host of global physicochemical properties that can be calculated for any given molecular structure; the list below focuses on those that can be derived solely from QM calculations. It should be pointed out that all the descriptors outlined below are sensitive to molecular conformations, and thus underscore the need to consider structure dynamics in computational models of chemical toxicity.

k k

Quantum Mechanics Approaches in Computational Toxicology 43

2.6.1.1 Electrostatic Potential, Dipole, and Polarizability Electrostatic potential (ESP) reflects the degree to which a positive or negative test charge is attracted to or repelled by a molecule. ESP can be computed for any position r according to Equation 2.5. nuclei∑ Z 1 V (r)= k − 휙(r′) 휙(r′)dr′ (2.5) ESP | − | ∫ | − ′| k r rk r r ESP is especially useful when visualized on molecular surfaces, as it provides information about local polarity. In doing so, one can differentiate regions of local negative and positive potential, which may be indicative of chemical reactivity. Mapping of the ESP to a 3D grid of the molecular surface can be used to identify common patterns in the ESPs of several molecules when the goal is to correlate such features with another chemical property, for example, biological activity. Considered in conjunction with steric fields, ESP can be a powerful tool in predicting toxicity against a specific biological target in a read-across type approach. An important limitation in using ESP is that it represents ground states of molecules; when externally perturbed, such as in the course of a chemical reaction, considerable reorganization of the charge occurs. Figure 2.3 illustrates the utility of ESP mapped onto 3D molecular structures of two structurally dissimilar inhibitors of acetylcholinesterase (AChE). ESPs k were calculated using the Poisson-Boltzmann equation, where red and blue k areas mark regions of negative and positive potentials around the molecule. First, ESPs were determined for the ground states in aqueous solution. While structurally different, the two molecules display general similarities in their respective ESPs. Because inhibitor conformations change upon binding biological targets to accommodate steric requirements of the pocket and to maxi- mize favorable interactions with pocket residues, ESPs were also calculated for inhibitors within binding pockets of AChE. In this case, regions of negative and positive potentials can be identified that have comparable spatial distribution on the molecular surface. In vitro bioactivities of the two inhibitors, expressed

as pIC50, are also close at 5.65 and 5.63, respectively [9–11]. Molecular dipole is a related property best derived from quantum-mechanical calculations. A molecule has a dipole if the centers of positive and negative charge do not coincide. This separation of charged centers allows for a calculation of a molecular dipole moment, 휇, according to Equation 2.6 as a product of charge, q, and distance, r. 휇 = q × r (2.6) Molecular dipole moment is a metric of intensity of the electrical ﬁeld around a molecule. It provides a measure of how favorable a potential electrostatic interaction can be with a nearby molecule – for example, greater molecular dipole is associated with more favorable energies of hydration. This is because water is a polar solvent capable of forming dipole-dipole interactions with

k k

44 Computational Toxicology

O O O O O O N O O O N

Global minima in aqueous solution:

Lowest-energy structure in binding pocket (bottom):

Lowest-energy structure in binding pocket (top): k k Figure 2.3 Electrostatic potentials computed for two structurally different inhibitors of acetylcholinesterase computed using UCSF Chimera v1.11.2. Bound structures were obtained using flexible docking in Autodock Vina. (See color plate section for the color representation of this figure.)

the solute. To this end, dipole moment has been frequently used in predictive models to estimate transport across different media, for example, diffusion through a phospholipid bilayer. Additionally, dipole moment can characterize many host-guest interactions, such as electrostatic interactions of a ligand in a protein’s active site. The caveat in using dipole moment as a descriptor is in that dipoles, just like other physicochemical properties, can be very conforma- tionally dependent. Consider the simple case of a 1,2-dichloroethane, which has an anti-conformer that dominates in the gas phase (most calculations are performed in the gas phase), while its gauche-conformer is more populated in aqueous solution. While the gauche-conformer has a molecular dipole, the anti-conformer does not. To this end, predicting a specific toxicodynamic event via dipole-dipole interactions would lead to divergent outcomes depending on the conformer selected. Both ESP and molecular dipole result from a specific distribution of electron density within the molecule. However, such a distribution readily changes in response to an electric field created by a surrounding medium. The ability of the electron cloud to distort in response to an external field is referred

k k

Quantum Mechanics Approaches in Computational Toxicology 45

to as polarizability. Upon distortion, a dipole is typically induced in the molecule, adding to any permanent dipole already present. Often, the larger the molecular volume, the more polarizable the electrons. Polarizability can be a useful descriptor in predictive models; for example, C − Ibondsare known to be more reactive than C − Cl bonds in substitution and elimination reactions despite iodine (I) being less electronegative than chlorine (Cl). In this case, the large polarizability of I makes up for lower electronegativity. From a modeling perspective, without considering polarizability, a reactant model can be inadequate to predict molecular interactions because when a hard nucleophile approaches a C—X bond, it can induce a large dipole moment if X is highly polarizable. In QM, polarizability may be calculated by solving the coupled perturbed Hartree-Fock (CPHF) equations with electric ﬁeld perturbations. Alternatively, we can determine polarizability via QSPR (quantitative structure–property relationship) models; Hansch and Kurup showed that one can use summation of the valence electrons in a molecule as a rudimentary measure of its polarizability [12]. Moreover, the same study found this parameter to correlate with nerve toxicity in insects, amphibians, and mammals for a wide range of chemicals.

2.6.1.2 Global Electronic Parameters Derived from Frontier Molecular k k Orbitals (FMOs) Descriptors outlined in the previous section can be useful predictors of biological activity that relies on noncovalent interactions. In describing covalent interactions, we need to use molecular orbital theory and quantum-mechanical calculations. Biochemical reactions implicated in toxicodynamic events involve either Lewis acid–base, radical, or redox chemistry. The importance of frontier orbitals on chemical reactivity is well known, and is summarized in the frontier molecular orbital (FMO) theory pioneered by Fukui [13]. The general premise of FMO for acid–base chemistry is that the closer the energy of the HOMO 휀 ( HOMO) of the nucleophile (often the biological target) and the energy of 휀 the LUMO ( LUMO) of the electrophile (often the toxicant), the greater the HOMO-LUMO orbital overlap and the more facile the reaction, assuming comparable sterics. An analogous concept exists for radical chemistry, where the highest occupied molecular orbital is singly occupied molecular orbital (SOMO). Possible orbital interactions include SOMO-SOMO, which is a very rapid reaction that stabilizes two free radicals, or SOMO-HOMO and SOMO-LUMO reactions, which are reactions that proceed smoothly if the energy diﬀerence is small (akin to HOMO-LUMO reactions). An electron-rich free radical that has high potential energy behaves as a nucleophile and interacts with the LUMO of another molecule. Conversely, an electron-poor free radical that has low potential energy behaves as an electrophile and interacts with the HOMO in another molecule.

k k

46 Computational Toxicology

In a simple scenario, the FMO theory is used to assess a series of electrophilic toxicants against the same biological target. In such a case, one 휀 can calculate LUMO with the assumption that the lower the computed value, the more electrophilic the chemical. Increased electrophilicity is consistent with increased reactivity, and thus greater toxicity. Alternatively, to describe chemical reactivity broadly, in a chemical acting as Lewis acid or abase,the 휀 energy difference between HOMO and LUMO orbitals (Δ LUMO–HOMO)of the toxicant alone can provide useful guidance. For covalent reactions, an assumption can be made that a molecule is more reactive if the difference is small than if the difference is large. Voutchkova et al.andKostalet al. 휀 showed that Δ LUMO–HOMO along with log K o/w as a measure of bioavailability can be used to distinguish chemicals with none-to-low concern for aquatic toxicity from chemicals posing greater hazard [14–16]. The authors chose this nonspecific measure of covalent reactivity with the intention of including diverse mechanisms of action involved in acute and chronic aquatic toxicity pathways. Importantly, the FMO theory applies to soft-acid–soft-base reactions, according to Pearson’s hard and soft acids and bases (HSAB) principle. The HSAB principle dictates that soft acids (large and polarizable) preferentially coordinate with soft bases, and hard acids (small, often charged) react with k hard bases, assuming that all other factors, such as steric effects within a k molecule, are equal. Whereas soft–soft reactions have a small HOMO–LUMO energy gap and are covalent in nature, hard–hard reactions have a large HOMO–LUMO energy gap and are ionic in nature. There is a multitude of global electronic parameters that can be derived from FMOs and used to inform reactivity of toxicants. Table 2.2 lists the most

Table 2.2 Examples of global electronic parameters calculated from frontier molecular orbitals.

Parameter Deﬁnition Computational approach ( ) 휕 E 휀 + 휀 Chemical 휇 =−휒 = LUMO HOMO 휕 휐 potential, 휇 N (r) 2 ( ) ( ) 휕2E 휕휇 휀 − 휀 Chemical 휂 = = LUMO HOMO 휕N 2 휕N 2 hardness, 휂 휐(r) 휐(r) 1 2 Chemical S = 휂 휀 − 휀 softness, S LUMO HOMO 휇2 휀2 + 2휀 휀 + 휀2 Chemical 휔 = HOMO HOMO LUMO LUMO 2휂 4(휀 − 휀 ) electrophilicity, LUMO HOMO 휔

k k

Quantum Mechanics Approaches in Computational Toxicology 47

prominent ones that are applicable to problems encountered in computational toxicology, along with their definition and computational approach. A more detailed review can be found elsewhere [17]. Chemical potential, 휇, which is the negative of electronegativity in the Parr definition [18], measures the escaping tendency of electrons at constant external potential, 휐. Chemical hardness, 휂, captures the resistance of chemical species to electron transfer and is expressed as the second derivative of energy, E, with respect to the number of electrons, N, at constant external potential. Both chemical potential and hardness are related to the ionization potential (IP) and electron affinity (EA), which in turn can be determined 휀 휀 using HOMO and LUMO according to Koopman’s theorem. Global softness can be expressed as the inverse of chemical hardness; the approximation 휀 휀 of softness and hardness using HOMO and LUMO is consistent with the principles of HSAB and FMO theory discussed previously. The electrophilicity index, 휔, measures the second-order energy change of an electrophile as it is saturated with electrons [19]. The term “electroaccepting power” is sometimes used [20, 21] and this concept has shown to be useful in understanding electrophilicity of a wide variety of systems [22–24]. Gázquez et al.proposed that the same expression used for “electroaccepting power” can be applied to define “electrodonating power” (or nucleophilicity) of the system. In this ∓ 휇∓ 휇 휂∓ k case, ΔN =−( sys − env)∕ , the optimum amount of charge transferred, k would be negative when the system is donating charge to the environment (nucleophilicity index) and positive when the system is accepting charge from the environment (electrophilicity index) [21]. Thus, whether or not the system is gaining stability in the electron-transfer process would depend on the difference in the chemical potential of the system and the environment [25]. In Table 2.3, four different Michael acceptors, which are soft electrophiles, are shown along with glutathione (GSH)-binding rate constants and global electronic indices calculated from HOMO and LUMO energies at the mPW1PW91∕MIDIX+ level of theory. Organic chemistry principles dictate that the observed trend in reactivity is based on electronic effects: acrylates tend to be less reactive owing to competing resonance, although this effect is reduced by the electron-withdrawing effect of unsaturated C = C∕C ≡ Cbonds. Computed molecular electrophilicity and softness indices are generally con- sistentwiththistrend,exceptfortheC= CversusC≡ C case. However, when 휀 휔 the HOMO of GSH is considered in the calculations of or S, the predicted reactivity trend is consistent with experimental data across the entire series.

2.6.2 Local (Atom-Based) Electronic Parameters The value of local descriptors is in describing reactivity in a speciﬁc part of the molecule. The endgame is to capture reaction energetics that could be obtained from far more demanding reaction-coordinate modeling using

k k

48 Computational Toxicology

Table 2.3 Global electronic parameters for 3-penten-2-one, propargyl acrylate, allyl acrylate, and methyl acrylate derived from HOMO and LUMO energies at the mPW1PW91/MIDIX + level of theory.

O O O O

O O O

Exptl log kGSH 3.10 1.71 1.29 1.06 (L/mol/min) Chemical 1.90 1.88 1.79 1.81 electrophilicity, 휔 (eV) Chemical softness, 0.0889 0.0771 0.0782 0.0755 S (eV) Electrophilicity, 휔 1.88 1.79 1.72 1.71

w/HOMOGSH (eV) Softness, 휔 0.0931 0.0910 0.0894 0.0892

w/HOMOGSH (eV) k k simple and computationally inexpensive calculations of steric and electronic parameters of the toxicant’s (and biological target’s) ground state(s). This is particularly useful when large databases of compounds need to be screened rapidly for their toxic effects in comparative hazard assessment. While a speedy evaluation of molecular interactions between a xenobiotic and a biological target is possible (e.g., docking as used in computational drug discovery), the method employed is usually too simplistic to allow for reliable comparative assessment, particularly when covalent interactions are involved [26]. To this end, using local descriptors offers an alternative approach, which may take advantage of higher level of theory at the expense of model size. The list below is by no means exhaustive, but simply offers a few examples that have found use in predictive toxicology.

2.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) Local (atom-based) analogs of global electronic parameters can be derived to help define the preferred site for a chemical reaction in a molecule and/or to compare relativities of two or more structurally similar compounds. Some of these local electronic parameters are outlined in Table 2.4, as rationalized within the framework of density functional theory [27]. Local hardness is omitted from Table 2.4 as it contains information already provided by local softness. To evaluate local softness and electrophilicity, a Fukui function is definedinthefirstrowofTable2.4.AFukuifunctionisanormalizedfunction

k k

Quantum Mechanics Approaches in Computational Toxicology 49

Table 2.4 Local electronic parameters derived from density functional theory.

Fukui ( ) ( )− ≅ 𝝆 ( )−𝝆 ( )≅𝝆 ( )=|𝝓 ( )|2 𝝏𝝆(r) f r N r N−1 r HOMO r HOMO r function, f(r) f(r)= ( )+ ≅ 𝝆 ( )−𝝆 ( )≅𝝆 ( )=|𝝓 ( )|2 𝝏 f r N+1 r N r LUMO r LUMO r N 𝝊(r) ( )− ≅ ( )− ( ) f r qN r qN−1 r ( )+ ≅ ( )− ( ) f r qN+1 r qN r 1 |휙 (r)|2 Local softness, s(r)= s(r)− ≅ HOMO 휂( ) 휀 휀 s(r) ( r) LUMO − HOMO 훿휌(r) = |휙 |2 훿휇 LUMO(r) v(r) + s(r) ≅ 휀 휀 = f (r)S LUMO − HOMO (휀 + 휀 )2 Local 휔(r)=f (r)휔 휔( )− ≅ |휙 ( )|2 HOMO LUMO r HOMO r 휀 휀 electrophilicity, 4( LUMO − HOMO) 휔(r) (휀 + 휀 )2 휔( )+ ≅ |휙 ( )|2 HOMO LUMO r LUMO r 휀 휀 4( LUMO − HOMO)

that measures the propensity of a reactant to accept or donate electrons from or to another chemical system. Since for a molecular system the derivative of 휌 k electron density, , with respect to the number of electrons is discontinuous, k the Fukui function has different formalisms for electrophilic (f −) and nucleophilic (f +) attacks on a molecule. From Table 2.4, N refers to the closed-shell system, whereas N + 1andN − 1 refer to the system with an added and removed electron, respectively. On the basis of the work of Yang and Mortier [28], a Fukui function condensed to atoms can be evaluated through integration over atomic regions using population analysis. To this end, f − and f + on a particular atom (sometimes referred to as the Fukui index)canbecalculated using partial atomic charges. Although omitted from Table 2.3 for brevity, local softness and electrophilicity can also be expressed in terms of condensed Fukui functions. The quantitative interpretation of local electronic parameters has been debated upon for many years. It is agreed that local softness and hardness provide pointwise measures of the localized concentration of the corresponding global parameter [27]. From a local extension of the HSAB principle, it can be stipulated that the preferred site of the molecule to react in an orbital-controlled interaction is the region characterized by the maximum in the Fukui function. Conversely, if a reaction is charge-controlled (i.e., ionic reactions), it cannot be described by local hardness as the HSAB principle does not consider electrostatic effects. One should also be cautious in making extensive comparisons between different sites within the same molecule and across different molecules. Torrent-Sucarrat et al. recommend that only atoms with distinctively high softness/hardness within a molecule be used

k k

50 Computational Toxicology

in comparisons to other atoms in the same or other molecules [27]. In other words, one cannot draw conclusions from small differences and small values because in those cases local softness/hardness only represents the atomic contribution to the global parameter. Local electronic parameters have found use in predicting chemical reactivity in toxicity pathways. For example, Wondrousch et al., showed that the local electrophilicity index can be employed as a predictor of binding rates of Michael acceptors to GSH, outperforming global electrophilicity [29, 30]. The utility of condensed-to-atoms Fukui functions is shown in Figure 2.4. In Figure 2.4a, the four α, β-unsaturated carbonyls are soft electrophiles and known skin sensitizers [31]. The maxima in f + of the four compounds are consistent with the preferred sites of attack by skin protein nucleophiles [32]. While the haptenation mechanism for compounds 1, 3,and4 is 1,4-(Michael) addition, 1,2-addition (Schiff-base formation) is favored for compound 2 owing to steric hindrance of the γ-carbon. Therefore, the electronic effect of the methyl substituents effectively replaces their steric significance. When multiple 1,4-additions are possible (compounds 3 and 4), the less hindered site is identified correctly in each case. For 4, an alternate approach is provided in Figure 2.4b by considering the atomic contributions to the LUMO orbital in the LCAO-MO (molecular orbital represented as a linear combination k of atomic orbitals) method (see Table 2.4); the largest coefficients suggest k preferred attack sites. Fukui indices can be used to guide transition-state calculations; free energies of activation were briefly considered here at the attack sites suggested by Fukui indices. Calculated with the PM6 method, the low barrier indicates higher reactivity for compound 1 than for 2, 3,and4, which is consistent with higher sensitization potential for 1, as determined from local lymph node assays (LLNAs) [31].

(a)12344 (b) 1,2-Addition O 0.162 O 0.139 O 0.116 Disfavored 1,4- 0.076 0.169 addition 0.116 0.126 0.124 0.078 O 0.072 0.072 0.072 Favored 1,4-addition ΔG‡ (kcal/mol) 13.1 31.5 30.8 31.5 Sensitization Extreme Moderate Moderate Moderate potential

Figure 2.4 (a) Fukui indices, f +, computed for 4α,β-unsaturated carbonyls using Hirshfeld charges at the mPW1PW91/MIDIX+ level of theory. Maxima in the Fukui function are labeled with a black dot and a corresponding value; black circles mark the next highest values. Free energies of activation were calculated with the PM6 semiempirical method in the gas phase. Sensitization potential categories were derived from LLNA EC3% values [31]. (b) The LUMO orbital for 4.

k k

Quantum Mechanics Approaches in Computational Toxicology 51

2.6.2.2 Partial Atomic Charges Partial atomic charges are not only useful in determining Fukui indices; they can be used directly as descriptors of intra- and intermolecular electrostatic interactions. Many methodologies have been formulated to compute partial charges, which can be divided into four classes, I–IV. Class I charges are calculated using classical models of dipoles or are derived directly from experimental data. Partial equalization of orbital electronegativity is an exemplary method implemented by Gasteiger and Marsilli [33]. The advantage of Class I charges is high computational speed and so they are best applicable for quick assessments of large datasets of compounds. Class II charges involve direct partitioning of the molecular wave function or electron charge density into atomic contributions. The most popular schemes are Mulliken, Löwdin, natural charges (natural population analysis (NPA)) and Hirshfeld charges. From a practical perspective, Hirshfeld charges are perhaps the least sensitive to basis set size or choice of basis within this group [34]. Class III charges are determined from analysis of physical observables, which are calculated from the molecular wavefunction. The most prominent examples include CHarges from ELectrostatic Potentials using a Grid based method (CHELPG) and Merz-Kollman (MK) charge schemes, which are both derived from a molecular ESP (ESP, Section 2.6.1.1). Being derived from molecular ESP, k CHELPG, and MK charges are useful in characterizing intermolecular short- k and long-range electrostatic interactions. Their biggest drawback is in computing partial charges for atoms not on the molecular surface. Inﬂuenced by molecular conformations, partial charges for “buried” atoms can vary widely even if the ESP is insensitive to these conformational changes. To this end, prediction of intramolecular interactions using ESP charges can be questionable. Lastly, class IV charges are derived by a semiempirical mapping of a precursor charge of type II or III to reproduce experimentally determined observables such as dipole moments. The CM (Charge Model) 1–5 are good examples, and currently available to the user in various software packages, such as Gaussian or GAMESS. CM5 charges, derived from Hirshfeld population analysis, show high accuracy across diﬀerent chemical classes and remarkable independence of the level of theory used, and thus can be generally recommended [34].

2.6.2.3 Hydrogen-Bonding Interactions Hydrogen bonds are quintessential in biochemistry. Proteins and nucleic acids

are composed of numerous NH2 and OH groups that can donate hydrogen bonds and C = O groups that can accept them. To this end, hydrogen bonds guide the shape and function of biomolecules. They are also important in enzymatic catalysis to stabilize a ligand in a binding pocket. Hydrogen bonds can be used as descriptors either by counting atomic donor and acceptor sites or by assessing the “actual” number and energetics of the hydrogen bonds formed in

k k

52 Computational Toxicology

molecular simulations. In statistical mechanics, one can compute radial distribution functions which provide the normalized probability of having one molecule a given distance from another molecule – such interaction can be reflective of hydrogen bonding, in which case integration of these functions yields the number of hydrogen bonds present. Additionally, energy pair distributions can be calculated, which record the average number of molecules that interact with the system in question and their corresponding energies. It is important to note that while hydrogen bonding is mostly electrostatic in nature and can therefore be represented by the Coulomb potential between two atom-based charges, very strong hydrogen bonds have some orbital character. For those systems, high-level quantum mechanical calculations should be used to provide accurate results. The utility of predicting the number and strength of hydrogen-bonding interactions in aqueous medium as a metric of human skin permeability is illustrated on three phenols in Table 2.5. The well-known corrosiveness of these phenols can affect permeability rates; therefore, short duration of the test and very low concentrations are key to preserving the stratum corneum intact and providing meaningful metrics. Among the three phenols, the most permeable is 2,4-dichlorophenol, which can only donate and accept one hydrogen bond; Cl···H—OH interactions are considerably weaker. 1,2,3-Trihydroxybenzene is k more permeable because it can accept and donate nearly three hydrogen bonds; k however, its ability to form intramolecular hydrogen bonds reduces bonding to nearby water. 1,2,4-Trihydroxybenzene is the most water soluble and least permeable in the series as its substitution pattern accommodates surrounding water molecules more effectively than 1,2,3-trihydroxybenzene. Computed

Table 2.5 Hydrogen bonding (HB) as an inverse metric of human skin permeability.

OH OH OH OH

CI CI OH HO OH

Experimental log K P −4.70 −6.37 −7.46 (cm/s) HB donors/acceptors 1.0/1.0 2.8/2.8 2.9/3.3 Interaction energies −6.5 −13.8 −19.8 (kcal/mol) Dipole (D) 2.5 2.3 3.5

Skin permeability values were determined from in vitro studies [35]; hydrogen bonds and molecular dipole moments were calculated from MC simulations utilizing CM1A charges on the solute and TIP4P water model, averaged over 8 × 106 conﬁgurations. A cutoﬀ of −4kcal∕mol was used to specify hydrogen bonding in energy pair distributions.

k k

Quantum Mechanics Approaches in Computational Toxicology 53

energies of the intermolecular hydrogen bonds for the three compounds (last row in Table 2.5) are consistent with the experimental trend in skin permeability; the more favorable the interaction with water, the less permeable the compound. Note that the molecular dipole, a metric frequently used to inform solubility in polar medium, does not underline the trend in skin permeability for this series.

2.6.2.4 Bond Enthalpies This section segues into an ensuing discussion of descriptors derived from explicit interactions of two or more chemicals. Thus far we have largely sidelined radical chemistry, although Fukui functions, for example, can be derived for a radical attack on a molecule [36]. Bond dissociation energy (BDE) describes the enthalpy change for a homolytic cleavage of a chemical bond, that is, separation into free radicals. Understanding how readily molecules cleave their bonds to form free radicals is important to redox cycling. For example, the metabolism of quinone-like compounds involves enzymatic reduction by one or two electrons to form the corresponding semiquinone or hydroquinone, respectively. In the reverse process, the hydroquinone and semiquinone can be oxidized by molecular oxygen, generating a superoxide radical anion, which triggers production of other reactive oxygen species that k can lead to oxidative stress [37]. k Two antioxidants, hydroquinone (HQ) and t-butyl hydroquinone (tBHQ), were contrasted in their ability to undergo oxidation to the corresponding quinones using bond dissociation energies (Figure 2.5). Since tBHQ is an asymmetric molecule, two oxidation pathways are possible. The calculations suggest that oxidation of tBHQ is more facile than that of HQ. The diﬀerence in bond dissociation energies is about 2.5 kcal/mol, favoring tBHQ. The calculations shown in Figure 2.5 were performed at the M06-HF∕6-31+ G(d) level of theory in the gas phase.

2.6.3 Modeling Chemical Reactions In the previous section, the first step toward considering the biological target was to observe how the toxicant responds when an electron is added to or removed from its molecular wavefunction. The next step might be to include an atomistic yet highly reduced representation of the target, focusing on the chemical region of interest. Modeling chemical reactions translates to computing the energy profile of a reaction along its reaction coordinate(s). A detailed outline of the pathway is not always necessary; analysis of the reactant and product minima is sufficient to reveal reaction thermodynamics, that is, the favorability of a reaction to proceed toward products. Moreover, kinetic feasibility can be gauged by calculating transition-state energetics, although these calculations are far more difficult and resource intensive. In practice, a series of

k k

54 Computational Toxicology

3.5 Major tBHQ pathway 3 Minor tBHQ pathway ΔBDE 2.5

1. 5 BHQ (kcal/mol) t

– 1 HQ

H 0.5

ΔΔ 0 –0.5 OH O – O· O· O

OH OH OH O – O

Figure 2.5 Two-electron oxidation of hydroquinone (HQ) and t-butyl hydroquinone (tBHQ) to quinones, calculated at the M06-HF/6-31+G(d) level of theory in the gas phase. The solid black line represents energy diﬀerence between the HQ and tBHQ pathways, each recorded relative to enthalpies of the fully reduced HQ and tBHQ, respectively. The gray line represents the diﬀerence between the HQ pathway and the energetically less favorable k tBHQ pathway. Each specie considered in the oxidation process is recorded below the graph k with t-butyl substituents omitted for clarity. The species resulting from superoxide radical anion generation, phenoxy radical, and quinone are about 2.5 and 2.7 kcal/mol lower in energy in the (major) tBHQthanintheHQpathway.

educated guesses for minima and saddle points along the reaction coordinate can save computational time. Even then, in order to include covalent reactivity in descriptor calculations, a substantially reduced form of the biological target (and possibly the toxicant as well) is necessary to assess reaction energetics with adequate level of theory and obtain results in reasonable time frames. To consider electrostatic eﬀects of the medium surrounding the reactive centers, a hybrid QM/MM description of the system can be used. The calculation of transition states deserves further discussion. A transition state refers to a surface around a transition structure, the highest point on the minimum energy path that connects reactants to products on the free energy surface of the reacting system, that is, the reaction coordinate (RC). A free energy surface can be analyzed using a sampling method; however, as this is a computationally demanding exercise for QM or QM/MM calculations, a geometry minimization algorithm is often used instead to locate the highest saddle point along the RC. This state is characterized by a single imaginary frequency corresponding to a normal mode of the reaction coordinate. For geometry minimizations, following the reaction coordinate in the forward (i.e., toward products) and reverse (i.e., toward reactants) directions from the transition

k k

Quantum Mechanics Approaches in Computational Toxicology 55

structure is often crucial to verifying that the desired transition structure was in fact found. Reduced to a stationary point, a transition structure that appears to have the correct geometry may correspond to a diﬀerent process than the one of interest. The notion that a transition state allows for a population of molecules, that is, that some variation in degrees of freedom other than the reaction coordinate is permitted, is important to transition state theory.

k T ≠ k = B e−ΔG ∕RT (2.7) h Equation 2.7 is a frequently used expression of the transition state theory, which relates activation free energy, ΔG≠, to the rate constant of a unimolecular reaction. Transition state theory supposes that an equilibrium exists between the transition state and the reactants, and hence assumes population distribution of both states. In practice, ΔG≠ is calculated as the diﬀerence in free ≠ energy between the transition state and the reactants, G − GR,whereG = U0 + PV − kBTlnQ where U0 is the internal energy at 0 K and Q is the partition function. The ability to accurately calculate reaction rates is very important in toxicology. Mulliner et al. showed that calculated reaction barriers can be used to pre-

dict log kGSH in screening for electrophilicity-driven toxicity of α, β-unsaturated carbonyls [38]. Table 2.6 illustrates four different Michael acceptors for which k calculated barriers underline the observed trends in reaction kinetics. k In a different study, Kostal et al. examined the relevance of computed free energies of activation and reaction on the mutagenicity potential of simple epoxides [39]. The study assumed that all compounds were readily bioavailable, and that the chloride anion could be used as a model for DNA nucleotides given comparable nucleophilic strength. The study found that a cutoff value in free energies of reaction effectively separated mutagens from non-mutagens for the series of compounds considered. More recently, Zhang et al.investigated

Table 2.6 Reaction barriers correlated to GSH-binding rate constants for methylacrolein, 3-penten-2-one, allyl acrylate, and ethyl crotonate.

O O O O O O

Exptl log kGSH 2.31 1.43 1.29 −0.79 (L/mol/min) ΔE≠ (kJ/mol) 29.6 67.5 72.2 103.2

Reaction barriers were calculated at the B3LYP/6-31G(d,p) level of theory using methane thiol as a model nucleophile [38].

k k

56 Computational Toxicology

Table 2.7 Reaction energetics as a predictor of mutagenicity potentials for 2,2-diﬂuorooxirane, 2,2-dichlorooxirane, 2,3-dichlorooxirane, and 2,2,3-trichlorooxirane.

F O CI O O CI O F CI CI CI CI CI

Mutagenic potential ++ − − ΔG≠ (kcal/mol) 12.2 16.3 28.0 21.1 ΔG (kcal/mol) −26.7 −17.4 −14.3 −11.7 / All values were calculated at the MP2 6-31+ G(d, p) level of theory in aqueous solution using a continuum solvation model, IEF-PCM [39].

a diverse set of aliphatic epoxides, substituted styrene epoxides, and PAH

epoxides using DFT calculations [40]. Activation energies for SN2reactions between the epoxides and the guanine N7 site were well-correlated with the epoxides’ mutagenic potential; expectedly, higher mutagenic activity was associated with lower activation energies. Table 2.7 brieﬂy illustrates how reaction energetics can be used to distinguish mutagenic from non-mutagenic epoxides in a simple substitution series. In this k series, the mutagens have noticeably smaller free activation energies and forma- k tion of the corresponding alcohol is thermodynamically more favorable. Since reaction occurs on the less-substituted carbon, it is the electrostatic repulsion between the halogen and the incoming nucleophile that accounts for lower reactivity of the 2,3-substituted epoxides. Free energy changes associated with a reaction coordinate can sometimes be predicted using carefully chosen electronic parameters and physicochemical properties derived from ground-state structures. Figure 2.6 illustrates this approach for a series of skin-sensitizing chemicals that undergo haptenation

with skin proteins via the nucleophilic substitution mechanism (SN2) [41]. This approach is considerably less demanding than reaction pathway modeling and may be preferable when accuracy is of lesser concern than the ability to process compounds automatically (i.e., with minimal expert intervention) and fast.

2.6.4 QM/MM Calculations of Covalent Host-Guest Interactions Consideration of the entire (or substantial part of the) biological target is rarely a sound strategy in predictive toxicology using QM calculations, even when the nonreactive regions are described classically (i.e., QM/MM calculations). This is due to the enormous number of degrees of freedom that need to be sampled in MC or MD simulations to study reaction free energy surfaces. That being said, such approaches are sometimes used in computational chemistry to study enzymatic reactions involving a single or relatively few ligands. Such

k k

Quantum Mechanics Approaches in Computational Toxicology 57

0 ‡

25 rxn G G Δ Δ 20 –20 15 (kcal/mol) (kcal/mol) Predicted Predicted 10 Predicted –40 10 15 20 25 –40 –20 0 ‡ (a)Computed ΔG (kcal/mol) (b) Computed ΔGrxn (kcal/mol)

Figure 2.6 Linear models for free activation energies (a) and free energies of reaction (b) for † nucleophilic substitutions of halides, epoxides, and tosylates; ΔG and ΔGrxn values were computed in aqueous solution at the M06-2X/6-311 + G(d,p) level of theory; ≠ ΔG = 1706.38sα − 27.26EE − 243.69S − 1.76SASAα + 35.72(S × SASAα) − 4.02; N = 15; 2 2 휇 휇 R = 0.98; Radj = 0.97; RMS = 0.96. ΔGrxn = 801.01sα − 4.12 + 8.90SASAα + 2.04( × SASAα) 2 2 − 70.04; N = 15; R = 0.95; Radj = 0.93; RMS = 0.35. sβ = local softness on the α carbon; EE = electrostatic solvation energy; S = global softness; SASAα = surface accessible solvent area on the α carbon; 휇 = chemical potential [41].

calculations are rigorous enough to elucidate or propose novel chemical mechanisms, but are too resource intensive to carry out in the context of k typical hazard assessments. To this end, one could envision restrained use of k these calculations to ﬁll in data gaps in molecular mechanisms of toxicity for a class of chemicals. Since to the best of our knowledge, these calculations have not yet been employed in toxicology and no suitable examples exist, the approach is demonstrated using our recent study of the tautomerization mechanism in the microphage migration inhibitory factor (MIF). MIF is a critical upstream regulatory protein of the innate and acquired immune response. It is a keto-enol tautomerase capable of shifting the equilibrium from keto to the enol form for a host of substrates, such as dopachrome [42] and p-hydroxyphenylpyruvate (HPP) [43, 44]. HPP is an intermediate in the metabolism of phenylalanine, the aromatic side chain of which is hydroxylated to form tyrosine. In HPP tautomerization, it is expected that both a general acid and a base are involved. Preceding our investigation, the catalytic role of a nearby proline residue (Pro1) as a general base has been demonstrated by several experimental studies (Figure 2.7) [45–48]. However, the identity of the catalytic acid was not known, although proximal residues Tyr95 or Lys32 have been suspected in other studies (Figure 2.7). Our computational exploration of the dynamics of the bound HPP substrate revealed that Tyr95 and Lys32 are either not positioned well to donate a proton to the substrate or their calculated pKa’s suggest this is not a favorable process (Figure 2.7a). However, we observed that there are two proximal residues changing ionization state in the pH range 5–9, Pro1 and His62. Of these two, it is unlikely that His62 plays any role in the MIF catalytic mechanism as the

k k

58 Computational Toxicology

Asn212 1.7 Å Ile64 2.8 Å Tyr95

H2 2.4 Å H1 3.0 Å 3.4 Å Pro1 Lys32

(a) (b)

Figure 2.7 (a) Active site of MIF (1CA7) with bound HPP in the keto form from QM/MM/MC simulations. (b)Truncated MIF–HPP complex with about 680 water molecules; the ligand is marked in black. (See color plate section for the color representation of this ﬁgure.)

MIF mutant, His62T, possesses the same catalytic efficiency as the wild-type k [49]. Thus, our focus shifted to Pro1 as both a potential catalytic base and acid. k Computationally, this system’s reaction free energy surface can be studied using free energy perturbation (FEP) calculations in conjunction with MC simulations (MC/FEP). In practical terms, reaction coordinates are gradually incre- mented while all other internal degrees of freedom are sampled at every step. To reduce cost, the QM region only involved the substrate and side chains of the key residues in the binding pocket, totaling 83 heavy atoms; the rest of the system (Figure 2.7b) was treated classically with the optimized potentials for liquid simulations (all-atom) (OPLS-AA) force field. To further reduce cost, an inexpensive semiempirical method (Pairwise distance directed gaussian modification of the parametrized model number 3 (PDDG/PM3)) was used. Even then, to analyze a single proton transfer involved in the keto-enol tautomerization required about 18 billion calculations. A sample of a reaction free energy surface describing a proton transfer from Pro1 to HPP to yield the enolate is shown in Figure 2.8. Our study found that a proton transfer from HPP to Pro1 yielded an enolate intermediate that was well-positioned to be re-protonated on the oxygen by Pro1. Furthermore, this process was found to be kinetically most favorable and corresponded to an overall free energy change in agreement with experimental results. One could envision how this computational approach is applied to computational toxicology to fill mechanistic data gaps in key toxicodynamics phenomena. If a molecular mechanism is elucidated for a chemical class, then more simplistic methods, such as those illustrated in previous sections of this

k k

Quantum Mechanics Approaches in Computational Toxicology 59

4.1 enolate intermediate TS 4.0 1.7 Å

3.9 1.9 Å TS 2.3 Å

3.8

3.7

H-HPP + (Å) H-Pro1 Enolate intermediate 3.6 1.8 Å 1.8 Å 3.5 0.0 0.5 1.0 1. 5 2.0 2.0 Å 2.3 Å H-HPP – H-Pro1 (Å) (a)

(b)

Figure 2.8 (a) Computed 2D free energy map for the H2 proton transfer (see Figure 2.7). The white dashed line follows the minimum free energy path. (b) Snapshots of the transition state (TS) and the enolate intermediate illustrating relevant electrostatic interactions. The k resolution based on a single FEP window is 0.025 Å. (See color plate section for the color k representation of this ﬁgure.)

chapter, could be applied within that chemical class to screen new compounds for interactions with the biological target.

2.6.5 Medium Effects and Hydration Models Including a solvent in a computational model effectively increases system size. Solvation can be considered in all models described above: a single structure used to calculate physicochemical properties or a model that considers electrostatic or covalent interaction between multiple structures. Solvation affects electronic distribution in a molecule, altering electronic properties such as orbital energies, partial atomic charges, dipole moments, as well as molecular volume and accessible surface area by perturbing conformational equilibria. Processes in solution can be viewed as occurring on a lower free-energy surface than equivalent gas-based transformations. Since any chemical process takes place between minima on the free energy surface, if these states are characterized by differential solvation then the process will have a different equilibriumconstantinsolutionthaninthegasphase.Thesameappliesto transition states; a transition structure may be more or less well solvated than the reactant(s), implying smaller or larger free energy of activation than a corresponding gas-phase process. Many examples of this phenomenon

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60 Computational Toxicology

are known. For the tautomeric equilibrium between 4-hydroxypyridine and 4-pyridone, aqueous solvation changes the equilibrium constant by some six orders of magnitude [50]. In epoxide-forming cyclization reactions, solvation can drastically alter reaction rates depending on the ring substitutions [7]. Perhaps the most studied example is a nucleophilic substitution reaction between a chloride ion and methyl chloride. While the gas-phase activation free energy is about 3 kcal/mol, the diffuse negative charge associated with the transition structure compared to a chloride ion leads to preferential solvation of the reactants, and the rate in solution decreases by more than 15 orders of magnitude [51]. Despite these and many other solvent effects documented in the literature, gas-phase conditions are typically invoked in predictive modeling in toxicology owing to lower computational cost. In computational models, the solvent can be represented either implicitly or explicitly. For implicit models, the charge distribution of the solvent around the solute is replaced by a continuous electric field that represents a statistical average over all solvent degrees of freedom at thermal equilibrium. Generalized Born/surface area (GB/SA), polarizable continuum models (PCM) and the uni- versalsolvationmodelbasedonsoluteelectrondensity(SMD)arefrequently used examples. Performance of implicit solvation models varies. For neutral compounds, carefully parameterized models exhibit average errors over large k datasets on the order of about 0.5 kcal/mol. A single-charged species (−1∕+1) k poses more difficulties experimentally, and solvation free energies are accurate to about ± 2 − 5kcal∕mol. Reliable data for species of higher charge are extremely scarce, so no valid comparison can be made. The effect of solvation on calculated energetics is demonstrated in Table 2.8. Three benzyl halide skin sensitizers, 1-chloromethylpyrene1 ( ), 4-nitrobenzyl

Table 2.8 Reaction energetics computed for three benzyl halide skin sensitizers at the M06-2X/6-311 + G(d, p) level in gas phase and in aqueous solution.

CI O N+ –O Br Br 1 2 3

Sensitization potential Extreme Extreme Strong ΔG≠, gas (kcal/mol) −2.6 −13.4 −3.1 ΔG≠, aq (kcal/mol) 16.2 13.3 7.5 ΔG, gas (kcal/mol) −27.5 −37.1 −36.3 ΔG,aq(kcal/mol) −31.4 −36.2 −35.3

Methyl thiolate was used as a model nucleophile; polarizable continuum model (IEF-PCM) was used to model aqueous environment. Sensitization potency category was determined from LLNA EC3% values [32].

k k

Quantum Mechanics Approaches in Computational Toxicology 61

bromide (2), and benzyl bromide (3) were compared in their propensity to

react with cysteine residues of the Keap1 protein via SN2mechanism.Free energies of activation and reaction were calculated both in the gas phase and in aqueous solution using a polarizable continuum model (IEF-PCM) at the M06-2X∕6-311+G(d, p) level of theory. Table 2.8 shows lower barrier and more negative free energies for 2 and 3 than 1 in both gas phase and in solution, consistent with the fact that bromide is a better leaving group than chloride. In examining solvent effects, free activation energies increase owing to more favorable solvation of charge-localized reactants than charge-diffuse transition structures. Furthermore, 2 is kinetically favored in the gas phase, while 3 is favored in solution; the difference is considerable, about 16 kcal/mol. It can be proposed that the electron-withdrawing effect of the para-nitro group is more pronounced in solution than in the gas phase as increased charge separation is favored in a polar solvent. As it is a resonance withdrawing effect, the charge on the methylene carbon is made more negative by this effect (−0.50 vs −0.13 from Mulliken population analysis). Thus, the attack of the negatively charged nucleophile is less favorable on 4-nitrobenzyl bromide in aqueous solution. Note that neither gas nor aqueous phase reaction energies underline the differences in sensitization potency between 1, 2,and3 [32]. Skin permeability is an important factor and would need to be considered alongside reaction k energetics if a predictive model were to be developed. k A computationally more demanding alternative to implicit representation of the solvent is to include solvent molecules in the model explicitly. Explicit models are needed when knowledge of the explicit behavior of the surroundings is deemed to be important; the perfect example was presented in Section 2.6.2.3, where MC simulations of substituted phenols in aqueous solution were used to assess hydrogen bonding between the solutes and the solvent as a measure of skin permeability. The downside of explicit models is greater computational cost – explicit representation of a condensed phase leads to a system characterized by enormous number of degrees of freedom. Properties of such systems must be determined as statistical averages over phase space, as discussed in Section 2.5. To reduce computational costs, simple atomistic representations of the solvent are usually adopted, the simplest being an MM model.

2.7 Rational Design of Safer Chemicals

Thus far we have considered computational chemistry techniques that canbe applied to solve problems in predictive toxicology, whether to screen chemicals for their hazard or to fill mechanistic data gaps in toxicity pathways. Predicting toxicity of existing chemicals is fundamentally different from designing new molecules that fulfill certain functions yet have minimal biological activity.

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62 Computational Toxicology

Given the complexity of biochemical pathways in any target species, our limited understanding of these pathways, limited resources, and the far-reaching connotation of “chemical safety,” two approaches can be postulated: a nonspecific approach that considers elements of bioavailability and reactivity broadly to cover multiple endpoints and modes of action, and a highly refined approach that focuses on one type of molecular event. An example of the former was published by Voutchkova et al.andKostalet al., who developed design guidelines for chemicals with minimal acute and chronic aquatic toxicity using just two properties: the octanol-water partition/distribution coefficient (log P/D)and the HOMO-LUMO gap (Δ휀) [14, 15]. These properties were used to demar- cate a “safer chemical space”; any existing or newly designed chemical that falls within this space was postulated to be about 10 times more likely to have low aquatic toxicity than chemicals that do not meet the criteria (Figure 2.9). Since log P,logD,andΔ휀 canberelatedtomolecularstructure,theycan also be used qualitatively as design guidelines for developing new chemicals without carrying out calculations. While basic principles of organic chemistry can be applied to infer the degree of lipophilicity versus hydrophilicity from a chemical structure (as reflected in log P or D), correlation between Δ휀 and structure is harder to understand. Larger Δ휀 is generally associated with more stable molecules; a molecule trapped in a deep well on a free energy surface will k be both thermodynamically and kinetically stable. Ruiz-Morales investigated k polycyclic aromatic hydrocarbons (PAHs), which are potential carcinogens, and their fluorescence emission spectra to draw links between molecular size, structure, and the band gap, Δ휀 [52]. The band gap was found to increase going from linear arrangement to the most compact structure, which also increased

10 10

8 8

6 6

4 4 HOMO–LUMO gap (eV) HOMO–LUMO gap –2 0 2 4 6 –2 0 2 4 6

log Po/w log Do/w

Figure 2.9 Scatter plots of the octanol-water partition and distribution coeﬃcients (log P and log D) versus HOMO-LUMO gap (Δ휀) calculated at the mPW1PW91/MIDIX+ level of theory. The 500+ compounds represented are colored by category of concern for acute aquatic toxicity (red = high concern; orange = medium concern; yellow = low concern; green = no concern) based on a 96-h toxicity assay of the fathead minnow [14]. The highlighted upper-left quadrant marks the “safer chemical space” (log P/D < 1.7; Δ휀>6eV), which should be targeted in designing new molecules. Source: Adapted from Kostal et al. 2015 [14]. (See color plate section for the color representation of this ﬁgure.)

k k

Quantum Mechanics Approaches in Computational Toxicology 63

the number of localized resonant sextets. PAHs with zigzag structures and full resonance had the largest Δ휀 values, making them thermodynamically and kinetically stable. Stable PAHs are less likely to undergo metabolic oxidation to become potent carcinogens. The results were extrapolated to the fused aromatic ring region in asphaltenes, experimental Δ휀 values of which were obtained from fluorescence emission data, and were compared with the calculated Δ휀 values of free PAHs. Note that the design guidelines described in Figure 2.9 omitted function as a design criterion. In a recent study, Burello proposed a computational screening approach to design safer nanomaterials that considers both toxicity and functionality [53]. The work was based on the calculation of key physicochemical properties of nanomaterials. Some were related to inhalation toxicity (size, conduction band energy, charge, and solubility), and others to functionality (band gap, transmittance). These properties were proposed as a screening tool to select a pool of candidates for further experimental testing and development. For broad design rules, functional requirements may easily overlap with safety requirements. In a trivial example, consider a chemical that needs to be reactive in a certain species to function (e.g., an insecticide) but should not be reactive in other species. To this end, there is value in highly refined design approaches that can distinguish between two very similar biological targets. k Such approach should, for example, distinguish between a chemical covalently k interacting with an insect acetylcholinesterase and a mammal variant of this enzyme. Free energy perturbation calculations noted in Section 2.6.4 as means to study mechanisms of complex enzymatic reactions can also be used to capture such minute differences in free energies of binding. For noncovalent host-guest interactions, this process becomes much more computationally feasible as the entire system can be described classically. In an FEP calculation, the free energy associated with perturbing a state A of a system into state B is related to an average of a function of their energy difference evaluated by sampling for state A in the Zwanzig equation (Equation 2.8). ⟨ ( )⟩ − → EB EA ΔG(A B)=GB − GA =−kBT ln exp − (2.8) kBT A From Equation 2.8, states A and B may be different atoms or molecules in a classical treatment, in which case ΔG obtained is for mutating one molecule (or part of a molecule) into another, or they may correspond to different geometries of a molecule in a quantum-mechanical treatment, in which case a free energy map is generated along one or more reaction coordinates (see Section 2.6.4). Among the numerous existing applications of free energy perturbation calculations, the most prominent one is lead optimization of inhibitors in computer-guided drug discovery. In a recent study, which could in future find its analog in predictive toxicology, Cole et al. used FEP calculations in conjunction with MC and MD simulations to investigate binding of

k k

64 Computational Toxicology

F Figure 2.10 Benzyloxazole molecule used in the FEP O study by Cole et al. [54] and Bollini et al. [55]. The R NH group was iteratively modiﬁed to optimize binding aﬃnity toward HIV-RT. Source: Adapted from Cole R F N Cl et al. 2015 [54] and Bollini et al. 2013 [55].

benzyloxazole inhibitors to both the wild-type and the Y181C variant of the drug target HIV-RT (Figure 2.10) [54]. In their study, tyrosine to cysteine change in the binding pocket introduced a hydrophobic cavity, which preferred bulky nonpolar substituents on the benzyloxazole. Iterative mutations of the substituents according to Equation 2.8 revealed the most favorable free energy of binding for isopropyl and ethyl substituents. This result was confirmed with experimental studies that showed sub-10 nM potency for either substituent against both viral strains; by comparison, a methyl analog had a potency by about two orders of magnitude lower [55]. It is not difficult to envision that a similar approach could be applied to minimize activity against a specific biological target. In conjunction with broader design rules, such approach might offer viable strategy for the design k of safer chemicals. k

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38 Mulliner, D., Wondrousch, D., and Schuurmann, G. (2011) Predict- ing Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations. Org. Biomol. Chem., 9 (24), 8400–8412. 39 Kostal,J.,Voutchkova-Kostal,A.,Weeks,B.et al. (2012) A free energy approach to the prediction of oleﬁn and epoxide mutagenicity and carcinogenicity. Chem. Res. Toxicol., 25 (12), 2780–2787. 40 Zhang, J., Wang, C., Ji, L., and Liu, W. (2016) Modeling of toxicity-relevant electrophilic reactivity for guanine with epoxides: estimating the hard and soft acids and bases (HSAB) parameter as a predictor. Chem. Res. Toxicol., 29 (5), 841–850. 41 Kostal, J. and Voutchkova-Kostal, A. (2016) CADRE-SS, an in silico tool for predicting skin sensitization potential based on modeling of molecular interactions. Chem. Res. Toxicol., 29 (1), 58–64. 42 Rosengren, E., Bucala, R., Aman, P. et al. (1996) The immunoregulatory mediator macrophage migration inhibitory factor (MIF) catalyzes a tautomerization reaction. Mol. Med., 2 (1), 143–149. 43 Rosengren,E.,Aman,P.,Thelin,S.et al. (1997) The macrophage migration inhibitory factor MIF is a phenylpyruvate tautomerase. FEBS Lett., 417 (1), 85–88. 44 Lubetsky, J.B., Swope, M., Dealwis, C. et al. (1999) Pro-1 of macrophage k migration inhibitory factor functions as a catalytic base in the phenylpyru- k vate tautomerase activity. Biochemistry, 38 (22), 7346–7354. 45 Swope, M., Sun, H.W., Blake, P.R., and Lolis, E. (1998) Direct link between cytokine activity and a catalytic site for macrophage migration inhibitory factor. EMBO J., 17 (13), 3534–3541. 46 Stamps, S.L., Fitzgerald, M.C., and Whitman, C.P. (1998) Characterization of the role of the amino-terminal proline in the enzymatic activity catalyzed by macrophage migration inhibitory factor. Biochemistry, 37 (28), 10195–10202. 47 Taylor, A.B., Czerwinski, R.M., Johnson, W.H. et al. (1998) Crystal structure of 4-oxalocrotonate tautomerase inactivated by 2-oxo-3-pentynoate at 2.4 angstrom resolution: analysis and implications for the mechanism of inactivation and catalysis. Biochemistry, 37 (42), 14692–14700. 48 Taylor, A.B., Johnson, W.H., Czerwinski, R.M. et al. (1999) Crystal structure of macrophage migration inhibitory factor-complexed with (E)-2-ﬂuoro-p-bydroxycinnamate at 1.8 angstrom resolution: Implications for enzymatic catalysis and inhibition. Biochemistry, 38 (23), 7444–7452. 49 Bendrat, K., AlAbed, Y., Callaway, D.J.E. et al. (1997) Biochemical and nuta- tional investigations of the enzymatic activity of macrophage migration inhibitory factor. Biochemistry, 36 (49), 15356–15362. 50 Schlegel, H.B., Gund, P., and Fluder, E.M. (1982) Tautomerization of for- mamide, 2-pyridone, and 4-pyridone: an ab initio study. J. Am. Chem. Soc., 104, 5347–5351.

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51 Chandrasekhar, J., Smith, S.F., and Jorgensen, W.L. (1985) Theoretical examination of the SN2 reaction involving chloride ion and methyl chloride in the gas phase and aqueous solution. J. Am. Chem. Soc., 107 (1), 154–163. 52 Ruiz-Morales, Y. (2002) HOMO–LUMO gap as an index of molecular size and structure for polycyclic aromatic hydrocarbons (PAHs) and asphaltenes: a theoretical study. I. J. Phys. Chem. A, 106 (46), 11283–11308. 53 Burello, E. (2015) Computational design of safer nanomaterials. Environ. Sci.-Nano., 2 (5), 454–462. 54 Cole, D.J., Tirado-Rives, J., and Jorgensen, W.L. (2015) Molecular dynamics and Monte Carlo simulations for protein-ligand binding and inhibitor design. Biochim. Biophys. Acta, 1850 (5), 966–971. 55 Bollini, M., Gallardo-Macias, R., Spasov, K.A. et al. (2013) Optimization of benzyloxazoles as non-nucleoside inhibitors of HIV-1 reverse transcriptase to enhance Y181C potency. Bioorg. Med. Chem. Lett., 23 (4), 1110–1113.

k k

Part II

Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical

k k

Computational Approaches for Predicting hERG Activity Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade

LabMol – Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, Brazil

CHAPTER MENU

Introduction, 71 Computational Approaches, 73 Ligand-Based Approaches, 73 Structure-Based Approaches, 77 k Applications to Predict hERG Blockage, 77 k Other Computational Approaches Related to hERG Liability, 82 Final Remarks, 83

3.1 Introduction

The human ether-à-go-go related gene (hERG) is a gene (kcnh2) that encodes for a protein named Kv11.1, the α subunitofpotassiumionchannels.ThehERG channels are expressed in a variety of tissues, but its clinical signiﬁcance is more understood in the heart. This voltage-gated channel contributes to the coordi- nation of the heart beat by mediating the repolarization of the cardiac action potential [1]. The structure and function of the heart are commonly evaluated by performing electrocardiography. In this examination, the electrical activity of the heart is recorded, where the QT interval is the measure of the duration of ventricular depolarization and repolarization in an electrocardiogram [2]. Abnormalities in the structure of hERG caused by diﬀerent mutations are associated with increase or decrease of the QT interval, both related to sudden death. Short QT syndrome is a very rare condition caused by mutation and it is not discussed in this chapter. The long QT syndrome, also termed Torsade de Pointes, may be caused by genetic defects or induced by drugs [3]. Chemically induced prolongation of the QT interval is caused by the blockage of hERG

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72 Computational Toxicology

and this is an important clinical issue that is a point of focus of current drug development and regulation [4, 5]. The hERG channel earned notoriety for being associated with potentially fatal cardiac arrhythmia [6]. According to the WITHDRAWN database [7], twenty-seven drugs have been withdrawn from the market because of their blockage of hERG channels, including blockbusters [8] such as astemizole, terfenadine [9], cisapride [10], sertindole [11], and grepafloxacin [12]. This unfor- tunate condition is both related to the physiological importance of the channel and its three-dimensional structure. The hERG channel has high ligand promiscuity, mainly due to its large hydrophobic intracellular binding pocket and its multiple states (open, inactive, and closed) [13]. The high ligand promiscuity and risk of sudden death triggered great concern for hERG liability. Assessment of chemically induced QT interval prolongation and potential proarrhythmic risk has been made mandatory in clinical trials since 2005 by the US Food and Drug Administration (FDA) [14, 15]. There are several in vitro and in vivo assays performed during preclinical studies for evaluation of hERG [14]. Among the in vitro assays, conventional patch-clamp electrophysiology remains the preferred method. This method evaluates the electric current passing through hERG channels expressed in cells, before and after test compound addition [16]. The most common cell lines include human k embryonic kidney (HEK) 293 cells, Chinese hamster ovary (CHO) cells, or k Xenopus laevis oocytes (XO) cells. Usually, these cells present similar outcome and discrepancies are related with poor standardization of protocols [17, 18]. In vivo electrophysiology assays are usually performed in dogs and monkeys, but other species are used as well. The ionic mechanisms of repolarization in adult rats and mice differ from those in humans. Therefore, these animals are not suited for evaluation of hERG [14]. These assays are often delayed until the late stage of preclinical development, owing to cost and resource constraints. As an alternative, transgenic zebrafish models have been validated as a high-throughput animal model [19]. Despite all the testing guidelines, hERG blockage is still missed in clinical trials and may go undiscovered for years. Recent studies have shown that current in vitro and in vivo assays are not fully predictive of potential arrhythmia risks [5, 20]. For instance, rubidium efflux has shown to be oversensitive [21] and fluorescence assays are prone to experimental artifacts [22]. In addition, in general, drugs with faster overall kinetics and drugs with higher affinity for the open state than to the inactivated state induce more QT prolongation. These characteristics of drug-hERG interaction are likely to be more arrhythmogenic

but cannot be predicted by IC50 measurement alone [23]. To overcome this issue, there have been eﬀorts to develop new in vitro assays that aim to modern- ize and improve the quality of early hERG evaluation [5]. The Comprehensive in vitro Proarrhythmia Assay (CiPA) considers an approach involving a multiple ion channel panel, in silico evaluation, human cardiomyocyte stem cell

k k

Computational Approaches for Predicting hERG Activity 73

assays, and in human phase 1 ECGs [24]. Despite some progress, experimental testing is still expensive and time consuming, which has prompted scientists to search for more cost-eﬀective and mechanistic paradigms to assess drug liability. As a practical solution, computational approaches have earned recognition to manage chemical-biological data and evaluate chemicals not yet tested in any experimental protocol [25].

3.2 Computational Approaches

The recent increase of hERG blockage data, mostly produced by automated hERG assays [26], has been contributing to a rapid increase in the number of compounds tested, helping populate bioactivity chemical databases such as ChEMBL [27]. These large datasets promote great possibilities for computational studies, such as structure-activity relationship (SAR) analysis, quantitative structure-activity relationship (QSAR) modeling, and pharmacophore modeling [28]. The publicly available hERG data jumped from 15 molecules [29], when the ﬁrst in silico model was developed (2002), to 6690, as used by the most recent study [30]. As of date, a full crystal structure of hERG is not available yet, but structural details for hERG and molecular docking have been k performed by homology modeling [31]. k

3.3 Ligand-Based Approaches

Most of the ligand-based approaches consist of QSAR models, but several pharmacophore models have been proposed as well. QSAR modeling is an important approach used to design novel bioactive compounds or to evaluate chemical safety. This approach employs statistical or machine learning techniques to establish predictive correlations between intrinsic chemical properties (chemical descriptors) and measured bioactivity or biological properties, such as toxicity, and the resulting models are used to predict the respective target properties of novel or untested compounds [32, 33]. Several QSAR models to predict hERG blockage have been published over the years. A comprehensive analysis of all the publications up to 2013 of these models has been made by our group and published elsewhere [34]. As of 2014, a critical analysis reveals that the vast majority of the published QSAR models do not comply with the standard validation procedures and the diﬀerent statistical criteria described in the best practices of QSAR modeling [35, 36]. Most of those models are indeed not compliant with the Orga- nization for Economic Co-operation and Development (OECD) guidance on QSAR model development and validation [37]. More speciﬁcally, the primary drawbacks of the majority of published QSAR studies are as follows: (i) most

k k

74 Computational Toxicology

models do not have proof of passing the Y-randomization test [38–71]; (ii) no proof of applicability domain (AD) estimation is provided [38–63, 71–75]; and (iii) model predictivity is not acceptable [76–78]. As a consequence, despite a significant number of QSAR models for hERG blockage being available in the literature, only very few models could actually be employed to predict hERG blockage [77, 79–81]. Also, most of the models and associated datasets used to build them are not available online to the scientific community. Here,weupdatethecomprehensiveanalysisofQSARmodelsforpredict- ing hERG blockage recently made by our group [34], commenting on the most interesting works published recently. The summary of these publications can be found in Table 3.1. All these studies used collated data on inhibition of hERG K+ channels on various cells evaluated by using the patch-clamp technique. A critical analysis of recent models reveals that most of the models present the same failures as reported in the previous study [34]. These major drawbacks compromise the practical use of these models for reliable assessment of drug-induced QT syndrome. A few improvements are worth commenting on. It has been shown that acids and zwitterions have less affinity for hERG than bases and neutral compounds [90]. Nikolov et al. [82] showed that a higher predictivity of local QSAR model using a decision tree over a global QSAR model based on a larger diverse training set for acids and zwitterionic k ampholytes, but at the expense of coverage. In a recent study [86], the authors k developed QSAR models for predicting the blockage of multiple ion channels, including hERG. These models have been implemented in a software called the Cardiac Safety Simulator (https://www.certara.com/software/pbpk- modeling-and-simulation/cardiac-safety-simulator/), along with models for slow delayed rectifying potassium current, peak sodium current, and late calcium current. Despite the innovative approaches, the authors did not follow the best practices for model development and validation [36], not curating properly the datasets and there is no proof of Y-randomization evaluation. In another recent study [30], the authors removed from the hERG dataset extracted from ChEMBL [91] the assays known not to be predictive of hERG, which is a practice that should be considered by modelers from now on, but unfortunately the authors did not make these models publicly available. The models developed by Braga et al. [34, 85] still represent an innovative and useful tool for the scientific community. The authors developed both binary with correct classification rate (CCR) = 0.8 and multiclass models for hERG using the ChEMBL data containing 5984 compounds. Robust and externally predictive binary (CCR = 0.8) and multiclass models (accuracy = 0.7) were developed. These models have been implemented in a public web app available at http://www.labmol.com.br/predherg. This app has been updated for this chapter and the improvements are discussed in Section 3.5.1. Several pharmacophore models have been generated over the years [38, 48, 59, 77, 81, 89, 92–98]. A pharmacophore is, according to IUPAC [99],

k k (Continued) Yes Yes [85] No No [86] Yes NoNo [82] No [84] 0.7 0.93 Yes Yes [34] 0.8 − ≈ 11.25% ≈ 0.78 = 0.83 = 0.91 = 0.92, 0.94 0.83, 0.84, 0.67 0.92 0.68 0.90 0.88, 0.75 0.90, 0.84, 0.86 binary = multiclass ======2 RMSE Acc NRMSE Se Se Se Acc BAC BAC Sp Sp R Sp k k Statistical/ ML approach Performance AD Y-rand References DT SVM, RF, Tree bagging, GBM ANN Structural, atomic, conformational MACCS, FeatMorgan, Pharmacophore, PubChem Experimental data, PADEL Various WOMBAT-PK Dataset reference/ database Descriptors QSAR studies for predicting hERG blockage, published during the period 2014-2016. Number of compounds 236 137426444899 [83] ChEMBL, Global, ECFP5,984 LCB ChEMBL239 PLR Morgan, CDK Tox-database SVM Table 3.1

k k e; e No No [88] No No [89] No No [30] -randomization. Y 0.93 Yes No [87] 0.83 -rand, 0.78 − − Y 0.72 0.81 0.90 0.87 − − − − − 0.76 0.86 0.72 = 0.69 0.54 0.64 = 0.63 0.79 0.75 ======2 pred -nearest neighbors; LCB, Laplacian-corrected Bayesian; ML, machine r Se Sp Se Acc Sp k k k NN, k NN k Statistical/ ML approach Performance AD Y-rand References RP AUC NB Se GBM SVM Sp PADEL Physicochemical, topological OCHEM, Fenitchel Dataset reference/ database Descriptors (Continued) Number of compounds 400172 [71]6690 CORAL ChEMBL 587 Monte Carlo [60] Pharmacophore learning; NB, naïve Bayes; NRMSE,error; normalized RP, root-mean-square recursive error; partitioning; PLR, Se, partial sensitivity; logistic Sp, regression; specificity; RF, SVM, random support forest; vector RMSE, machines; root-mean-squar ECFP, extended connectivity fingerprints; GBM, gradient-boosting method; Table 3.1 Acc, accuracy; AD, applicability domain; ANN, artificial neural networks; BAC, balanced accuracy; CDK, chemistry development kit; DT, decision tre

k k

Computational Approaches for Predicting hERG Activity 77

“an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a speciﬁc biological target and to trigger (or block) its biological response.” A pharmacophore model can be generated by superposing a set of active molecules and extracting common chemical features that are essential for their bioactivity or by probing possible interaction points between the macromolecules of the active ligands and target. Most of the pharmacophore hypotheses reported for hERG were generated using a limited number of compounds. They also were typically shown to be less predictive than QSAR models. A few groups have integrated pharmacophore models as descriptors for QSAR models [48, 77, 81, 89].

3.4 Structure-Based Approaches

Owing to the absence of hERG experimental 3D structures, homology modeling techniques have been applied to potassium channels that were used as templates. Several homology models have been proposed so far [100–104], most of them better contributing to comprehending possible channel functioning and drug binding interactions rather than being used as a predictive tool. The structure of hERG is proposed as consisting of a tetramer containing six α-helical transmembrane segments defined as S1–S6. The voltage sensor domain (VSD) k is embedded by the S1-S4 segments, which defines the open, closed, and inac- k tivated states. These states are administered by the positively charged lysine and arginine residues in the S4 helix in response to changes in the membrane potential. The pore domain is formed by the S5 and S6 segments, which allows potassium ions to cross the membrane. A selectivity filter loop is present in these segments along with a S5-P linker. Both N-andC-terminal domains are located on the intracellular side of the membrane [1, 105]. Usually, hERG inhibitors interact with the pore module. Several homology models indicated that important amino acids include T623, S624, and V625 from the P1, and residues G648, Y652, and F656 located on the helix S6 [106–108]. A recent study indicated that there is a high-affinity aromatic binding determinant for blockers located in helix S5, F557 [31]. Common templates for hERG channels are the crystal structure of KcsA of Streptomyces lividans in the closed state [109], KvAP of Mus musculus [110], MthK of Methanothermobacter [111], and more recently the Kv1.2 of Rattusnorvegicus [112, 113]. The last three are in the open state. A structural representation of the hERG channel is presented in Figure 3.1.

3.5 Applications to Predict hERG Blockage

Several tools, essentially from academic groups, are freely available to predict the hERG blockage. We can also mention commercial tools such as

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Y623 Y623

S624 S624

Y652 Y652

F656 F656

(a) (b)

Figure 3.1 Structural representation of hERG channel generated through homology modeling. This model was generated using the open conformation of the hERG channel (UniProt accession number: Q12809) and the KvAP crystal structure (PDB code: 1ORQ) of Mus musculus [110] as template. The model was generated using a similar protocol reported by Farid et al. [102]. (a) Tetrameric representation of hERG channel. (b) Dimeric k representation of S5 and S6 segments. The residues usually involved in drug interaction are k represented by sticks. Each black sphere represents a potassium ion. (See color plate section for the color representation of this ﬁgure.)

StarDrop - ADME QSAR models (Optibrium Ltd., http://www.optibrium.com/

stardrop/), which has a hERG model that predicts pIC50 values for inhibition of hERG K+ channels expressed in mammalian cells. This model uses 158 descriptors, including a mixture of physicochemical properties and ﬁngerprints. The model was trained using 135 compounds, with 35 compounds in the test set. The quality of the models during the external validation achieved a Q2 of 0.72 and RMSE of 0.64 log unit. An important feature of the StarDrop module is the ability to use a user’s hERG data to improve the built-in model. Another commercial tool is QuikProp from Schrödinger Suite (Schrödinger, LLC, http://

www.schrodinger.com/), designed to predict pIC50 values for inhibition of hERG K+ channels expressed in mammalian cells. The model was trained using 47 compounds, and the quality of the models is R2 of 0.76 and RMSE of 0.8 log unit. The AdmetSAR (http://lmmd.ecust.edu.cn:8000) is an open webserver for many endpoints and it has a binary QSAR classiﬁcation model for hERG blockage, developed from an academic group [114]. The model was trained > using 368 molecules including 79 strong hERG inhibitors (pIC50 6.0 mol/L)

k k

Computational Approaches for Predicting hERG Activity 79

≤ and 289 weak hERG inhibitors (pIC50 6.0 mol/L). The Pred-hERG web app (http://www.labmol.com.br/predherg) is an app developed by the LabMol - Laboratory of Molecular Modeling and Drug Design of the Federal University of Goias, Brazil [34, 85]. This app is based on statistically signiﬁcant and externally predictive QSAR models of hERG blockage. The models were built using the largest publicly available dataset of structurally diverse compounds including a variety of drug classes (see Section 3.6 for more information).

3.5.1 Pred-hERG Web App The Pred-hERG is a web app available for early identiﬁcation of putative hERG blockers and non-blockers in chemical libraries, based on binary and multiclass QSAR models. Recently, we released Pred-hERG 4.0, an enhanced version based on ChEMBL v.21 and other public data with 16,932 chemical records in raw data. After a careful data curation [115], 8489 compounds including 4437 non-blockers (activity ≥ 10 μM), 2753 weak/moderate blockers (1 μM ≤ activity ≤ 10 μM), and 1299 strong blockers (activity ≤ 1 μM) remained for the modeling. To the best of our knowledge, this is the largest publicly available hERG dataset [85, 116]. We succeeded in developing a robust and externally predictive binary (CCR ≈ 0.82) and multiclass models (accuracy ≈ 0.7). These models are available at Pred-hERG web app, which k is freely available to the public at http://www.labmol.com.br/predherg. k Prediction time for a compound is just a few seconds. Three following outcomes are available for the users: prediction by binary model, prediction by multiclass model, and the probability maps of atomic contribution (Figure 3.2). As shown in Figure 3.2, the Pred-hERG web app provides highly accurate predictions along with predicted probability of fragment contributions represented in a ﬁgure, allowing end users to better understand the prediction and propose new compounds. For binary models, the probability of the compound to be a hERG non-blocker or blocker is reported in parenthesis (in this order). The predicted probability represents the result of a consensus prediction for 500 decision trees from a random forest model (e.g., 375 decision trees predicted the compound to be blocker, so the probability to be blocker is 75%). Multiclass models have similar outcome, but the user will receive the probability of a compound to be a non-blocker, weak/moderate blocker, or strong blocker, respectively. The association of fragment importance derived from QSAR models clearly helps in ending the stigma attached to QSAR as a “black box.” Recently, we have shown that toxicity prediction solely based on structural alerts is unreliable in most cases and should be avoided [117]. Therefore, alerts

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Figure 3.2 Outcome interpretation from the Pred-hERG web app. Binary prediction, multiclass prediction, and predicted probability maps (PPM) extracted from binary models using Morgan fingerprints with 2048 bits. In the PPMs, green atoms or fragments represent contribution toward blockage of hERG, while pink indicate that it contributes to decrease of hERG blockage, and gray means no contribution. Gray isolines delimit the region of split between the positive (green) and the negative (pink) contribution. (See color plate section for the color representation of this figure.) k k should be used only as a hypothesis of toxicity mechanism and after validation by QSAR. For illustrating this, we selected two well-known structural alerts for hERG and compared them with predictions made by QSAR. The presence of a tertiary amine is one of the most described substructure alerts for hERG blockage [50, 118]. Previously [34], we presented 16 examples showing in detail the electronic and steric change in the environment of the tertiary amine that could transform a potent hERG blocker to a less potent blocker or even to a non-blocker compound. Figure 3.3a shows the compares theuse of a tertiary amine as a predictor of hERG blockage versus the predictive power of QSAR models. In the studied dataset, we found 5984 compounds (3436 blockers and 2548 non-blockers) with at least one tertiary amine. Employing only the alert, the probability of success in selecting a hERG blocker from this dataset is 57%. To evaluate the true predictive power of the developed QSAR models, we rean- alyzed the external sets during the fivefold external cross-validation, quantifying the predictions only for the compounds having at least one tertiary amine moiety for each external fold. We found a probability of 84% of success in selecting a hERG blocker (selectivity ≈ 0.84), with a good correct classification rate (CCR ≈ 0.8) and good ability to classify non-blocker compounds as well (specificity ≈ 0.76). The Pred-hERG combines quantitative toxicity prediction with a visualization output, ensuring both high predictivity and interpretability of models. In the PPMs, green atoms or fragments represent a contribution toward blockage

k k

(a)

(b) k k

Figure 3.3 Comparison of structural alerts and the Pred-hERG QSAR models for prediction of hERG blockage. (a) Tertiary amines. (b) Aryl chloride. PP, predicted probability. (See color plate section for the color representation of this ﬁgure.)

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of hERG, while pink indicate contribution to decrease of hERG blockage, and gray indicate no contribution. Gray isolines delimit the region of the split between the positive (green) and the negative (pink) contributions (see Figure 3.2). Owing to its high predictive power associated with a visualization method that allows easy model interpretation, Pred-hERG represents an innovative tool to predict hERG blockage contributing to the design of safer compounds. Fur- thermore, Pred-hERG 4.0 implements the largest publicly available dataset for hERG blockage, which spans the largest chemical space in terms of available tools for the use of the scientiﬁc community.

3.6 Other Computational Approaches Related to hERG Liability

In addition to ligand and structure-based approaches to predict and/or understand characteristics that influence hERG blockage, other computational approaches have been proposed to better comprehend this phenomenon. Recently [119], a data mining approach that identifies from electronic health k records (EHRs) common clinical features associated with the use of drugs k that do or do not prolong the QT interval identified that the combination of two drugs not associated with QT prolongations appeared to increase the liability for long QT syndrome. EHR offer unprecedented opportunities to comprehend post-marketed drug issues [120]. As another example, Romero et al. [121] developed a computational approach to reveal the pharmacological properties of drugs that are most likely to induce long QT prolongation that would be expected to result from genetic variants. From now on, new computational studies must address current knowledge to better predict hERG blockage. The CiPA recommends that in silico models of the human ventricular myocyte should use data obtained at physiological temperatures, but as temperature may have an effect on channel gating and drug binding rate [122], a base model dealing with temperature-dependent gating changes without drugs and a pharmacodynamics component simulating temperature-dependent drug binding kinetics should be used [123]. The role of hERG is well characterized in ventricular repolarization. How- ever, other ion channels are also related to the cardiac electrical activity, and, therefore, evaluation of Torsades de Pointes based only on hERG appears now to be not enough to predict cardiac toxicity [124]. Few computational studies have introduced multiple ion approaches to predict cardiac toxicity [71, 86], but well-validated models are required to use these models on a large scale. We suggest that current hERG models should be used along with other ion channels models to better predict cardiac toxicity.

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Computational Approaches for Predicting hERG Activity 83

3.7 Final Remarks

Despite some progress made in the last decade to predict hERG blockage, we observe that these efforts still do not fully guarantee that new chemicals do not induce QT prolongation. The absence of a crystal structure of hERG associated with current flaws in experimental assays reveals that there is significant scope for development of innovative studies using modern data and considering key information gathered in recent years. Certainly, the newly proposed experimental assays for hERG will increase the quality and the correlation of these assays with human response, allowing computational modelers to generate more comprehensive and predictive models.

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83 Doddareddy, M.R., Klaasse, E.C., Shagufta et al. (2010) Prospective validation of a comprehensive in silico hERG model and its applications to commercial compound and drug databases. ChemMedChem, 5, 716–729. 84 Liu, L., Lu, J., Lu, Y. et al. (2014) Novel Bayesian classification models for predicting compounds blocking hERG potassium channels. Acta Pharmacol. Sin., 35, 1093–1102. 85 Braga, R.C., Alves, V.M., Silva, M.F.B. et al. (2015) Pred-hERG: a novel web-accessible computational tool for predicting cardiac toxicity. Mol. Inform., 34, 698–701. 86 Wisniowska,´ B., Mendyk, A., Szle˛k, J. et al. (2015) Enhanced QSAR models for drug-triggered inhibition of the main cardiac ion currents. J. Appl. Tox- icol., 35, 1030–1039. 87 Gobbi, M., Beeg, M., Toropova, M.A. et al. (2016) Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds. Toxicol. Lett., 250–251, 42–46. 88 Chavan, S., Abdelaziz, A., Wiklander, J.G., and Nicholls, I.A. (2016) A k-nearest neighbor classification of hERG K+ channel blockers. J. Comput. Aided Mol. Des., 30, 229–236. 89 Wang, S., Sun, H., Liu, H. et al. (2016) ADMET evaluation in Drug discovery. 16. Predicting hERG blockers by combining multiple pharmacophores k and machine learning approaches. Mol. Pharm., 13, 2855–2866. k 90 Waring, M.J. and Johnstone, C. (2007) A quantitative assessment of hERG liability as a function of lipophilicity. Bioorg.Med.Chem.Lett., 17, 1759–1764. 91 Willighagen, E.L., Waagmeester, A., Spjuth, O. et al. (2013) The ChEMBL database as linked open data. JCheminform, 5, 23. 92 Aronov, A.M. (2008) Tuning out of hERG. Curr. Opin. Drug Discov. Dev., 11, 128–40. 93 Aronov, A.M. (2006) Common pharmacophores for uncharged human ether-a-go-go-related gene (hERG) blockers. J. Med. Chem., 49, 6917–6921. 94 Du, L.-P., Tsai, K.-C., Li, M.-Y. et al. (2004) The pharmacophore hypotheses of I(Kr) potassium channel blockers: novel class III antiarrhythmic agents. Bioorg Med Chem Lett, 14, 4771–4777. 95 Durdagi, S., Duff, H.J., and Noskov, S.Y. (2011) Combined receptor and ligand-based approach to the universal pharmacophore model development for studies of drug blockade to the hERG1 pore domain. J. Chem. Inf. Model., 51, 463–474. 96 Kratz, J.M., Schuster, D., Edtbauer, M. et al. (2014) Experimentally validated hERG pharmacophore models as cardiotoxicity prediction tools. J. Chem.Inf.Model., 54, 2887–2901. 97 Matyus, P., Borosy, A.P., Varro, A. et al. (1998) Development of pharmacophores for inhibitors of the rapid component of the cardiac delayed rectifier potassium current. Int. J. Quantum Chem., 69, 21–30.

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98 Peukert, S., Brendel, J., Pirard, B. et al. (2004) Pharmacophore-based search, synthesis, and biological evaluation of anthranilic amides as novel blockers of the Kv1.5 channel. Bioorg.Med.Chem.Lett., 14, 2823–2827. 99 Wermuth, C.G.C., Ganellin, C.C.R., Lindberg, P., and Mitscher, L. (1998) Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl. Chem., 70, 1129–1143. 100 Schmidtke, P., Ciantar, M., Theret, I., and Ducrot, P. (2014) Dynamics of hERG closure allow novel insights into hERG blocking by small molecules. J.Chem.Inf.Model., 54, 2320–2333. 101 Durdagi, S., Deshpande, S., Duff, H.J., and Noskov, S.Y. (2012) Modeling of open, closed, and open-inactivated states of the hERG1 channel: structural mechanisms of the state-dependent drug binding. J. Chem. Inf. Model., 52, 2760–2774. 102 Farid, R., Day, T., Friesner, R.A., and Pearlstein, R.A. (2006) New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg. Med. Chem., 14, 3160–3173. 103 Masetti, M., Cavalli, A., and Recanatini, M. (2008) Modeling the hERG potassium channel in a phospholipid bilayer: molecular dynamics and drug docking studies. JComputChem, 29, 795–808. 104 Stary, A., Wacker, S.J., Boukharta, L. et al. (2010) Toward a consensus k model of the hERG potassium channel. ChemMedChem, 5, 455–467. k 105 Durdagi, S., Subbotina, J., Lees-Miller, J. et al. (2010) Insights into the molecular mechanism of hERG1 channel activation and blockade by drugs. Curr.Med.Chem., 17, 3514–3532. 106 Sanguinetti, M.C., Chen, J., Fernandez, D. et al. (2005) Physicochemi- cal basis for binding and voltage-dependent block of hERG channels by structurally diverse drugs. Novartis Found Symp., 266, 159–166. 107 Kamiya, K., Niwa, R., Morishima, M. et al. (2008) Molecular determinants of hERG channel block by terfenadine and cisapride. J. Pharmacol. Sci., 108, 301–307. 108 Lees-Miller, J.P., Duan, Y., Teng, G.Q., and Duff, H.J. (2000) Molecular determinant of high-affinity dofetilide binding to HERG1 expressed in Xenopus oocytes: involvement of S6 sites. Mol. Pharmacol., 57, 367–374. 109 Doyle, D.A., Morais Cabral, J., Pfuetzner, R.A. et al. (1998) The structure of the potassium channel: molecular basis of K+ conduction and selectivity. Science, 280, 69–77. 110 Jiang, Y., Lee, A., Chen, J. et al. (2003) X-ray structure of a voltage-dependent K+ channel. Nature, 423, 33–41. 111 Jiang, Y., Lee, A., Chen, J. et al. (2002) Crystal structure and mechanism of a calcium-gated potassium channel. Nature, 417, 515–522. 112 Long, S.B., Tao, X., Campbell, E.B., and MacKinnon, R. (2007) Atomic structure of a voltage-dependent K+ channel in a lipid membrane-like environment. Nature, 450, 376–382.

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113 Long, S.B., Campbell, E.B., and Mackinnon, R. (2005) Crystal structure of a mammalian voltage-dependent Shaker family K+ channel. Science, 309, 897–903. 114 Cheng, F., Li, W., Zhou, Y. et al. (2012) admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem.Inf.Model., 52, 3099–3105. 115 Fourches, D., Muratov, E., and Tropsha, A. (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 50, 1189–1204. 116 Alves, V.M., Braga, R.C., Silva, M.F.B., Muratov, E., Fourches, D., Tropsha, A. et al. (2014) Pred-hERG: A novel web-accessible computational tool for predicting cardiac toxicity of drug candidates. 248th Am. Chem. Soc. Natl. Meet., vol. Abstracts, San Francisco, CA. 117 Alves, V.M., Muratov, E.N., Capuzzi, S.J. et al. (2016) Alarms about structural alerts. Green Chem, 18, 4348–4360. 118 Springer, C. and Sokolnicki, K.L. (2013) A ﬁngerprint pair analysis of hERG inhibition data. Chem. Cent. J., 7, 167. 119 Roden, D.M., Mosley, J.D., and Denny, J.C. (2016) Finding a needle in a QT interval big data haystack. J. Am. Coll. Cardiol., 68, 1765–1768. 120 Peissig, P.L., Santos Costa, V., Caldwell, M.D. et al. (2014) Relational k machine learning for electronic health record-driven phenotyping. J. k Biomed. Inform., 52, 260–270. 121 Romero, L., Trenor, B., Yang, P.-C. et al. (2015) In silico screening of the impact of hERG channel kinetic abnormalities on channel block and susceptibility to acquired long QT syndrome. J. Mol. Cell. Cardiol., 87, 271–282. 122 Windley MJ, Mann SA, Vandenberg JI, Hill A. Temperature eﬀects on kinetics of Kv11.1 drug block have important consequences for in silico proarrhythmic risk prediction. Mol. Pharmacol. 2016. DOI: 10.1124/mol.115.103127. 123 Li, Z., Dutta, S., Sheng, J. et al. (2016) A temperature-dependent in silico model of the human ether-à-go-go-related (hERG) gene channel. J. Pharmacol. Toxicol. Methods, 81, 233–239. 124 Kramer, J., Obejero-Paz, C.A., Myatt, G. et al. (2013) MICE Models: superior to the HERG model in predicting Torsade de Pointes. Sci. Rep., 3, 2100.

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Computational Toxicology for Traditional Chinese Medicine Ni Ai and Xiaohui Fan

Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, PR China

CHAPTER MENU

Background, Current Status, and Challenges, 93 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions, 99 Conclusion, 114 k k

4.1 Background, Current Status, and Challenges

In the year 2015, Dr Youyou Tu won the Nobel Prize in Physiology or Medicine for her discovery of artemisinin that has saved millions lives of those suffering malaria around the world. Despite numerous debates on the relationship between artemisinin and traditional Chinese medicine (TCM), there is no doubt that inspiration from an ancient TCM medical book documenting the application of Artemisia annua L., the original herb of artemisinin, for the treatment of malaria plays a critical role in the identification of this blockbuster drug. This new triumph reawakened global interest in TCM again, as a valuable resource for novel structural scaffold identification and complementary and alternative pharmacotherapy for various common and rare human illnesses. The recent 2015 statistics from the Ministry of Industry and Information Technology of China report that the combined sales of TCM patent prescriptions and prepared decoctions have approached approximately one-third (29.26%, more than US$ 111 billion) of the annual revenue of the national pharmaceutical industry. These data reflect the expected outcome from heavy investment in the TCM sector from the Chinese government and favored use of traditional medicines through the state medical insurance program. From a global pharmaceutical trade point of view, the picture is not

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so promising in that the combined export of TCM products excluding raw herbs from China only reached US$ 487 million in revenue for the year of 2015 and this was mostly concentrated in the Asia Pacific countries, suggesting that the appeal of TCM remedies to Western patients remains unclear. Across the globe, after 2000 years of clinical practice and decades of scientific studies, researchers, pharmaceutical companies, and policymakers are keener than ever to uncover the unknown scientific merits of a TCM and to then take it into modern healthcare in the twenty-first century. To do so, it is important to incorporate the traditional knowledge of TCM which has accumulated over centuries with current understanding of pharmacotherapy and ensure it meets the mainstream standards on safety and efficacy. Compared to pharmacological studies, relatively few reports focus on toxicological consequences of TCMs. Because of their natural origin, people often think TCMs are toxicity free. Before the 1980s, the toxicology-related information was incomplete for many TCM patent prescriptions on the market and the application of these medicines caused serious uncertainty in the clinic. With increasing use of TCM, the number of adverse drug reaction (ADR) reports to the state ADR monitoring center has been climbing annually. There are a number of TCM toxicity incidents which have occurred in the past two decades, including nephrotoxicity of TCM with aristolochic acids [1], interstitial pneu- k monia caused by Xiao Chai Hu Decoction [2], and fatal allergic reactions to k TCM injections [3]. The toxicity and ADRs of TCM impact a great number of patients and have even led to death, drawing considerable attention about TCM safety in China and around the world. Safety concerns with regard to TCM is now one of the major hurdles limiting exports of TCM patent prescriptions from China. Several TCMs with cinnabar, arsenic sulfide, aconite, and aristolochic acids have been put on the blacklist of items forbidden for import into the United States, European Union, and other countries. This increasing anxiety regarding TCM safety is due to limited information available on toxicological effects and proper clinical use. In 2013, the Medicines and Healthcare Products Regulatory Agency of United Kingdom announced a warning that the aconitine composition in Zhengtian pills may cause cardio- and nervous-system toxicity. This is not surprising as Aconite is marked as a toxic TCM that should be used with caution. However, recently some traditionally nontoxic TCMs such as Polygonum multiflorum have been associated with hepatotoxicity due to improper clinical usage [4–6]. Thus comprehensive and detailed toxicological research into TCMs is urgently needed to recognize potential toxic effects, identify toxic substances, and uncover related mechanisms, which will provide critical knowledge that will guarantee safe use of TCMs in the clinic. Further- more, this information will play an essential role in the modernization and internationalization of TCM, which now finds a place in the strategic planning of the Chinese government.

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Biochemical and histopathological tests have produced valuable phenotypic toxicity data for a few TCMs but efforts are far from complete considering there are more than 600 TCMs listed in the Chinese Pharmacopeia (2015 edition). In addition, our understanding of their toxicity is significantly lacking at the cellular and molecular level, which hampers the identification of toxic substances and elucidation of potential mechanisms involved. TCMs are different from chemical drugs in many ways. A single TCM usually is composed of hundreds of chemical substances. The complexity of the system exponentially increases when multiple TCMs are combined together to form the patent prescription under investigation. It is a challenging task to isolate, extract, and identify individual chemicals in the TCM, and then test each one for safety is practically impossible. Computational toxicology is a feasible solution in the prioritization of TCM compounds for experimental evaluation with high risk. On the basis of experimental data and chemical structures, a set of predictive computational models can be generated for various toxic endpoints, which have been extensively applied in the risk assessment of new chemical entities at various stages of drug discovery and development of time- and cost-efficient methods that will effectively increase the success rate. With the advent of state-of-the-art analytical instruments and following decades of research, structural data is now available for more than 10,000 compounds originated from over 600 TCMs in k the Chinese Pharmacopeia (2015 edition) [7]. However, only a limited number k of those compounds were tested for toxicity. In general, construction of these in silico models for toxicity prediction needs the integration of biological and chemical information sources of these compounds; however, data collection of TCM molecules is rather poor in terms of their quantity and quality when compared to similar information on synthetic chemicals. Taking advantage of a wide battery of previously established predictive models [8], it is possible to rebuild them using the latest algorithm or directly apply these to assess the potential activities of TCM molecules on related toxic endpoints. According to the availability of toxic mechanisms, these computational models can be divided into two categories: quantitative structure-toxicity relationship (QSAR) models and toxic-target-based models. Computational toxicological studies on TCMs are sparsely reported. Most of these works focus on prediction of a TCM’s organ toxicity including hepatoxicity, cardiotoxicity, and nephrotoxicity. Various computational algorithms are utilized to construct QSAR models for screening potential toxic compounds. Drug-induced liver injury is one of the major reasons attributed to drug candidate failure during development and even for drug withdrawal from the market after approval [9, 10]. As mentioned previously, TCM-caused liver injury have been reported in the clinic and has led to serious concern on the medication safety of TCMs. It is of urgent importance to evaluate the hepatotoxic potential of chemical substances in TCMs. Huang et al. [11] extracted the details of drugs

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found in the Liver Toxicity Knowledge Base with known liver injury information to establish the predictive model for hepatotoxic compounds in Chinese herbal medicine (CHM) using the random forest (RF) method. Their internal cross validation and external validation both yielded a high accuracy of 79% and 87%, respectively. More than 6000 molecules in the TCM Database@Taiwan are marked by their model with liver damaging potential. Literature mining confirms predicted liver toxicity of the top 100 molecules, which demonstrates the validity of this model. Similarly, Ye et al. [12] employed three tree-based models to predict hepatotoxic compounds in TCM. This was different from the previous study as their training set included both synthetic drugs and natural compounds from TCM. In this study, boosting trees showed good overall accuracy (82%) and better specificity compared with the other two tree algorithms (the decision tree and RF). In addition, the mixture of training chemicals is proven to yield a positive effect on prediction of the external validation set. From a novel perspective of traditional TCM theory, Liu et al. [13] addressed the problem of TCM hepatoxicity prediction holistically rather on individual compounds. Their survey of known clinical and animal studies first selected 107 hepatotoxic and 431 non-hepatotoxic CHMs using a set of defined inclusion and exclusion criteria. A logistic regression model was then trained by these CHMs and 24 independent variables that described different properties k and flavors of TCM in the traditional theory, such as warm, cold, and astrin- k gent, among others. Their analysis revealed statistically significant relationships between hepatotoxic CHM with certain ancient descriptions on properties and flavors, which could be a promising tool for liver toxicity evaluation of new drugs from CHMs. The kidney plays a key role in drug metabolism and excretion and several TCMs are linked with nephrotoxicity. To prevent toxic effects of TCM on this organ, Wang et al. [14] constructed a QSAR model by the k-nearest neighbor (kNN) and support vector machine (SVM) using a public drug side-effect database (SIDER). Both models demonstrated comparable predictivity on internal cross validation but kNN showed better performance on TCM compounds in the external set. In their study, of Zhang et al. [15] performed keyword-based searches on DrugBank, TOXNET, and PubMed to prepare a training set of nephrotoxic compounds for the construction of a nephrotoxicity model. Using SVM, the classification model for nephrotoxicity yielded reasonably good predictive capacity. Interestingly, the author has also established another classification model for renal tubular injury, one of three major pathological mechanisms for nephrotoxicity. The TCM compounds predicted with nephrotoxicity from the first model would then be subject to a secondary mechanism-oriented classification model. Out of the 10 compounds output from two-step screening, the literature supported 6 compounds that can cause tubular necrosis with various experimental or clinical evidence.

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There are an increasing number of reports on cardiotoxicity of TCM, mostly focusing on known toxic TCMs based on traditional theory. The inhibition of the hERG potassium channel has been associated with cardiotoxicity of several drugs such as terfenadine. Lei et al. [16] built a computational model to predict hERG inhibition for TCM compounds. Two feature elimination algorithms (RF and SVM) were applied and model construction was performed by four separate methods, boosting tree, SVM, discriminate analysis, and RF. Their results indicated that RF yielded the QSAR model with best performance (82.5% accuracy). Screening on more than 1,600 compounds from 60 toxic TCMs suggested 72 compounds that can potentially inhibit this ion channel. This in silico hERG inhibition model is a useful tool for early identification of cardiotoxicity in TCM drug discovery. Zhang et al. [17] reported the application of an SVM model on CNS toxicity prediction of TCM. Using a training set with more than 500 chemicals, an SVM classifier with reasonable predictive ability was built for identifying CNS toxic compounds over nontoxic ones. Among 13 compounds predicted with neurotoxicity in Radix Sophorae Subprostratae, the literature verified toxic activity of four TCM compounds. For computational evaluation of acute toxicity of TCM, there is only one report available that a QSAR model was constructed and applied to over 1,600 compounds from 60 toxic TCMs. In this study, 89.7%, 10.2%, and 0.1% of TCM compounds were predicted to exhibit k acute/low, medium, and high toxicity, respectively, which provides important k data for guiding clinical usage of these TCM. In clinical practice, TCM is often prescribed together with chemical drugs. Since pharmacokinetics and pharmacodynamics processes are largely unknown for most TCMs, little information is currently available on TCM–drug interactions (TDI). However, the potential presence of these interactions with high risk should not be neglected because this may lead to unexpected adverse reactions and loss of efficacy of concomitantly used chemical drugs. Our recent review on in silico models for drug–drug interactions [18] has provided an in-depth summary on the latest development on this topic, in which a general workflow is also proposed to investigate TDI. Taking Salvia miltiorrhiza as an example of a TCM, we focused on the clinically reported TDI of this commonly used medicinal herb with warfarin, the anticoagulant for treating cardiovascular diseases. Since warfarin is highly human serum albumin (HSA)-bound in the plasma, the interaction of S. miltiorrhiza with this drug may occur at this target, which could increase free drug concentration in vivo and lead to bleeding issues observed clinically. A molecular docking study of over 70 compounds in S. miltiorrhiza identified 6 compounds that specifically interact with the warfarin-binding site of HSA and fluorometric experiments later confirmed our predictions, which sheds some light on the possible mechanism of HSA-mediated S. miltiorrhiza-warfarin interactions [19]. Similarly, docking techniques have been used to study TCM compounds with drug-metabolizing enzymes CYP2D6 [20], CYP2C19 [21],

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and other related proteins, such as PXR [22], which provide target specific information on TDI. QSAR models have also been constructed to understand the interactions between TCM and UGT1A4 [23] and the drug transporter OATP1B1 [24]. Results from these studies indicate that special attention may be needed when selecting TCM co-administered with drugs for therapeutic applications. As reviewed above, most of the computational toxicological studies focus on the prediction of organ toxicities of TCM compounds, mainly through machine learning methods. In addition, there have been several attempts in the evaluation of TDI due to the simultaneous binding/inhibition on important proteins participating in drug metabolism and excretion using the molecular docking technique. It is noteworthy that these in silico models usually predict toxicity potential of individual compounds in TCMs. However, TCMs or modern TCM preparations, which are complex systems with more than a hundred substances, are usually used as a whole and the relative contents of these compounds may be critical to their efficacy and toxicity, which is a difficult problem that needs to be handled by the aforementioned computational models. In addition, compounds in TCMs are secondary metabolites of the plants or animals and often share the same scaffold with diverse substitutions. The occurrence of activity cliffs is very likely to happen on these structural k analogs and this needs to be kept in mind to decrease prediction errors. Oral k dosing is the conventional administration route for TCMs; extensive in vivo metabolism may occur as these metabolites could be potentially active or toxic ingredients of the TCMs. Previous studies normally overlooked this important group of substances, thus a complete picture of the toxicity profile of a TCM could not be fully derived. Owing to sparse and limited experimental information on TCM compounds, most of the current computational models are trained with synthetic compounds, which could be another potentially problematic issue in these studies. Differences of their chemical space between these two sets of compounds have been reported and this raises concern regarding the applicability of these models to TCM. More efforts would be required in evaluating the impact of this problem and it might be better to construct the TCM-specific predictive models when sufficient data are available. Computational toxicological techniques have been extensively applied in various stages of discovery and development of synthetic drugs. Owing to their effect in decreasing the time and cost related to drug toxicity, these models are now recognized as key components of the whole pipeline. Relatively speak- ing, TCM computational toxicology is in its early phrase and more work is warranted to demonstrate its full potential in TCM-related studies, such as deciphering the toxic mechanisms and research and development of modern TCM patent prescriptions.

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4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions

In the following section, we present a case study on the application of computational techniques to evaluate the potential risk of transporter-mediated drug–TCM interactions. The organic anion transporter 1 (OAT1)-mediated drug–drug interactions have received considerable interest for their potential influence on drug efficacy and toxicity. Recently, several TCMs have been shown to modulate OAT1-transporting activity, which has raised concern about the TCM–drug interactions that may occur. However, information about OAT1-mediated TCM–drug interactions are extremely limited and large-scale evaluation has not been performed for the majority of the TCMs. In this work, we have elucidated important structural features of inhibitors for OAT1 via pharmacophore modeling based on available in vitro data. The reliability of the model was validated by an external dataset and seven out of eight known inhibitor drugs in the test set were successfully mapped to the current OAT1 inhibitor pharmacophore. All the medicinal TCMs reported in the Chinese Pharmacopoeia were then subject to screening by the validated model. The screening results revealed 144 potential OAT1 inhibitors and 11 TCMs out of k 611 TCMs that could likely interfere with the ability of OAT1 to transport its k substrates. Literature curation confirmed the OAT1 inhibitory activities for five of these identified TCMs, suggestive of TCM–drug interactions mediated by OAT1. Further study would be required to exploit the clinical relevance of OAT1-mediated TCM–drug interactions.

4.2.1 Introduction to OAT1 and TCM Recently, the United States Food and Drug Administration (FDA) and Euro- pean Medicines Agency issued guidelines for the investigational conditions that are required prior to approval and both agencies involved the potential drug–drug interactions (DDIs) mediated by seven identified transporters, including organic anion transporter 1 and 3 (OAT1 and 3), organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and 1B3), organic cation transporter 2 (OCT2), multidrug resistance transporter 1 (MDR1), and the breast cancer resistance protein (BCRP) [1, 25]. Clearly, regulatory authorities have recognized the potential clinical significance of transporter-mediated DDIs and their impact on drug safety based on results from many preclinical and clinical studies, as well as post-marketing reports [26–29]. By modulating transporter activities, these DDIs may influence the pharmacokinetics of the concomitantly used drugs and further lead to adverse effects by affecting absorption, distribution, metabolism, and excretion (ADME) of drugs. To predict transporter-mediated DDIs, many studies have been conducted to

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characterize these transporters, identify their substrates or inhibitors, and elucidate drug specificity [30–32]. Obviously, drugs that are currently used in theclinichavebeenthemajorfocusofthesestudies. TCM has been practiced in Eastern Asian countries for centuries and it still significantly contributes to the current healthcare system in China with great efficacy in the treatment of various diseases, such as infections, cardiovascular diseases, and diabetes. [33–35]. With their increasing use as health supplements and complementary/alternative medicines in Western countries, we are currently observing more concomitant occurrence of TCMs and drugs in a clinical setting [36, 37]; therefore, more attention is required regarding the potential adverse effects related to the combination of TCMs and drugs. Similar to DDIs, it is possible that transporter-mediated TCM–drug interactions also occur and lead to altered blood drug concentration and plasma half-life, therefore influencing the therapeutic effects of drugs or causing adverse events [38–40]. For drugs with a narrow therapeutic index, such as digoxin, aminophylline, and cyclosporin A, it is especially important to monitor for adverse drug reactions when used in combination with TCM [41–43]. However, it is difficult to evaluate TCM–drug interactions as only limited information is available for clinical practitioners and researchers to make decisions regarding the combinational usage of TCM and drugs. More- k over, the potential for TCM–drug interactions poses a threat to the general k public owing to the false impression of safety induced by the natural origin of TCM. Therefore, it is critical to evaluate transporter-mediated TCM–drug interactions, including molecular mechanisms and the possible individual molecule perpetrators in TCMs, in order to establish a reliable system to evaluate the safety of TCM–drug usage. As one of the major pathways for drug elimination, active secretion in the proximal tubules of the kidney, particularly transporter-mediated active secretion, has been investigated for its involvement in DDI [44, 45]. Significant attention has focused on OAT1 [45, 46], which belongs to the Solute Carrier 22 (SLC22; organic cation/anion/zwitterion transporters) family and is expressed in the basolateral membrane of renal proximal tubule cells [47, 48]. OAT1 is one of major constituents of the renal organic anion transport pathway and plays a key role in the distribution and renal tubular secretion of a variety of endogenous and exogenous organic anions [48, 49]. In accordance with its crucial role in renal active secretion of many drugs, such as nonsteroidal anti-inflammatory drugs, antiviral drugs, antibiotics, diuretics, antineoplas- mics, antiepileptics, OAT1-mediated DDIs have been observed clinically [50]. For example, when used with the known OAT1 inhibitor probenecid, the renal excretion of benzylpenicillin and ciprofloxacin was reduced [51]. Recent studies have demonstrated the inhibitory effect of individual compounds in TCM and how these can modulate the ability of OAT1 transporting its substrates [52–54]. Thus the possibility of OAT1-mediated TCM–drug

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interactions should be seriously considered when TCM are co-administrated with drugs that are transported by OAT1 [52, 55]. Each TCM usually consists of multiple compounds as a complex mixture and there are more than 600 TCMs that are currently used in clinical practice. Therefore, it is important to develop an effective and rapid screening approach for putative TCMs with OAT1-modulating activities. Computational modeling of drug–transporter interactions has showed great potential to identify DDIs mediated by various transporters, including OAT1 [56–58]. Since OAT1 is a transmembrane protein and there is no crystal structure available for structural details on ligand-protein interactions, we constructed a structural model of OAT1 for a molecular-docking study that was followed by the pharmacophore modeling technique [59] to predict whether TCMs reported in the Chinese Pharmacopoeia could interact with this renal drug uptake transporter. To our knowledge there have been no large-scale efforts to evaluate potential TCM interactions with OAT1. The goal of our current study was to use previously published OAT1 inhibitors, to build and test a pharmacophore model for OAT1 inhibition, which could be useful to facilitate identification of TCM-OAT1 interactions that could then be tested experimentally. Results from this preliminary study provide valuable information to understand and estimate OAT1-mediated TCM–drug interactions, which may possess k important clinical relevance. k

4.2.2 Construction of TCM Compound Database Information about clinically relevant TCMs was collected from the Chi- nese Pharmaceutical Encyclopedia (2010 edition) [60]. Literature mining and database searching were then performed to identify small molecule compounds that have been described in these TCMs by the keyword-based approach. This process resulted in chemical structures of 10,914 compounds from 611 TCMs after removing inorganic compounds and metals. Two-dimensional (2D) and three-dimensional (3D) structural conﬁgurations of these compounds were generated using the Molecular Operating Environ- ment (MOE) (Chemical Computing Group, Montreal, Canada) and saved in a database format for further computational evaluations. The TCM compound database was updated to remove counter-ions and corresponding protonation stage of the compounds was decided under pH value of 7.4. The 3D structure of each compound was constructed using the MMFF94x force ﬁeld using MOE. Then the low energy conformers for each compound were generated using the conformation import methodology implemented in MOE.

4.2.3 OAT1 Inhibitor Pharmacophore Development More than 50 compounds were collected from the UCSF-FDA TransPortal website [61] (http://bts.ucsf.edu/fdatransportal/) as known inhibitors of

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human OAT1 and used for pharmacophore generation. These compounds have been experimentally validated to inhibit para-aminohippuric acid (PAH) uptake via OAT1. Since the goal of this study was to construct a representative inhibitor pharmacophore model and apply it to identify OAT1 inhibitors from TCMs that may induce herb-drug interactions clinically, we considered potent

inhibitors with IC50 less than 50 μM as OAT1 actives in this training set, while drugs with IC50 more than 50 μM were included as OAT1 inactives to derive unfavorable features for inhibition. This process led to 28 compounds (24 actives and 4 inactives) for inhibitor pharmacophore generation. To explore the conformational space of these OAT1 inhibitors, a fragment-based high-throughput conformer generation approach accessible as the “conformation import” method in MOE was used to generate conformation clusters for each molecule for further calculations. Pharmacophores are the key features that are important for biological activity and their geometric arrangement in space. The initial OAT1 inhibitor pharmacophore model was developed using only ﬁve molecules from 24 inhibitors with

thelowestIC50 data, including mefenamic acid, diclofenac, indomethacin, ketoprofen, and naproxen. The pharmacophore elucidation protocol in MOE was applied in this step. Atoms in the compounds were annotated according to the Uniﬁed Scheme of MOE pharmacophore searching application. The remaining k less-active OAT1 inhibitors and inactives were then mapped to this model and k their chemical and spatial features were incorporated into the model.

4.2.4 External Test Set Evaluation Following the development of the OAT1 inhibitor pharmacophore model, we performed model validation by utilizing it as query for in silico screening of drugs in the NIH clinical collection (NCC) and NCC2 (Evotec, USA) whose OAT1 inhibitory activities have been determined experimentally [56]. The test set included 10 OAT1 actives and 8 OAT1 inactives. The predictive capability of our pharmacophore model was evaluated by identiﬁcation of molecules with conﬁrmed inhibitory activities from the NCC and NCC2 databases.

4.2.5 Database Searching After model validation, the pharmacophore model was then used to search the TCM compound database. Hits exhibiting a good ﬁt to the generated pharmacophore model were retrieved from our TCM compound database for further evaluation. The root mean square deviation between centroids of pharmacophore features and annotated points in the compounds were calculated to evaluate the quality of the pharmacophore mapping. PubMed (http://ncbi.nlm.nih.gov/pubmed/) was searched for the likely connection between OAT1 and these computationally identiﬁed TCM compounds or their original TCMs. Jarvis-Patrick clustering of TCM compounds mapped to

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OAT1 inhibitor pharmacophore was performed in MOE through 2D MACCS structural keys (MDL Ltd, CA, USA) and resulted in partitioning into 32 categories by (sub)structure similarity.

4.2.6 Results: OAT1 Inhibitor Pharmacophore Twenty-four known OAT1 inhibitors were used to develop the OAT1 inhibitor pharmacophore model and the inhibitors had three common features: a negative ionizable center (F1), a hydrophobe (F2), and the third feature (F3) that can be an aromatic center or a hydrophobic centroid, along with six excluded volumes (Figure 4.1). This pharmacophore model successfully mapped 19 < inhibitors among the 24 highly active OAT1 inhibitors (IC50 50 μM); however, it failed to identify two thiazide diuretic drugs (chlorothiazide and

F3 Aro|Hyd

V6 F2 Aro k k

F1 Ani

V1 V2

Figure 4.1 An OAT1 inhibitor pharmacophore model that consists of a negative ionizable feature (F1, red), one hydrophobe (F2, yellow), and a third feature that can be an aromatic center or a hydrophobic centroid (F3, yellow). In addition, six excluded volumes shown as

gray spheres were present in this model. A potent OAT1 inhibitor, bumetanide (IC50 = 6 μM), has been displayed with the model and the atoms are colored by atom type (carbon, gray; nitrogen, blue; oxygen, red; phosphorus, yellow). (See color plate section for the color representation of this ﬁgure.)

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trichlormethiazide). This is apparently due to the absence of a negative anionic center in these compounds, which is the essential feature in our model. Recent results have showed that chlorothiazide may work as substrates of OAT1 but it is not clear whether this drug binds to the same site as PAH does [62]. Future work is planned to use these drugs to elucidate the important pharmacophoric features for OAT1 substrates and comparison of the substrate and inhibitor pharmacophores would highlight their chemical similarities/dissimilarities. The carboxyl group was present for the other three drugs missed by the model (adipate, α-ketoglutarate, and glutarate). However they all have small size in terms of volume, suggesting that this may be an important factor for the current OAT1 inhibitor pharmacophore. 4.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation Duan et al. [56] identified 10 highly potent OAT1 inhibitors from clinical drug libraries and those drugs established the test set to evaluate our OAT1 inhibitor pharmacophore model (Table 4.1). Mefenamic acid and Ketoprofen were the only two common drugs in the training set and test set and these were excluded from analysis. Pharmacophore comparison on the remaining eight active molecules in the test set showed that seven were mapped to the current OAT1 inhibitor pharmacophore. The only drug missed was nitazoxanide, k which does not possess a negatively charged moiety that appears essential. k Furthermore, none of inactives (eight drugs) matched with the current OAT1 inhibitor model. These results attest to the specificity of the model. The same group that published inhibitory activities of the test set drugs also generated a non-discriminative inhibitor pharmacophore for OAT1 and OAT3, which partially overlapped with our model on one negative ionizable feature and an aromatic center. However, the inter-feature distances were varied between two models and our model included an additional feature that could be aromatic or hydrophobic. These two common features therefore appear most important for inhibitors interacting with OAT1. The distance between these two features was 4.1 Å for our model versus 5.7 Å in previous model and this distance variation may relate to the selection of different OAT1 substrates during the experiments, as a subtle difference in local receptor conformations can be induced in the same binding site by various substrates. Inhibitors were selected in this study when PAH was used as substrate for OAT1, while Duan et al. used 6-carboxyfluorescein to determine inhibitory activities of clinical drugs. We speculated that these two sets of inhibitors may interact with an overlapping binding site region of OAT1 based on shared common features. 4.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore Using our OAT1 inhibitor pharmacophore model as a query, we searched through the TCM compound database and mapped 144 compounds from

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Table 4.1 Representative molecules used for OAT1 inhibitor pharmacophore model generation and validation.

Molecules Detailsa) References

Mefenamic acid Non-steroidal [80] anti-inﬂammatory drug

(IC50 = 1 μM) Novobiocin An aminocoumarin antibiotic [81]

(IC50 = 38 μM) Ketoprofen A non-steroidal [80] anti-inﬂammatory drug

(IC50 = 4 μM) Training set Fluvastatin A hypolipidemic drug to [82] hypercholesterolemia and to prevent cardiovascular disease

(IC50 = 26 μM) Probenecid A uricosuric drug to treat gout [83] and hyperuricemia

(IC50 = 6.5 μM)

Meclofenamic acid A non-steroidal anti-inﬂammatory drug k k Ketorolac A non-steroidal [56] anti-inﬂammatory drug Telmisartan An angiotensin II receptor

Test set antagonist Oxaprozin A non-steroidal anti-inﬂammatory drug Amlexanox An anti-inﬂammatory and anti-allergic immunomodulator

a) Activities were reported against OAT1.

over 10,000 compounds. The hit rate is around 1.3% and it showed that our model is rather selective against the large database. Since the same compounds may exist in different TCMs, there were 101 unique TCM compounds after removing the duplicates, which originate from 85 kinds of TCM (about 13% of all TCMs). For example, Rhein, the active compound in Rheum sp., is found in five other TCMs from our database. The majority of 85 TCMs only had one compound mapped to the model (Figure 4.2) and their potential to be involved in TCM–drug interactions probably would be insignificant considering the relative low quantity of a single compound in one TCM. About 11.5% (11/87) of TCMs mapped by our method included more than two compounds that were predicted as OAT1 inhibitors and detailed investigations would be valuable for

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106 Computational Toxicology

8 Radix salvia, Herba portulacae

7 Senna leaf

inhibitor 6

OAT1 5 Rheum sp.

4 Cohosh, fennel, propolis

3 Radix scutellariae, Lycium spp., Hawthron, Aristolochia debilis

2 Number of predicted Number of predicted 1

0102030 40 50 60 70 Number of TCMs

Figure 4.2 The distribution of predicted TCM compounds with OAT1 inhibitory activity in medicinal TCMs. The black bars represent TCMs with three or more compounds mapped to the pharmacophore model and the names of these TCMs are listed in the ﬁgure. k k their role in TCM–drug interactions (listed in Figure 4.2). A literature survey of these 11 TCMs showed that Radix Scutellariae, with three OAT1 inhibitors predicted by the model, inhibited the uptake of PAH through OAT1 about 51% by a single TCM preparation [63]. Among 11 selected TCMs, Aristolochia debilis is known for its nephrotoxicity that is related to uptake of compounds from this TCM by OATs [64] and three TCM compounds (aristolochic acids) in this herb were predicted to show strong interactions with OAT1, which is consistent with previous experimental data [65]. A further PubMed search was performed for several predicted TCM compounds to conﬁrm their direct inhibitory activities against OAT1. In agreement with results from pharmacophore searching, components of Radix Salvia,salvianolic acids, lithospermic acid, and rosmarinic acid, have all been demonstrated to competitively inhibit OAT1 experimentally [66]. Rhein, found in two of the selected TCMs (Rheum sp. and sennae leaf), is the major constituent of these two TCMs and displayed a potent nanomolar activity for OAT1 in vitro and a calculated DDI index of 5.0 [54], suggesting high probability of occurrence of herb-drug interactions when taken with drugs that are transported by OAT1. In this study, several cinnamic acids, such as ferulic acid and sinapinic acid, were predicted to inhibit OAT1 activities by the pharmacophore model, which was also consistent with published results [52]. Table 4.2 summarizes the information for these TCM compounds and Figure 4.3a-h shows how these compounds matched to the common features of OAT1 inhibitor

k k (Continued) References [52] a) 0.19 [84] M) DDI index 𝛍 ( b) b) c) 50 0.077 5.0 [54] 0.84 5.6 OH OH OH

k OH k OH O O 2 O OH NO OH O O HO O O OH OH O O HO Example TCM compounds with experimental information about interactions with OAT1. TCM compound Chemical structure IC Aristolochic acid I Salvianolic acid A Rhein Table 4.2

k k References [53] a) 0.01 [52] 1.6 [52] c) M) DDI index 𝛍 ( b) b) 50 20.8 0.32 11.02 9.01 0.03 [53] OH OH OH HO k O k OH HO O OH OH O O O O O O HO O HO O HO O O HO HO HO HO HO (Continued) i values were reported in the corresponding references. K TCM compound Chemical structure IC Ferulic acid Rosmarinic acid Sinapinic acid Lithospermic acid c) Calculation was not done owing to lack of information. a) DDI index were calculated using previously reported data. b) Table 4.2

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Computational Toxicology for Traditional Chinese Medicine 109

(a) (b)

(e) (f)

(g) (h)

Figure 4.3 TCM compounds mapping to the OAT1 inhibitor pharmacophore. The pharmacophore consists of a negative ionizable feature (red) and two hydrophobic features (yellow). For clarity, the excluded volumes are not shown here. (a) rhein; (b) aristolochic acid I; (c) salvianolic acid A; (d) lithospermic acid; (e) rosmarinic acid; (f) ferulic acid; (g) sinapinic acid; (h) and isoferulic acid. (See color plate section for the color representation of this ﬁgure.)

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Table 4.3 Structurally similar TCM compounds without experimental validation.

Validated TCM compound Chemical structure compound Tsa)

Isoferulic acid O Ferulic acid 1.0 HO OH

Cimicifugic acid Lithospermic acid 0.77 O O

HO HO COOH OH Lonchocarpric acid O OH Lithospermic acid 0.59

k O O O k

a) Tanimoto score (Ts) using 2D MACCS ﬁngerprint.

pharmacophore. Isoferulic acid, a structural analog of ferulic acid, was also selected by our pharmacophore and we expected this compound would inhibit OAT1 because of great structural similarity to ferulic acid (Table 4.3). These cinnamic acids exist in ﬁve selected TCMs; however, their involvement in drug interactions needs more experimental investigation.

4.2.9 Discussion TCM has continuously displayed beneficial therapeutic effects against various diseases in the clinic, including cardiovascular diseases, infectious diseases, and cancers [67]. As a form of complementary/alternative medicine, it is accepted in Western countries, such as the United States, and a recent survey [36] suggested that 20% of the population reported the use of herbal medicines that make up 80% of TCMs. However, compared to its broad clinical applications, pharmacological studies on TCM are limited, particularly on their potential toxicological or adverse effects. For example, there are extremely limited studies on TCM–drug interactions when used concomitantly. Some

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Computational Toxicology for Traditional Chinese Medicine 111

clinical evidence is available to support the existence of TCM–drug interactions [68, 69]. Most of the attention has been focused on how TCMs disrupt activities of drug-metabolizing enzymes such as cytochrome P450 [70]. Despite the important role of transporters in drug absorption and disposition which has been well recognized, information about TCM-transporter interactions is sparse and as most of the studies have concentrated on P-glycoprotein [71]. Few studies have been conducted to assess the ability of TCMs as renal transporter substrates or inhibitors, which clearly is a possible mechanism for TCM–drug interactions. Therefore, it is critical to characterize the level of TCM-renal transporter associations in order to prevent unexpected drug toxicity when TCM and drugs are administrated concomitantly. For synthetic drugs, experimental screening of their capabilities to modulate OAT1 activity has been performed to prevent potential DDIs [56]. However, this approach is probably not feasible to screen TCM compounds owing to the enormous number of compounds to be tested. Prioritization for experimental testing would be required to speed up the process and gain more valuable information on TCM-transporter interactions. Computational modeling in combination with in vitro experiments has been used to enrich our knowledge about substrates and inhibitors of renal transporters [72, 73], which also would provide useful information about TCM-renal transporter interactions. k We present such an approach aimed at the identification of OAT1 inhibitors k from TCM by building an OAT1 inhibitor pharmacophore model using previously published in vitro data on inhibitors of this transporter. Tomeasure model accuracy, additional literature information was used to form a validation set with experimental observations that could be compared with predicted results. A further literature search confirmed an important relationship between compounds in medicinal TCMs and OAT1 suggested by the current computational model, which may possess significant clinical implications by impacting pharmacokinetics or pharmacodynamics of drugs co-administrated with TCMs. In vitro experiments have shown that the same drug displayed altered levels of inhibition when different substrates of one transporter were present, suggesting that inhibitors may interact with the transporter through diverse patterns [61]. This observation makes it difficult to generate one comprehensive pharmacophore hypothesis to map all known inhibitors. Here we are interested in potent OAT1 inhibitors from TCMs, which then may show clinically relevant interactions with other drugs; thus a highly specific pharmacophore model would be preferred. Toestablish a single OAT1 inhibitor pharmacophore model, it is important to select inhibitors whose OAT1 activities are evaluated by inhibiting uptake of the same substrate, therefore focusing on explaining a unique OAT1 inhibitory mechanism. In this study, the OAT1 substrate is PAH, which is commonly used to measure renal plasma flow due to its primary secretion by renal tubules. Although the PAH-dependent OAT1 inhibitor pharmacophore model only accounts for one specific inhibition mechanism, it

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112 Computational Toxicology

would represent a large portion of OAT1 inhibitors and display high specificity to the PAH binding site of OAT1. Using 24 highly active inhibitors that disrupt the ability of OAT1 to transport PAH, a three-feature pharmacophore model was constructed to represent important structural elements of OAT1 inhibitors. We found that the negative ionizable feature was important for potent OAT1 inhibitors. However, several drugs without this feature, such as thiazide diuretics, also strongly blocked transport of the OAT1 substrate, suggesting they may interact with the transporter through a different inhibitory mode and a separate pharmacophore will be needed to identify OAT1 inhibitors functioning similarly to those drugs. Fur- thermore, comparison with another published OAT1 inhibitor pharmacophore [56] revealed two overlapping features (one negative center and an aromatic center) that possibly are required for inhibitor binding. In our model, one additional feature (F3) suggested a minimal size threshold for OAT1 inhibitors by an approximate 8 Å separation between F1 and F3. During the model evaluation step, the current model successfully mapped seven out of eight OAT1 active drugs from an external test set and showed great discrimination between actives and inactives, indicating that the model can identify potent inhibitors that are not in the training set. OAT1 substrate pharmacophores with common features have been constructed to identify metabolites transported by OAT1 k [57, 74]. Comparison with these models again highlighted the importance of k the anionic center, which appears in both inhibitor and substrate models. In addition to pharmacophore modeling, quantitative structure-activity relationships have been developed for a series of antiviral compounds and mouse OAT1 inhibitors showed higher polar surface areas relative to those for mouse OAT3 and OAT6 [75]. This information is also valuable for computational screening for OAT1 inhibitors. There have been no previous database screening efforts with TCM compounds against OAT1 on a large scale. The current effort therefore presents a first attempt to perform this task using our OAT1 inhibitor pharmacophore and a very small percentage of TCM compounds; 144 TCM compounds matching the key features of pharmacophore model were identified by our model as potential OAT1 inhibitors and retrieved from a large database with over 10,000 compounds. This high selectivity may partially relate to the PAH-specific OAT1 inhibitor pharmacophore we constructed. Several TCM compounds with reported OAT1 inhibitory activities were also revealed by our method (shown in Table 4.2), such as Rhein with nanomolar potency. The OAT1 activities remain to be experimentally validated for a large portion of TCM compounds identified in this work. Chemical similarity analysis indicated that 144 TCM compounds belonged to 32 distinct structural clusters and more than half of the predicted compounds (68 compounds) can be grouped into two clusters (data not shown). All TCM compounds with previously known OAT1 activities (shown in Table 4.2) were found in these two clusters

k k

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except Rhein, which was located in a separate cluster. These results showed predicted TCM compounds in these two clusters shared good structural similarities and they very likely modulate OAT1 in a similar manner as those confirmed TCM compounds. On the basis of the number of predicted OAT1 inhibitors in each TCM, 11 TCMs were selected and investigated as potential perpetrators for OAT1-mediated TCM–drug interactions. Additional literature search revealed experimental evidence to support the associations with OAT1 for 5 out of these 11 TCMs (Salvia miltiorrhizae, A. debilis, Scutellaria baicalensis, senna leaf, and Rheum sp.). The current pharmacophore model also mapped hydroxycinnamic acid, a class of aromatic acid or phenylpropanoids with a C6—C3 skeleton, such as ferulic acid and sinapinic acid, to common features of potent OAT1 inhibitors. These organic acids are found in five unconfirmed TCMs (cohosh, hawthorn, fennel, propolis, and Herba portulacae). Table 4.3 lists the Tanimoto scores of three TCM compounds from these unconfirmed TCMs with validated compounds, which suggests that these unconfirmed compounds share good structural similarity with the validated compounds. It is possible they also would inhibit OAT1 activity. Since the quantitative content of these acids in TCM are not available, experiments are warranted to prove the participation of interactions with OAT1 for these TCMs. Notably, k all these five TCMs can be used as dietary material, which suggests that they k could be consumed in large quantities by people. For example, black cohosh is a popular dietary supplement among women for management of menopausal symptoms. The extract of this plant has been shown to moderately inhibit the transport of estrone-3-sulfate uptake by another organic anion transporter, OATP-B [76]. Clearly, according to the previous report and our prediction, it is worth exploring the potential OAT1 inhibitory activity of black cohosh. Lycium spp., a well-known culinary fruit, which is another TCM selected by our method and estimated to be involved in OAT1-mediated TCM–drug interactions in this study. Although there is no information about OAT1 activity of Lycium spp., a clinical-evidence-based evaluation indicated that this fruit can induce interactions with warfarin [77, 78]. More experimental testing is required to demonstrate the interactions between Lycium spp. and OAT1 and the possibility of disrupting OAT1 substrate drug uptake by this TCM. Renal active secretion of drugs would involve both transporter-mediated basolateral uptake and apical efflux. In this study, we focused on the potential influence of TCM on the OAT1-mediated basolateral uptake, which is a portion of the complex dynamics of drug movement across the renal membrane. To gain a more complete understanding of the potential of TCM–drug interactions, it is important to characterize the contribution from other renal transporters, including another important uptake transporter OAT3 and the apical efflux transporter (multidrug resistance protein 4) [79]. More studies on interactions between TCM and these transporters are currently ongoing

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114 Computational Toxicology

to clarify the mechanism of transporter-mediated TCM–drug interactions in kidney.

4.3 Conclusion

In conclusion, we developed an OAT1 inhibitor pharmacophore to estimate inhibitory activity of TCMs on this important renal transporter, which might potentially induce OAT1-related TCM–drug interactions and lead to unexpected renal accumulation of concomitantly used OAT1 substrate drugs. Eleven TCMs were predicted to inhibit OAT-mediated substrate uptake and ﬁve of these were subsequently validated by reported experimental data. The computational pharmacophore approach used in the study could be useful in evaluating modulation of other drug transporters by TCMs. The results of the study should provide helpful information related to drug interactions in TCM safety research. Furthermore, our results can be used as future references for general public and clinical practitioners about the potential risk of combinational usage of TCM and drugs, as well as helping to make related regulatory policies. k k Acknowledgment

The authors acknowledge the funding on this work from National Youth Top-notch Talent Support Program and the National Natural Science Foundation of China (No. 81173465).

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46 Sekine, T., Watanabe, N., Hosoyamada, M. et al. (1997) Expression cloning and characterization of a novel multispecific organic anion transporter. J. Biol. Chem., 272, 18526–18529. 47 Sweet, D.H., Wolff, N.A., and Pritchard, J.B. (1997) Expression cloning and characterization of ROAT1. The basolateral organic anion transporter in rat kidney. J. Biol. Chem., 272, 30088–30095. 48 Miyazaki, H., Sekine, T., and Endou, H. (2004) The multispecific organic anion transporter family: properties and pharmacological significance. Trends Pharmacol. Sci., 25, 654–662. 49 Rizwan, A.N. and Burckhardt, G. (2007) Organic anion transporters of the SLC22 family: biopharmaceutical, physiological, and pathological roles. Pharm. Res., 24, 450–470. 50 Uwai, Y., Taniguchi, R., Motohashi, H. et al. (2004) Methotrexate–loxoprofen interaction: involvement of human organic anion transporters hOAT1 and hOAT3. Drug Metab. Pharmacokinet., 19, 369–374. 51 Jaehde, U., Sorgel, F., Reiter, A. et al. (1995) Effect of probenecid on the distribution and elimination of ciprofloxacin in humans. Clin. Pharmacol. Therap., 58, 532–541. 52 Wang, L. and Sweet, D.H. (2013) Interaction of natural dietary and herbal k anionic compounds and flavonoids with human organic anion transporters k 1 (SLC22A6), 3 (SLC22A8), and 4 (SLC22A11). Evid. Based Complement. Altern. Med.: eCAM, 2013, 612527. 53 Wang, L. and Sweet, D.H. (2012) Potential for food-drug interactions by dietary phenolic acids on human organic anion transporters 1 (SLC22A6), 3 (SLC22A8), and 4 (SLC22A11). Biochem. Pharmacol., 84, 1088–1095. 54 Wang, L., Pan, X., and Sweet, D.H. (2013) The anthraquinone drug rhein potently interferes with organic anion transporter-mediated renal elimination. Biochem. Pharmacol., 86, 991–996. 55 Uwai, Y., Ozeki, Y., Isaka, T. et al. (2011) Inhibitory effect of caffeic acid on human organic anion transporters hOAT1 and hOAT3: a novel candidate for food–drug interaction. Drug Metab. Pharmacokinet., 26, 486–493. 56 Duan,P.,Li,S.,Ai,N.et al. (2012) Potent inhibitors of human organic anion transporters 1 and 3 from clinical drug libraries: discovery and molecular characterization. Mol. Pharm., 9, 3340–3346. 57 Kouznetsova, V.L., Tsigelny, I.F., Nagle, M.A., and Nigam, S.K. (2011) Elu- cidation of common pharmacophores from analysis of targeted metabolites transported by the multispecific drug transporter–Organic anion trans- porter1 (Oat1). Bioorg.Med.Chem., 19, 3320–3340. 58 Ahn, S.Y., Eraly, S.A., Tsigelny, I., and Nigam, S.K. (2009) Interaction of organic cations with organic anion transporters. J. Biol. Chem., 284, 31422–31430.

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59 Guner, O.F. and Bowen, J.P. (2013) Pharmacophore modeling for ADME. Curr. Top. Med. Chem., 13, 1327–1342. 60 Committee, N.P. (2010) ChinesePharmacopoeia, Medicine Science and Tech- nology Press of China, Beijing. 61 Morrissey, K.M., Wen, C.C., Johns, S.J. et al. (2012) The UCSF-FDA Trans- Portal: a public drug transporter database. Clin.Pharmacol.Therap., 92, 545–546. 62 Juhasz, V., Beery, E., Nagy, Z. et al. (2013) Chlorothiazide is a substrate for the human uptake transporters OAT1 and OAT3. J. Pharm. Sci., 102, 1683–1687. 63 Lin, C.C., Fan, H.Y., Kuo, C.W., and Pao, L.H. (2012) Evaluation of chinese-herbal-medicine-induced herb–drug interactions: focusing on organic anion transporter 1. Evid. Based Complementary Altern. Med.: eCAM, 2012, 967182. 64 Lebeau, C., Debelle, F.D., Arlt, V.M. et al. (2005) Early proximal tubule injury in experimental aristolochic acid nephropathy: functional and histo- logical studies. Nephrol. Dial. Transplant., 20, 2321–2332. 65 Babu, E., Takeda, M., Nishida, R. et al. (2010) Interactions of human organic anion transporters with aristolochic acids. J. Pharm. Sci., 113, 192–196. k 66 Zhang, J., Pan, X., Wang, C. et al. (2012) Pharmacophore modeling, k 3D-QSAR studies, and in-silico ADME prediction of pyrrolidine derivatives as neuraminidase inhibitors. Chem. Biol. Drug Des., 79, 353–359. 67 Robinson, M.M. and Zhang, X. (2011) World Medicines Situation 2011, Tra- ditional Medicines: Global Situation, Issues and Challenges,WHO. 68 Shi, S. and Klotz, U. (2012) Drug interactions with herbal medicines. Clin. Pharmacokinet., 51, 77–104. 69 Coxeter, P.D., McLachlan, A.J., Duke, C.C., and Roufogalis, B.D. (2004) Herb–drug interactions: an evidence based approach. Curr. Med. Chem., 11, 1513–1525. 70 Wu, J.J., Ai, C.Z., Liu, Y. et al. (2012) Interactions between phytochemicals from traditional Chinese medicines and human cytochrome P450 enzymes. Curr. Drug Metab., 13, 599–614. 71 Eichhorn, T. and Eﬀerth, T. (2012) P-glycoprotein and its inhibition in tumors by phytochemicals derived from Chinese herbs. J. Ethnopharmacol., 141, 557–570. 72 Kido, Y., Matsson, P., and Giacomini, K.M. (2011) Proﬁling of a prescription drug library for potential renal drug–drug interactions mediated by the organic cation transporter 2. J. Med. Chem., 54, 4548–4558. 73 Nigam, S.K., Bush, K.T., and Bhatnagar, V. (2007) Drug and toxicant handling by the OAT organic anion transporters in the kidney and other tissues. Nat. Clin. Pract. Nephrol., 3, 443–448.

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74 Wikoff, W.R., Nagle, M.A., Kouznetsova, V.L. et al. (2011) Untargeted metabolomics identifies enterobiome metabolites and putative uremic toxins as substrates of organic anion transporter 1 (Oat1). J. Proteome Res., 10, 2842–2851. 75 Truong, D.M., Kaler, G., Khandelwal, A. et al. (2008) Multi-level analysis of organic anion transporters 1, 3, and 6 reveals major differences in structural determinants of antiviral discrimination. J. Biol. Chem., 283, 8654–8663. 76 Fuchikami, H., Satoh, H., Tsujimoto, M. et al. (2006) Effects of herbal extracts on the function of human organic anion-transporting polypeptide OATP-B. Drug Metab. Dispos., 34, 577–582. 77 Rivera, C.A., Ferro, C.L., Bursua, A.J., and Gerber, B.S. (2012) Probable interaction between Lycium barbarum (Goji) and Warfarin. Pharmacother- apy, 32 (3), e50–e53. 78 Leung, H., Hung, A., Hui, A.C., and Chan, T.Y. (2008) Warfarin overdose due to the possible effects of Lycium barbarum L. Food Chem. Toxicol., 46, 1860–1862. 79 Smeets, P.H., van Aubel, R.A., Wouterse, A.C. et al. (2004) Contribution of multidrug resistance protein 2 (MRP2/ABCC2) to the renal excretion of p-aminohippurate (PAH) and identification of MRP4 (ABCC4) as a novel PAH transporter. J. Am. Soc. Nephrol., 15, 2828–2835. k k

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Pharmacophore Models for Toxicology Prediction Daniela Schuster

Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, Austria

CHAPTER MENU

Introduction, 121 Antitarget Screening, 125 Prediction of Liver Toxicity, 125 Prediction of Cardiovascular Toxicity, 127 Prediction of Central Nervous System (CNS) Toxicity, 128 k Prediction of Endocrine Disruption, 130 k Prediction of ADME, 135 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies, 136

5.1 Introduction

For the virtual screening-based discovery of bioactive compounds, pharmacophore models have been successfully used for three decades now. However, the concept of a pharmacophore, then also called toxicophore or haptophore, was first defined by Ehrlich [1] and later redefined by Schueler [2, 3]. It is defined by the IUPAC as “… the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response. A pharmacophore does not represent a real molecule or a real association of functional groups, but a purely abstract concept that accounts for the common molecular interaction capacities of a group of compounds toward their target structure.” [4]. Pharmacophore models consist of so-called chemical features that represent specific molecular interaction types such as hydrogen bonds, aromatic interactions, charged centers, metal interactions, or hydrophobic contacts. There are also steric constraints such as exclusion volumes or shape features that limit the size and extent of mapping compounds (Figure 5.1) [7].

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* # § H HBD HBA Xvol *

Figure 5.1 Visualization of exemplary pharmacophore models with selected chemical features in diﬀerent modeling programs. Interactions of equilin cocrystallized with 17β-hydroxysteroid dehydrogenase 1 (PDB entry 1equ [5]) are shown. *, LigandScout; #, discovery studio; §, molecular operating system (MOE) [6]. The functionalities are abbreviated as H, hydrophobic; HBD, hydrogen bond donor; HBA, hydrogen bond acceptor; Xvol, exclusion volume. (See color plate section for the color representation of this ﬁgure.)

Because of their universal representation of essential chemical functionalities for bioactivity, pharmacophore models are highly successful and widely applicable over all kinds of macromolecular targets. As in silico bioactivity predictions are being applied more and more in toxicology studies to reduce experimental efforts, especially animal testing, pharmacophore models are also being investigated for their applicability in this area. This chapter gives a brief introduction to the pharmacophore technology, present applications k in toxicity prediction, and finally makes a statement on the future use of k pharmacophoremodelsfortheso-calledantitargets. In general, pharmacophore models are used to investigate whether compounds have a set of chemical functionalities that are typical for a specific bioactivity. There are two types of pharmacophore models: structure-based and ligand-based models [7]. Structure-based models are directly retrieved from 3D structures of targets with bound active compounds, usually from X-ray co-crystal structures or NMR structures. Additionally, a docking pose in a target structure or even in a homology model may be used as a template. Nowadays, structures refined by molecular dynamics simulations are also employed for structure-based pharmacophore modeling [5]. If the 3D structure of the target alone is available, a grid may be used to identify hot spots for ligand binding in the structure cavities and define the locations of pharmacophore features [6, 8]. For generating structure-based pharmacophore models, (possible) protein-ligand interactions are directly translated into pharmacophore features forming a starting model for systematic refinement [7, 9]. If structural data are absent or not sufficient to sufficiently describe the properties of active compounds, ligand-based pharmacophore models can be developed based upon the 3D alignment of highly active, preferably rigid molecules. These models just describe the common chemical features of active ligands and may therefore not translate into direct interactions with the target. Owing to their focus on the presence of essential chemical functionalities at defined places in the 3D space and not on the entire structure of a compound, pharmacophore

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models are exceptionally well suited to discover novel chemotypes for a certain activity [10, 11]. In addition to the already described models, quantitative pharmacophores can be generated, which – similarly to a quantitative structure–activity relationship (QSAR) model – aim to predict the potency of the screened molecules. The collection of training molecules for such quantitative models should comprise at least 16 compounds spanning more than three orders of activity [12]. To assure the quality of the activity data, those molecules must have been experimentally investigated using the same biological assay, preferentially in the same lab, and tested by the same scientist. These quantitative models are not primarily preferred for virtual screening, but for ranking molecules according to their expected potency. For more detailed descriptions of the generation, validation, and applications of pharmacophore models, the interested reader is referred to standard literature in the field [13, 14] and to some extent excellent recent reviews, for example [7, 15–17]. One important aspect of selecting pharmacophore models for prospective screening is their predictive quality. It is essential to only use well-validated models to prospectively screen xenobiotic databases and select compounds for further in vitro investigation. Although there is no single path leading to high quality models, a few general recommendations can be given. First, the qual- k ity of the compound data for building and theoretically validating the model k iscrucial.Amodelcanonlybeaspreciseastheunderlyingdata.Buthow can one recognize high-quality data? First of all, it is definitely tempting to use structure-activity databases such as the ChEMBL [18] or PubChem [19]. These databases offer ready-to-download chemical structures along with activity data on many targets. Although tremendously useful as a starting point, these databases suffer from two drawbacks: First, they are not comprehensive and cover only a fraction of the published activity data of chemicals. Second, in the automated processing of thousands of structure and activity data, errors occur. This is a known problem and there are already efforts being made to improve the accuracy of the data [20]; however, all data coming from such databases should be verified with the original literature, just to be 100% sure. During the data collection step, it is crucial to also collect a large set of inactive molecules to train the model to discard those compounds in virtual screening. In the best case, a dataset of inactive compounds is available in literature or one of the large structure-activity databases. PubChem offers several datasets from otherwise unpublished in vitro tests and is a helpful source when searching for inactive compounds. The aim is to gather about 40-fold more inactive compounds than actives to properly mimic the success chances of a random high-throughput screening. If this amount of data on inactive compounds is not available, so-called decoys can be employed for the study [21]. A decoy is a molecule with similar physicochemical properties (molecular weight, lipophilicity, numbers of hydrogen bond donors/acceptors,

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etc.) as the active compounds but with a different chemical structure. These decoys have a very low chance of coincidentally being active and can therefore be used as putative inactive compounds in the model validation. However, models can be trained more accurately when data of tested inactives and not only decoys are used [22]. For the decision on the compounds to be included in the model calculation, the in vitro assays used for evaluating these compounds must also meet certain criteria. Because pharmacophore models predict the direct interaction of the ligand with the target, the biological assay readout must also give this result. Cell-based or in vivo assays include many possible ways for the ligand to be intercepted before even reaching the target. Additionally, these systems offer many other potential targets for the ligand. Accordingly, no conclusions on direct ligand binding can be drawn from those assays. Cell-free assays allowing for a direct access to the binding site are the minimum requirement for measuring protein-ligand binding. Once a suitable dataset has been assembled, structure- and ligand-based models can be calculated. For ligand-based models, it is advisable to preferentially use highly active compounds as the training set, because virtual hits are usually at least one order of magnitude less active than the training compounds of the screening model [23]. After the model’s computation, it k needs to be theoretically validated using the compounds from the dataset that k have not been used for model generation. Some commonly evaluated metrics for pharmacophore model quality include the yield of actives, enrichment factor, accuracy, and receiver-operating characteristic curves/area under the curve, as reviewed by Braga et al. [24]. Individual models with promising performance can additionally be manually refined by adding or deleting exclusion volumes, changing the size and shifting the location of features, marking features as optional, adding shape constrictions, or using customized features [9]. After successful theoretical validation, the model’s ability to recognize so far unknown active hits is experimentally tested. For cost and efficiency reasons, it is advisable to perform such experiments on in-house or commercial databases. The biological validation of the model’s predictions usually gives true positive hit rates between 5% and – in exceptional cases – 50 % or more. On the basis of new experimental data, models can be refined and again experimentally validated to verify this improvement. Finally, such a well-validated model is ready for prospective use and a steady and reliable performance of the virtual screening predictions can be expected. On the other hand, the use of models that have not been experimentally validated cannot be recommended. Nowadays, pharmacophore models are frequently combined with computationally more expensive screening methods such as docking or molecular dynamics simulations. Pharmacophore models are often used as fast pre-filters of large chemical databases. This strategy of virtual screening is very

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promising, because it covers diﬀerent aspects that are important for the activity of a compound: The pharmacophore model accounts for the presence of physicochemical features important for changing a biological result and the docking or molecular dynamics part independently calculates the ﬁtting of a compound into the binding site of the target. If a substance meets both criteria, the chance that a compound is active supposedly exceeds the success rates of the isolated virtual screening approaches.

5.2 Antitarget Screening

Regarding pharmacophore-based screening, there are only few differences in screening for a compound with a desired (target) or undesired (antitarget) effect. Actually, interference with a healthy organism can always be seen as unwanted and therefore, most known pharmacological targets are also antitargets in a toxicological sense. However, some targets are more susceptible to modulation by xenobiotics and more critical for health when modulated. Those targets have already attracted much attention and some pharmacophore-based screening studies have been conducted focusing on the identification of potentially harmful agents. In this chapter, some examples, k grouped by toxicological area, are shown. Owing to space constraints, it is not k possible to provide a comprehensive listing.

5.3 Prediction of Liver Toxicity

For the mechanism-based prediction of liver toxicity, especially nuclear receptors (NRs) involved in fatty acid and bile acid signaling have been investigated. Specifically, for the aryl hydrocarbon receptor (AhR), constitutive androstane receptor (CAR), estrogen receptor (ER), glucocorticoid receptor (GR), farnesoid X receptor (FXR), liver X receptor (LXR), peroxisome proliferator-activated receptor (PPAR), pregnane X receptor (PXR), and retinoic acid receptor (RAR), there is evidence of involvement in the development of hepatic steatosis [25]. One of the best-studied NR involved in liver steatosis is PPARγ,whichisthe pharmacological target of oral antidiabetic agents such as pioglitazone. This class of drugs, full PPARγ agonists, has been associated with weight gain and liver toxicity [26]. In terms of drug development, focus shifts toward PPARγ partial agonists in expectance of reduced side effects [27]. Owing to the wealth of structural and in vitro data for this receptor, the development and validation of pharmacophore models predicting PPARγ agonism is straightforward. There are even cocrystal structures with endocrine disrupters, for example, with the flame retardant tetrabromobisphenol A (Figure 5.2) [28]. Besides

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Figure 5.2 PPARγ with bound tetrabromobisphenol A (PDB entry 3osw). The pharmacophore model derived from this endocrine disruptor–receptor complex can be refined and used for virtual screening for other PPARγ ligands from environmental chemicals. Protein–ligand interactions are color coded according to Figure 5.1. (See color plate section for the color representation of this figure.) k k many pharmacophore models that were developed to find PPARγ agonists as novel therapeutic agents [29–32], some models have now also been specifically designed for toxicity prediction related to this target [33, 34]. However, until now the latter have not been used to prospectively identify potentially hepatotoxic compounds including in vitro experiments. Additionally, up to now, there is no sharp association of adipogenesis with full agonism, partial agonism, or antagonism on PPARγ. Pharmacophore modeling of (partial) NR agonism and antagonism may be challenging. Thus, although the modeling studies on PPARγ are quite advanced, it remains an interesting field for further and more in-depth research. A few experimentally validated pharmacophore models have been reported for LXR [35]; however, they were designed for finding novel lead candidates for the treatment of high levels of blood lipids via LXRβ agonism. In contrast, hepatotoxicity is associated with LXRα activation [36]. Still, because the lig- and binding sites of the two LXR subtypes are very similar, the models are also suitable to identify potentially hepatotoxic LXRα modulators. Similar to LXR, no specific pharmacophore models designed for hepatotoxicity prediction have been reported for FXR and CAR. The available experimentally validated models from drug discovery [37–39] could and should be used in the near future to identify compounds involved in NR-induced liver steatosis. For PXR, AhR, and RAR, some models have been reported, but they are so far not experimentally validated, for example, see Ref. [40].

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5.4 Prediction of Cardiovascular Toxicity

In the cardiovascular toxicity target group, one can ﬁnd predominantly ion channels and receptors associated with the regulation of blood pressure and heart rate. Probably the best known cardiovascular antitarget is the human ether-a-go-go related gene (hERG) potassium channel involved in cardiac repolarization. Several experimental and even marketed drugs have been removed from the market due to hERG activity [41]. Additionally, some calcium and sodium channels also are important ion channel antitargets. A whole chapter is dedicated to hERG-mediated toxicity in this book (Chapter 3), so in this part the focus lies on two hERG ligand pharmacophore modeling studies including experimental validation. The ﬁrst one was reported by Ekins et al. and focused on antipsychotic agents blocking the hERG channel. They developed a quantitative pharmacophore model optimized for predicting

the IC50 values for the hERG block [42]. In comparison, Kratz et al. followed a parallel screening approach by developing several qualitative pharmacophore models for hERG blockers and using all of them for screening commercial databases (Figure 5.3). From the hit lists, 50 compounds were selected for

testing, of which 20 significantly blocked the hERG channel with IC50 values ranging from 0.13 to 2.77 μM[43].Thesemodelswerealsousedtofind k hERG blockers from widely consumed herbal remedies. Alkaloids from ipecac k were thereby identified as micromolar inhibitors of this channel [44]. The compositions of the pharmacophore models developed by Ekins et al.and

Screening database Hits Hits Hits Active molecules Hits Hits

Inactive molecules

Figure 5.3 Pharmacophore models for hERG blockers used in a parallel way. Each model covers a different fraction of active compounds, but is restrictive enough not to find a large number of inactive hits. All hit lists together cover the vast majority of active compounds and find less false positive hits compared to one very general model designed to cover most active compounds at once.

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those by Kratz et al. are very similar. Common chemical features include four hydrophobic features and one positively ionizable group, preferably a tertiary amine. Compared to hERG, other cardiac ion channels such as the CaV1.2 or NaV1.5 channels are hardly investigated using pharmacophore models. To date, no experimentally validated pharmacophore model has been reported for these targets. In their pioneering work, Klabunde et al. designed ligand-based pharmacophore models for antitarget G protein-coupled receptor (GPCR) ligands,

among them the α1A adrenergic receptor antagonists [45]. This receptor regulates blood pressure by modulating the relaxation of the vascular muscle tone. Its unwanted inhibition can lead to orthostatic hypotension, dizziness,

and fainting spells [46]. Owing to diﬀerent classes of α1A receptor ligands, several models were built and theoretically validated against a set of 50 known active and nearly 1000 inactive compounds. A clear enrichment of active compounds was observed in this experiment. The models consisted of hydrophobic/aromatic outposts and a central positively ionizable feature. The class I model additionally contained a hydrogen bond acceptor. These pharmacophore models were later used in a prospective, multistep virtual screening campaign. Among the 80 tested virtual hits, 37 showed aﬃnities less

than 10 μM, three of them even less than 10 nM [47]. Similarly, α2A receptor k agonists can trigger hypotension [48]. However, no pharmacophore modeling k studies have currently been conducted for this mechanism.

The serotonin receptors 5-HT1A and 5-HT2B have also been associated with cardiovascular toxicity. 5-HT2B agonism can trigger valvular heart disease [49, 50]. While 5-HT1A activity is already the subject of pharmacophore modeling studies (see the following), 5-HT2B still needs attention in the near future to predict those serious eﬀects.

5.5 Prediction of Central Nervous System (CNS) Toxicity

Central nervous system (CNS) toxicity studies are very complex, because many physiological processes in the brain involve multiple GPCRs, of which there are several subtypes with diﬀerent functionalities and ligand selectivities. Moreover, many CNS-active compounds target several receptors and ion channels, thereby triggering diﬀerent responses. In their GPCR antitarget pharmacophore modeling work, Klabunde and Evers modeled chemical

functionalities of 5-HT2A, dopamine D2, and adrenergic α1A ligands and also compared them. All those receptors bind biogenic amines and therefore share structural features in the binding site that enable unspeciﬁc ligands to modulate several of them. The shared features comprised a positively ionizable nitrogen, several hydrophobic and/or aromatic features, and hydrogen bond

k k

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(a) (b) (c)

β1-Adrenoceptor Dopamine D3 receptor Histamine H1 receptor

(d) (e)

k Figure 5.4 Protein–ligand interactions determined by X-ray crystallography of exemplary k GPCRs. (a) β1-Adrenoceptor in complex with cyanopindolol (PDB entry 4bvn [51]); (b) dopamine D3 receptor in complex with eticlopride (PDB entry 3pbl [52]); (c) histamine H1 receptor in complex with doxepin (PDB entry 3rze [53]); (d) the protein–ligand interactions of all models superimposed onto each other; (e) all example models share two hydrophobic (yellow) features and a positively ionizable nitrogen (blue star). Figure inspired by Klabunde et al. [47]. (See color plate section for the color representation of this ﬁgure.)

acceptors (Figure 5.4) [45]. Compounds with a certain spatial arrangement of those chemical functionalities are susceptible to CNS toxicity, if they are able to cross the blood–brain-barrier.

Although it is also involved in blood pressure regulation, the 5-HT1A receptor is primarily associated with central eﬀects. Ngo et al. developed agonist and antagonist models for this receptor to use them for counter screening their

test candidates. These were potential α1 receptor antagonists for the treatment of benign prostatic hyperplasia (BPH). Some of the eﬀects modulated by the

5-HT1A receptor (deregulated sleep patterns, anxiety-like behavior, interrupted neural tone of the iris sphincter muscle, sexual dysfunction, inhibited bladder control) are especially unpleasant for BPH patients. Accordingly, they wanted to exclude potentially unselective compounds right at the beginning of their virtual screening campaign. Theoretical validation was followed by prospective virtual screening and biological testing of hits. Although the success of the selectivity prediction between both targets was moderate, a promising compound was identiﬁed in this study [54]. While the respective models may need

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reﬁnement, the strategy followed in this work is a very rational and promising one and can be recommended for similar studies. Most currently investigated CNS antitargets belong to the target group of GPCRs (e.g., histamine receptors, adrenoceptors, dopamine receptors, muscarinic receptors, opioid receptors, cannabinoid receptors) [55]. The promiscuity of many CNS-active ligands complicates the accurate understanding and prediction of CNS eﬀects of compounds [51]. The development of pharmacophore models for this important target class has long been delayed owing to the lack of publicly available X-ray structures. Dai et al. developed a large pharmacophore model collection for GPCRs based on the available X-ray structures and many homology models for most of the known GPCRs [52]. However, those models have so far not been experimentally validated and the usefulness of such a large, automatically generated model library remains to be determined. If this system works, it will be tremendously useful for further drug development and toxicity assessments.

5.6 Prediction of Endocrine Disruption

Endocrine disruptors are xenobiotics (substances in food, consumer products, k and the environment, e.g., drinking water), which interfere with human and/or k wildlife hormone biosynthesis, metabolism, or action. Thereby they have effects on male and female reproduction, cancer forms, neuroendocrinology, thyroid, metabolism, obesity, and cardiovascular endocrinology. Nowadays, endocrine disruptors are considered a public health threat and a lot of research is performed to identify potentially harmful xenobiotics [53]. Of course, testing hundreds of thousands of compounds and mixtures is a challenging and costly task and needs sophisticated planning to be also effective in identifying the most hazardous substances first. In this endeavor, in silico virtual screening tools are used to prioritize compounds for in vitro experiments. While also other virtual screening methods are intensively applied in this field, for example, QSAR models [56], pharmacophore models have already contributed to the identification of substances interacting with targets associated with endocrine disruption. For this reason, these examples are explained in more detail to show feasible workflows and success stories in the area. In the field of endocrine disruption, many studies focused on ligands directly binding to nuclear hormone receptors, which directly regulate sexual development, growth, fertility, and behavior. The effects of agonism and antagonism on the ERs, androgen receptor (AR), thyroid receptors (TRs), GRs, and progesterone receptors (PRs) are well understood, and those proteins are established drug targets. The association of unwanted modulation of these receptors with endocrine disruption is therefore obvious and those NRs are top-priority antitargets. In terms of prospectively identifying endocrine disruptors, there are

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currently no reports on successful studies. However, experimentally validated pharmacophore models for ERs [57–60], ARs [61–63], GRs [64], and TRs [65] could readily be used. Additionally, cofactor binding sites of the NRs are now recognized as druggable sites and must be considered in modulating NR activities [66]. For PRs, there are currently no suitable models available. More recently, the enzymes catalyzing the biosynthesis of hormones are also considered in evaluating endocrine disruption. These include already approved drug targets such as aromatase (Aro, CYP19) and 5α-reductase (5αR), but also investigational targets like 3β-, 11β-, and 17β-hydroxysteroid dehydrogenases (HSDs) [67]. For some of these targets, prospective virtual screening studies aiming at the identification of previously unknown endocrine disruptors have been reported. A series of papers investigated inhibitors for 11β-HSDs. The two isoforms 1 and 2 interconvert the active glucocorticoid cortisone and inactive cortisol, thereby regulating intracellular glucocorticoid concentrations in various tissues (Figure 5.5). Because glucocorticoids have multiple effects in different tissues, the shifting of their concentrations can trigger advantageous or unwanted effects. In brief, selective 11β-HSD1 inhibition is evaluated as a strategy to treat obesity, Alzheimer’s disease, depressive disorders, and the metabolic syndrome. k 11β-HSD2 inactivates glucocorticoids. This function is especially important in k tissues expressing the mineralocorticoid receptor, which can also be activated by cortisone, not only by its usual agonist aldosterone. 11β-HSD2 inhibition may therefore cause apparent mineralocorticoid excess, accelerate atherogen- esis promoting cancer, decreasing testosterone levels in the testes, and causing fetal development disorders [68]. The in vivo effect of 11β-HSD2 inhibition in pregnant women is well known from Finland, where many people consume high amounts of licorice, which contains the potent inhibitor glycyrrhizin. It has been shown that high licorice consumption in pregnancy has an adverse impact on fetal development in utero and also later in life [69]. The following projects can be seen as pioneering works in the field of pharmacophore-based endocrine disruptor identification, because they cover

Cortisone Cortisol

Figure 5.5 11β-HSDs catalyze the interconversion of the active glucocorticoid cortisone and its inactive metabolite cortisol [6].

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all the steps from model development, validation, virtual screening, and the successful prospective identification of enzyme inhibitors from environmental chemicals. Schuster et al. first developed and experimentally validated pharmacophore models for 11β-HSD1 and unselective inhibitors [70]. This first validation was performed on commercially available substances. Because of the endocrine-disrupting effects of 11β-HSD2 inhibition, they later used the unselective model to screen a 3D database of putative endocrine-disrupting chemicals [71]. Out of the over 76,000 compounds in the virtual screening database, 29 fitted into the model and 5 of them were biologically tested. The two compounds lasalocid and AB110873, an antibiotic used in chicken farms and a silane rubber coupling agent, significantly inhibited 11β-HSD2

with IC50 values in the low micromolar range. Both active hits were chemically very distinct from the currently known enzyme inhibitors, proving the scaffold-hopping potential of the pharmacophore model. It is noteworthy that the silane compound additionally directly activated the mineralocorticoid receptor in low micromolar concentrations, which would additionally increase the adverse effects triggered by 11β-HSD2 inhibition. The two models from reference [70] were later refined with new literature data and again used to virtually screen commercial, drug, and in-house natural product databases. Experimental validation of selected hits revealed several clinically used drugs k as 11β-HSD1, 2, or nonselective inhibitors. The antihypertensive furosemide, k the anti-inflammatory drug ibuprofen, and the natural products digitoxigenin, hecogenin, hispanolone, and marrubiin selectively inhibited 11β-HSD1. The fungicide ketoconazole, the calcium channel blocker lidoflazine, the vitamin B1 analog octotiamine, the antibiotic rifampicin, the food-flavoring agent monoolein, and the natural product gossypol were 11β-HSD2-selective inhibitors. The immunosuppressive rapamycin nonselectively inhibited both enzymes [9]. Finally, the Drugbank database consisting of 1543 FDA-approved drugs was virtually screened with one of the refined 11β-HSD inhibitor pharmacophore models. This led to the identification of several azole antifungals as 11β-HSD1 inhibitors. Further biological tests of additional fungicides from this class identified itraconazole and posaconazole as potent 11β-HSD2 inhibitors

with submicromolar IC50 values [72]. Although the pharmacophore-based virtual screening proved successful in these studies, the question raised was why the potent inhibitors itraconazole and posaconazole had not been identified by the model in the first place. First of all, one needs to check if the two compounds had been present in the Drugbank database – and they were. So theoretically, they could have been found in the initial virtual screening. Then, the azoles were fitted into all available models forβ 11 -HSD inhibitors. It turned out that the shape restriction that should prevent too large compounds from fitting the model was responsible for missing these potent but high-molecular-weight hits. Accordingly, in future studies, the shape should be deleted from the model before the virtual database screening. In general,

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going back to the pharmacophore models after the biological evaluation of virtual hits is a crucial step in the model development and reﬁnement cycle [9, 72]. Only in this way the application domain of the models can be broadened and their predictive power optimized only in this way. Whereas 11β-HSDs are catalyzing glucocorticoid metabolism, 17β-HSDs are key enzymes in the sex hormone metabolism network (Figure 5.6). Some of them are evaluated as drug targets and, accordingly, experimentally validated pharmacophore models for the screening for endocrine disruptors are available (Table 5.1). From this enzyme family, one study reported the prospective discovery of environmental chemical inhibitors: screening of an endocrine disruptor database for 17β-HSD3 ligands. 17β-HSD3 catalyzes the reduction of the 17-keto group to a hydroxyl group in the ﬁnal step of testosterone synthesis (Figure 5.6). Its inhibition therefore

O OH 17β-HSD1 H H

HHHH 17β-HSD2 HO HO k Estrone Estradiol k O 17β-HSD1 OH 17β-HSD5 H H 17β-HSD2 HHHH HO HO Dehydroepiadrosterone 5-Androstene-3β-17β-diol O 17β-HSD3 OH 17β-HSD5 H H 17β-HSD2 HHHH HO HO 4-Androstene-3,17-dione Testosterone O OH

H H 17β-HSD2 H H H H HO HO H H 5α-Androstanedione 5α-Dihydrotestosterone

Figure 5.6 Interconversion of sex hormones and their metabolites catalyzed by 17β-HSDs [6].

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Table 5.1 Experimentally validated pharmacophore models for 17β-HSD inhibitors.

Enzyme Model References

17β-HSD1 1 Structure-based pharmacophore model [73] 17β-HSD1 1 Structure-based pharmacophore model [74] 17β-HSD2 3 Ligand-based pharmacophore models [75] 17β-HSD3 2 Ligand-based pharmacophore models [76] 17β-HSD3 1 Ligand-based pharmacophore model [77] 17β-HSD5 4 Structure-based pharmacophore models [76]

decreases testosterone synthesis in the testis. The importance of this enzyme for normal sexual development is shown in patients suffering from a mutation in the 17β-HSD3 gene, so-called 17β-HSD3 deficiency or 46,XY disorder. These patients have female sexual characteristics at birth, but are genetically males. Because of the impaired testosterone synthesis in the fetal stage, they cannot develop male characteristics. However, at puberty, other testosterone sources than the one catalyzed by 17β-HSD3 become available and so the k children develop secondary male features [78]. It is therefore crucial that k this enzyme is not unintentionally inhibited in the critical phase of early life. Nashev et al. employed a pharmacophore-based virtual screening of an endocrine disruptors database for searching 17β-HSD3 inhibitors among environmental chemicals [79]. In their virtual hit lists, some representative chemical UV filters were reported. Because humans are directly exposed to this class of compounds, and it has already been known that some UV filters are bioavailable via cutaneous application, they were investigated in vitro. Additionally, other chemical UV filters not included in the database were tested. Indeed, the study identified several benzophenones and camphor derivatives as micromolar inhibitors of 17β-HSD3. Some of the active compounds were also found to antagonize AR activation by testosterone, which synergistically impairs testosterone action in the organism. As mentioned above, it is critical to return to the pharmacophore model for refinement once the new biological test data become available. In this case, the authors focused on the benzophenone class of compounds because, interestingly, their activities ranged from very low micromolar activity to inactivity. So the authors developed a structure–activity-relationship model for the benzophenone class of chemical UV filters (Figure 5.7). The information from this study suggests that industry should shift toward the use of benzophenones with etherified hydroxyl groups in UV screens and plastics to avoid potential antiandrogenic effects of these products.

k k

Pharmacophore Models for Toxicology Prediction 135

BP-1 BP-2 BP-3 BP-8 IC50 = 1.05 μM IC50 = 18.1 μM 93% rest activity* 86% rest activity*

Figure 5.7 Structure–activity relationship rationalization of chemical UV filters of the benzophenone class-inhibiting 17β-HSD3. The three hydrogen bond acceptors (red) and two aromatic rings (blue) are essential for bioactivity. Etherification of one of the hydroxyl groups inactivates the compound (arrows). *, Residual activity of the enzyme was measured at a compound concentration of 20 μM[6].(See color plate section for the color representation of this figure.)

Besides these paradigm studies on 11β-and17β-HSD inhibitors, other enzymes involved in sex steroid metabolism also need attention because their inhibition can cause endocrine disruption. These include 3β-HSDs, aldosterone synthase (CYP11B), Aro-synthesizing estrogen, and 5αR-synthesizing the potent androgen dihydrotestosterone. The in vivo impacts of the Aro and 5αR inhibition are well known. Aro inhibitors such as anastrozole and letro- zole are approved drugs to treat postmenopausal, estrogen-dependent breast k cancer. The 5α-reductase inhibitors ﬁnasteride and dutasteride are used for k treating BPH and androgenic alopecia. Currently, no pharmacophore-based virtual screening studies aiming at identifying endocrine disruptors are reported for these enzymes. However, published models for Aro inhibitors [80–82] could readily be used for this purpose and speed up the discovery of so far unrecognized active xenobiotics. Like the steroid receptors themselves, the substrates of the steroid- metabolizing enzymes are structurally very similar to each other. Accordingly, some of these enzymes also share substrates and inhibitors. Because substrates or products of the enzymes are often endogenous NR agonists, cross-activities of xenobiotics active on one of the enzymes or receptors are often observed, as exempliﬁed on the silane AB110873 or benzophenone-1. Therefore, the activities of a chemical needs to be determined against all of these related targets to make an informed decision on its endocrine-disrupting potential [70, 71, 73–75, 79].

5.7 PredictionofADME

As with hERG blocking, there is a chapter on absorption, distribution, metabolism, excretion (ADME) prediction in this book. The most important targets in this area are the xenobiotic-metabolizing CYP enzymes 1A2, 2C9,

k k

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2C19, 2D6, and 3A4 as well as eﬄux pumps such as P-glycoprotein. In this area, both the predictions of inhibitors and of substrates are of interest. These enzymes and transporters have been studied for decades now, and there are already some pharmacophore models applied to prospectively identify potential ligands. For example, pharmacophore models for CYP1A2 [76] and CYP2D6 [77] have been employed to identify natural products inhibiting the enzymes.

5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies

Although the pharmacophore approach has shown promising results in the case studies described above, antitarget screening compared to drug discovery screening has other requirements in terms of model quality. In drug discovery, the so-called cherry picking approach is followed, in which the virtual screening narrows down a large database to just a small fraction of hits, which have a high probability to be active. In contrast, toxicological screening must aim at the discovery of preferably all active compounds in a database. Therefore, the k application domain of the pharmacophore models should be very large, which k means that the models must recognize structurally diverse active hits from various activity ranges. This can be accomplished by generating very general pharmacophore models with few features (usually only three or four), creating partial query models with omitted features, or combining several more restrictive models in a parallel screening, as exemplified in the hERG study by Kratz et al. [43] Of course, such broad screening approaches will lead to a higher false positive rate; however, it enables the models to also find chemically distinct active compounds with unexpected activity, which would not be discovered by just looking at their structures. The application of pharmacophore models in toxicology studies also has limits: Since pharmacophore models are developed for compounds interacting with specific targets, they can only be used for predicting mechanism-based toxicity. A general toxicity prediction such as “mutagenic” or “irritant” is not within the scope of pharmacophore-based virtual screening. Another limitation is that much of the screening success depends on the composition of the database. For environmental chemical studies, it is a challenge to assemble databases of all chemicals an organism may be exposed to. For a good start, regulatory agencies publish lists of compounds approved for use in cosmetics, food additives, drugs, industrial chemicals, and more, for example, on the FDA homepage (www.fda.gov). These often contain the CAS numbers of the respective chemicals, which can then be used to extract the chemical structures of these compounds for the compilation of virtual screening databases.

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These compounds may be altered and metabolized before or when they enter the human organism and these metabolites can have different effects than the parent compounds. The same is true for natural products. Apart from this, not all natural compounds that we consume are known, therefore, they cannot be included in screening databases and suggested as active compounds in a virtual screening campaign. This problem is not restricted to pharmacophore-based studies but actually concerns all virtual screening endeavors that are independent of the screening method. Finally, pharmacophore models work on a single compound – one target at atime.However,xenobioticsareusuallytakeninasmixtures,beitasfood, nutraceuticals, cosmetic products, or other consumer products. In vitro and in vivo, such mixtures often have not one dominant, highly active ingredient but a mixture of compounds that leads to a synergistic effect [83]. The activity simulation of these mixtures is challenging and currently the focus of intensive investigation. It is to be determined how pharmacophore models can be best used for this task. In recent years, in silico activity profiling has become popular. In these calculations, one compound is screened against a panel of models representing different targets. This experiment unifies therapeutic target fishing for a compound and the concomitant prediction of possible adverse effects. This concept k has been developed for several virtual screening methods such as 2D similarity k search using fingerprints [84], combined 2D-3D similarity [85] machine learning [86], docking [87, 88], and pharmacophore models [89]. Of course, for such a high number of models, complete experimental validation is hardly feasible and therefore most of these pharmacophore models are of uncertain quality. Furthermore, the approaches are limited to certain targets and can therefore not predict all pharmacologically relevant activities. However, as the field advances, more and more high quality models are becoming available. Pharmacophore-based target fishing has already led to the identification of novel targets for natural products [90, 91] and its importance in the field is growing. Therefore, in silico activity profiling, although challenging, is bound to be a very powerful tool in toxicity research.

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72 Beck, K.R., Bächler, M., Vuorinen, A. et al. (2017) Inhibition of 11β-hydroxysteroid dehydrogenase 2 by the fungicides intraconazole and posaconazole. Biochem. Pharmacol., 130, 93–103. 73 Schuster, D., Kowalik, D., Kirchmair, J. et al. (2011) Identification of chemically diverse, novel inhibitors of 17β-hydroxysteroid dehydrogenase type 3 and 5 by pharmacophore-based virtual screening. J. Steroid Biochem. Mol. Biol., 125, 148–161. 74 Schuster, D., Nashev, L.G., Kirchmair, J. et al. (2008) Discovery of nonsteroidal 17β-hydroxysteroid dehydrogenase 1 inhibitors by pharmacophore-based screening of virtual compound libraries. J. Med. Chem., 51, 4188–4199. 75 Vuorinen, A., Engeli, R., Meyer, A. et al. (2014) Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors. J. Med. Chem., 57, 5995–6007. 76 Zhu, R., Hu, L., Li, H. et al. (2011) Novel natural inhibitors of CYP1A2 identified by in silico and in vitro screening. Int J Mol Sci., 12, 3250–3262. 77 Hochleitner, J., Akram, M., Gostner, J.M. et al. (2017) A novel combinatorial approach for the discovery of cytochrome P450 2D6 inhibitors from nature. Sci. Rep., 7, 8071. k 78 Faienza, M.F., Giordani, L., Delvecchio, M., and Cavallo, L. (2008) Clinical, k endocrine, and molecular findings in 17beta-hydroxysteroid dehydrogenase type 3 deficiency. J. Endocrinol. Invest., 31, 85–91. 79 Nashev, L.G., Schuster, D., Laggner, C. et al. (2010) The UV-filter benzophenone-1 inhibits 17β-hydroxysteroid dehydrogenase type 3: virtual screening as a strategy to identify potential endocrine disrupting chemicals. Biochem. Pharmacol., 79, 1189–1199. 80 Schuster, D., Laggner, C., Steindl, T.M. et al. (2006) Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors. J. Chem. Inf. Model., 46, 1301–1311. 81 Muftuoglu, Y., Leimgruber, S.S., Sharlow, E.R. et al. (2012) An integrated strategy to identify new aromatase inhibitors. Curr. Trends Pharmacol., 16, 15–24. 82 Neves, M.A.C., Dinis, T.C.P., Colombo, G., and Sá e Melo, M.L. (2009) An efficient steroid pharmacophore-based strategy to identify new aromatase inhibitors. Eur. J. Med. Chem., 44, 4121–4127. 83 Cedergreen, N. (2014) Quantifying synergy: a systematic review of mixture toxicity studies within environmental toxicology. PLoS One, 9, e96580. 84 Keiser, M.J., Roth, B.L., Armbruster, B.N. et al. (2007) Relating protein pharmacology by ligand chemistry. Nat. Biotechnol., 25, 197–206. 85 Gfellerr, D., Grosdidier, A., Wirth, M. et al. (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acid Res., 1,1–7.

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86 Filimonov, D.A., Lagunin, A.A., Gloriozova, T.A. et al. (2014) Prediction of the biological activity spectra of organic compounds using the pass online web resource. Chem. Heterocycl. Compd., 50, 444–457. 87 Li, H., Gao, Z., Kang, L. et al. (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acid Res., 34, W219–W224. 88 Vedani, A., Dobler, M., Hu, Z., and Smiesko, M. (2015) OpenVirtual- ToxLab – a platform for generating and exchanging in silico toxicity data. Toxicol. Lett., 232, 519–532. 89 Liu, X., Ouyang, S., Yu, B. et al. (2010) PharmMapper server: a web server for potential drug tarrget identification using pharmacophore mapping approach. Nucleic Acid Res., 38, W609–W614. 90 Rollinger, J.M., Schuster, D., Danzl, B. et al. (2009) In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med., 75, 195–204. 91 Duwensee, K., Schwaiger, S., Tancevski, I. et al. (2011) Leoligin, the major lignan fom Edelweiss, activates cholesteryl ester transfer protein. Atheroscle- rosis, 219, 109–115. k k

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145

Transporters in Hepatotoxicity Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker

Department of Pharmaceutical Chemistry, University of Vienna, Wien, Austria

CHAPTER MENU

Introduction, 145 Basolateral Transporters, 146 Canalicular Transporters, 148 Data Sources for Transporters in Hepatotoxicity, 148 In Silico Transporters Models, 150 k Ligand-Based Approaches, 150 k OATP1B1 and OATP1B3, 150 NTCP, 154 OCT1, 154 OCT2, 154 MRP1, MRP3, and MRP4, 155 BSEP, 155 MRP2, 156 MDR1/P-gp, 156 MDR3, 157 BCRP, 157 MATE1, 158 ASBT, 159 Structure-Based Approaches, 159 Complex Models Incorporating Transporter Information, 160 In Vitro Models, 160 Multiscale Models, 161 Outlook, 162

6.1 Introduction

Transmembrane transporters are essential for regulation of the uptake and eﬄux of endobiotics and xenobiotics at the cellular level as well as in barrier tissues (e.g., blood–brain barrier, kidney, liver, enterocytes). Among them,

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MRP1 MRP3 MRP4 MRP5 MRP6 OSTα

OSTβ OCT1 NTCP BSEP MDR3

OCT3 BCRP Bile P-gp OATP1B1

OAT7 MATE1 OATP1B3 MRP2

OAT2 OATP2B1 ABCG5/G8 ATP8B1

Blood Hepatocyte Blood

Figure 6.1 Transporters located in the hepatocyte. The medium grey symbols represent the canalicular transporters and dark grey ones the basolateral transporters. Cycles represent uptake transporters and ellipses refer to efflux transporters. The arrows define the direction of transport. k k hepatic transporters possess a vital role, as the liver is the main organ of metabolism and detoxification [1, 2]. Figure 6.1 depicts the main hepatic transporters and their respective location in the hepatocyte. In the following section, we will briefly introduce their significance in selected liver toxicity manifestations.

6.2 Basolateral Transporters

Regarding the basolateral uptake transporters, the sodium (Na+) taurocholate co-transporting polypeptide (NTCP) is quite important in the enterohepatic circulation of bile salts, thus contributing to liver homeostasis [3, 4]. It has been proposed that the mechanistic basis of some hepatotoxic – and, in particular, cholestatic - drugs includes the inhibition of NTCP [5]. In addition, the potential association of organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1 and OATP1B3) inhibition with hyperbilirubinemia, a pathological accumulation of conjugated or unconjugated bilirubin in sinusoidal blood [6, 7], is worth mentioning. Hyperbilirubinemia can be drug-induced [6, 7] or genetically induced, such as in the case of the Rotor syndrome [7–13]. Figure 6.2 shows the cycle of bilirubin and how transporters might be involved in the development of this condition.

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Bilirubin cycle in liver

Sinusoidal

MRP1 MRP4 MRP3 OATP1B1 OATP1B3

Hepatocyte Bilirubin UGT1A1

Glu Glu BCRP MRP2

Canalicular

Figure 6.2 The cycle of bilirubin in the liver. Bilirubin is taken up from sinusoidal blood by k OATP1B1 and OATP1B3. It is metabolized by UGT1A1 into mono- and bi-glucuronidated k products that are exported into bile primarily by MRP2 and in smaller extent (smaller arrow) by BCRP. A portion of the glucuronidated or unglucuronidated bilirubin is eﬄuxed into sinusoidal blood by MRP4 and the cycle is repeated. Source: Adapted from Sticova and Jirsa 2013 [11].

For the other major basolateral uptake transporters, such as the organic anion transporters (OATs) and the organic cation transporters (OCTs), there is low incidence for a potential role in toxicity phenotypes in the liver. However, there is one exception, namely, some polymorphisms and mutations in human OCT1 that lead to decreased transport activity of OCT1 in the liver, which can obstruct the biliary excretion of hydrophobic cationic drugs [14]. Regarding the basolateral efflux transporters, the organic solute transporter alpha-beta (OSTα–OSTβ) dimer is upregulated as a protective mechanism against the accumulation of toxic bile salts in the hepatocyte [15]. The same accounts for most of the multidrug resistance-associated proteins (MRPs). Several reviews describe an increase in mRNA levels of MRP1, MRP3, MRP4, andMRP5[4],aswellasanincreaseinproteinlevelsofMRP3andMRP4 [16] in hepatobiliary pathological conditions. Moreover, MRP3 as well as MRP1 may act as a compensatory mechanism to alleviate the potential toxic effects of high bile acid concentrations in the liver, when the canalicular efflux transporters such as the bile salt export pump (BSEP) and multidrug resistance-associated protein 2 (MRP2) are blocked [1, 17].

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6.3 Canalicular Transporters

For canalicular transporters, the most prominent example is the contribution of both genetically – [3, 18–21] and drug-induced [18, 20, 22–24] BSEP inhibition in the development of cholestatic conditions. MRP2, due to its important role in bilirubin and bile salts transport, is also suggested to be correlated with drug-induced hyperbilirubinemia [11, 25] and cholestasis [26–28]. Similarly, BCRP is also believed possibly contribute to the efflux of bilirubin conjugates into bile [11]. Deficiency of BCRP is also suspected to result in accumulation of toxic bile salts in the liver, which induce toxicity issues [29]. MDR3 main- tains the integrity of the membrane and conducts the phospholipid flow across the canalicular membrane of the hepatocyte [30]. It has also been associated with genetically – [1, 16, 26, 29–33] and drug-induced [16, 26, 29, 30, 34, 35] cholestatic conditions. Furthermore, MDR1 (P-glycoprotein, P-gp) is also expressed in the liver. MDR1 plays a prominent role in drug resistance during cancer therapy [36, 37] and has also been associated with drug-drug interactions. Nevertheless, in most of the cases of drug-induced hepatotoxicity or cholestasis, the implication of P-gp is attributed to its localization in several organ membranes and its great number of its substrates, rather than to direct effects in the liver [38, 39]. k The ATP-binding cassette subfamily G members 5 and 8 (ABCG5 and k ABCG8) heterodimer, the ATPase class I type 8B member 1, also known as ATPase-aminophospholipid transporter (ATP8B1 or FIC1), the multidrug and toxin extrusion transporter 1 (MATE1), the cystic fibrosis transmembrane conductance regulator (CFTR), the copper-transporting P-type ATP-ase (ATP7B), and the manganese transporter SLC30A10 are also liver transporters with an important physiological role. Despite the fact that they are associated with several diseases – including manifestations of liver toxicity, to our knowledge they are not associated with any pathological drug-induced liver condition. With this list of transporters and their important role it becomes evident, that any distortion in the proper function of hepatic transporters might result in manifestation of hepatotoxic phenomena. Therefore, knowledge of the inhibitory profile of drugs currently in the market, as well as the ones under development, is vital in order to avoid potential side effects. One step in this direction is the collection of the available data and another step further is the development of robust predictive models for these transporters.

6.4 Data Sources for Transporters in Hepatotoxicity

Currently several large-scale initiatives collect and predict toxicity data for both drugs and environmental chemicals. These include, among others,

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projects funded by the innovative medicines initiative (IMI) such as eTOX (http://www.etoxproject.eu/) and MIP-DILI (http://www.mip-dili.eu/), the Horizon 2020 EU-ToxRisk project (www.eu-toxrisk.eu) and the Toxicology in the 21st Century (Tox21) initiative [40] (http://tox21.org). EU-ToxRisk aims at advancing in vitro and in silico tools for toxicology, thereby focusing on mechanism-based approaches. Adverse outcome pathways (AOPs) introduced by the Organisation for Economic Co-operation and Development (OECD) play an important role here. One example for an AOP relevant to hepatotoxicity is “cholestatic liver injury induced by inhibition of the BSEP (ABCB11)” [41]. Searching for data on hepatotoxicity in bioactivity databases, such as ChEMBL [42, 43] or PubChem [44], is difficult owing to the way biological data are organized. While searches for bioactivity data for protein targets are straightforward, hepatotoxicity as a “target” is more difficult to define. For example, an assay search in ChEMBL version 22 [43] (accessed October 5, 2016) for “hepatotoxicity” returns 585 different assays mentioning hepatotoxicity in the assay description. Here, the target is for example the tissue Liver, the cell-line hepatocyte, or the general target ADMET. However, the phenotype “hepatotoxicity” is available as target directly (CHEMBL1697861) and is connected with 31 assays. These include, among others, datasets mined from literature [45, 46], the drug induced liver injury prediction system k (DILIps) training set [47], and the food and drug administration (FDA) liver k toxicity knowledge base benchmark dataset (LTKB-BD) [48]. Of note for hepatotoxicity, but not yet available in ChEMBL, is a recent work by Chen et al. [49], where a reference list for drug-induced liver injury (DILI) was presented. While identifying activity values for a specific transporter is more straightforward, interpreting the data can be challenging. As an example, a search for BCRP easily identifies the human protein (CHEMBL6020), which shows a total of 1799 bioactivity values. While a large portion of the values are reported as

IC50 values in nanomolar (nM) units (615), others are given as inhibition in percentage (357), activity in percentage or fold increase of control (278), or

EC50 in nM (213). Several activities are reported as ratios (58) or other activity types (275), for example, fluorescence intensity, drug transport, intrinsic activity, or permeability. This makes a direct comparison of the values rather difficult. In addition, measurements of different assay setups cannot always be directly compared, as shown for P-gp inhibitors [50]. To retrieve bioactivity values for transporters (e.g., to build computational models),alistofrelevanttransportersisneededfirst.Thiscanbeachieved by reviewing the literature, but data collections such as the Gene Ontology (geneontology.org) [51] can be helpful as well. For example, the molecular function of “canalicular bile acid transmembrane transporter activity” (GO:0015126) can be used to retrieve a list of BSEP proteins from different organisms.

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6.5 In Silico Transporters Models

Table 6.1 summarizes some of the available computational models of hepatic transporters implicated in hepatotoxicity, namely, BSEP, MRP2, MDR1, BCRP, MATE1, OCT1, OCT2, OATP1B1, OATP1B3, MRP3, MRP4, NTCP, ASBT, and OATPs. Owing to the heterogeneity of experimental reports in terms of assay types, test concentrations, and experimental conditions, most computational studies focus on classiﬁcation models of varying prediction performances. These models are built to distinguish inhibitors from non-inhibitors [79]. Only a few models for prediction of binding aﬃnity or inhibition at a quantitative level are available. Their predictivity is usually limited to small sets of compounds with measurements from assays with similar experimental conditions [79].

6.6 Ligand-Based Approaches

Considerable progress has been made in the development of in silico prediction models for canalicular transporters such as BSEP, MRP2, MDR1, and BCRP. In addition, there were also recent advances for in silico models for basolateral k transporters. k

6.7 OATP1B1 and OATP1B3

Karlgren et al. proposed a computational model for OATP1B1 [52] based on 146 compounds (2/3 training set; 1/3 test set) using orthogonal partial least-squares discriminant analysis (OPLS-DA). The model used a set of molecular descriptors and achieved a performance of 80% sensitivity and 91% specificity for a test set. Subsequently, they reported classification models for OATP1B1, OATP1B3, and OATP2B1 inhibitors at a 20 μM potency threshold, with accuracies between 75% and 93% [53]. Following a proteochemomet- ric modeling approach, De Bruyn et al. [80] combined protein-based and ligand-based molecular descriptors using random forest (RF) as classifier. They used 2,000 compounds for training and 54 compounds as an external test set. An additional OATP1B1 classification model was published by van de Steeg et al. [81] Their Bayesian model was based on a training set of437 compounds (37 inhibitors and 400 non-inhibitors) and an internal set of 155 compounds for validation (12 inhibitors and 143 non-inhibitors), resulting from the screening of a commercial library of 640 FDA-approved drugs. The overall model performance was greater than 80%, both for leave-one-out cross-validation and external validation. Kotsampasakou et al. [54] developed

k k [56] [53] [53] [53] [52] [55] [55] [61] et al. [62] [62] [58] [58] [57] et al. et al. et al. et al. et al. [59] (Continued) et al. et al. et al. et al. et al. et al. et al. et al. [54] [54] et al. et al. [60] et al. Dataset size (training set/TS, EV) References 98/48 Karlgren NN)NN) 109/27 42/11 Sedykh Sedykh k k k k 70% 162/299 Xu 85% 95/96 Ahlin 80% (RF, 75% (RF, 75%60% 118/60 5/10 Karlgren Greupink 92% 150/75 Karlgren 87% 1725/209 Kotsampasakou 85% 1708/201 Kotsampasakou 79% 150/75 Karlgren 0.710.73 107/0 107/0 Pajeva Pajeva 0.810.970.770.80 28/6 28/6 29/0 60/20 Suhre Suhre Van Zanden Tawari ======2 2 2 2 2 2 R R R R Q Q inhibitors and 93% non-inhibitors NN (Cl. subst.) Acc. NN (Cl. inhib) Acc. k k RF (Cl. inhib); RF (Cl. subst.); 3D-QSAR (CoMFA) Pharmacophore (combinations) Acc. CoMSIA SVM (Cl. inhib) Acc. Pharmacophore CoMFA RF (Cl. inhib) Acc. Model summary (best model) Performance (TS, EV) PLS (Cl. inhib) Acc. Summary of the best-performing models for transporters. Transporter OCT2 2D-QSAR OCT1 OPLS-DA (Cl. inhib) Acc. OATP2B1 PLS (Cl. inhib)NTCP Pharmacophore Acc. Acc. MRP1 Stepwise multiple regression OATP1B3 PLS (Cl. inhib) Acc. OATP1B1 OPLS-DA (Cl. inhib) Correctly predicted 81% Table 6.1

k k [64] [64] . [64] [71] [145] [55] [55] [55] [55] [55] [67] [70] [68] et al. et al. [63], et al. et al. et al [66] et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. [65] Pedersen [69] Dataset size (training set/TS, EV) References

k k 72% 79/39 Pedersen 98%89% 50/12 74/18 Sedykh Sedykh 75% 964/240 Pinto 77%87%85% (TS), 86% (EV) 257/61 772/85 (TS), 418 (EV) 150/38 Broccatelli Zheng Sedykh 80% (TS), 89% (EV) 670/168 (TS), 156 (EV) Montanari 87% 437/187 Warner 89% 77/19 Sedykh 89% 163/86 Pedersen 67% 51/13 Sedykh 0.82 20/5 Ng 0.95 37/0 Saito ======2 2 R R ) Ki RF (Cl. inhib) Acc. SVM (Cl. inhib)SVM (Cl. subst.) Acc. Acc. OPLS-DA (Cl. inhib) Acc. RF (Cl. inhib) Acc. OPLS-DA Acc. Naive Bayes (Cl. inhib)SVM (Cl. inhib) 81% 75% 973/300 1201/407 Chen Klepsch SVM (Cl. inhib) Acc. SVM (Cl. inhib) Acc. Multiple linear regression Model summary (best model) Performance (TS, EV) (Continued) Transporter MRP2 SA-PLS (binding aﬃnity, MDR1 LDA (Cl. inhib) Acc. BSEP SVM (Cl. inhib) Acc. MRP3 SVM (Cl. subst.) Acc. MRP4 SVM (Cl. subst.) Acc. Table 6.1

k k [76] [73] [55] [55] minant [77] et al. [78] [72] et al. [74] et al. et al. et al. et al. et al. et al. et al. ndom forest; ´ c [75] 978 Montanari

k k 87% (TS), 67% (EV) 96/32 (TS), 147 (EV) Eri 88% 120/30 Sedykh 94% 80/20 Sedykh 0.730.680.89 29/1 23/4 31/1 González Zheng Rais ======2 2 2 R R R (10-fold CV) -nearest neighbor; SA-PLS, simulated annealing-partial least squares. k NN, ) ) ) k Ki Ki Ki NN (Cl. subst.) Acc. Linear regression (binding affinity, RF (Cl. inhib) Acc. Linear regression (binding affinity, k Pharmacophore (Cl. inhib) 66% 30/79 Pan Logistic regression (Cl. inhib) 64% (leave-sources-out CV); 83% OPLS-DA (Cl. inhib) 79% 80/43 Matsson affinity, BCRP ANN, SVM (Cl. inhib) Acc. ASBT Linear regression (binding SVM, support vector machine; PLS, partialdiscriminant least analysis; squares CoMFA, comparative regression; molecular OPLS-DA, field orthogonal analysis; partial CoMSIA, least-squares comparative projection molecular to similarity latent index structures analysis; LDA, linear discri Cl. inhib., classification of inhibitors; Cl. subst., classification of substrates; Acc., accuracy; TS, test set; EV, external validation set; RF, ra analysis; SMO, Kohonen self-organizing maps; BPNN,artificial back-propagation neural neural network; network; QSAR, quantitative structure–activity relationship; ANN, The type of transporter and the summary for the best model (algorithm, performance, data size, and publication)original are provided.

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a set of classiﬁcation models for OATP1B1 and OATP1B3 inhibition based on 1,700 curated compounds from the literature. Virtual screening of DrugBank drugs followed by biological testing of 10 top-ranked hits conﬁrmed the validity of the models, yielding in an accuracy of 90% for OATP1B1 and 80% for OATP1B3, respectively.

6.8 NTCP

A study by Greupink et al. [56] proposed a ligand-based common feature pharmacophore model consisting of two hydrogen bond acceptors and three hydrophobicfeatures.Thismodel,basedonﬁveNTCPsubstrates,wasthen applied to screen large chemical libraries. In the virtual screening procedure, 10 compounds were selected out of which 6 notably inhibited taurocholate uptake in NTCP overexpressing cells.

6.9 OCT1

Three pharmacophore models have been reported for OCT1 so far [82–84]. k k Ahlin et al. [57] investigated the inhibition patterns of OCT1 using registered oral drugs to develop predictive computational models. Increased lipophilicity and positive net charge were found to be key physicochemical properties that positively correlated with OCT1 inhibitory activity. Moreover, dipole moment and multiple hydrogen bonds were found to be negatively correlated. The data were used to generate orthogonal partial least-squares projection to latent structures discriminant analysis (OPLS-DA) models for OCT1 inhibitors so as to discriminate the inhibitors from the non-inhibitors. The ﬁnal model correctly predicted 82% of the inhibitors and 88% of the non-inhibitors from the test set.

6.10 OCT2

A 2D-QSAR model based on 34 OCT2 inhibitors that inhibit tetraethylammo- nium (TEA) transport was reported by Suhre et al. [58]. Another study by Zolk et al. [85] analyzed 26 commonly used drugs for inhibition of MPP+ uptake. A signiﬁcant correlation was found between the topological polar surface area (TPSA) and activity on MPP+ uptake inhibition. Kido et al. [86] experimentally screened 910 compounds, of which 244 compounds inhibited OCT2-mediated transport of 4-(4-(dimethylamino)styryl)-N-methylpyridinium(ASP+). Using computational analysis, molecular charge was identiﬁed as one of the key

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properties for diﬀerentiating inhibitors from non-inhibitors. The 10 most potent OCT2 inhibitors were used to generate a two-point pharmacophore, showing a pattern of an ion-pair interaction site and a hydrophobic aromatic site separated by 5.0 Å. Xu et al. [59] designed a scheme for screening combinations of pharmacophores based on hypotheses established using 162 OCT2 inhibitors. The ﬁnal model comprises four individual pharmacophores. The combinatorial model provided an overall accuracy of about 70% on a test set containing 81 OCT2 inhibitors and 218 non-inhibitors.

6.11 MRP1, MRP3, and MRP4

van Zanden et al. [60] studied the effect of flavonoids on MRP1 and MRP2 transfected MDCKII cells. A QSAR model for the inhibition of MRP1 was obtained [60]. Pharmacophore-based models are reported for MRP1 inhibition by Chang et al. [87], Tawari et al. [61], and Pajeva et al. [62]. Owing to lack of experimental measurements, very few computational studies exist for the basolateral bile acid efflux transporters MRP3 and MRP4 (Table 6.1). Sedykh et al. [55] reported classification models of MRP4 inhibitors k at a 10 μM threshold with accuracy of 70% on external dataset. The modeling k was based on a rather small set of 64 molecules. In a recent study, Akanuma et al. [88] attempted structural analysis of MRP4 transport for several groups of β-lactam antibiotics.

6.12 BSEP

For the human BSEP,Warner et al. [20] used a recently described in vitro membrane vesicle BSEP inhibition assay to quantify transporter inhibition for a set of 624 compounds. A support vector machine (SVM) learning model, employing in-house descriptor sets comprising 2D, 3D, and fingerprint-like features, led to prediction accuracy of 87%. Relating a set of physicochemical properties of the compounds to BSEP inhibition, they demonstrated that lipophilicity and molecular size are significantly correlated with BSEP inhibition. The model could be further used to minimize the propensity of drug candidates to inhibit BSEP. Saito et al. [63] reported a BSEP inhibition model based on multiple linear regression using 37 diverse druglike compounds and their chemical fragment descriptors. However, the model was not validated further to evaluate its applicability. The model proposed by Hirano et al. [89], based on as few as 37 compounds, does not allow in silico profiling of chemically diverse compound libraries. Later, Pedersen et al. [90] built two OPLS-DA models on 163 compounds. They report an accuracy of 89% on a test set of randomly selected 86

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compounds. Nevertheless, none of the aforementioned models were applied in prospective studies to mark BSEP inhibitors in real-life settings. Inamorerecentstudy,Montanariet al. [65] developed a classification model based on a set of physicochemical descriptors. The model revealed the importance of hydrophobicity, aromaticity, and H-bond donor characteristics in distinguishing inhibitors from non-inhibitors. One major finding of these studies was bromocriptine - a known drug - being identified as BSEP inhibitor. The accuracies of the BSEP models on external datasets ranged from 70% to 90%.

6.13 MRP2

Several publications have proposed prediction models for MRP2 inhibition (Table 6.1) using linear and nonlinear modeling methods. For linear models, mainly partial least squares (PLS) regression and discriminant analysis were used, while nonlinear modeling methods include SVM, k-nearest neighbors (kNN), and RF [55, 64, 91]. Ng et al. developed a QSAR model of binding affinity to rat MRP2 for 25 methotrexate analogs as well as a pharmacophore for their binding mode [66]. Zhang et al. [91] have constructed a pharmacophore k for MRP2 inhibitors, which performed slightly worse than their SVM-based k model. Pinto et al. [68] applied different machine learning methods for the development of models for putative substrate/non-substrate classification for MRP2. Although the prediction performance is not excellent, the study can be marked as the first of its kind for classification of a huge set of putative MRP2 substrates and non-substrates.

6.14 MDR1/P-gp

P-gp is a thoroughly studied ABC transporter protein. A number of ligand-based approaches have been proposed already, including conventional methods such as Hansch analysis, linear and nonlinear classification algorithms, pharmacophore modeling, and even more advanced methods such as supervised and unsupervised artificial neural networks [92–97]. One of the groundbreaking contributions is the work of Broccatelli et al. [69], who used a combination of molecular field analysis, pharmacophore-based representation of the compounds, as well as physicochemical descriptors to develop both global and local models for P-gp inhibitors. The final model indicated that flexibility, hydrophobic surface area, and log P are the discriminating physicochemical properties for inhibitors and non-inhibitors. The model, which was

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based on 1275 compounds extracted from 61 studies, also points toward shape, a 3D descriptor/feature, as a crucial discriminative property. With a reported accuracy of 86%, the model demonstrated a sensitivity of 0.9, a specificity of 0.8, and Cohen’s kappa of 0.7 when tested on an external set. In addition to binary classifiers, a number of other 2D-QSAR models [98–107] and machine learning methods were successfully applied for prediction of P-gp substrates and inhibitors [108, 109]. Wang et al. [109] used unsupervised machine learning methods such as Kohonen self-organizing maps, which were also employed to predict P-gp substrates and inhibitors. The best model, based on a dataset of 206 compounds, correctly predicted 83% of substrates and 81% of inhibitors. Models based on recursive partitioning and Naïve Bayes methods were developed by Chen et al. [70] on a dataset containing 1273 compounds. The best model accurately predicted 81% of the compounds in the test dataset. Klepsch et al. [71] used BestFirst as a feature selection method using a dataset of 1608 P-gp inhibitors and non-inhibitors. Random forest and SVM models were reported as the best classifiers, accurately predicting a total of 86% and 83% of the training set compounds and 73% and 75% of the test set compounds, respectively. Different studies, employing a range of simple to complex methods, showed satisfactory prediction performance and have contributed to identification of k molecular features that are involved in P-gp mediated MDR reversal. However, k the applicability of the models is questionable, taking into account the still relatively small number of molecules investigated in each of these studies [110].

6.15 MDR3

Multidrug resistance protein 3 (MDR3) is the closest homologe to P-gp sharing a sequence identity of 75%. Only five substrates could be identified in previous studies [111]. Regarding inhibitors, a study by He et al. [34] led to the discovery of nine drugs that inhibit MDR3, while a more recent study by Mahdi et al. showed inhibition of MDR3 by antifungal azoles. In addition, their data indicated a potential increased cholestatic effect in case of simultaneous inhibition of BSEP and MDR3 [35]. However, this information is not sufficient to establish in silico prediction models.

6.16 BCRP

Several global machine learning-based classiﬁcation models have been proposed to predict BCRP inhibition. Eric´ et al. [72] extracted and merged

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literature data on BCRP inhibition to build neural network and SVM models based on 96 compounds. The models provided test set accuracies over 82%, sensitivities over 83%, and specificities over 80%. Matsson and colleagues [73] developed models that could distinguish BCRP inhibitors from non-inhibitors using a diverse training set of 80 compounds and the descriptors log D and polarizability. The best model had a sensitivity of 83% and a specificity of76% on a test set of 43 compounds. Pan et al. [74] developed a Bayesian classification model and a set of pharmacophores on 203 compounds. Screening the collaborative drug discovery (CDD) database [112] with these models led to selection and testing of 33 compounds. Among them, two compounds, flunarizine and pimozide, showed significant BCRP inhibition at 10 μM. All these models were built on rather small datasets, without using all the data available at the respective times of their studies. Montanari et al. [113] compiled the largest set of 978 BCRP compounds available up to now by extracting information from 47 different studies. The authors reported an accuracy of 0.92 and an area under the ROC curve (AUC) of 0.85 in cross validation based on a naïve Bayes model. Later on, this dataset was used [75] to build a global binary classification model for prediction of BCRP inhibition. The final model was used to screen all the approved drugs in DrugBank to identify potential BCRP inhibitors. Ten drugs were selected and k tested in BCRP-expressing PLB985 cells. Among them, two drugs, cisapride k (IC50 = 0.4 μM) and roflumilast (IC50 = 0.9 μM), showed inhibition in the sub micromolar range.

6.17 MATE1

Protein-ligand interactions for organic cation transporters and the multidrug and toxin extrusion (MATE) transporter have been investigated using pharmacophores and quantitative structure-activity relationships [58, 82, 85, 86, 114]. In a recent study, Astorga et al. [114], characterized the relative selectivity of MATE1 and MATE2-K for some clinically important organic cations (OCs). Novel inhibitors for these transporters were identiﬁed and predictive models of

MATE1 selectivity were developed. Using the IC50 values, a common-feature pharmacophore could be developed along with quantitative pharmacophores for hMATE1. Furthermore, a Bayesian model suggesting molecular features favoring and not favoring the interaction of ligands with hMATE1 was introduced [114]. In another study, Wittwer et al. [115] proposed an RF classiﬁcation model to identify MATE1 inhibitors and non-inhibitors. The average AUC for 10 tests was 0.70 ± 0.05 (permutation test; p-value < 0.0001), indicating that models of good quality had been obtained.

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6.18 ASBT

Efforts from Zheng et al. [67, 77], Rais et al. [78, 116], and González et al. [76] provided several QSAR models and pharmacophore models for ASBT binding affinity, with R2 values between 0.68 and 0.89. All were trained on small congeneric series of conjugated bile acid derivatives. Classification QSARs of ASBT inhibitors based on 10 and 100 μM potency thresholds were reported by Sedykh et al. [55] and Zheng et al. [67], respectively. To summarize this part, based on the data presented in Table 6.1, confined size of datasets has been a major limitation in developing highly accurate in silico prediction models to identify the drug interaction potential of hepatic transporters. The conformational flexibility of membrane transporters, the diverse chemical space covered by their substrates, and the inconsistency in data availability from experimental assays limit the predictive power of computational models even further.

6.19 Structure-Based Approaches

As stated earlier, the nonnavailability of resolved 3D structures of a number of k membranetransportersisthereasonforlimitedprogressinstructure-based k approaches for transporter interaction prediction. However, in recent years, a number of 3D structures of ABC transporters have been resolved [117, 118]. Thus, improved performance of experimental approaches [119] has led to the development of structure-based models with decent performance. Bikadi et al. [120] used SVM prediction and molecular docking approaches to predict P-gp substrate binding modes. Dolghih et al. [121] separated P-gp binders from non-binders via induced fit docking into the crystal structure of mouse P-gp (PDB ID: 3G60) [117] and using the docking score for subsequent classification. Further, Chen et al. [93] performed docking studies using 245 P-gp substrates and non-substrates, but could not clearly separate them on the basis of the Glide docking scores [122]. Klepsch et al. [123] docked a set of propafenones into a homology model of human P-gp. The study revealed that the binding poses are consistent with QSAR data, indicating that the observations can be exploited in identification of new P-gp inhibitors [124]. This study was further extended to structure-based classification of nearly 2000 compounds, which showed a prediction accuracy of 61% for the external test set compounds [71]. Although ligand-based approaches, owing to their high speed and accuracies, remain the method of choice for classification of transporter ligands, structure-assisted docking models show reasonable prediction accuracies in addition to providing valuable information on putative protein-ligand interactions at the molecular level.

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6.20 Complex Models Incorporating Transporter Information

As described in the introduction, there is ample of evidence for the association between hepatic transporters and toxicity manifestations in the liver. This knowledge generated the idea that transporter information (inhibition, expression, or upregulation) could be incorporated within in vitro or in silico models, together with other assay data and physicochemical and/or biological descriptors. This is also in line with the FDA recommendations for transporters to be tested during drug development [125, 126]. Curiously, despite the fact that information on drug-transporter interactions is quite important and there are several in vitro and in silico models available for transporters per se, as outlined in the next section, there are only few studies combining the transporters information with other data.

6.21 In Vitro Models

There have been some well-established assays for hepatic transporters inhibition to predict liver toxicity. Especially in the case of BSEP, whose inhibition is k linked with cholestasis, the respective screening is considered essential at the k early stages of drug development. However, although there are several methods to measure BSEP inhibition, not all of them are equally suitable. In their review, Kis et al. [22] describe several appropriate in vitro methods that can predict BSEP-drug interactions. Furthermore, Szakács et al. present several in vitro methods and models for elucidating the ADMET profile of ABC transporters [127]. Thomson et al. have proposed a combination of assays for cytotoxicity [128]. Their suggestion is the use of a hazard matrix based on covalent binding, incon- junction with an array of five in vitro assays, addressing cytotoxicity in different cell lines and inhibition of the canalicular transporters BSEP and MRP2, with individual cutoff values for each assay. Aleo et al. have shown that the severity of human DILI is highly associated with the dual inhibition of mitochondrial function and BSEP, flagging them as two very important liability factors that should be checked during pharmaceutical screening [129]. Another study by Schadt et al. [130] proposed a methodology based on a compilation of assays to predict DILI for drug candidates. Among these assays are BSEP inhibition, glutathione adduct assay, CYP3A time-dependent inhibition, cytotoxicity in human hepatocytes, mitochondrial toxicity, and cytotoxicity in NIH 3T3 mouse fibroblasts. As a training set, 81 marketed or withdrawn compounds with differing DILI classes (according to FDA) were used. The resulted modeling approach yielded a performance of 79% overall accuracy, 76% sensitivity, and 82% specificity for the external test set composed of 39 compounds [130].

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On a slightly different level, Dawson et al.s’ [18] testing of 85 drugs for human BSEP inhibition, as well as its rat ortholog Bsep, followed by statistical analysis showed that inhibition of BSEP/Bsep correlates with the drug potential to cause 2 < DILI with an r = 0.94. Moreover, all drugs with human BSEP IC50 300 μM had molecular weight > 250, ClogP > 1.5, and nonpolar surface area > 180 Å [18]. Similarly, in the work of Köck et al. [131], 88 drugs (100 μM) were investigated regarding their inhibitory effect on MRP3- and MRP4-mediated substrate transport. 50 BSEP non-inhibitors (24 non-cholestatic; 26 cholestatic) and 38 BSEP inhibitors (16 non-cholestatic; 22 cholestatic) were examined. MRP4 inhibition was associated with an increased cholestatic risk among BSEP non-inhibitors. In this group, for each 1% increase in MRP4 inhibition, the odds of the drug being cholestatic increased by 3.1%. By implementing a cutoff value of 21% for inhibition, which predicted a 50% chance of cholestasis, 62% of the cholestatic drugs inhibited MRP4 (P < 0.05). Nevertheless, merely 17% of non-cholestatic drugs were MRP4 inhibitors. Among BSEP inhibitors, MRP4 inhibition did not provide additional predictive value for cholestatic potential, as almost all BSEP inhibitors were also MRP4 inhibitors. The study failed to prove statistically significant association of MRP3 inhibition and cholestasis, regardless of the drug’s capability to inhibit BSEP. k k 6.22 Multiscale Models

During the last decades, there has been a vast development in biomedical research, which allows the investigation of biological systems with higher level of detail and accuracy [132]. Multiscale models, that is, complex models that couple high- and low-resolution models thus allowing the study of biological systems from atomic to macroscopic levels [133], have made considerable contribution in this direction. The virtual liver network (VLN) is a characteristic example where several multiscale models are combined to simulate the function of a single organ [132]. Similar initiatives have also taken place previously for heart, such as the Virtual Heart (http://thevirtual heart.org/) [134] and the Living Heart Project (http://www.3ds.com/products- services/simulia/solutions/life-sciences/the-living-heart-project/) [135]. They combine information from the level of molecular targets, move toward molecular pathways/processes, then cellular/tissue processes, and end up at a tissue or whole-organ endpoint. This approach, apart from modeling the physiological function of an organ, can further be implemented for modeling whole-organ toxicity [136]. These multiscale models might facilitate the discovery of potentially hazardous drugs/chemicals at the early stages of drug discovery in a more eﬃcient way than the single models, as more parameters that contribute to toxicity are taken into account.

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In this direction, Diaz Ochoa et al. [137] developed a multiscale modeling framework for spatiotemporal prediction of substances’ distribution that may result in hepatotoxicity. This framework consists of cellular models, a 2D liver model, and a whole-body model. Several mechanistic, genome-based in silico cells composite the 2D liver model and the whole-body model, including also the function of MRP2, MRP3, and MRP4. In principle, they use cellular systems for kinetic modeling and their aim was not only to calculate the drug concentration in the organ, but also the cell viability [137]. Another systems biology approach based on the analysis of dynamic adaptations in parameter trajectories (ADAPT) pointed out the important role of liver X receptor (LXR) activation for the development of steatosis [138]. Hijmans et al. showed that both input and output fluxes to hepatic triglyceride content can be induced by LXR activation, and during the early stages of LXR activation, steatosis can be induced by just a small imbalance between input/output fluxes of triglycerides. For the modeling analysis, mRNA levels of several mice genes were used, including Abcg1, which is known for its major role in cholesterol efflux from macrophage foam cells [139], and Abcg5, which forms a heterodimer with Abcg8 to translocate cholesterol and other plant sterols from the canalicular membrane into bile [16, 19, 39]. In addition, recent modeling approaches in our lab concerning prediction k of hepatotoxicity endpoints by incorporating transporter interaction profiles k follow the multiscale model concept. Apart from the prediction of hepatotoxicity endpoints, these models also aim to investigate the putative link of transporters inhibition with the respective toxicity endpoints. Initially, we used physicochemical descriptors of chemical compounds together with predictions of OATP1B1 and OATP1B3 inhibition [54] to predict hyperbilirubinemia [140]. In total a dataset of 836 compounds (86 positives and 749 negatives) for hyperbilirubinemia was used for training. Combination of MetaCost [141] and SMO (the SVM implementation in the WEKA [142] software package) using 93 interpretable 2D MOE [143] descriptors gave a performance of 68% accuracy and AUC. However, with respect to hyperbilirubinemia-transporter association, we only saw a weak relationship. For sure, more studies are expected in this field, which will allow targeting complex in vivo endpoints on a more sophisticated level than conventional machine learning methods currently allow.

6.23 Outlook

Transmembrane transport proteins represent a considerable fraction of the human genome. Their substrates cover a broad chemical space and range from

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neurotransmitters via hormones up to a large panel of xenobiotics. Further- more, they are also strongly involved in ADME and toxicity. One of the organs where a proper transporter homeostasis plays an important role is the liver. Imbalance in the function of the numerous transport proteins expressed in the liver has a big impact in its physiological function and subsequently in human health. In the past decade, the community has faced a tremendous increase in knowledge on transmembrane transporters, their function, and their ligands. Several high-resolution structures were deposited in the Protein Data Bank, and specialized databases composed of inhibitors and substrates for transport proteins became available. These served in the development of in silico models for predicting transporter ligands. However, coverage is still quite limited and there is a strong need for high-quality data for particular transporters (NTCP, MRPs, MDR3) in order to develop more robust models for transporter inhibition. Fur- thermore, as generally observed for all target classes, the data available suffer from a “positive data bias,” that is, they are heavily biased toward biologically active compounds. In addition, in most cases, the respective assay conditions are not available in a standardized form, which renders it difficult to compare data retrieved from different assays. Thus, it would be of major importance to have public available data depositories, which allow the deposition of both k positive and negative data. These transporter data hubs should also follow the k findable accessible, integratable reuse (FAIR) principles of data access [144] and allow data upload in a standardized format, especially with respect to assay conditions. With respect to in silico toxicity prediction tools, multiscale models and virtual organs might be the near future of toxicity prediction. They are able to capture the necessary information from the molecular interaction with individual targets to the cellular response up to the whole tissue or organ. Of course, this is a complex challenge, but the first success stories for the heart demonstrate the advantage of a more holistic view on organ function and dysfunction. In addition, in this case, high-quality data are the key. They need to be provided on different levels, ranging from molecular interactions up to time/concentration series of solutes. In our opinion, all the tools necessary to pursue such a task for the liver are there already, and it just needs a concerted effort to make it happen. Finally, following the increasing automation in life sciences, genotyping of patients will become routine soon. This opens up the whole field of single nucleotide polymorphisms (SNPs) and their consequences on response rates to medication. In addition, in the field of transporters, numerous SNPs are known which influence function and ligand recognition. This will add another layer of complexity to holistic prediction tools, but finally will link transporter informatics to precision medicine.

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Acknowledgments

We gratefully acknowledge ﬁnancial support provided by the Austrian Science Fund, Grant F3502 (SFB35 – Transmembrane Transporters in Health and Disease). Additionally, the research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under Grant Agreements No. 115002 (eTOX) resources of which are composed of ﬁnancial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.

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Cheminformatics in a Clinical Setting Matthew D. Krasowski1 and Sean Ekins2

1Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA 2Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA

CHAPTER MENU

Introduction, 175 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays, 177 Similarity Analysis Applied to Therapeutic Drug Monitoring Immunoassays, 187 Similarity Analysis Applied to Steroid Hormone Immunoassays, 191 k Cheminformatics Applied to “Designer Drugs”, 195 k Relevance to Antibody-Ligand Interactions, 202 Conclusions and Future Directions, 202

7.1 Introduction

Detection and measurement of drugs, drug metabolites, and steroid hormones in body ﬂuids is commonly used in clinical medicine and forensic science [1–4]. For example, blood concentrations of steroid hormones such as cortisol, estradiol, progesterone, and testosterone assist in the evaluation of endocrinology and reproductive disorders. Detection of anabolic steroids that are potentially abused as performance-enhancing drugs is important in competitive athletics [5, 6]. Therapeutic drug monitoring (TDM) involves determination of serum/plasma concentrations of medications and/or metabolites to guide drug dosing and avoid toxicity [7]. Lastly, drug of abuse and toxicology (DOA/Tox) analyses are used widely in emergency medicine, management of patients on pain medications, competitive athletics, and forensics [8]. The two main technologies for clinical analysis of drug, drug metabolites, and steroid hormones are immunoassays (antibody-based assays) [2, 3, 9] and chromatography/mass spectrometry (MS) [10, 11]. Immunoassays use poly- clonal or monoclonal antibodies, with an increasing trend toward monoclonal

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antibody-based assays [2]. A basic immunoassay approach is exemplified by enzyme-linked immunosorbent assay (ELISA) which uses antibodies bound to a solid support such as multiwell microplates. Modern clinical chemistry analyzers often employ “homogeneous” immunoassays where the entire analysis occurs in the liquid phase. The advantages of immunoassays include low technical complexity for laboratory staff, high throughput, and wide commercial availability of Food and Drug Administration (FDA)-cleared assays. However, one of the main challenges with immunoassays is cross-reactivity with compounds that are structurally related to the target molecule(s) of the assay [12]. For TDM or measurement of steroid hormones, cross-reactivity can lead to potentially misleading results [7]. On the other hand, immunoassay cross-reactivity can be useful for DOA/Tox analysis in allowing for detection of a class of drugs such as benzodiazepines or opiates [7]. The usefulness of the assay becomes a balance of how well the immunoassay detects the intended target(s) compared to the assay’s cross-reactivity with unintended targets [13, 14]. Chromatography with or without MS represents the other common approach for drug and steroid hormone analysis in body fluids [10, 11, 15, 16]. Gas chromatography (GC) and high-performance liquid chromatography (HPLC or simply LC) are used either on their own or in combination with MS. GC/MS and LC/MS/MS (HPLC coupled with tandem mass spectrometry) can k provide definitive and specific identification of drugs, drug metabolites, and k steroid hormones. Chromatography/MS methods used in DOA/Tox analysis are often used to confirm (verify) positive screening results obtained using initial analysis by immunoassay or other similar technology [10, 11]. MS-based methods are also increasingly used for the analysis of compounds that are not detected by commercially available immunoassays. These include the broad and very diverse class of “designer drugs” such as the amphetamine-like stimulants (popularly referred to by names such as “bath salts” and “plant food”), synthetic cannabinoids, or novel anabolic steroids abused as performance-enhancing drugs [17, 18]. MS-based methods also allow for the differentiation of compounds that are very close in structure such as steroid hormones or vitamins and their metabolic intermediates or synthetic analogs [15, 16]. While a steadily increasing number of larger clinical laboratories are adopting MS-based techniques, relatively few hospital- or clinic-based clinical laboratories use this technology. The main barriers for adoption include high capital cost of instrumentation (e.g., LC/MS/MS analyzers typically have pur- chase prices of $200,000-300,000 USD), technical complexity of operation, and labor-intensive steps in sample preparation and results analysis [15, 16]. There are also relatively few FDA-cleared chromatography/MS assays available in the United States for the analysis of drugs, drug metabolites, and steroid hormones. This places the burden on clinical laboratories to validate their own assays, placing this testing in the category of “laboratory-developed tests” [19]. In terms of United States regulations under the Clinical Laboratory Improvement

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Amendments of 1988 (CLIA’88 or simply CLIA), laboratory-developed tests are placed in the highest complexity category for laboratory assays, which have the most stringent requirements for the qualifications of testing personnel and supervisory staff. In contrast, most marketed immunoassays for drug or steroid hormone analysis are in the CLIA moderate or waived complexity categories that have less stringent constraints than high-complexity tests. Thus, many hospitals and clinics refer chromatography/MS-based testing to off-site commercial reference laboratories, resulting in much slower turnaround time than automated immunoassays. Given that immunoassays will likely continue to be used widely in clinical medicine and forensics, better understanding and prediction of immunoassay cross-reactivity has multiple useful applications [13, 14, 20, 21]. First, computational algorithms can help prioritize cross-reactivity testing studies. This becomes especially important given that some of the compounds of interest are controlled substances (potentially requiring special licenses for acquisition) or drug metabolites that may be difficult and/or costly to obtain. Second, computational methods can rationalize existing in vitro cross-reactivity data and help extrapolate whether compounds observed to cross-react in one immunoassay are likely to affect other immunoassays. Third, computational prediction of cross-reactivity can help formulate hypotheses generated from k toxicology data. For instance, early toxicology case reports of designer drug k abuse may have data such as otherwise unexplained positive immunoassay drug screens [14]. Computational methods provide a means to evaluate likelihood that a given compound will cross-react with a particular immunoassay. Lastly, computational modeling of cross-reactivity can provide insight into the complex interactions between antibodies and their targets. This chapter reviews the application of computational methods for understanding and predicting immunoassay cross-reactivity, using this as a model system for applying cheminformatics to a clinical application.

7.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays

DOA/Tox screens are widely used in a variety of clinical settings such as emergency departments, substance abuse treatment programs, and pain management clinics [8]. Although there are multiple methods that can provide rapid detection of drugs and drug metabolites, immunoassays currently represent the most widely used methodology [3, 22]. Immunoassays have mostly replaced alternative technologies such as thin-layer chromatography and chemical spot assays, although these older methods still have niche applications (e.g., spot assays for ﬁeld testing of conﬁscated narcotics). DOA/Tox screens are available in a broad range of formats from point-of-care kits to assays performed on

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high-throughput clinical chemistry analyzers. The most common specimens for analysis are urine, saliva (oral fluid), and blood [8]. Emerging sample types include hair and fingernails, both of which require extensive specimen processing but have the benefit of a wider detection window than urine, oral fluid, or blood [23]. In the specialized realm of newborn drug testing, umbilical cord tissue and meconium (newborn’s first stools) are the most common specimens to detect maternal drug use over the course of the pregnancy [24]. Immunoassays commonly are used as a positive/negative “screen” and as such can provide a rapid qualitative assessment whether a class of drugs is present in a patient sample [3, 8, 22]. Depending on the setting, samples showing positive screens may be further tested by confirmatory methods such as GC/MS or LC/MS/MS. Common targets of DOA/Tox screens are individual drugs or drug classes such as amphetamines (e.g., amphetamine, methamphetamine), benzodiazepines (e.g., alprazolam, clonazepam, diazepam, lorazepam, midazolam), cocaine, opiates (e.g., codeine, heroin, hydrocodone, morphine, oxycodone), and tetrahydrocannabinol (THC; the active component of marijuana/cannabis). DOA/Tox screening presents differing challenges based on the specific target(s) of the assay [14]. In particular, an assay intended to detect cocaine use can focus on the specific metabolite benzoylecgonine without need to cross-react with other molecules. In contrast, a clinically k useful benzodiazepines screening assay ideally would detect the commonly k used benzodiazepines (and/or metabolites) while not cross-reacting with other “off-target” compounds. Data on DOA/Tox immunoassay cross-reactivity may be found in the assay package inserts provided by the manufacturer [14]. Assay manufacturers typically test a variety of drugs and metabolites within the targeted class along with frequently used medications (e.g., acetaminophen, diphenhydramine, salicylates) that would also be commonly found in patient samples. For some immunoassays, there is also data in the published literature related to cross-reactivity that has been discovered after assay marketing. Classic examples of post-marketing published reports of immunoassay cross-reactivity include fluoroquinolone antibiotic (e.g., ciprofloxacin) cross-reactivity with opiates assays [25, 26], fentanyl cross-reactivity with lysergic acid diethylamide (LSD) immunoassays [27], and sertraline (antidepressant) cross-reactivity with benzodiazepines assays [28]. Overall, the amount of cross-reactivity data reported in the package inserts for marketed DOA/Tox immunoassays varies considerably, with extensive data reported in some package inserts and minimal data reported in others [14]. Likewise, the published literature on immunoassay cross-reactivity is highly variable, with more attention on cross-reactivities likely to have higher clinical or forensic impact [13, 14]. There is thus an opportunity to utilize computational methods to provide a more systematic conceptual approach to DOA/Tox immunoassay cross-reactivity. As proof of concept, we used the in silico method of molecular

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similarity analysis, which determines the similarity between molecules independent of any in vitro or in vivo data [13, 14]. Molecular similarity can be assessed at the one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) levels [29–34]. Common 2D methods use fragment bit strings compared by use of the Tanimoto coefficient which scales the results from 0 (maximally dissimilar) to 1 (maximally similar). 3D-similarity methods typically involve the determination of a pharmacophore pattern that models how the arrangement of chemical features and distances between them are associated with biological activity [35]. For our initial studies using similarity approaches, we compiled cross- reactivity data for 84 marketed DOA/Tox immunoassays covering 18 classes of drugs [14]. Cross-reactivity studies for DOA/Tox immunoassays generally report the concentration of a compound that produces reactivity equivalent to a specified concentration of the target molecule of the assay [13, 14]. For example, opiate assays customarily use morphine as the target molecule, with a cutoff of either 300 or 2,000 ng/mL as the reference for positivity (the latter higher cutoff used in employment drug testing). For each DOA/Tox immunoassay, compounds were assigned to the following categories: strong true positives (high degree of cross-reactivity for an intended target of the assay), weak true positive (low degree of cross-reactivity for an intended k target of the assay), strong false positive (high degree of cross-reactivity for an k off-target compound), weak false positive (low degree of cross-reactivity for an off-target compound), true negative (no cross-reactivity for an off-target compound), and false negative (no cross-reactivity for an intended target of the assay). To this data, we applied two different 2D similarity methods (MDL public keys; long-range functional class fingerprint – FCFP - description six keys) along with 3-point and 4-point pharmacophore-based fingerprints [14]. Figure 7.1 shows an example of 2D similarity applied to the drug of abuse phencyclidine (PCP, also known as “angel dust”) as the target compound. Of the five other compounds shown, 2D similarity is highest to 4-phenyl-4-piperidino-cyclohexanol (PCP metabolite) and two compounds (dextromethorphan and meperidine) reported to cross-react with PCP immunoassays [14, 36]. PCP has low similarity to ketamine (a drug with similar pharmacologic properties) and essentially no 2D similarity to ibuprofen (a common over-the-counter medication). Neither ketamine nor ibuprofen have been reported to cross-react with PCP immunoassays. At a broad level, MDL public keys similarity measures were significantly associated with cross-reactivity for DOA/Tox immunoassays [14]. For the MDL public keys data, approximately 46% of the strongly cross-reactive compounds (either false or true positives) had Tanimoto similarity coefficients of 0.8 or higher to the target compound of the assay. In contrast, only 1.4% of true negatives have similarity coefficients in this range. Thus, a cutoff of 0.8 has a positive predictive value of approximately 78% in

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Figure 7.1 Illustration of structural similarity. Using phencyclidine (PCP) as the target compound, 2D similarity to five different compounds was calculated using MDL public keys and the Tanimoto coefficient; three of these (dextromethorphan, chlorpromazine and k tramadol) have been reported to cross-react with at least some marketed PCP k immunoassays, and the other two (ketamine and ibuprofen) have not been reported to cross-react with PCP screening assays. PCP has the highest similarity (in descending order) to dextromethorphan, chlorpromazine, and tramadol. PCP has low structural similarity to ketamine (despite having similar pharmacological properties to PCP) and essentially no structural similarity to ibuprofen. (Source: Krasowski 2009 [13]. https://bmcemergmed .biomedcentral.com/articles/10.1186/1471-227X-9-5. Licensed under CC-BY 2.0.)

identifying compounds capable of strong cross-reactivity versus the true negatives. Conversely, 66% of true negatives have similarity coefficients of less than 0.4 to the target compound of the assay compared to only 0.2% (3 of 1,681) for strongly cross-reactive compounds. Interestingly, the three compounds with strong cross-reactivity but low 2D similarity to the target molecule of the assay were all amphetamine derivatives cross-reacting with amphetamines immunoassays - 3,4-methylenedioxymethamphetamine (MDMA/Ecstasy); 3,4-methylenedioxy-α-ethyl-N-methylphenethylamine (MDBD); and 3,4-methylenedioxy-N-ethylamphetamine (MDEA). Adopting a lower cutoff of 0.4 would have a negative predictive value of 99.8% in distinguishing true negatives from strongly cross-reactive compounds. Similarity comparisons help illustrate the challenges of broadly specific DOA/Tox immunoassays intended to detect multiple drugs within a class [14]. Figure 7.2 shows similarity data using MDL public keys for cross-reactivity of marketed amphetamines, barbiturates, benzodiazepines,

k k

Cheminformatics in a Clinical Setting 181

(a) Amphetamines – d-amphetamine 1

Methamp- Phentermine 0.8 hetamine keys)

0.6 Benz- phetamine None

MDA

0.4 MDMA Similarity (MDL public 0.2

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives (b) Barbiturates – secobarbital 1 k Thiamylal k

0.8

Primidone

Amino- 0.6 glutethimide None

0.4 Similarity keys) (MDL public 0.2

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives

Figure 7.2 Similarity of drugs and drug metabolites relative to the target compounds for four broadly speciﬁc DOA/Tox assays. Cross-reactivity data for four DOA/Tox assays were sorted into six categories. The similarity (using MDL public keys and the Tanimoto coeﬃcient) of each tested compound to the target compound of the DOA/Tox assay was plotted. (a) Amphetamine assays (using d-amphetamine as the target). (b) Barbiturate assays (using secobarbital as the target compound). (c) Benzodiazepine assays (using diazepam as the target compound). (d) TCA assays (using desipramine as the target compound). (Source: Krasowski 2009 [14]. Reproduced with permission from the American Association for Clinical Chemistry.

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182 Computational Toxicology

0.8 keys) Methaqualone

0.6 Rilmazafone Flupirtine None

Nefopam Diazoxide 0.4 Similarity (MDL public 0.2

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives k k (d) Tricyclics – desipramine 1

0.8

Mianserin

0.6 None Amoxapine

0.4 Similarity keys) (MDL public 0.2

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives

Figure 7.2 (Continued)

k k

Cheminformatics in a Clinical Setting 183

and tricyclic antidepressant immunoassays. Amphetamine immunoassays generally have as their intended detection target the most clinically relevant amphetamines, namely, amphetamine, methamphetamine, and MDMA/Ecstasy. Amphetamine immunoassays often have either amphetamine or methamphetamine as the target hapten for immunoassay design [14]. How- ever, there are other compounds such as phentermine that have equal or greater similarity to amphetamine or methamphetamine than illicit amphetamines or amphetamine-like drugs such as MDEA or MDBD (Figure 7.2a). Barbiturate immunoassays typically have a short-to-intermediate acting barbiturate such as secobarbital as the target hapten [14]. The cross-reactivity profile of barbiturates immunoassays is generally good, with the exception that certain barbiturates such as methohexital are not detected (Figure 7.2b). Benzodiazepine immunoassays have classically used diazepam or related metabolites such as nordiazepam as the target hapten for immunoassay design [14]. One of the main challenges for benzodiazepine immunoassays is that there are currently a number of benzodiazepines used clinically or illicitly [37]. The 2D similarity of some benzodiazepines to diazepam is relatively low and overlaps with some out-of-class compounds (Figure 7.2c). Tricyclic antidepressants (TCAs) represent a class of medications used for the treatment of major depression, obsessive-compulsive disorder, chronic pain, insomnia, k and a variety of other conditions [38, 39]. As illustrated in Figure 7.2(d), TCAs k share structural similarity to a variety of other clinically relevant compounds, resulting in a fairly large number of off-target compounds capable of producing positive screening results in TCA immunoassays. The propensity for false positives significantly limits the clinical utility of TCA immunoassays, as a high percentage of positive screens may be attributable to off-target compounds (e.g., cyclobenzaprine) in the clinical setting. Cannabinoid immunoassays generally use δ-9-tetrahydrocannabinol (δ-9- THC) or related THC metabolites as the target [14]. The 2D similarity of THC to its own metabolites is much higher than to out-of-class compounds (Figure 7.3a). Most marketed cocaine immunoassays target the primary metabolite benzoylecgonine [13, 14]. With the exception of the related drug atropine, benzoylecgonine has little 2D similarity to other clinically relevant drugs (Figure 7.3b). Classic opiates are compounds such as codeine and morphine that are derived from the opium poppy [40]. Semisynthetic derivatives of opiates include buprenorphine, heroin (3,6-diacetylmorphine), hydrocodone, oxycodone, and oxymorphone. Opioids is a term that encompasses opiates as well as synthetic compounds such as fentanyl that do not contain the core opiate structure and are thus of a different structural class of drugs [41]. Other examples of non-opiate opioids include meperidine, methadone, and propoxyphene. Opioids are among the most heavily prescribed medications in the United States. Abuse of prescription opioids is a major public health

k k

184 Computational Toxicology

(a) Cannabinoids 1

Cannabitetrol 0.8 Cannabicyclol Lovastatin keys) 0.6

CBC None None

0.4

Similarity (MDL public 0.2

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives

Cocaine metabolite – (b) benzoylecgonine 1 k Cocaine k

Atropine 0.8 Norcocaine

Ecgonine 0.6 None None

0.4

Similarity keys) (MDL public 0.2

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives

Figure 7.3 Similarity of drugs and drug metabolites relative to the target compounds for four DOA/Tox assays. Cross-reactivity data for four DOA/Tox assays were sorted into six categories. The similarity (using MDL public keys and the Tanimoto coeﬃcient) of each tested compound to the target compound of the DOA/Tox assay of the DOA/Tox assay was

plotted. (a) Cannabinoid assays (using 9-carboxy-11-nor-Δ9-tetrahydrocannabinol as the target compound). (b) Cocaine metabolite (benzoylecgonine) assays. (c) Opiate assays (using morphine as the target compound). (d) Phencyclidine assays. (Source: Krasowski 2009 [14]. Reproduced with permission from the American Association for Clinical Chemistry.)

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Cheminformatics in a Clinical Setting 185

Opiates – morphine (c) 1

Oxymorphone Bupre- 0.8 Naloxone norphine

Fluoro- Normorphine keys) quinolones 0.6 None

0.4

0.2 Similarity (MDL public

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives

Phencyclidine k (d) 1 k

0.8

Dextro- 0.6 methorphan None None None

0.4

0.2 Similarity keys) (MDL public

0 Strong Weak Strong Weak Tr ue False true true false false negatives negatives positives positives positives positives

Figure 7.3 (Continued)

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186 Computational Toxicology

concern, with over 20,000 deaths in the United States attributed to overdose of these drugs [42]. Figure 7.3c shows 2D similarity data for opiate immunoassays that use morphine as the molecular target. The strong true positives have higher similarity to morphine than compounds causing false positives. Several fluoroquinolone antibiotics that can cause false positives on some opiate assays [42] have similarity coefficients between 0.5 and 0.6 using MDL public keys. In contrast, buprenorphine does not generally cross-react with standard opiate assays despite a similarity of approximately 0.8 with morphine. Likewise, oxycodone (one of the most heavily prescribed opiates) [43], along with its metabolite oxymorphone, cross-reacts only weakly with most of the marketed opiates immunoassays. Thus, designers of opiate immunoassays face a difficult challenge in cross-reacting with clinical opiates without unintended cross-reactivity with off-target compounds such as fluoroquinolone antibiotics [13, 14]. PCP is a drug of abuse that has faded considerably in use since the 1980s and 1990s [44]. As illustrated in Figure 7.3d, the intended targets of PCP immunoassays (namely, the parent drug and unique metabolites) have higher 2D similarity to PCP than compounds that are false positives or true negatives, consistent with generally good clinical performance of PCP immunoassays. Nevertheless, therearesomecompoundsthatcanproducePCPimmunoassayfalsepositives k under certain conditions. A good example is dextromethorphan, an opioid-like k drug that is a common component of over-the-counter cough suppressant and cold/flu medications. At recommended doses, urine concentrations of dextromethorphan do not reach levels that will cross-react with PCP immunoassays. However, dextromethorphan is sometimes abused, often by teenagers and young adults who rapidly consume very large doses of dextromethorphan (typically obtained over-the-counter or via the Internet) to achieve intoxication [36, 45–47]. At these very high doses, urine concentrations of dextromethorphan will cross-react with PCP immunoassays. Similarly, meperidine also has weak cross-reactivity with PCP immunoassays but may cause immunoassay positivity in individuals who consume very high doses of meperidine [14]. Overall, 2D similarity studies of DOA/Tox immunoassays performed better using the MDL public keys than with FCFP [14]. The main limitation of using FCFP was that the similarity coefficients for true negatives overlapped more substantially with those for true positives, limiting the positive predictive value. FCFP performs particularly poorly with immunoassays for benzodiazepines, opiates, propoxyphene, and TCAs, with many true positives having lower similarity coefficients than the average similarity for true negatives. In our proof-of-concept studies, we also explored 3D similarity classification approaches by using 3- or 4-point pharmacophore fingerprints. However, we found that even with extensive exploration, these algorithms were too restrictive and missed true positives, including many with known strong cross-reactivity.

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Cheminformatics in a Clinical Setting 187

We utilized similarity analyses to predict additional cross-reactive compounds for 10 immunoassays (amphetamines, barbiturates, benzodiazepines, cocaine, cannabinoids, methadone, opiates, PCP, propoxyphene, and TCAs) [14]. We tested 46 such compounds and identiﬁed 14 previously unre- ported cross-reactivities for two marketed immunoassays. Eight of the cross-reactivities had not previously been reported for any marketed immunoassay for the respective class of drugs. The results of these studies indicate that computational predictions can be used to enhance cross-reactivity experimental testing.

7.3 Similarity Analysis Applied to Therapeutic Drug Monitoring Immunoassays

Analysis of drugs and drug metabolites in body fluids for TDM frequently uses immunoassays [7]. In contrast to DOA/Tox assays, which are frequently utilized as qualitative screens for classes of drugs, TDM assays generally need to provide an accurate quantitative concentration of a single compound, typically either a parent drug or an active metabolite [21]. The most common specimen for TDM is blood (often serum or plasma but sometimes whole blood), k although other body fluids such as urine or cerebrospinal fluid may be ana- k lyzed in certain situations [7]. Immunoassays used for TDM ideally have high specificity for the target of the assay, with minimal or no cross-reactivity with structurally related compounds [21]. Achieving high assay specificity becomes especially challenging when there are multiple metabolites or other structurally similar drugs that may be present. The quantitative nature of TDM means that assay interference can potentially produce misleading values that can lead to inappropriate clinical decisions [12]. Cross-reactivity data for TDM immunoassays is usually generated by either of two main methods [21]. In the first approach, the potential cross-reactive compound is tested alone and its signal compared to that of a defined concentration of the target compound. In the other common approach, the potential cross-reactive compound is tested together with a specified concentration of the target compound and its signal expressed as an equivalent concentration of the target compound. The two approaches have subtle strengths and weaknesses. For example, the first approach can better define whether a cross-reactive compound can produce an erroneous signal when the target drug is not present at all. The second approach replicates the scenario when a patient is taking the target drug but also another drug that cross-reacts with the TDM immunoassay. Occasionally, package inserts simply state that a compound “cross-reacts” (or similar language) without providing quantitative data. For proof of concept in the application of computational studies to TDM immunoassay cross-reactivity, 96 marketed versions of 28 TDM

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188 Computational Toxicology

immunoassays were analyzed, compiling data from assay package inserts and published literature [21]. For each assay, compound cross-reactivity was broadly classified into Strong Cross-Reactives, Weak Cross-Reactives, and Non-Cross-Reactives. Similar to the DOA/Tox assays discussed in the previous section, similarity measures using MDL public keys performed better than FCFP coefficients in separating out Strong and Weak Cross-Reactives from Non-Cross-Reactives. Using MDL public keys, approximately 60% of the Strong Cross-Reactives had similarity coefficients of at least 0.8 relative to the target molecule of the immunoassay, while only 3.2% of Non-Cross-Reactives had similarity coefficients of 0.8 or higher. Conversely, nearly 50% of Non-Cross-Reactives had similarity coefficients of less than 0.4 to the target molecules, while only 5% of Strong Cross-Reactives fit in this category. There were only five TDM immunoassays (carbamazepine, gentamicin, salicylates, tobramycin, and topiramate) that had examples of Strong Cross-Reactives with similarity coefficients of 0.5 or lower. In all cases, the Strong Cross-Reactives with these low similarity coefficients were reported for only a single marketed assay, suggesting that unique features of the specific assay may contribute to interference by these compounds that have low structural similarity to the target compound of the immunoassay. Similarity comparisons demonstrate the varying challenges of achieving k specificity for individual drugs using TDM immunoassays [21]. Figure 7.4 k shows similarity data using MDL public keys for cross-reactivity of marketed cyclosporine (immunosuppressant), lamotrigine (anticonvulsant), theophylline (used for treatment of asthma and newborn apnea), and valproic acid (anticonvulsant). In the case of cyclosporine, only metabolites of the parent drug show high specificity to the target compound (Figure 7.4a). In clinical practice, cyclosporine immunoassays consistently give higher blood concentrations on average than chromatographic assays, likely due primarily to cross-reactivity of the immunoassays with one or more cyclosporine metabolites [48]. Tacrolimus (another immunosuppressant) immunoassays contend with not only the high similarity of tacrolimus metabolites but also the closely related drug sirolimus, another immunosuppressant that might be used concomitantly with tacrolimus in organ transplant recipients. Similar to cyclosporine, tacrolimus immunoassays often give higher blood concentrations than chromatographic assays owing to metabolite cross-reactivity with the immunoassay [49]. Lamotrigine is a newer generation anticonvulsant drug that has little 2D similarity to other clinically relevant drugs and even to its own metabolites (Figure 7.4b). In contrast to cyclosporine and tacrolimus immunoassays, lamotrigine immunoassays perform very similarly to chromatographic assays, indicating that immunoassay cross-reactivity with metabolites is minimal [50]. Theophylline immunoassays encounter the challenge of differentiating theophylline from other methylxanthines such as caffeine and theobromine (found in chocolate and tea) [51, 52] (Figure 7.4c). This cross-reactivity can especially

k k

Cheminformatics in a Clinical Setting 189

(a) Cyclosporine 1 AM9 AM4N AM1 AM19 AM1c

0.8

Sirolimus Oxytocin keys) 0.6

0.4

Similarity (MDL public 0.2

0 Strong cross- Weak cross- Non-cross- reactives reactives reactives

(b) Lamotrigine k 1 k

Lamotrigine N-methyl 0.8 Lamotrigine N-oxide metabolite

Trimethoprim 0.6 Lamotrigine N2-glucuronide

0.4

Similarity keys) (MDL public 0.2

0 Strong cross- Weak cross- Non-cross- reactives reactives reactives

Figure 7.4 Similarity of drugs and drug metabolites relative to the target compounds for four TDM assays. Cross-reactivity data for four TDM assays were sorted into three categories. The similarity (using MDL public keys and the Tanimoto coeﬃcient) of each tested compound to the target compound of each assay was plotted. (a) Cyclosporine assays. (b) Lamotrigine assays. (c) Theophylline assays. (d) Valproic acid assays. (Source: Krasowski 2009 [21]. Reproduced with permission from Wolters Kluwer Health, Inc.)

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190 Computational Toxicology

0.8 keys) 0.6

Xanthines and relatived 0.4 compounds Similarity (MDL public 0.2

0 Strong false Weak false Tr ue positives positives negatives k k (d) Valproic acid 1

0.8

0.6 Valproic acid derivatives/ Ibuprofen metabolites 0.4 Similarity keys) (MDL public 0.2 Phenobarbital

0 Strong false Weak false Tr ue positives positives negatives

Figure 7.4 (Continued)

k k

Cheminformatics in a Clinical Setting 191

be an issue in adults, who commonly ingest caﬀeine and theobromine from beverages or foods. In contrast to the above assays, valproic acid has fairly low similarity to its own metabolites and also to other clinically relevant drugs, allowing for high assay speciﬁcity [53] (Figure 7.4d).

7.4 Similarity Analysis Applied to Steroid Hormone Immunoassays

Immunoassays are frequently used in the clinical setting for quantitation of blood and urine concentrations of steroid hormones such as cortisol, estradiol, and testosterone [1, 4]. Similar to DOA/Tox and TDM analysis, the most common alternative approach for measurement of steroid hormones are chromatographic/MS-based assays [15, 16]. Although the use of chromatographic assays has been increasing, many clinical laboratories continue to use immunoassays for routine steroid hormone analysis. Cross-reactivity is one of the major limitations of steroid hormone immunoassays. Interfering compounds can be structurally related endogenous molecules (e.g., 17-hydroxyprogesterone for progesterone immunoassays), drugs (including herbal compounds and performance-enhancing drugs), or k natural products [12, 54]. Cross-reactivity data is included in manufacturer k package inserts and in the published literature. Using a similar approach to the TDM immunoassays described above, we classified steroid hormone immunoassay cross-reactivity data into Strong Cross-Reactives, Weak Cross-Reactives, Very Weak Cross-Reactives, and Non-Cross-Reactives [20]. For some of the potentially cross-reactive compounds, there are published reference ranges for concentrations in blood. This allows for an estimate of the apparent concentration in blood that will be caused by a given cross-reactive compound. This is only a rough estimate as there are multiple factors that influence cross-reactivity in actual patient samples. We studied cross-reactivity for five steroid hormones: cortisol, dehydroepiandrosterone (DHEA) sulfate, estradiol, progesterone, and testosterone. DHEA sulfate and estradiol immunoassays did not have any compounds that showed strong cross-reactivity [20]. For DHEA sulfate immunoassays, only one compound (pregnenolone sulfate) was estimated to produce any clinically significant impact on apparent DHEA sulfate measurements, and this was only in the setting of the very highest pregnenolone sulfate concentrations in pregnancy. For estradiol immunoassays, no compound was estimated to produce clinically relevant cross-reactivity. Even estriol, which becomes the major estrogen during pregnancy, has weak enough cross-reactivity to not impact apparent estradiol concentrations. For cortisol, progesterone, and testosterone immunoassays, all compounds with documented strong cross-reactivity had 2D similarity coefficients of 0.8

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192 Computational Toxicology

or higher using MDL public keys, with the majority in this group exceeding 0.9. 2D similarity was less successful at differentiating Weak Cross-Reactives, Very Weak Cross-Reactives, and Non-Cross-Reactives [20]. 2D similarity may be insufficient to resolve the subtle features involved in causing weak versus no cross-reactivity. Figures 7.5 and 7.6 demonstrate cross-reactivity and similarity predictions for cortisol and testosterone immunoassays, respectively. Figure 7.5 shows that two common cortisol analogs, prednisolone and 6-methylprednisolone, can produce cross-reactivity resulting in apparent cortisol concentrations that overlap or even exceed typical reference ranges for humans [55]. Similarly, Figure 7.6 shows that 6-methyl testosterone (an anabolic steroid) can produce clinically significant cross-reactivity in males and females, whereas nandrolone (another anabolic steroid) and norethindrone (a progestin commonly found in female oral contraceptives) can cause increases in apparent testosterone concentrations that would be clinically relevant in females. Overall, the computational studies with steroid hormone immunoassays demonstrate that 2D similarity measurements can narrow the search for strongly cross-reactive compounds, all of which had high similarity coefficients (0.8 or higher) [20]. This feature can be useful in prioritizing compounds for cross-reactivity testing. For example, the list of anabolic steroids abused as k performance-enhancing drugs continues to expand. Definitive identification of k novel anabolic steroids requires sophisticated chromatographic/MS analysis, as may be done in international athletics competitions [5, 6]. Anabolic steroids and other performance-enhancing drugs may be also used in animals such as racehorses [56]. Indeed, for some of the anabolic steroids, there are much more detailed pharmacokinetic studies in horses than in humans [57].

Figure 7.5 Cortisol immunoassay cross-reactivity and similarity predictions. (a) The plot shows the cortisol reference range for adults (highlighted in yellow) in comparison to the predicted apparent cortisol concentrations produced on the Roche Elecsys Cortisol assay by 6-methylprednisolone, prednisolone, 21-deoxycortisol (healthy controls and patients with 21-hydroxylase deficiency), and 11-deoxycortisol (healthy controls, patients with 11β-hydroxylase deficiency, and following metyrapone challenge). Table 1 contains the concentration ranges and percentage of cross-reactivity values from which the estimated apparent cortisol concentrations are derived. (b) Two-dimensional similarity of compounds to cortisol is shown, sorted by degree of cross-reactivity in the Roche Cortisol assay (horizontal line in each column indicates average similarity within that group). Similarity values vary from 0 to 1, with 1 being maximally similar. The compounds are subdivided into categories of strong cross-reactivity (5% or greater, black circles), weak cross-reactivity (0.5-4.9%, red squares), very weak cross-reactivity (0.05-0.49%, blue triangles), and no cross-reactivity (<0.05%, green diamonds) to the Roche Cortisol assay. (Source: Krasowski 2014 [20]. https://www.researchgate.net/publication/264396906_Cross-reactivity_of_ steroid_hormone_immunoassays_Clinical_significance_and_two-dimensional_molecular_ similarity_prediction. Licensed under CC-BY 2.0.) (See color plate section for the color representation of this figure.)

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Cheminformatics in a Clinical Setting 193

(a)

1000

100

1 Apparent cortisolApparent (ng/mL) 0.1

0.01 Cortisol deficiency) deficiency) -hydroxylase Prednisolone β (21-hydroxylase 11-Deoxycortisol 11-Deoxycortisol 11-Deoxycortisol 21-Deoxycortisol 21-Deoxycortisol (11 k controls) (healthy controls) (healthy k 6-Methylprednisolone (b) (metyrapon challenge) 1

0.8

0.6

0.4 2D Similarity cortisol to 0.2

0 Cross-reactivity Strong Weak Very weak None (5% or greater) (0.5–4.9%) (0.05–0.49%) (<0.05%)

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194 Computational Toxicology

(a) 1000

100

0.1

Apparent testosterone (ng/mL) testosterone Apparent 0.01

0.001 Males Females deficiency) Nandrolone Testosterone Norethindrone k (21-hydroxylase k Androstenedione Androstenedione (healthy controls) (healthy Methyltestosterone (b) 1

0.8

0.6

0.4 2D Similarity testosterone to 0.2

0 Cross-reactivity Strong Weak Very weak None (5% or greater) (0.5–4.9%) (0.05–0.49%) (<0.05%)

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Cheminformatics in a Clinical Setting 195

Figure 7.6 Testosterone immunoassay cross-reactivity and similarity predictions. (a) The plot shows the testosterone reference range for males and females (highlighted in yellow) in comparison to the predicted apparent testosterone concentrations produced on the Roche Elecsys Testosterone II assay by methyltestosterone, norethindrone, nandrolone, and androstenedione (healthy controls and patients with 21-hydroxylase deficiency). Table 5 contains the concentration ranges and percent cross-reactivity values from which the estimated apparent testosterone concentrations are derived. (b) Two-dimensional similarity of compounds to testosterone is shown, sorted by degree of cross-reactivity in the Roche Testosterone II assay (horizontal line in each column indicates average similarity within that group). Similarity values vary from 0 to 1, with 1 being maximally similar. The compounds are subdivided into categories of strong cross-reactivity (5% or greater, black circles), weak cross-reactivity (0.5-4.9%, red squares), very weak cross-reactivity (0.05-0.49%, blue triangles), and no cross-reactivity (<0.05%, green diamonds) to the Roche Testosterone II assay. (Source: Krasowski 2014 [20]. https://www.researchgate.net/publication/264396906_ Cross-reactivity_of_steroid_hormone_immunoassays_Clinical_significance_and_two- dimensional_molecular_similarity_prediction. Licensed under CC-BY 2.0.) (See color plate section for the color representation of this figure.)

7.5 Cheminformatics Applied to “Designer Drugs”

“Designer drugs” are a varied group of psychoactive compounds typically discovered by modifications of existing drug classes such as amphetamines, k cannabinoids, and opioids [17, 18, 58–67]. Two current broad categories k of designer drugs are the amphetamine-like stimulants [17, 59, 65] and the synthetic cannabinoids [58, 60, 67, 68], each of which encompasses a chemically diverse set of chemical structures. Figures 7.7 and 7.8 show representative chemical structures of these two classes of designer drugs. The amphetamine-like stimulants are related to amphetamine, methamphetamine, and MDMA/Ecstasy and are sometimes referred to as “bath salts,” stemming from a misleading sales name under which some of these products were sold [17, 59, 65]. Detailed descriptions of the chemical synthesis and psychoactive effects of these compounds is accessible in the published literature, including two books by Shulgin and Shulgin that describe hundreds of compounds in detail [69, 70]. The amphetamine-like drugs can be further broken down into subcategories such as 2C compounds, β-keto amphetamines, piperazines, and tryptamine (Figure 7.7). Methylone, methylenedioxypyrovalerone (MDPV), and mephedrone are three of the more common amphetamine-like designer drugs. The illegal distribution patterns of amphetamine-like drugs shift as law enforcement and regulatory agencies target specific drugs within this class. Within the United States, increasing numbers of amphetamine-like drugs are designated as Schedule I controlled substances (no valid medical use and illegal for use outside of restricted research protocols), which limits the ability to sell and distribute these compounds [71]. The synthetic cannabinoids are also chemically diverse (Figure 7.8) and were originally synthesized and evaluated for medical applications [58, 60, 67, 68].

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196 Computational Toxicology

k k

Figure 7.7 Representative chemical structures of amphetamine-like drugs. Abbreviations: MDMA, 3,4-methylenedioxy-N-methamphetamine; MDPV, 3,4-methylenedioxy pyrovalerone; MDPBP, 3′,4′-methylenedioxy-α-pyrrolidinobutiophenone. (Source: Krasowski 2014 [74]. https://www.researchgate.net/publication/262531018_Using_cheminformatics_ to_predict_cross_reactivity_of_designer_drugs_to_their_currently_available_ immunoassays. Licenced under CC BY 2.0.) Similar to THC from cannabis/marijuana, the synthetic cannabinoids bind to cannabinoid receptors in the nervous system, producing a range of psychoactive and neurological eﬀects. However, clandestine laboratories began to produce synthetic cannabinoids for personal use, often in products marketed as “legal highs” (with misleading descriptions such as “incense,” “potpourri,”

k k

Cheminformatics in a Clinical Setting 197

k k

Figure 7.8 Representative chemical structures of cannabinoids. (Source: Krasowski 2014 [74]. https://www.researchgate.net/publication/262531018_Using_cheminformatics_to_ predict_cross_reactivity_of_designer_drugs_to_their_currently_available_immunoassays. Licenced under CC BY 2.0.).

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“K2,” or “spice,” or simply designated as “not for human consumption” or “for research use only”). Many of the synthetic cannabinoids were discovered by J.W. Huffman; hence, many are designated as part of the “JWH” series [72]. JWH-018 [1-naphthyl-(1-pentylindol-3-yl)methanone] and JWH-250 [2-(2-methoxyphenyl)-1-(1-pentylindol-3-yl)] are two of the most commonly abused synthetic cannabinoids. As shown in Figure 7.8, some synthetic cannabinoids such as JWH-018 (a naphthoylindole) differ considerably in chemical structure from THC, while other compounds such as HU-210 (1,1-dimethylheptyl-11-hydroxy-THC) are analogs of THC. Detection of designer amphetamine-like stimulants and synthetic cannabinoids presents a substantial technical and logistic challenge for clinical and forensic toxicology laboratories [73]. These compounds can generally be detected by chromatography/MS methods; however, maintaining methods to keep pace with shifting trends in designer drug use is difficult for all but the most dedicated laboratories with expertise in this area. DOA/Tox screening immunoassays based on amphetamine, methamphetamine, and/or MDMA/Ecstasy as the target molecule(s) will only detect a small subset of designer amphetamine-like drugs [74, 75]. THC immunoassays generally do not show cross-reactivity with synthetic cannabinoids that lack the classic cannabinoid chemical backbone found in THC, a finding consistent with k similarity analysis. In general, the synthetic cannabinoids show very low 2D k similarity to 9-carboxy-THC, with very few compounds having a Tanimoto coefficient greater than 0.5. Despite the technical complications, some manufacturers have marketed ELISA immunoassays for the amphetamine-like stimulants and synthetic cannabinoids [76–80]. Figure 7.9 shows 2D similarity using MDL public keys applied to the target molecules used in marketed synthetic cannabinoids immunoassays, namely, prominent urinary metabolites of JWH-018, JWH-073 [1-butyl-1H-indol-3-yl)-1-naphthenyl-methanone], and JWH-250 [74]. As can be seen, the 2D similarity of synthetic cannabinoids to these target molecules varies considerably, even within the JWH series. This is consistent with the observed cross-reactivity of these assays in detecting the more closely structurally related compounds and not other synthetic cannabinoids [77]. This limits the practicality of immunoassays for synthetic cannabinoids, especially given the assay development and regulatory costs involved in bringing in vitro assays to the market [73]. The amphetamine-like stimulants represent a somewhat different challenge from the synthetic cannabinoids [73]. Many of these stimulants are based on the same core structure as amphetamine and methamphetamine, resulting in some cross-reactivity with amphetamines immunoassays. To better predict the cross-reactivity of amphetamine-like stimulants for amphetamine screening immunoassays, we sorted 42 amphetamine-like compounds into categories based on whether the compounds caused a clinically positive result

k k

(a) JWH-018 N-pentanoic acid metabolite (b) JWH-073 N-butanoic metabolite 1 1 AM-2201 AM-2201 N-4-OH N-OH-pent

0.8 WIN 55,212- 0.8 WIN 55,212- 2 and 3 Benzoyl- 2 and 3 Benzoyl- ecgonine ecgonine

0.6 0.6

JWH-175 JWH-175 0.4 0.4

0.2 0.2 2D Similarity to JWH-018 metabolite 2D Similarity JWH-018 to 2D Similarity JWH-073 metabolite to k k 0 0 JWH JWH Other Other series series series series AM, UR, AM, UR, synthetic synthetic Cannabis Cannabis RCS, XLR RCS, RCS, XLR RCS, compounds compounds Other drugs Other drugs Endogenous Endogenous cannabinoids cannabinoids cannabinoids cannabinoids and metabolites and metabolites

Figure 7.9 Similarity comparisons of cannabinoids. Test compounds are divided into broad categories (JWH series, AM/UR/RCS/XLR series, other synthetic cannabinoids not possessing the chemical backbone of THC, cannabinoids sharing chemical backbone of THC, endogenous eicosanoid cannabinoids, and non-cannabinoids). (a) 2D similarity of the N-pentanoic acid metabolite of JWH-018 to 168 other compounds. (b) 2D similarity of the N-butanoic acid metabolite of JWH-073 to 168 other compounds. (c) 2D similarity of the N-4-hydroxy metabolite of JWH-250 to 168 other compounds. (d) 2D similarity of 9-carboxy-THC to 168 other compounds. (Source: Krasowski 2014 [74]. https://www.researchgate.net/ publication/262531018_Using_cheminformatics_to_predict_cross_reactivity_of_designer_drugs_to_their_currently_available_immunoassays. Licenced under CC BY 2.0.)

) Continued ( 7.9 Figure and metabolites and metabolites cannabinoids cannabinoids cannabinoids cannabinoids Endogenous Endogenous Other drugs Other drugs compounds compounds RCS, XLR RCS, XLR Cannabis Cannabis synthetic synthetic AM, UR, AM, UR,

series series series series Other Other

JWH JWH

0 0

k to JWH-250 metabolite2D Similarity k

0.2 2D Similarity to 9-carboxy2D Similarity THC 0.2

CB-52

0.4 0.4

CB-25

JWH-071

0.6 0.6

Codeine

0.8 0.8

Idiethanone

Cannabipiper-

M9 Metab M9

RCS-4

1 1

-4-hydroxy metabolite -4-hydroxy N JWH-250

THC 9-carboxy metabolite 9-carboxy THC (c) (d)

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at 5,000 ng/mL, at 20,000 but not 5,000 ng/mL, or only at 100,000 ng/mL [75]. While the pharmacokinetics of these compounds are varied, urine concentrations can often reach 5,000–20,000 ng/mL in abusers but would exceed 20,000 ng/mL only in extreme overdoses. The dataset included three widely used, commercially available immunoassays: Abbott Diagnostics, CEDIA (cloned enzyme donor immunoassay), and EMIT (enzyme-multiplied immunoassay technique). Receiver operator characteristic (ROC) curve analysis compared various 2D similarity methods (including consensus methods that averaged similarity between the test molecule and target compounds such as amphetamine and MDMA/Ectasy) as well as 3D shape and pharmacophore. In general, 2D consensus methods performed well. However, there were differences among the data for the different immunoassay techniques. In particular, 2D consensus methods performed well for AbbottandEMITdata,while3DmethodsperformedwellforCEDIA data. We utilized the best-performing modeling methods in predicting the likelihood that various other amphetamine-like compounds would cause cross-reactivity. One way to rationalize these findings is that amphetamine screening immunoassays have been designed by manufacturers to cross-react well with amphetamine, methamphetamine, and sometimes MDMA/Ecstasy, as these k have been traditionally the most clinically relevant amphetamines [74, 75]. An k antibody cross-reacting only with amphetamine but not methamphetamine or vice versa would have limited clinical utility. However, manufacturers have adopted different methodologies and approaches for their immunoassays to achieve similar purposes. Interestingly, the CEDIA amphetamines assay uses three different monoclonal antibodies (using amphetamine, methamphetamine, and MDMA/Ectasy, respectively, as the haptens) [75]. The superiority of a 3D pharmacophore based on methamphetamine in predicting CEDIA cross-reactivity for amphetamine-like compounds may suggest that the pharmacophore for methamphetamine represents a “middle ground” between the smaller amphetamine molecule and the larger MDMA/Ectasy molecule that contains methamphetamine within its substructure. The computational modeling methods applied to amphetamine-like stimulants and synthetic cannabinoids should also be applicable to new drugs of abuse such as designer opioids and benzodiazepines [81–83]. Reports of abuse and toxicity of these compounds have recently emerged. Similarly to the amphetamine-like stimulants and synthetic cannabinoids, the designer opioids and benzodiazepines often have history dating back decades, often discovered in medicinal chemistry efforts and then abandoned for therapeutic purposes [81]. The use of similarity methods could help predict whether existing opiates and benzodiazepine assays are likely to cross-react with the new designer drugs, thus helping to prioritize experimental cross-reactivity testing.

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7.6 Relevance to Antibody-Ligand Interactions

The results of our studies raise interesting questions about the structural interactions between diagnostic antibodies and their target ligands. To our knowledge, an X-ray crystallographic or nuclear magnetic resonance (NMR) structure of a diagnostic antibody used for DOA/Tox screening or steroid hormone analysis has not been reported. However, there are crystallographic structures of antibodies evaluated for the treatment of addiction or overdoses of amphetamine [84], cocaine [85, 86], morphine [87], and PCP [88]. There have also been a number of studies looking at the 3D structure of antibodies that bind steroid hormones. These studies illustrate the complexity and heterogeneity inherent in interactions between antibodies and ligands. For example, a crystallographic study of two different estradiol antibodies revealed that antibodies that bound estradiol with equivalent specificity could have substantially different amino acid sequence, ligand orientations, and ligand-binding pockets [89]. Several studies of anti-testosterone antibodies revealed how directed mutagenesis could be used to obtain higher antibody specificity for ligands [90–92]. Crystallographic studies showed that potentially therapeutic anti-cocaine and anti-PCP antibodies interact with all portions of the ligand [85, 86, 88], whereas the study of an anti-morphine antibody k demonstrated interaction more with the hydrophobic portion of the ligand, k while the hydrophilic half of morphine was mostly exposed to the solvent [87]. The crystallographic structure of digoxin (a relatively large drug molecule used to treat congestive heart failure and arrhythmias) with a Fab antibody fragment showed the carbohydrate portions of the drug exposed to the solvent and unbound by the antibody [93]. This heterogeneity of antibody-ligand molecular interactions undoubtedly underlie some of the observed variation in cross-reactivity of marketed immunoassays for DOA/Tox and steroid hormones.

7.7 Conclusions and Future Directions

Cross-reactivity between structurally related compounds remains a challenge in the clinical use of immunoassays. Similarity analysis represents a computational tool to identify compounds likely to cross-react with immunoassays. Our studies have shown that 2D similarity using MDL public keys as molecular descriptors are especially useful for this purpose. FCFP and pharmacophore ﬁngerprints have more limited use and are best suited for studying very close structural analogs (e.g., methamphetamine and related designer amphetamine-like drugs). Other molecular ﬁngerprints could be evaluated in the future.

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A limitation of similarity approaches is that these do not account for the complex 3D molecular interactions between ligand and antibody or cases where the antibody only interacts with portions of the ligand, as seen with crystallographic structures of antibodies against morphine and digoxin [87, 93]. For target compounds such as these, similarity searching using substructures may be useful. An additional limitation of similarity methods is that these do not account for the complicated and often nonlinear concentration dependence of cross-reactivity. Weak cross-reactivity for many compounds with a given immunoassay may be clinically insignificant if suitably high concentrations are not commonly present in specimens being analyzed. However, as seen with abuse of dextromethorphan resulting in false positive PCP screens, some compounds with weak cross-reactivity for an immunoassay may be clinically significant if very high concentrations can be found in body fluids in some circumstances. Furthermore, there are no currently good computational models for the interaction of multiple cross-reacting substances present at once in the same specimen. This type of situation can result from drugs such as benzodiazepines which may have multiple metabolites with varying degrees of cross-reactivity. Predicting the ability of mixtures to cross-react is certainly likely to require training sets with which to build and test models. k Overall, similarity analysis represents a computational tool for understand- k ing and predicting cross-reactivity of immunoassays. Use of the Tanimoto coefficient provides an easily understood quantitative measure for structural similarity between compounds. Similarity analysis can guide investigations of potentially cross-reacting compounds. The computational ease of similarity calculations allows for screening of even large databases of therapeutic drugs, herbal compounds, endogenous molecules, and toxins. These results can help prioritize the much more costly and time-consuming process of experimental cross-reactivity testing. Similarity analysis also provides a logical framework for regulatory agencies and immunoassay manufacturers to focus cross-reactivity testing schemes and guidelines on those compounds most likely to interfere. In future, as the datasets increase in size, we could expand beyond the molecular similarity approach to using machine learning methods. For example Bayesian, support vector machine, trees, and deep learning, which have all found use in ADME/Tox and other areas of drug discovery, could be readily applied to predicting cross-reactivity [94].

Acknowledgment

We are very grateful to our many collaborators for assisting us on the studies described.

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Part III

Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives

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Computational Tools for ADMET Proﬁling Denis Fourches1, Antony J. Williams2, Grace Patlewicz2, Imran Shah2, Chris Grulke2, John Wambaugh2, Ann Richard2, and Alexander Tropsha3

1Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA 2National Center for Computational Toxicology, Oﬃce of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA 3UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

CHAPTER MENU k Introduction, 213 k Cheminformatics Approaches for ADMET Proﬁling, 214 Unsolved Challenges in Structure Based Proﬁling, 230 Perspectives, 234 Conclusions, 237

8.1 Introduction

Large quantities of ADMET-related chemical biological data have been released in the public domain in the past decade [1, 2]. This growth was mainly due to (i) large federally sponsored high-throughput screening (HTS) programs (e.g., Tox21, ToxCast) [3–6] with on-going eﬀorts to test thousands of chemicals against hundreds of relevant short-term assays; (ii) development of online repositories (e.g., ChEMBL, PubChem) [7, 8] that actively collected, integrated, and stored chemogenomics data extracted from literature and/or directly deposited by researchers; and (iii) contributions from pharmaceutical companies that publicly released enormous amounts of internal screening data not considered to be strategic anymore (e.g., GSK PKIS and antimalarial sets) [9, 10]. Importantly, there is a novel growing source of absorption, distribution, metabolism, excretion, and toxicology (ADMET) data contributed by academic screening centers (e.g., Harvard Institute of Chemistry and Cell Biology - ICCB, UNC-Chapel Hill’s Center for Integrative Chemical Biology

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and Drug Discovery - CICBDD) [2, 11]. The latter are not only transforming drug discovery but also enabling ADMET-relevant high-throughput screening campaigns driven by multidisciplinary government-academic teams [11–13]. With the skyrocketing amount of data available in the public domain, it has been hypothesized that ADMET prediction models would become more and more reliable. Yet it is not clear whether the current generation of ADMET predictors have already benefited from this data profusion. It is also unclear what level of accuracy the current models are providing when predicting complex endpoints (e.g, cytochrome 3A4 inhibition, renal clearance, P-gp inhibition) for compounds not present in the models’ training sets. With the evolving deployment of ADMET predictors on mobile devices, all key actors of the drug discovery pipeline, from synthetic and medicinal chemists to regulators, will have easy access to computational ADMET tools requiring, on the surface, little-to-no knowledge in cheminformatics. This begs the questions of whether such tools are sufficiently reliable to accurately assess the ADMET profile of a new chemical, whether medicinal chemists can use these models to bias the molecular design process, or whether regulators are willing to consider current ADMET models as ready-to-use property forecasting tools. These are complex issues and the goal of this chapter is to address some of them from the perspectives of both academic molecular modelers and computational and k toxicology research scientists at regulatory agencies worldwide. k In this chapter, we discuss (i) different modeling techniques and approaches (e.g., chemical curation workflow, QSAR modelability index, chemical (biological) read-across) that have proven helpful in obtaining predictive ADMET models, (ii) the utility of those models for regulatory purposes and their potential relevance to adverse outcomes pathways (AOPs), (iii) the numerous challenges (e.g., biological data curation, activity and toxicity cliffs, multi-endpoint modeling, in vitro/in vivo continuum) yet unsolved by the current generation of cheminformatics approaches and associated software tools, and (iv) perspectives for developing the next generation of computational ADMET predictors (e.g., accessibility with mobile devices, metabolite profiling, structure-exposure-activity relationships). This chapter is not exhaustive and it reviews some computational models and approaches that have been developed for ADMET profiling. Since there are a number of excellent published reviews on ADMET [14–16], this chapter solely aims to give the authors’ point of view on the predictive value and overall relevance of key cheminformatics techniques introduced and tested in a number of case studies.

8.2 Cheminformatics Approaches for ADMET Proﬁling

In this section, we highlight some of the cheminformatics approaches and protocols that have demonstrated usefulness and high relevance for ADMET

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proﬁling in various published case studies and/or have attracted the attention of the research community. Again, this is not an exhaustive list of methods. Recent reviews by Beck and Geppert [14] and Moroy et al. [17] will be interesting reading for those who wish to delve further into this topic.

8.2.1 Chemical Data Curation Prior to ADMET Modeling There is community-wide excitement regarding the rapid growth and availability of chemical-biological data in the public domain. However, serious alerts concerning the poor quality and irreproducibility of these chemical and biological records have appeared in the recent literature [18–21]. Chemical curation is essential because the reliability of cheminformatics models is totally dependent on the quality of the data used for model development. Several studies [22–26] have demonstrated that the prediction performances of models can be negatively influenced by inaccurate representations of chemical structures and uncertainties in experimental data. Therefore, prior to any analysis or modeling studies of ADMET records, a thorough curation of the dataset needs to be accomplished, followed by chemical structure standardization. Ascertaining the accuracy of the chemical data in large datasets is an arduous task, which is often impossible to complete fully. The chemical curator k must evaluate the linkage between each chemical structure and the value k oftheresponsevariablethatwillbemodeled.Assumingthedatasetisan aggregation extracted from a public database (e.g., PubChem, ChEMBL), to map the provenance of every dataset record to its originating reference and verify the chemical identity associated with each value would be very time consuming. Even if that mapping were done, verifying the correctness of the chemical identifiers associated with the value in the originating publication would require accessing supplier information (e.g., material safety data sheets, certificates of analysis) or analytical chemistry results for the sample tested. With this level of curation, the rate of model development would grind to a crawl, so determining the reasonable amount of curation necessary for the intended modeling purpose is vital. A dataset intended for building a model to be used in a regulatory context needs a different level of curation compared to a dataset intended for screening or hypothesis generation. At a minimum, it is appropriate to evaluate the consistency of the provided chemical identifiers from the dataset. If a dataset record contains a name, registry identifier (e.g., Chemical Abstract Service Registry Number, European Inventory of Existing Commercial Chemical Substances Number), and a chemical structure, it should be determined when the identifiers may be conflicted (i.e., map to different substances) on the basis of other public sources of chemical information. A curator can then examine the inconsistencies, choose either to keep the record and assign it to the structure that seems most likely, or eliminate the record from the set because of the uncertainty caused by the conflict. Many of the cases encountered may

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involve distinct chemical structures being associated with names or registry numbers that are less well defined (e.g., a mixtures of positional isomers represented by a single isomer). The decision to use a representative structure is made based on the curator’s discretion, but the implications of imprecise mappings of data to structures for model interpretation and application should be clearly conveyed. More details are given in the final section of this chapter from the perspective of chemogenomics data curation (i.e., generally referring to the collection of large amounts of chemical-biological target activity data generated in HTS programs). Once the dataset has been curated, the structural representation of the chemicals must be standardized so that chemicals are represented by consistent rules. It is essential to ensure, for example, that (i) a given functional group (e.g., nitro and sulfo groups) is consistently represented for each molecule in the dataset, (ii) salts and counterions are either properly formatted or treated according to well-defined rules, and (iii) there are no duplicate chemicals in the modeling set to be utilized for training a QSAR model. We [24, 26–29] have worked to establish best practices for chemical data curation and data preprocessing prior to modeling, largely focused on compiling accurate chemical structures. For example, EPA’s recently expanded the Distributed Structure-Searchable Toxicity (DSSTox) database k that is powering EPA’s newly released Chemical CompTox Dashboard k (https://comptox.epa.gov/dashboard). The latter focuses heavily on ensuring accurate and consistent associations of chemical structures used in modeling with source chemical identifiers, such as chemical names and Chemical Abstracts Service (CAS) registry numbers, which are most commonly linked to published biological data. General rules for chemical data treatment and curation have been established from the experience gained from processing and analyzing various types of ADMET datasets. The following are the rules with respect to chemical structures:

1. Chemical descriptors must be computed from standardized 2D structures, which helps to ensure that the different chemotypes (e.g., nitro groups and other specific tautomeric substructures) present in chemical structures are consistently represented in the same form. 2. Establishing consistency of chemical structures assigned to substances across multiple chemical databases helps to flag potentially incorrect structures, especially when it comes to chemicals with complex stereochemistry. 3. Resolving the presence of structural duplicates is essential prior to QSAR modeling using either structural descriptor – (which is derived from the mol-file) or identifier – (e.g., InChI) based methods to detect structural duplicates in a given dataset. CAS registry numbers, chemical names, and/or SMILES are not well suited to this task.

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4. Modelers and ﬁnal users of ADMET predictions should ensure that models are based on well-documented, carefully curated chemical datasets. Otherwise, they themselves should spend time browsing manually through the chemical datasets, looking for structural inconsistencies or evident mis- takes in the molecular structures. It has been shown that even a relatively small fraction of erroneous structures in a dataset of less than 10% leads to a signiﬁcant decrease in model accuracy [30].

8.2.2 QSAR Modelability Index The prediction performances of a QSAR model are directly influenced by the dataset used to build the model, especially its size, structure diversity, and the uncertainties and distribution of chemicals’ experimental activity (e.g., ratio of actives to inactives, range of quantitative potency values). The reliability of models is also dependent on the modeling workflow used to generate, select, and validate the models. The types of variable selection techniques, cross-validation protocols, application domains, or consensus scoring approaches also impact the nature and the quality of the ADMET models. A single study can employ multiple types of machine learning techniques, descriptor calculations, and modeling workflows in order to identify the most k predictive QSAR models for a given ADMET endpoint. With the increasing k size of chemical datasets, such Combi-QSAR approaches [31] are becoming more onerous and expensive to conduct. Moreover, it has been shown that the prediction performances of various QSAR models tend to reach a maximum for a given chemical dataset no matter how many modeling techniques are employed [32, 33]. Recently, the concept of “dataset modelability” has been introduced. It implies a pre-modeling estimate of the feasibility to achieve highly predictive QSAR models for a given dataset of chemicals tested for the same biological endpoint. Golbraikh et al. [34] envisioned the concept of modelability based on the analysis of multiple datasets that included significant fractions of activity cliffs (i.e., very similar compounds with very different activities) [35], these cliffs being directly responsible for the poor prediction performance of the QSAR models built using these datasets. The authors introduced the “MODelability Index” (MODI) as a quantitative scoring technique to quickly evaluate whether reliable global QSAR models could be obtained for a given dataset. MODI is computed as follows. The first nearest neighbor (i.e., the most structurally similar compound in the dataset) is determined for each compound of the set on the basis of the computation of the chemicals’ dissimilarity using a series of molecular descriptors. Then, the algorithm analyzes whether or not each compound and its first neighbor belong to the same activity class (e.g., both compounds are annotated as being CYP3A4 inhibitors). If the two compounds do not belong to the same activity class,

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then they can be recognized as activity cliffs. The MODI algorithm analyzes how many pairs of such activity cliffs are present in each category of chemicals according to the following formula: ∑K same 1 N MODI = i (8.1) K total i=1 Ni same where K is the number of classes (K = 2 for a binary dataset), N i is the number of compounds of the ith activity class that have their first nearest neighbors total belonging to the same activity class i; N i is the total number of compounds belonging to the class i. It was shown that if the value of MODI for a particular dataset does not exceed 0.6, then it is impossible to develop a model with an accuracy exceeding 60% with even the most sophisticated modeling algorithms. There are other dataset indices, such as the ligand-based SALI [36] and structure-based ISAC [37] that were developed specifically for identifying and quantifying activity cliffs in chemical datasets. These indices are complementary to MODI and allow for a better assessment of the readiness and suitability of global QSAR approaches for a given dataset. Thus, it is important to evaluate the feasibility of model building for any dataset before significant efforts are expended in an effort to develop reliable and externally predictive models. k 8.2.3 Predictive QSAR Model Development Workflow k There have been several recent reviews outlining critical steps and best practices for developing rigorous and externally predictive QSAR models (e.g., [38–40]). Here we outline some key steps of the modeling workflow for achieving models of the highest statistical significance and external predictive power. We emphasize, however, that model development and validation are critical but not exclusive components of the modeling process. Other factors, such as data accuracy or dataset modelability, can strongly affect modeling outcomes irrespective of the rigor of the data mining and statistical modeling techniques used. Additionally, the ultimate success of the ADMET model depends on (i) the accuracy of primary experimental data used for model development, (ii) the accurate linkage of chemical structure representations to such data, (iii) the rigor of the computational tools and proper use of statistical model validation techniques, and (iv) the mechanistic transparency, interpretability, accessibility, and ease of use of both computational tools and models by the research and regulatory community. Anexampleofanestablished,structure-focusedstrategyforQSARmodel development and validation [38] is shown in Figure 8.1. It describes the workflow for delivering validated QSAR models that can be employed for virtual screening, yielding computational hits that should be ultimately confirmed experimentally. It starts by carefully curating chemical structures and, if possible, associated biological activities to prepare the dataset for subsequent

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Original Y-randomization dataset Multiple training Split into sets Structure training, test Combi-QSAR curation/ and external modeling harmonization validation sets

Multiple Activity test sets prediction Only accept models that passed both Database internal and screening using Validated predictive external applicability External validation models with high accuracy domain using applicability internal & external filters domain accuracy Experimental validation

Figure 8.1 Predictive QSAR modeling workflow [38]. k k calculations (see Section 8.3.1). Then, an n-fold external validation protocol is typically employed, where the dataset is randomly divided into n nearly equal parts, with n − 1 parts systematically being used for model development and the remaining fraction of compounds used for model evaluation. The sphere exclusion protocol [41] is then used to rationally divide the remaining subset of compounds (the modeling set) into multiple training and test sets that are used for model development and validation, respectively. Note that the division of the modeling set into training and test sets can also be done randomly, with the modeling set compounds being initially sorted (or stratified) according to the endpoint, from the most to least active molecule. Once the dataset is split into training and test sets, multiple QSAR techniques are employed based on the combinatorial exploration of all possible pairs of descriptor sets and various supervised data analysis techniques (combi-QSAR) to select models characterized by high accuracy for predicting both training and test sets data. The model acceptability thresholds are typically characterized by the lowest acceptable value of the leave-one-out (LOO) cross-validation R2 (q2) for the training set and by the conventional R2 for the test set; default threshold values of 0.7 are used for both q2 and R2. All validated models are finally tested as one ensemble using the external validation set. A critical step of the external validation is the use of an applicability domain (AD), which is defined uniquely for each model used in the consensus (ensemble) prediction of the external set. If the external validation demonstrates significant global

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predictive power of the models, they can be employed for virtual screening of available chemical databases to identify putative active compounds. More specific details about the algorithms and related approaches have been given in earlier publications [38, 39]. Models resulting from the QSAR workflow (Figure. 8.1) can be used to prioritize the selection of chemicals for experimental testing and validation. Clearly, experimental validation constitutes the ultimate test of the model-based prediction success. The focus on experimental validation, in turn, shifts the emphasis on ensuring good (best) statistics for the model that fits known experimental data toward generating testable hypotheses about purported bioactive (e.g., toxic) compounds. When possible, all derived QSAR models should be benchmarked against other existing predictors available in the public domain developed with the same datasets, as well as (with new datasets) against commercial packages. Consensus approaches integrating models developed with different QSAR methods and/or often by different groups are becoming increasingly popular. One of the first examples of such collaborative development of highly accurate QSAR models was in application to aquatic toxicity [32], where the best QSAR model emerged on combining individual models developed by six different groups. Recently, a similar approach was applied to the collaborative modeling k of estrogen receptor activity [42], where the final model integrated contribu- k tions from more than a dozen different groups. A significant advantage of this consensus approach, in addition to its higher statistical accuracy due to the use of ensemble modeling, is the potentially higher community acceptance of the resulting models as “gold standards,” given the community involvement in building the models. The biggest limitation of the consensus modeling approach, as applied in practice in the above-mentioned studies, is the need to maintain access to a complicated assembly of distinct models, some based on proprietary software, in order to generate new predictions. Such models could be maintained by publicly accessible web servers such as OChem (https:// ochem.eu) and ChemBench (http://chembench.mml.unc.edu) that enable the development of consensus models according to the above workflow. This workflow is used to develop each model contributing to the ensemble, which ensures the rigor of the consensus model.

8.2.4 Hybrid QSAR Modeling An important challenge for computational toxicology is achieving fast and accurate estimation of environmental hazards and human health risks with minimal to no dependence on animal testing [43, 44]. To address this challenge, the EPA launched the ToxCast project in 2007 to repurpose “oﬀ-the-shelf” HTS methods, commonly employed in the pharmaceutical industry, toward the evaluation of hundreds, to now thousands of chemicals in the environment

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that pose potential exposure and toxicity risks [45]. Additionally, EPA has partnered with the National Institutes of Environmental Health Sciences’ National Toxicology Program (NIEHS/NTP), the National Institutes of Health’s National Center for Advancing Translational Sciences (NIH/NCATS), and the US Food and Drug Administration (FDA) to form the Tox21 consortium (http://tox21.org). The Tox21 project aims to develop novel, alternative approaches to toxicity testing, minimizing the use of animals, with the goal of moving the field of predictive chemical toxicology forward under the paradigm of in vitro to in vivo extrapolation [46]. These projects have generated extensive quantitative in vitro HTS data for thousands of environmental chemicals, drugs, food additives, and so on, as well as for approximately 100 drugs that failed clinical trials due to toxicity issues in hundreds of experimental systems [47]. As of today, 8599 unique substances have been tested in Tox21 across about 100 assay endpoints, whereas ∼3,800 substances comprising the ToxCast chemical library have been screened in up to 800 assay endpoints [48, 49]. ToxRefDB is another EPA database that contains the in vivo regulatory repeat dose study data for roughly 1000 chemicals, with an overlap of roughly 750 chemicals with ToxCast and Tox21 [50]. In addition, toxicogenomics data, consisting of microarray gene panel screening, provides another dimension k of experimental knowledge that is potentially useful for predictive chemical k toxicity modeling and is becoming increasingly available [51, 52]. In addition to screening data, guideline study data, both in vivo and in vitro, are becoming increasingly available, often encouraged by new regulations. In that context, the European REACH regulation [53] comes with data requirements for chemicals that both manufacturers and importers must satisfy. And, these requirements are mainly dictated by annual tonnage production levels. As of the end of 2016, the REACH database lists 9801 unique substances studied across 3609 assays [54]. In addition, there is a significant amount of in vivo animal data deposited, thus far. The proliferation of publicly available in vitro and in vivo bioactivity data for hundreds to thousands of chemicals creates an opportunity for innovative computational frameworks that integrate chemical and biological factors for systematic investigation of endpoint toxicities. New workflows for hybrid modeling are beginning to address the challenges of processing and exploiting diverse experimental data streams in the context of the in vitro–in vivo extrapolation paradigm. The success of such integration depends on whether the alternative data streams (e.g., HTS assays) provide unique and relevant information to complement traditional, structure-based QSAR models. Figure 8.2 shows a plot comparing compound similarity in chemistry space versus the space of multiple qHTS descriptors: each point represents a pair of compounds, with x-axis values representing chemical similarities in the space of qHTS descriptors (i.e., biological profile similarity) and y-axisvaluesconveyingthechemical

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** CAS#79-43-6 CAS#141-44-4 Cl O CAS#92-52-4 O Cl Cl Structurally different Cl OH CAS#116-06-3 Both toxic chemicals can be close in O Both non-toxic biological space N NH O S 12

10 Similar chemicals 8 can be distant in biological space 6 *** CAS#101-96-2 Toxic 4 NH

2 NH CAS#793-24-8 NH Space of chemical descriptors

0 NH 0123456789 Non-toxic Space of qHTS descriptors * Biological dissimilarity due to insignificant variation (noise). Between “toxic” compounds; Between “non-toxic” compounds; Between “toxic” and “non-toxic” compounds

Figure 8.2 Chemical and biological similarities do not correlate. Each point represents a pair k of compounds characterized by pairwise chemical similarity (y-axis) versus biological k similarity (x-axis). Examples of apriorioutliers that should be ﬂagged and analyzed separately are shown. (See color plate section for the color representation of this ﬁgure.)

structure similarity of the same pair (computed as a Tanimoto coefficient). This plot clearly indicates that the two distinct sets of compound features are uncorrelated and may be complementary in a sense that the same pair of compounds could be viewed as chemically similar but produce vastly different biological responses (activity cliff ); or vice versa, compounds with similar biological profiles could have highly dissimilar chemical structures. This suggests that the concomitant use of short-term assays and chemical descriptors in a hybrid modeling strategy may prove advantageous in establishing models of the highest predictive power. Conventional QSAR models often have limited power due to the complexity of the observable biological effect, which can be mediated through multiple biomolecular interaction networks. Additional factors limiting ADMET QSAR models include low-quality data, over-extrapolation, and poor endpoint definition [55, 56]. Thus, it is unlikely that significant gains would come from using novel machine learning techniques or new chemical descriptors to model the same dataset. The hope is that major improvements can be achieved by utilizing alternative data sources, such as short-term biological assays, that can serve as inputs for modeling of the higher-order biological effects, along with traditional chemical descriptors [57–59].

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(a) Chemical features QSAR Consensus Biological data model Biological model (b) Chemical features Combined matrix of Hybrid Biological data features model

(d) Chemical features J QSAR o Multi- i space Biological data n Biological model t k model Optimization k Figure 8.3 Strategies for utilizing diverse data streams for predicting higher-order biological eﬀects.

In Figure 8.3, we outline several strategies for integrating in vitro biological data streams and chemical descriptors to predict in vivo eﬀects. These approaches include simple consensus (a), mixed-feature (a), and hierarchical (c) modeling, and multispace optimization (d); for more comprehensive reviews, see Refs [59, 60]

8.2.4.1 Simple Consensus This is the most straightforward approach where two models of the same endpoint are developed independently using either chemical descriptors or biological assay data. Both models are applied concurrently to predict external datasets and the predicted values are averaged (for continuous modeling) or annotated using some voting scheme (e.g., by majority for categorical data).

8.2.4.2 Mixed Chemical and Biological Features This approach employs both chemical structural data (i.e., chemical descriptors) and in vitro assay data (i.e., biological descriptors) as joint chemical-biological descriptors; the approach is illustrated by studies of carcinogenicity and acute toxicity [57, 58] described below. This approach was

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used to build improved models for predicting carcinogenicity and employed bioactivity data generated for a set of 1408 compounds tested across six human cell lines [61]. A total of 383 compounds were identified for which data were available from both the Berkeley Carcinogenic Potency Database and HTS studies. Compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62% overall prediction accuracy for rodent carcinogenicity applied to this dataset. Importantly, the prediction accuracy of the global model was significantly improved (to 73%) when chemical descriptors were augmented by the HTS data, which were regarded as biological descriptors. Another study [57] used cell viability qHTS (i.e., quantitative HTS, screened at multiple doses) for the same 1408 compounds across the 13 cell lines used. A total of 690 of these compounds were identified for which rodent acute toxicity data (i.e., toxic or nontoxic) was also available. The classification kNN QSAR modeling method was applied to these compounds using either chemical descriptors alone or as a combination of chemical and qHTS biological (hybrid) descriptors as compound features. The external prediction accuracy of models built with chemical descriptors only was 76%. In contrast, the prediction accu- k racy was significantly improved to 85% when using hybrid descriptors. These k studies suggest that combining qHTS profiles, especially the dose-response qHTS results, with conventional chemical descriptors considerably improves the predictive power of computational approaches for rodent acute toxicity assessment. A similar approach can be employed for the modeling of any ADMET endpoint where both in vitro and in vivo results are available. A clear limitation of the hybrid chemical-biological modeling approach is that experimental HTS data are required in order to use models for the prediction of in vivo effects for new chemicals. However, the barrier to generating such data is steadily decreasing as programs such as ToxCast and Tox21 continue to generate new HTS data for many chemicals. Another possible avenue to be explored is the development of a panel of rigorous QSAR models predicting the outcome of HTS assays; if successful, such models could replace experimental testing of new chemicals and could expand into areas of chemical space that are less amenable to HTS testing (e.g., DMSO insolubles, volatiles, commercially unavailable chemicals) such that predicted activity values could be used in hybrid QSAR modeling.

8.2.4.3 Two-Step Hierarchical Workﬂow As previously indicated, the development of hybrid models that require experimental data as inputs can be justiﬁed only if the barrier to generating such data is low. Ideally, however, and for many practical applications (e.g., to virtual screening), experimental data would only be required for the training set, with

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k in vitro– Compounds NN LD50 Whole Class 2 in vivo above the models for dataset Compounds line class 2 compounds Generate regression Modeling k line between IC50 and NN classification set LD50 using moving models for class 1 regression method vs class 2

Compounds kNN LD50 on the line Class 1 models for Compounds class 1 Compounds compounds below the line

External validation set

Figure 8.4 Two-step hierarchical k-nearest neighbor (kNN) QSAR workﬂow to develop an

enhanced rat acute toxicity (LD50) model by using cytotoxicity data (IC50) as biological profile descriptors. k k structure-only models used to predict activity for new compounds. A hierarchical modeling workflow [62] illustrating this type of workflow is shown in Figure 8.4. First, in vitro to in vivo correlation profiles are generated for all compounds in the modeling set. These compounds are then clustered into several subsets based on the discovered relationships and, lastly, QSAR models are developed for individual subsets. The modeling set compounds are, thus, partitioned into two or more subclasses, enabling development of a classification QSAR model using chemical descriptors only for predicting membership in each subclass. Thus, for an external compound, the first classification QSAR model is used to assign it to one of the subclasses, and the second QSAR model is used to predict quantitative compound toxicity. This workflow would not rely on the experimental assays once the development of initial models is completed. Potentially, this approach allows one to analyze thousands of pairwise in vitro to in vivo relationship plots based on the ToxCast database. Studies such as those described above have shown that the accuracy of QSAR models utilizing chemical descriptors only for predicting complex toxicity endpoints can be improved by adding biological descriptors derived from in vitro assays. However, in a recent investigation [63] involving 127 drugs from the Japanese Toxicogenomics Project (TGP), hybrid models did not show higher accuracy than those using toxicogenomic descriptors alone (i.e., chemically perturbed gene expression profiles) for predicting hepatotoxicity. This observation prompted the development of a new way of integrating

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chemical and biological information to achieve models of higher accuracy and interpretability, which is described in the following.

8.2.5 Chemical Biological Read-Across The prediction of chemical-induced bioactivity through traditional global QSAR modeling approaches, such as those described above, is essentially based on various perceptions of structural similarity. Although we have described eﬀorts to extend traditional global QSAR modeling approaches through the use of hybrid chemical-biological descriptors, the overall perspective of hybrid QSAR models still remains that of a global modeling approach. A more focused approach, which better aligns with a regulatory perspective in evaluating new chemicals, starts from the chemical of interest and creates a local neighborhood of structure-activity inferences, which in turn can be used to build a weight-of-evidence (WOE) argument. To capitalize on the emerging data streams mentioned above, the chemical-biological read-across (CBRA) approach [64] was developed to infer a compound’s activity (e.g., acute toxicity) from those of its chemical and biological analogs. CBRA employs the experimentally obtained biological measurements (e.g., gene expression proﬁles, cytotoxicity screening, cytochrome P450 inhibition) regarded as k biological descriptors, as well as computed chemical descriptors to assess k similarity between chemical substances. As various types of chemical-induced bioactivity (e.g., carcinogenicity, hepatotoxicity) can be evaluated by CBRA, there is a need for user-oriented software capable of rapidly conducting and analyzing the outcomes of this approach. The CBRA standalone software (see Figure 8.5) provides a user-friendly graphical interface to (i) upload both chemical and biological descriptors and export CBRA outputs, (ii) build, visualize, and customize the radial plots illustrating the relative similarity of a chemical of interest to previously tested compounds, and (iii) help in analyzing structure-activity relationships. The use of 2D radial plots facilitates the transparency of the analysis by depicting the relative similarities between a query chemical and its closest analogs. By integrating computed structural descriptors and experimentally measured biological responses, CBRA maxi- mizes the use of all available data to ensure both reliability and interpretability of hybrid QSAR models. CBRA requires users to prepare and upload both chemical and biological descriptors, and can process thousands of compounds within the same project. Although the complete biological matrix should contain as many rows as the number of compounds and +as many columns as the number of measured biological properties, incomplete matrices with a small fraction of unavailable measurements are tolerated by the program and are taken into account for similarity calculations. CBRA calculates two similarity matrices, one based on chemical descriptors

and another based on biological descriptors. Similarity (Si)iscomputedasa

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(a)

(c)

(b)

k k Figure 8.5 Main window of the CBRA program divided into three parts: (a) selection of input ﬁles, (b) colored radial plot, (c) molecular structure viewer, textual information, and options.

standard Tanimoto similarity coeﬃcient. Then two sets of nearest neighbors

are determined by CBRA from the similarity matrices: kchem neighbors in the chemical space and kbio neighbors in the biological space. The predicted activity of a given compound (Apred) is calculated from the similarity-weighted aggre- gate of the activities Ai of its k nearest neighbors in both chemical and biological spaces (Equation 8.2). ∑k ∑k bio ⋅ chem ⋅ i=1 Si Ai + j=1 Sj Aj Apred,DSRA = ∑ ∑ (8.2) kbio kchem i=1 Si + j=1 Sj

where Ai and Si are the activity and Tanimoto similarity values, respectively, of the nearest neighbors of a compound in bioactivity space, and Aj and Sj are the activity and Tanimoto similarity values of the nearest neighbors of a compound in chemistry space. Statistical criteria are automatically calculated to determine the prediction performance of CBRA. CBRA software also implements a unique radial plot

visualizer (Figure 8.5) to illustrate how Apred is computed and the nearest neighbors for a given compound. Overall, CBRA enables users to conduct chemical biological read-across and visualize the neighborhood of chemicals using innovative radial plots. This technology facilitates the incorporation

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of diverse data streams to infer, for example, in vivo effects of chemicals from their molecular structures and in vitro biological profiles. This makes CBRA a potentially appealing tool for drug discovery and chemical hazard assessment. Importantly, CBRA technology is freely accessible for academic laboratories and regulators and is easily customizable for supporting specific needs requiring additional code implementation. A similar approach to CBRA has been independently developed by Shah et al. [65] with the goal to begin formalizing the process of read-across in a regulatory setting, which is traditionally a qualitative expert-driven assessment. They proposed a generalization of the CBRA approach called a generalized read-across (GenRA), which can be based on either chemical-only or biological-only similarity, or a hybrid chemical-biological similarity read-across, depending on biological data availability. Approximately 3200 chemical structure descriptors were generated for a set of about 1780 chemicals and supplemented with bioactivity data from just over 800 in vitro assays from EPA’s ToxCast program. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors characterized by chemical descriptor and/or bioactivity information. In this study, the read-across predictions were of in vivo toxicity effects as observed across 10 different guideline or guideline-like study types, spanning 574 unique k types of toxic effects across 129 targets. Objective measures of performance k were also established to evaluate the GenRA predictions, both in terms of the optimal performance for a given number of neighbors and similarity threshold, and in terms of the local neighborhood. The bioactivity descriptors were often found to be more predictive of in vivo toxicity than chemical descriptors alone or even a combination of both, but this was dependent on both the toxicity effectofinterestaswellasthelocalneighborhood.Workiscurrentlyongoing to extend the approach to evaluate the utility of other chemical descriptors that can better characterize expert knowledge and to disseminate the approach as a publicly available, web-based tool. The latter will permit a read-across prediction to be made for a given chemical of interest and assess the confidence in the prediction by modifying the number of neighbors and the similarity index. Figure 8.6 provides a graphical representation of a local neighborhood for a target chemical, in this case Benoxacor. The figure shows the nearest neighbors,wherethetargetchemicalisattherootofthetreeandtheneigh- bors are organized radially in an anticlockwise manner starting with the most similar chemical and ending with the least similar. The similarity index (Jaccard similarity) is weighted by the type of descriptor. The boxes above each analog provide an aggregated summary of the experimental information per study type, for example, mgr = multigenerational, where red boxes reflect adverse effects and green reflect no adverse effects. GenRA predictions are represented as orange and blue boxes for predicted adverse effects and no predicted adverse effects, respectively.

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Computational Tools for ADMET Proﬁling 229 chr_brain mgr_body_weight mgr_bone mgr_intenstive_large mgr_urinary_bladder sub_Intenstine_small sub_prostate sub_spleen sub_urinalysis sub_water_consumption ) ) + – 1,2,3,6-Tetrahy (+) Act (–) Act (NT) Act ( Pred ( Pred drophthalimide Octyl decyl phthalate

Dioctyl 1.00c phthalate 1.00c

1.00c

Dibutyl 0.81c phthalate

0.77c Benoxacor

1,2-Benzenedica 0.72c 0.63c rboxylic acid, di-C9-11-branched alkyl esters, 0.72c 0.65c C10-rich k 0.66c k

4-Nonylphenol, Butyl benzyl branched phthalate

Trioctyl Monobutyl trimellitate phthalate Dimethyl adipate

Figure 8.6 Graphical representation of a local neighborhood for Benoxacor [65].

8.2.6 Public Chemotype Approach to Data-Mining An approach to data mining and subsequent modeling that relies more heavily on a local-chemistry perspective is being pursued within EPA’s ToxCast research program using a public set of “ToxPrint” chemical features (https:// toxprint.org/). A ToxPrint fingerprint file (i.e., a matrix of binary values [1,0] for each chemical-feature pair) can be generated within the associated public Chemotyper application (https://chemotyper.org/) for an imported chemical SDF file, or can be generated within the commercially available CORINA Sym- phony software (see https://www.mn-am.com/products/corinasymphony). Both the ToxPrints and Chemotyper were developed by Altamira (Altamira,

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Columbus, OH) and Molecular Networks (Molecular Networks, Erlangen, GmbH) under contract from FDA. ToxPrint chemotypes (or ToxPrints) are coded in an open, XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be visualized and searched within the Chemo- typer application [66]. The ToxPrint set consists of 729 uniquely defined CSRML features specifically designed to provide broad coverage of inventories consisting of tens of thousands of environmental and industrial chemicals, including pesticides, cosmetics ingredients, food additives, and drugs, as well as to capture salient features important to the safety assessment workflow within FDA. The Chemotyper Windows application, with the ToxPrint CSRML file loaded, enables a user to import an SDF file, superimpose ToxPrints onto their chemicals of interest, view and filter chemicals based on one or more ToxPrints, and export a ToxPrint fingerprint file for the user-imported structures. Significant advantages of the ToxPrint chemotype approach over other available fingerprinting methods (e.g., PubChem, MACCS), that are motivating its varied applications within EPA, are the ease with which the public ToxPrints can be generated, visualized, chemically interpreted, shared across projects, communicated, and used to profile inventories and define local chemistry domains. Several examples using ToxPrints to profile, assess relative structural diversity, and compare distinct chemical inventories were k presented in a recent survey of the ToxCast chemical landscape [49]. Other k ToxCast research applications are employing ToxPrints to detect bioactivity enrichments within local chemotype domains for chemical subsets (e.g., flame retardants) and individual assays (e.g., the Tox21 mitochondrial membrane potential disruption assay). Such enrichments can inform and guide follow-up studies and can be used to generate initial structure-activity hypotheses. In addition, chemotype enrichments computed for hundreds of ToxCast and Tox21 HTS assays provide an objective basis for enhancing and exploring putative bioactivity linkages within local chemical domains defined by the ToxPrints. Eventually, however, it is envisioned that chemotype sets, such as ToxPrints, would be employed across a continuum of methods, that is, for guiding interpretation and use of global QSAR models, defining local applicability domains within global QSAR models, and providing an objective local chemistry basis for comparing distinct models. Workflows that automate the process of using ToxPrint chemotypes for data mining and exploration will also be key to engaging toxicologists and regulators and potentially overcoming some of the existing barriers to adoption of global QSAR models.

8.3 Unsolved Challenges in Structure Based Proﬁling

The current generation of computational proﬁlers used for lead optimization and chemical risk assessment has shown tremendous progress regarding

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prediction reliability and broadened applicability domain. Still, important challenges remain, especially when it comes to the identification of erroneous data points prior to modeling, the identification and treatment of activity and toxicity cliffs, and in vitro to in vivo extrapolation. In this section, we recapitulate the main challenges that face predictors and give some indications how to solve them.

8.3.1 Biological Data Curation As mentioned in Section 8.2.1, public data pose many questions and potential challenges when it comes to determining the quality of a particular chemical measurement. We recently defined the “five i’s” danger [27, 28, 67] as using potentially incomplete, incorrect, inaccurate, inconsistent, or irreproducible data points in a given modeling study. To illustrate this point, the study by Prinz et al. [68] observed that only 20−25% of published assertions regarding purported biological functions for novel deorphanized proteins were in line with their in-house measurements at Bayer Pharmaceuticals. Similarly, Begley and Ellis [69, 70] conducted a complementary study at Amgen, resulting in an even lower rate of reproducibility of 11%. Ekins et al. [71] recently analyzed the potential bias of dispensing techniques (tip-based vs acoustic) on the quality, reproducibility, and modelability of HTS data. The authors found these two k different techniques to significantly influence the experimental responses mea- k sured for the same compounds tested in the same assay. Although both dispensing techniques were deemed acceptable, this example emphasizes the dramatic influence of subtle experimental variations in the lack of accuracy and/or reproducibility of the measurements. Of critical importance are the consequences for molecular modelers who depend on the quality of the experimental data used for building models. Therefore, the implementation of key steps for ensuring biological data curation prior to the modeling of ADMET data is also critical. The three main steps of our biological data curation workflow are recapitulated in the following [27, 28]: 1. Duplicate analysis with checking of the bioactivity concordance:Thesame compound may be present many times in the same database [72]. As an example, we underline cases of identical chemicals purchased from different suppliers tested in the same assays, resulting in different internal identifiers with potentially different experimental responses [73]. If modelers build QSAR models using datasets containing structural duplicates in both training and test sets, the models will be skewed toward enriched local activity domains and, if activities are similar, may artificially inflate prediction performances [26]. To sufficiently account for structure-based details (such as stereo, salt, or tautomeric distinctions), 1D or 2D descriptors, or a unique text-based structure representation, such as an InChI string (or key) should be used on the sets to identify duplicates. Note that distinct chemical

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names and/or CAS registry numbers in the public domain are commonly mapped to identical structures (e.g., parent to salt, stereo to no stereo), or vice versa (e.g., one name points to multiple structures); hence, these identifiers are particularly unreliable for identifying duplicate records. Once identified, chemical-activity duplicates should be set aside and analyzed further before being included in a training set. Some possibilities requiring further processing include the following: (a) The ADMET activity value for one compound is dramatically different from the value associated with the duplicated compound. One of the two values may be wrong (e.g., labeling error, incorrect unit). Neighborhood analysis using structure-based clustering could help find which value is more likely to be correct. (b) The removal of counterions or the normalization of functional groups has modified the representation of one substance (or both) resulting in duplicates. It is recommended that the user verify the original records and check the activity values associated with the two compounds. 2. Analysis of the intra- and inter-laboratory experimental variability:The bioactivity of a compound is usually measured in multiple replicates using the same assay. Companies can test thousands of compounds in duplicate or triplicate assays (e.g., metabolic stability). Obviously, such a dataset is k critical to analyzing the global experimental variability of a given assay, k as well as to spot any local variability within a chemical series. Even in academia, reference compounds can be tested hundreds of times over months and years. These data points, often ignored, can provide critical estimation of experimental variability. Usually, modelers rely on partial informationreportedinapublishedarticle(e.g.,“± 0.2 log units”) [74]. Thus, most modeling studies consider experimental variability to be a constant. As a result, the expected accuracy for any QSAR model of ADMET endpoints cannot exceed the experimental variability. For modelers dealing with screening data, it is recommended that the user analyze the baseline history of the target/endpoint of interest and the reference controls. The baseline history (e.g., per plate, per batch, per week, per month) is critical to identifying false-positives and false-negatives [75]. Inter-laboratory variability can best be assessed if many duplicate compounds are being measured at several independent laboratories. 3. Identification of mislabeled compounds: Individual QSAR models can be used to form an ensemble model, enabling consensus predictions for any chemical. Consensus models have been shown to afford higher prediction performances and broader applicability domains [38]. Importantly, consensus models can also be employed to identify and potentially correct mislabeled compounds in a dataset. A compound can be considered as “suspicious” if (i) all individual QSAR models involved in the consensus

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model cannot predict its activity accurately; and (ii) that compound is clustered with structurally similar compounds with dissimilar bioactivities.

8.3.2 Identification and Treatment of Activity and Toxicity Cliffs Activity cliffs [35] (or toxicity cliffs) are extremely challenging for cheminformatics modeling approaches. In a given ADMET dataset, modelers should identify all suspicious pairs and determine if some of these pairs of similar compounds with dissimilar ADMET profiles are true activity cliffs. There are several types of activity cliffs [35] defined by how the similarity of compounds is computed (e.g., 2D, 3D, matching molecular pairs). Each pair of activity cliffs needs to be analyzed according to the following principles: 1. Bioactivities for each compound must be checked against the original data source for accuracy and potential mislabeling issues. For instance, a compound originally annotated as a 10 nM inhibitor (very active)mightbe corrected to a 10 mM inhibitor (inactive). 2. The two chemicals must be analyzed and the structural differences interpreted. What functional group or structural feature differentiating the two structures is likely to cause that variation of activity? If one group is identified, what is the best molecular modeling technique capable to taking k that structural difference into account to differentiate the two compounds? k Three-dimensional differences should also be considered, especially in the context of receptor-ligand interactions disrupted or modified by one compound of the activity cliff pair.

8.3.3 In Vitro to In Vivo Continuum in the Context of AOP With the advent of adverse outcome pathways (AOPs), there exist many opportunities to reconsider the manner in which QSARs are developed in the future and to evaluate common assumptions underpinning AOPs. A case in point is the AOP for skin sensitization. Research in the field of skin sensitization dates back many decades, which made this endpoint a convenient case study from which to construct the first AOP, published by the OECD in 2012. The AOP outlines the various key events that need to occur in order to induce skin sensitization. The framework has also proved useful to put into context the different nonanimal test methods that are in development or have been formally validated against the specific key events. Recent research has focused on developing integrated testing and assessment approaches to help in assessing skin sensitization potential on the basis of both in silico and in vitro information. Our research used the AOP construct to test the role that penetration may play in limiting sensitization [72]. Typically, skin penetration is modeled using parameters that reflect hydrophobicity and size. Thresholds have been set that state that substances that exceed specific log octanol-water

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partition coefficient (log K ow) and molecular weight (MW) values will not be sensitizers as skin penetration will be limited. We evaluated a large body of skin sensitization data, as submitted for the purposes of the EU REACH regulation, to test the scientific validity of these thresholds. Highly hydrophilic ≤ substances, e.g., log Kow 1, are expected not to penetrate effectively to induce sensitization, and it is assumed that substances must have a MW < 500 to penetrate effectively through the skin to induce sensitization. 1482 substances

were identified with skin sensitization data and measured log K ow values. ≤ 525 substances had a measured log Kow 1; 100 of those were sensitizers. There was no significant difference in the incidence of sensitizers above and

below this log K ow threshold [76]. Reaction chemistry principles that had been established for lower MW and more hydrophobic substances were found to be ≤ as valid in rationalizing the skin sensitizers with a log Kow 1. Fewer examples were identified when a similar evaluation was performed for substances with a MW > 500. However again there were no special explanations for the 13 sensitizing substances identified with a MW > 500; the same reaction chemistry principles were sufficient in rationalizing the sensitization responses [76]. This study underscores the need to sufficiently scrutinize, using objective methods and modern approaches, commonly held assumptions and guiding principles based on chemical structure and computed properties. k We have also considered the utility of the validated nonanimal test methods k and the extent to which they are capable of correctly identifying sensitizers that require activation in order to induce their sensitization response [73]. A dataset of 127 substances with outcomes from the local lymph node assay (LLNA) and alternative test methods were compiled and evaluated. A reaction chemistry evaluation was first performed that determined which substances were nonreactive, directly acting, or indirectly acting. For the substances predefined as indirectly acting, the reaction chemistry mechanisms were evaluated for each of them in more detail to determine whether the substance was likely to act as a pre-hapten or pro-hapten or both. Pre-haptens undergo activation externally before coming into contact with the skin or on the skin (e.g., through air oxidation processes – autoxidation). Pro-haptens undergo activation within the skin, mainly through metabolic processes [77, 78].

8.4 Perspectives

This ﬁnal section focuses on the future developments and perspectives for the next generation of computational predictors. Indeed, as chemistry-based forecasting tools are increasingly available on all sorts of devices [79–81], more researchers will be drawn into using them directly, without the help of cheminformaticians and computational chemists to explain and interpret the validity of the prediction results. These increasingly feasible use scenarios create increasing pressures for the development of reliable, easy-to-use, and transparent ADMET proﬁlers for various types of chemicals.

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8.4.1 Profilers on the Go with Mobile Devices There is a growing compendium of cheminformatics software tools available and fully functional on tablets, smartphones, and other mobile devices. This is a new step in technology portability that finally addresses one of the initial promises of cheminformatics, which is to assist experimentalists, researchers, or safety assessors in chemical-relevant decision making as close as is possible to the lab bench. For instance, the following tablet applications are notable: PyMol (developed by Schrodinger; https://itunes.apple.com/us/app/pymol/id548668638) allows users to browse and visualize protein structures and protein-ligand complexes; ChemSpider (developed by Molecular Materials Informatics; https://itunes .apple.com/us/app/chemspider/id458878661) enables structural and text queries on the ChemSpider database; CompTox Mobile (developed by Kirill Blinov; https://itunes.apple.com/us/app/comptox-mobile/id1179517689) puts over 700,000 chemical structures onto a handheld device to be searched by chemical identifier and linking to a rich website of data; Chemical Engineering AppSuite (developed by John McLemore; https://itunes.apple.com/us/ app/chemical-engineering-appsuite/id526158171) integrates a collection of chemistry-related tools and databases; and Elemental (developed by k Dotmatics Limited; https://itunes.apple.com/us/app/elemental/id518655328) k allows users to draw and export compound structures. These applications are the first representatives of a new generation of “cheminformatics apps” with user-friendly, tablet-ready graphical interfaces that will offer direct and intuitive access to diverse chemogenomics data. It is very likely that the features offered by these applications will progressively integrate complex QSAR-based ADMET predictors coupled with sophisticated modules that will be capable of rapidly accessing and cross-searching chemical biological databases, visualizing and analyzing screening results, launching modeling and screening computations on remotely controlled workstations, and sharing chemical information in the cloud. Early examples of these capabilities are demonstrated with TB Mobile (developed by Collaborative Drug Discovery; https://itunes.apple .com/us/app/tb-mobile/id567461644) that makes available a set of molecules with activity against Mycobacterium Tuberculosis and links to pathways (in http://biocyc.org), genes (in http://tbdb.org) and literature (in PubMed); and PolyPharma (developed by Molecular Materials Informatics; https://itunes .apple.com/us/app/polypharma/id1025327772) that uses structure-activity relationships to view predicted activities against biological targets, physical properties, and off-targets to avoid. The Mobile Molecular Datasheet (MMDS) (developed by Molecular Materials Informatics; https://itunes.apple.com/us/ app/mobile-molecular-datasheet/id383661863) offers the ability to upload Bayesian models created by the user using open source descriptors and algorithms [82–84].

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8.4.2 Structure–Exposure–Activity Relationships The notion of chemical exposure has been largely ignored by molecular modelers, mainly due the lack of experimental data [85]. Although there are a handful of models for predicting both human and ecological exposure rates based upon basic chemical descriptors, without data to evaluate their predictive performance and domain of applicability, there may not be enough confidence to inform decision making [86]. One obvious data source for empirical evaluation of exposure modeling efforts has been the US Centers for Disease Control (CDC) National Health and Nutrition Examination Survey (NHANES) data, which reports chemical concentrations in urine and blood for a representative sample of the US population [87]. The NHANES exposure data, although extremely useful qualitatively, are notoriously difficult to interpret with respect to model evaluation [88, 89]. Unsurprisingly, efforts to link physicochemical properties and structure directly to exposure have met with, at best, limited success [90, 91]. However, a useful intermediate step between structure and exposure has been the recognition that the route of exposure, which is often dictated by how chemicals are used, can explain large differences in exposures within the NHANES data [91–93]. Among the chemical exposures indicated by the CDC NHANES urine data (which reflects the average exposure for the United States), nearly all those chemicals with k k urine analytes that were detectable (i.e., above the limit of detection) were used in consumer products and articles of commerce within the home. In contrast, those chemicals that were used exclusively outside the home were largely below the limit of detection except for the very highest quantiles of the US population (e.g., the 99th percentile). Thus, a model analysis performed by the EPA’s Exposure Forecasting (ExpoCast) project [92] was carried out, which tested for correlations between both physicochemical properties and coarse descriptors of product use. We found that only the use descriptors and the national production volume were predictive, but that these factors alone could explain roughly 50% of the chemical-to-chemical variance in exposure rates indicated by the NHANES data [91]. New models for indoor chemical exposure have been developed [94] and the ExpoCast project has worked to compile information on chemicals in consumer products [95, 96]. However, again these activities were data limited – only knowledge about chemical ingredients explicitly disclosed by manufacturers were available, which omits chemicals not reported either due to lack of regulatory requirement or lack of information (as in the case where constituents of a product are obtained from a third party). Therefore, new tools have been developed by the ExpoCast project to use machine learning to predict chemical use from structure. Thus, a paradigm of “structure predicts use,” which correlates exposure, currently allows for a tentative, but defensible prediction of exposure in the absence of other information.

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8.5 Conclusions

In this chapter, we highlighted several key modeling approaches, techniques, and protocols we believe already contribute to make ADMET prediction models more accurate and robust than ever. As emphasized by the issues of data reproducibility and chemical biological curation, it is clear that data growth and availability alone do not guarantee improved modeling reliability. However, the amount, diversity, and rate at which ADMET-relevant chemogenomics data are currently generated and released in the public domain, support and validate the notion of Big Chemical Data, which could potentially transform the way we store, analyze, visualize, model, and extract knowledge from chemical-biological data. In that context, the development of new integrative modeling approaches, still based on machine learning techniques but taking into account the chemical exposure in a subpopulation, 3D structures of relevant biological targets, HTS screening assay results, in vivo profiles in zebrafish models, or validated AOPs all together represent the next critical step of predictive ADMET modeling. Obviously, these next-generation models will not only allow for better profiling chemical hits early in the discovery pipeline but also help regulators prioritize chemicals to be tested with scrutiny for potential detrimental effects. Finally, we should emphasize k the necessity for educators to familiarize students with the aforementioned k concepts as early as possible in the curriculum. As these tools will continue to be more and more accessible to non-specialists (especially via mobile devices), the skills required to master the development of predictive ADMET models span several disciplines (e.g., cheminformatics, chemistry, biology, toxicology, bioinformatics) and necessitate dedicated classes and training workshops.

Acknowledgments

D.F. gratefully thanks the NCSU Chancellor’s Faculty Excellence Program, the Bioinformatics Research Center, and the NCSU Comparative Medicine Institute.

Disclaimer

This work has been internally reviewed at the US EPA and has been approved for publication. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US Environmental Protection Agency.

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60 Low, Y.S., Sedykh, A.Y., Rusyn, I., and Tropsha, A. (2014) Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr.Top.Med. Chem., 14 (11), 1356–1364. 61 Xia, M.; Huang, R.; Witt, K. L.; Southall, N.; Fostel, J.; Cho, M. H.; Jadhav, A.; Smith, C. S.; Inglese, J.; Portier, C. J.; Tice, R. R.; Austin, C. P. Compound cytotoxicity profiling using quantitative high-throughput screening. Environ.Health Perspect. 116 (3), 284–291. 62 Zhu, H., Ye, L., Richard, A. et al. (2009) A novel two-step hierarchical quantitative structure–activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environ. Health Perspect., 117 (8), 1257–1264. 63 Low, Y., Uehara, T., Minowa, Y. et al. (2011) Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem. Res. Toxicol., 24 (8), 1251–1262. 64 Low, Y., Sedykh, A.Y., Fourches, D. et al. (2013) Integrative chemical–biological read-across approach for chemical hazard classification. Chem. Res. Toxicol., 26, 1199–1208. 65 Shah,I.,Liu,J.,Judson,R.S.et al. (2016) Systematically evaluating read-across prediction and performance using a local validity approach k characterized by chemical structure and bioactivity information. Regul. k Toxicol. Pharmacol., 79, 12–24. 66 Yang,C.,Tarkhov,A.,Marusczyk,J.et al. (2015) New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling. J.Chem.Inf.Model., 55 (3), 510–528. 67 Moore, K.D., Eyestone, K., and Coddington, D.C. (2013) The big deal about big data. Healthc. Financ. Manage., 67 (8), 60–66, 68. 68 Prinz, F., Schlange, T., and Asadullah, K. (2011) Believe it or not: how much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov., 10 (9), 712. 69 Begley, C.G. and Ioannidis, J.P.A. (2015) Reproducibility in science: improving the standard for basic and preclinical research. Circ. Res., 116 (1), 116–126. 70 Begley, C.G. and Ellis, L.M. (2012) Drug development: raise standards for preclinical cancer research. Nature, 483 (7391), 531–533. 71 Ekins, S., Olechno, J., and Williams, A.J. (2013) Dispensing processes impact apparent biological activity as determined by computational and statistical analyses. PLoS One, 8 (5), e62325. 72 Baurin, N., Baker, R., Richardson, C. et al. (2004) Drug-like annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7 million compounds. J. Chem. Inf. Comput. Sci, 44 (2), 643–651.

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73 Veith, H., Southall, N., Huang, R. et al. (2009) Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat. Biotechnol., 27 (11), 1050–1055. 74 Kalliokoski, T., Kramer, C., Vulpetti, A., and Gedeck, P. (2013) Comparability of mixed IC50 data – a statistical analysis. PLoS One, 8 (4), e61007. 75 Fourches, D., Sassano, M.F., Roth, B.L., and Tropsha, A. (2013) HTS navigator: freely-accessible cheminformatics software for analyzing high-throughput screening data. Bioinformatics, 30, 588–589. 76 Fitzpatrick, J.M., Roberts, D.W., and Patlewicz, G. (2017) Is skin penetration a determining factor in skin sensitization potential and potency? Refuting the notion of a log Kow threshold for skin sensitization. J. Appl. Toxicol, 37 (1), 117–127. 77 Patlewicz, G., Casati, S., Basketter, D.A. et al. (2016) Can currently available non-animal methods detect pre and pro-haptens relevant for skin sensitization? Regul. Toxicol. Pharmacol., 82, 147–155. 78 Dumont, C., Barroso, J., Matys, I. et al. (2016) Analysis of the local lymph node assay (LLNA) variability for assessing the prediction of skin sensi- tisation potential and potency of chemicals with non-animal approaches. Toxicol. Vitr., 34, 220–228. k 79 Clark, A.M., Ekins, S., and Williams, A.J. (2012) Redeﬁning cheminformatics k with intuitive collaborative mobile apps. Mol. Inform., 31 (8), 569–584. 80 Williams, A.J., Ekins, S., Clark, A.M. et al. (2011) Mobile apps for chemistry in the world of drug discovery. Drug Discov. Today, 16 (21–22), 928–939. 81 Ekins, S., Clark, A.M., and Sarker, M. (2013) TB mobile: a mobile app for anti-tuberculosis molecules with known targets. J. Cheminform., 5 (1), 13. 82 Ekins, S., Clark, A.M., and Wright, S.H. (2015) Making transporter models for drug–drug interaction prediction mobile. Drug Metab. Dispos., 43 (10), 1642–1645. 83 Clark, A.M., Dole, K., Coulon-Spektor, A. et al. (2015) Open source Bayesian models. 1. Application to ADME/Tox and drug discovery datasets. J.Chem.Inf.Model, 55 (6), 1231–1245. 84 Clark, A.M. and Ekins, S. (2015) Open source Bayesian models. 2. Mining a ‘big dataset’ to create and validate models with ChEMBL. J. Chem. Inf. Model, 55 (6), 1246–1260. 85 Egeghy, P.P., Judson, R., Gangwal, S. et al. (2012) The exposure data landscape for manufactured chemicals. Sci. Total Environ., 414, 159–166. 86 Mitchell, J., Arnot, J.A., Jolliet, O. et al. (2013) Comparison of modeling approaches to prioritize chemicals based on estimates of exposure and exposure potential. Sci. Total Environ., 458–460, 555–567. 87 Crinnion, W.J. (2010) The CDC fourth national report on human exposure to environmental chemicals: what it tells us about our toxic burden and

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Computational Toxicology and Reach Emilio Benfenati, Anna Lombardo, and Alessandra Roncaglioni

IRCCS – Istituto di Ricerche Farmacologiche “Mario Negri”,Laboratory of Environmental Chemistry and Toxicology, Italy

CHAPTER MENU

A Theoretical and Historical Introduction to the Evolution Toward Predictive Models, 245 Reach and the Other Legislations, 247 Annex XI of Reach for QSAR Models, 248 The ECHA Guidelines and the Use of QSAR Models within ECHA, 255 k Conclusions, 266 k

9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models

Quantitative structure–activity relationship (QSAR) models are a versatile set of models that are used in many fields including chemistry, biology, and engineering. A QSAR model is designed on the basis of the endpoint that is envisaged. Consequently, it is quite obvious that numerous QSAR models can be designed. Furthermore, there are many ways to build a model for the same endpoint, using different chemicals to train the model, different chemical descriptors, or different algorithms. In this chapter, we discuss how the conceptual approach toward QSAR models may vary considerably. Historically, QSAR models evolved on the basis of the purpose for which they were required; therefore, it may be useful to first introduce the differences among the older and more recent perspectives, in particular in case of the use of QSAR models for regulatory purposes. Old QSAR models aimed to explore possible rules for a paticular outcone, to understand better the factors affecting the phenomenon under evaluation. It is only more recently that there has been interest in the possible use of QSAR models to predict the effect of a single molecule, avoiding the need to carry out

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an experimental test. This means that initially the interest was on the overall phenomenon, and not on a single substance to assess, trying to understand and explain the driving forces governing the phenomenon under investigation; the property data of the chemicals were known in this case, while what was unknown was the factor governing it. In the initial times of the QSAR modeling, a good publication was able to show that a certain descriptor could be associated with the observed property, such as, for example, aquatic toxicity. Initially, in most of the cases, some physicochemical descriptors were investigated, and the identification of the “best” one, useful to explain the phenomenon, was the challenge: if the model was working using certain descriptor(s), then the successful message of the study was about the role of these descriptors. There was almost no interest (i) in the validation of the model using other chemicals, (ii) in considering the model as substitute for the in vivo animal model, (iii) in the use of the model to predict the properties of a new chemical. In the case of the use of QSAR to predict the effect of a substance, the descriptors of the model are known, and the value that is lacking is the property effect of the chemical. While the initial studies were theoretical ones, later on the interest was on the practical use, to predict the properties of chemicals for regulatory purposes. This required a stricter process, compared to the initial studies, k with the inclusion of model validation. The reasons for this are mainly related k to the practical implication of the model and to the regulatory context. This generated the enforcement of procedures to verify the predictivity of the model not in terms of the descriptor’s correctness, but of the property value of the individual substance. The initial models were self-explanatory in a certain way: the aim was not to have a universal model, but the understanding of a phenomenon, which applied to the population of substances under examination. If it is the individual substance that is of interest, instead, we would also need a conceptual approach to verify whether the substance of interest is related or not to the population of the chemicals which are intrinsically the basis of a certain model. The need to apply a certain model to a certain substance poses theissueofdefiningtheboundariesofthemodel.Theso-calledapplicability domain ofthemodelbecomesanelementofthemodel. The theoretical explanation associated with a QSAR model still influences the acceptance of the results of the model. Indeed, it is appreciated if the model has an explicative power and is transparent in the indication of the factors affecting the property values. Quite often, this requires some simplification. In order to cope with more complex situations, more factors should be taken into account, which are probably not all known. There is a duality in the function of QSAR models, which are asked tobe predictive, but at the same time explicit and explanatory. This is not always possible. Actually, quite often, statistical models can provide higher predictive

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performance [1]. It is anyhow possible to analyze and understand the useful elements applied to the different perspectives. This will become more and more important in view of the increasing number of models for the same endpoint. Quite often, combined models provide better results than a single model [1, 2]. The fact that the best result comes from more than one model implies that a single, simple explanation is not sufficient, and does not cover all factors affecting the properties of a substance. The increased complexity of the modern models is another major difference when they are compared with the older models, but this complexity is not a consequence of the recent use of the models for regulatory purposes. Indeed, the earlier conceptual differences we discussed addressed the use of the model, considering the output of the model: new knowledge on the mechanism, or new predicted values of chemical substances, for instance. Conversely, the increased complexity of the modern QSAR models is the result of a changed scenario of the possible inputs of the models. Indeed, more chemical descriptors are available today as input: thousands of them, and even more structural keys. Furthermore, more algorithms have been introduced and applied to QSAR models, boosting new generations of models. More and more data are available. This is not typically the case of in vivo data, but it becomes quiteimpressiveinthecaseofin vitro data. Considering the case of initiatives k such as Tox21 (https://www.epa.gov/chemical-research/toxicity-forecasting) k and the introduction of high-throughput screening methods in general, it becomes clear that completely new perspectives will open up very soon. This scientific progress introduces complexity of the methodology and more detailed results, and points toward challenges not only for science but also for the application of the QSAR models for regulatory purposes. Indeed, they demand further checking of the applicability of the legislative conditions. All these factors complicate the evaluation of the models. We summarize in Table 9.1 the key points we discussed above. We discuss these factors in practice in the following sections, where we introduce practical examples of QSAR models with particular attention to the issues related to their application for regulatory purposes.

9.2 Reach and the Other Legislations

REACH (the European regulation for the Registration, Evaluation, Authori- sation and Restriction of Chemicals) represented for Europe, but also out of Europe, a major change in the way to assess chemical substances. REACH does notonlywanttoprotecthumanhealthandtheenvironmentbutalsowantsto promote innovation. Since its ﬁrst article, innovation is clearly indicated as a key target. Innovation clearly means moving toward safer chemicals, removing

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Table 9.1 The main diﬀerences between old models and models for regulatory purposes.

Old models Models for regulatory purposes

Scientiﬁc interest Regulatory interest Theoretical approach Practical approach Interest in useful descriptors Interest in predicting property value of the target chemical Interest is on the focused domain of the Interest is in looking forward at a substances of the dataset substance not in the training set No interest in validation Validation required Applicability domain implicitly deﬁned Applicability domain to be discussed by the composition of the dataset

the ones not complying with this requirement, and thus asking industry to invent a novel strategy for better substances. Innovation also means having new methods to cope with the safety of substances, taking into account the opportunity to use alternative methods. Indeed, the first article of REACH and the other following articles mentioned k these methods. The need for alternative methods derives from multiple k considerations, including ethical issues and the need to have more efficient ways to assess chemical safety, taking into account the cost of certain tests, the time needed, and the available resources. In addition, there is also scientific need to cope with a lack of knowledge, because existing methods do not have solutions for all the problems. REACH clearly recognizes this, and foresees an update of the available methodologies, in order to use advanced procedures. QSAR models are mentioned clearly and repeatedly within REACH. Annex XI specifically identifies a series of requirements, which are codified for the first time in case of the European legislation in such detail. For this reason, it is of interest to analyze what REACH says [3]. REACH classifies QSAR models as part of the so-called non-testing methods (NTM) that span read-across and grouping. All of these are addressed in Annex XI.

9.3 Annex XI of Reach for QSAR Models

Annex XI in the case of QSAR models reports the following: “Results obtained from valid qualitative or quantitative structure–activity relationship models ((Q)SARs) may indicate the presence or absence of a certain dangerous property. Results of (Q)SARs may be used instead of testing when the following conditions are met:

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• results are derived from a (Q)SAR model whose scientific validity has been established, • the substance falls within the applicability domain of the (Q)SAR model, • results are adequate for the purpose of classification and labelling and/or risk assessment, and • adequate and reliable documentation of the applied method is provided. The Agency in collaboration with the Commission, Member States and interested parties shall develop and provide guidance in assessing which (Q)SARs will meet these conditions and provide examples.” REACH refers to the models as (Q)SAR models. We have simplified this discussion by using the acronym QSAR for both QSAR and SAR, thus including quantitative methods, as well as those which address categorical targets. In the following, we use the acronym QSAR “collectively,” without differentiating between QSAR and SAR. It is important to note the relevance of this annex and all its implications. REACH requires valid models in the case of QSAR models. However, for the other ways to generate data, say for in vivo and in vitro methods, REACH demands validated (not valid) methods. This means that for the laboratory methods, the user should get values using official procedures, as described by the international bodies. The use of the data obtained with other experimental k procedures is possible, but their role is of lower relevance, and should be k supported by other considerations or used as part of data from multiple sources or nature. In the case of QSAR models, validation is not required.

9.3.1 The First Condition of Annex XI and QMRF The first condition clearly explains the meaning of a valid requirement. Scientific validity is the basis of acceptance. Innovation is a key topic for REACH, as we have mentioned. In this strategy, there is a parallel consideration related to the US approach moving toward Tox21. When in vitro and in silico models are used in the United States, their validity is also assessed on the basis of scientific considerations, that is, referring to the state of the art based on the literature and methods described there. These novel in vitro and in silico methods are rapidly evolving, with new methods appearing continuously in the literature. It would be impossible to apply the process of the formal validation to all of them, as applied for the other tests. In the United States, it was decided to extend this flexibility to in vitro and in silico methods. REACH, however, has adopted this strategy for QSAR models only. There is another fundamental difference of QSAR methods, compared to the other methods, such as in vivo and in vitro ones.Inprinciple,thesemethodscan be applied to all substances. Conversely, as we mentioned above, QSAR models are not necessarily universal, and, in practice, their results, and thus their validity, depend on the chemical substance. REACH is the legislation devoted

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to chemical substances. Its target is to describe criteria for the safe use of the chemicals, and not necessarily to solve other problems, such as which model can be applied for which chemical. Indeed, each model has to be evaluated for the specific chemical. Thus, European Chemicals Agency (ECHA) does not provide a list of valid models, that is, those that are to be considered always valid. The first condition better defines the meaning of validity, referring tothe scientific context, as we discussed above. In many cases, the organization for economic co-operation and development (OECD) QSAR principles have been mentioned to describe a series of points to be checked [4]. The points are the following: 1. a defined endpoint 2. an unambiguous algorithm 3. a defined domain of applicability 4. appropriate measures of goodness-of–fit, robustness, and predictivity 5. a mechanistic interpretation, if possible. A debate is ongoing about these points. In addition, OECD representatives discussed about the opportunity to refresh them, updating them on the basis of evolution of the QSAR models. k These five OECD criteria are the basis of the QSAR model reporting format k (QMRF) and QSAR prediction reporting format (QPRF) [5]. The QMRF is a procedure to gather the information related to different models in a standardized way, so that it is easier to assess and compare them. The QMRF does not imply that a model is necessarily a good one. Some critiques have pointed out that the QMRF may be demanding for users, if they have to provide it for the model they want to use, and thus may represent a barrier for a more general use of QSAR models, soliciting a simplified version of the QMRF. Some studies compared different QSAR models, with particular attention to their application for REACH. For instance, EC-funded projects, such as ANTARES (http://www.antares-life.eu/), CALEIDOS (http://www.caleidos- life.eu/), and PROSIL (http://www.life-prosil.eu/), evaluated a number of models and endpoints. Whether the models had or did not have a QMRF was not related to the performance of the model. In fact, the QMRF is based on a formal check of the existence of certain pieces of information, and not to the quality of the results. However, these pieces of information may be very useful to define a kind of identity card of a model. Thus, this can be useful, also in light of the fourth conditions of Annex XI, demanding proper documentation. Now a certain number of models have been used for REACH. As we will see in the following, ECHA also used some models to provide examples of how to use QSAR models [6] and to identify substance candidates that meet Annex III criteria [7]; thus, there are

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models, which may be considered more common, so less documentation may be necessary to explain them. The QPRF is related to the QMRF. This document refers to the points addressed in the QMRF and applies them to the specific substance under evaluation. Thus, the QMRF refers to the QSAR model, while the QPRF focuses on the specific substance. The correct application of a model to a specific substance is closely related to the evaluation of the applicability domain of the model.

9.3.2 The Second Condition and the Applicability Domain As we said, REACH addresses the assessment of substances. Within this need, it is important to evaluate whether the use of a certain model is appropriate for the target chemical. As we have stated, the QSAR models are not universally applicable and thus the user should explain whether the applied model could be used. The conceptual basis for this evaluation is that a certain model is based on a set of compounds used to build up the model. This set of compounds is called training set. The target chemical to be evaluated with the QSAR model may be more or less related to the chemicals used to build up the model. For instance, if the model is based on aromatic substances, it may be questionable to apply this k model to an aliphatic substance. This does not mean that the prediction will be k wrong. However, it will not be possible to check the results of the models on previous similar substances, and thus the reliability of the prediction may be questionable. The applicability domain should not be evaluated only on the basis of chemical similarity. No perfect model exists, and thus there are outliers (i.e., substances which are wrongly predicted) within each model. If we imagine a model for mutagenicity based on 100 substances in the training set and a second model for bioconcentration factor (BCF), also built up using the same 100 substances, we will observe different outliers for the two models. For instance, this may happen because the model is missing the structural alert for the target substance, which is not identified as mutagenic. However, the prediction on BCF is commonly based on physicochemical descriptors, and thus it is possible that the BCF model correctly predicts the same chemical, that is an outlier for the mutagenicity model. Thus, to evaluate properly the applicability domain, tools used should notbe based only on chemical similarity. We address this in the following, using the example of the virtual models for property evaluation of chemicals within a global architecture (VEGA) algorithm for the applicability domain. We have stated that a QSAR is typically based on a training set. This is not always the case. Some models are actually collections of rules derived byhumanexperts.Anexampleofthisisamodelbasedonstructuralalerts for mutagenicity. In this case, human experts typically looked at the different

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studies reporting the mutagenic effect of different substances, and then extracted a series of rules, codifying the structural alerts associated with the adverse effect. The assumption is that if there is the structural alert in the molecule, the substance is mutagenic. However, it may be questionable whether the lack of structural alerts implies lack of mutagenicity, because the list of structural alerts for mutagenicity is not complete. Thus, in this case, the use of the applicability domain may be also difficult.

9.3.3 The Third Condition of Annex XI, and the Use of the QSAR Models The third condition addresses the requirement or appropriateness of a certain model.Amodelmaybeaperfectone,butnotusefulforREACHbecauseit does not address a specific endpoint of the REACH legislation. For instance, it may be a model to predict the color of a chemical. While the case in this exampleisquiteobvious,theremaybemanycasesthatrequireacloser consideration. The third condition mentions the possible use for classification and labeling or risk assessment. Thus, this condition asks to relate the output of the model to the needs identified by REACH. We note that there may be different needs in case of risk assessment and in case of classification k and labeling. Indeed, the toxicity value in case of risk assessment has to k be continuous, so that it can be used to calculate the predicted no effect concentration (PNEC). Conversely, in other cases, the model can provide as output a categorical value. For instance, to classify a substance as CMR (carcinogenic, mutagenic, or reprotoxic), a binary category is sufficient. We also notice that different endpoints are required within different regulatory frameworks. For instance, mutagenicity may refer to different endpoints, such as bacterial reverse mutation assay, which is an endpoint within REACH, while for the classification labeling and packaging (CLP) regulation [8], the classification refers to inheritable mutation of relevance for humans. Furthermore, even if the endpoint is the same, there are cases where different thresholds apply, as in the case of BCF,as different thresholds exist within different legislations. Indeed, in the REACH legislation, a compound with a BCF of 1500 is not considered bioaccumulative (the threshold is 2000), whereas in the CLP regulation, this is considered potentially bioaccumulative (with a threshold of 500). Even for persistant, bioaccumulative, and toxic (PBT) purposes, in the American legislation Toxic Substances Control Act [9], the threshold is 1000; therefore this compound is considered bioaccumulative, but not according to REACH. This may lead to classification of the compound as a PBT in America but not in Europe. In conclusion, a model may be a good model, it may be related to an endpoint relevant for REACH, but if it is based on thresholds which conflict with those indicated by REACH, it may be not applicable for REACH.

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We have discussed the format of the output, which should be adequate for the purpose. Thus, the definition of whether the output of the model is bioaccumulative or not may be inadequate for REACH. Furthermore, it is preferable that the input of the model also corresponds to the classical format of the experimental model. Indeed, most models use data obtained with experiments conducted according to the official guidelines as values of the training set. These conditions are obviously those that do not pose questions about their use. However, there may be situations that deviate from this criterion, and may produce values useful from the point of view of the output of the model, with the values containing suitable information for the desired target. We have already discussed that REACH has clearly mentioned that for some endpoints, the experimental tests are not ideal. In the future, there will be more and more data obtained using in vitro models with human cells. The experiments on humans are not ethical, but a different situation may arise from the use of cells. Modeling these data may provide very useful results particularly for human toxicity, but obviously deviate from the traditional data using laboratory animals. EC-funded projects, such as EU-ToxRisk, are proceeding in this direction, as also the Tox21 initiative in the United States. As a result, the adequacy of innovative models adopting this innovative perspective within a regulatory framework may be a point of debate. k A related, but easier situation may be the case of models which slightly k modify the initial data derived from the experimental assay. An example is the case of the bacterial reverse-mutation assays. It is acknowledged that using a single bacterial strain may not cover all possible instances provoking mutation. Similarly, metabolic activation closer to the human situation is also adopted, using the S9 fraction derived from animals. Thus, in practice, a complex experimental methodology is used, with the aim to mimic as closely as possible a series of effects of interest. Under these circumstances, one approach is to simulate closely the individual experimental assays. Another one is to relate to the overall mutagenicity assessment, as obtained out of the complete battery of the assays (10 assays are required according to the OECD guideline 471 [10]). The comparison between the results obtained with the two approaches indicates that the second one is more predictive [11]. Indeed, the overall set of numerous data about this kind of assays provides a robust and large basis. Conversely, if we split the training sets into 10 separate subsets, one for each of the 10 assays, we dramatically reduce the number of available items in each dataset, and for some of the subsets the number of items is very low. This affects the overall statistical relevance of the models. Conversely, using a single dataset with data on all assays, even if for some chemicals the complete set of values derived from the individual assay is missing, the values from related compounds compensate for the information to be extracted from the missing values. In other words, the overall picture derived from the thousands of values from these assays provides a strong basis for predictive models. Conversely,

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if we split the values into the 10 separate assays, we reduce the basis for each of them, and the overall combination of these 10 weaker models is not so predictive, because part of the data is lost.

9.3.4 Adequate and Reliable Documentation of the Applied Method As described previously, there is a very high number of in silico models, and the number is continuously increasing. It is a common situation to make use ofthemostrecentmodels.Thus,itisclearthatinthisveryrapidlyevolving scenario it is very difficult to relate to only some well-known, consolidated models. REACH requires to provide adequate information because, as we said, thereisnofinitelistofin silico approved models, but this list is continuously increasing. This requirement seems necessary from the point of view of the authorities which has to be informed about the model. However, this requirement is also very useful for the applicant, as a check, to verify if all the elements to evaluate a model are present and have been analyzed. The information is particularly necessary for models which are new, while it may be easier to refer to models that have been used by ECHA in its report on QSAR [6]. Indeed, ECHA has mentioned some models in its report, for example, EPISuite (https://www.epa.gov/tsca-screening-tools/epi-suitetm- k estimation-program-interface), VEGA (http://www.vega-qsar.eu/), T.E.S.T. k (https://www.epa.gov/chemical-research/toxicity-estimation-software-tool- test). We emphasize that other models also can be used, and the list of models which can be used is surely not restricted to these models. Furthermore, using these models does not mean that the results for the specific chemical under evaluation will be correct. There has been a debate about the level of documentation particularly in the case of commercial models. Indeed, commercial models in most cases are not explicit in the detailed information on the basis of the model, such as chemicals used to build up the model and the algorithm used. This information is typically available in the case of public models such as VEGA, EPISuite, or TEST.Authoritiessofarhavenotbeenrestrictiveintheuseofonemodelor another. Thus, it does not seem that there are aprioribarriersontheuseof commercial models related to the much more limited amount of information available. However, there may be some practical cases to be carefully evaluated in the case of the use of commercial models. For instance, when the user needs to discuss the similarity of the target compound with the chemicals in the training set and document the line of reasoning done, if the availability of the chemicals in the training set is precluded, this discussion may be impossible. In general, the pieces of information relevant for providing adequate documentation for supporting the prediction obtained are those requested also in the QMRF/QMPF.

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9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA

In the following, we provide some examples to better clarify what we discussed previously. For these examples, we use VEGA, mainly because it has been used by ECHA for the examples it reported. Another reason is that it is one of the systems which provides more detailed information about the elements to be used to evaluate the result. We show one example related to continuous value and one about categorical value.

9.4.1 Example of Bioconcentration Factor (BCF) In VEGA v1.1.3, three models for the estimation of BCF are available. They are built starting from different training sets and using different methods. In this example, we focus on the first model, the CAESAR model. The substance used as target is the 1,4-dimethylnaphthalene (CAS No. 571-58-4). The CAESAR model was published [12], evaluated [13] and compared with other models [14]. All these articles are published in peer-reviewed journals. This is in line with the first condition of Annex XI of REACH. This model is based on eight descriptors and two regression models combined together. k The training set is of 378 compounds and the test set of 95 compounds. The k experimental values assigned to these compounds were obtained according to the OECD Guideline 305 [15], as suggested in the ECHA Guidance R. 7c and R. 11 [16, 17]. The data used to build the model, together with the continuous output, make the results adequate for the REACH and CLP purposes: indeed, they supply a BCF value for the registration, for the classification and labeling, and for the PBT assessment. For the target compound, the predicted value is of 2.91 log (L/kg) and the result is considered reliable by the model (see Figure 9.1). In addition, the two single models, which are integrated within the CAESAR model, predict 2.74 and 2.9 log (L/kg), respectively (see Figure 9.1, sub-models 1 and 2). Since this model was built considering the REACH requirements, it generates a B/vB evaluation adding a confidence interval to the predicted value (when reliable) (see Figure 9.2). This may be useful in case of prediction close to the thresholds. The confidence interval is based on the threshold considered and on the reliability of the prediction. Therefore, it is compound specific. In this case, the confidence interval is the same for both the thresholds: 0.5. This means that the predicted value of 2.91 may become 3.41. This poses an alert on the B threshold (of 3.3 log units), but not on the vB threshold (of 3.7 log units). This increases the adequacy for the PBT assessment of the model. The reliability of the estimation is based on different parameters summarized in the applicability domain index (ADI; see Figures 9.3 and 9.4). The majority of the parameters are based on similar compounds found in the training and

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k k

Figure 9.1 The ﬁrst page of the output of the CAESAR model for the target compound.

test sets. The output reports the six most similar substances with their CAS No., structure, similarity index, and both experimental and predicted values. In this case, all the six similar chemicals have a similarity above 0.9 (where 1 is the identity and 0 indicates the complete diversity). In particular, the first two have a naphthalene with two methyl groups (as the target compound) on the same ring. The difference is the position of the methyl groups: ortho and meta in the similar compounds and on different rings in the target one. The ADI uses the similarity index of the two most similar compounds as first parameter. In this case, the ADI value is high (0.986). The ADI values are in the range between 0 and 1. Values above 0.85 are quite good, while values below 0.75 may be relate to substances less useful for the target compound. The second parameter of the ADI considers the accuracy of the prediction for the two most similar compounds. It is based on the error in prediction. Therefore, the lower this parameter is, the better the model behaves. In this case, the value is 0.102, which is a good value. Then the ADI considers the concordance between the predictions for the similar compounds and the target one. Also in this case, the lower, the better. The concordance index for the

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k k

Figure 9.2 The page with the conﬁdence interval.

target compound is 0.106. Another parameter evaluates the maximum error in prediction among the similar compounds. It spreads from 0 (low error) to 1 (high error). In this case, it is low (0.203). These parameters indicate that there are similar molecules, well predicted and with BCF values in agreement with the prediction done for the target compound. Then, the ADI considers the descriptors used. It veriﬁes whether the descriptors calculated for the target compound are inside the range of the descriptor values for the training set or not. In the positive case, as this one, the target compound is suﬃciently similar to the training set compounds; therefore, the prediction has a higher reliability. The last parameter evaluated in the ADI uses atom-centered fragments. It evaluates whether, in the target molecule, there are fragments not represented in the training set or are rare. This parameter spreads from 1 (no rare or unknown fragments found) to 0 (a high number of rare or unknown fragments found). Intheoutput,therearealsocharts,asinFigure9.5.Theyplotthecalculated

Mlog P (the log K ow used to estimate the BCF) and the experimental BCF of the chemicals used to build up the model. The Mlog P is the most important descriptor used for the calculation of BCF in the CAESAR model. The ﬁrst chart

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Figure 9.3 The list of the sixth most similar compounds.

shows the entire training set together with the target molecule. If it is in the cloud of the values, the prediction has a high reliability, as in this case. The second plot shows only the three most similar compounds (both the predicted and the experimental BCF values) and the target compound. In this case, the target compound is in the range of the BCF values of the similar compounds.

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k k

Figure 9.4 The applicability domain index and its components.

Note that in this figure, two compounds overlap because they have the same Mlog P, which is also the same as that of the target compound. The third similar compound has a higher Mlog P and a higher logBCF. Moreover, its logBCF is about 3, whereas the target compound has a lower predicted value. In conclusion, the target compound is well predicted and inside the applicability domain. Its predicted value, 2.92, is close to the B threshold, in particular considering that the experimental variability for BCF spreads from 0.42 [13] to 0.75 [18]. This confirms the value obtained with the confidence interval and means that this compound may be bioaccumulative but not very bioaccumulative. Moreover, this model is scientifically valid and well documented.

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Figure 9.5 The chart with the Mlog P and logBCF plots.

9.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction Historically, the bacterial reverse mutation assay (e.g., Ames test) has been widely used as a ﬁrst test in the evaluation of genotoxicity. In the REACH context, the reverse-mutation assay is recommended for all substances

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Figure 9.6 The panel to access information about the models.

produced/imported above 1 ton/year. Other more sophisticated in vitro or in vivo tests are required for higher tonnages or if a positive response is k expected or observed in the in vitro bacterial test. k VEGA has implemented several models addressing this type of test: four individual models based on various algorithms (statistical and knowledge-based ones) and a consensus model combining and weighting the results of the four individual models. Information about the models is available in the model’s guide (accessible through the question mark icon aside each model, Figure 9.6) which syn- thetically describes the model’s characteristics and statistical performance. Further details about some of the specific models are available in the literature. These pieces of information are useful to cover the needs for an adequate documentation and help in assessing the scientific validity of the model. About the adequacy for the purpose of satisfying REACH requirements, we have to highlight that most of the available data as the basis of the QSAR models for the Ames test are related to an overall call, which consolidates the results obtained on the different strains and in the presence or absence of metabolic activation. As explained previously, this approach allows including a larger amount of data and improves the coverage of the models. At the same time, it does not allow ensuring that all the appropriate strains required by the most updated guidelines (a specific combination of five strains tested in presence and absence of an exogenous source of metabolic activation) have been tested. This problem theoretically is more related to the flag assigned to negative compounds (where all strains are required to be negative) rather than those resulting positive (as the response observed in a single strain is enough to consider the compound positive). In practice, the use of the existing models

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to predict substances registered for REACH did not show any bias toward false negatives [2]. On the contrary, false positives are found in case of non-testing methods [19]. These considerations are related to the possibility of using QSARs to compile the dossier containing the required toxicological data. If we consider, on the other hand, the possibility to use QSAR results for the purpose of CLP and risk assessment, we have to highlight that in both cases the relevance of the bacterial reverse test is quite marginal compared to the other higher tier tests much more informative for the mutagenicity assessment. In these situations, they remain more applicable in a weight-of-evidence approach, while the use of a QSAR estimation for the bacterial test may be useful for screening and prioritization purposes. To address the reliability of the prediction and to deﬁne whether the compound falls in the model’s applicability domain, at ﬁrst glance, the analysis of the most similar compounds can be useful as in the case of BCF. As an example, we can consider ethyl 2-bromobutanoate (CAS No. 533-68-6) and related estimations given by the four models plus the consensus presented in Figure 9.7.

k k

Figure 9.7 Estimations provided by the four individual models plus the consensus for a chemical used as example (ethyl 2-bromobutanoate).

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k k

Figure 9.8 The six most similar compounds to the target present in the original dataset and their respective observed and predicted values.

Most of the models point toward a toxic assessment for this chemical but the kNN model, even though most of the estimations are not considered to be completely reliable. By analyzing the most similar compounds in this model (see Figure 9.8), it appears that only the ﬁrst and the sixth compounds contain

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a halogen (present also in the target), while the others do not contain this het- eroatom. These two compounds are also the only experimentally mutagenic examples among the similar compounds. However, the kNN model uses the ﬁrst four examples to derive its assessment. Therefore, only one compound among them is positive and hence the target compound is predicted as negative. In the documentation available for reasoning about some individual models, some relevant structural moieties are presented. These are fragments or structural alerts correlated either statistically (for the SARpy model) or by experts (for the ISS model), to the observed activity. The alert SA8 is shown in Figure 9.9, together with three of the most similar compounds. This alert

k k

Figure 9.9 The SA8 and the three most similar compounds of the training set with the same SA.

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provides a theoretical explanation about its presence and its associated mechanism. Indeed, this is an alert of the Benigni-Bossa rules, which have been described and characterized [20]. A closer look at the particular property eﬀect on quite similar compounds may be obtained by looking at Figure 9.8, where there are the two brominated compounds which are experimentally mutagenic. This evidence-based analysis is in line with the read-across evaluation. Moreover, it should be combined with the mechanistic basis provided by the structural alert. A further alert (SM93) is provided by VEGA, as reported in Figure 9.10, which is indeed more speciﬁc for the brominated

k k

Figure 9.10 The SA SM93 and the three most similar compounds of the training set with the same SA.

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aliphatic compounds, oﬀering further support to the overall assessment for mutagenicity. These evidence overrule the negative estimation provided by the kNN model.

9.5 Conclusions

We analyzed the multiple points to be evaluated in order to use in silico models within REACH. The in silico models are tools which are very useful to assess the properties of chemical substances, but the user should carefully evaluate whether the existing models may be useful. A series of conditions related to the model itself have to be satisfied. However, the final choice should be related to the use of the model for the substance of interest. Indeed, the same model may be good for one substance, but not for another. We described how modern models do not simply provide the predicted value, but offer many other elements, which have to be carefully evaluated, documented, and reported.Ifmorethanonemodelischosenforthesameproperty,theprocess hastoberepeatedonallofthem. The modern QSAR models are very valuable in assisting the human expert in making an overall assessment. The expert should take the final decision, but k the models may provide a reproducible way to scrutinize a series of elements k very important for the final judgement. Indeed, modern QSAR models are a source of valuable pieces of information besides the predicted value, such as values of related compounds and identification of structural alerts, which are actually related to read-across. As a matter of fact, modern QSAR models combine QSAR predictions with an evaluation very close to read-across. In this, they help the user making a kind of weight of evidence assessment, in particular, in the recommended case of using multiple models.

References

1 Amaury,N.,Benfenati,E.,Boriani,E.et al. (2007) Results of DEMETRA models, in Quantitative Structure–Activity Relationships (QSAR) for Pesticide Regulatory Purposes (ed. E. Benfenati), Elsevier Science Ltd, Amsterdam, pp. 201–281. 2 Cassano, A., Raitano, G., Mombelli, E. et al. (2014) Evaluation of QSAR models for the prediction of ames genotoxicity: A retrospective exercise on the chemical substances registered under the EU REACH regulation. J. Environ. Sci. Health C Environ. Carcinog. Ecotox. Rev., 32, 273–298. 3 Registration, Evaluation, Authorisation and restriction of Chemicals (REACH) Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December, 2006.

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4 OECD (2004) OECD principles for the validation, for regulatory purpose, of (Q)SAR models, https://www.oecd.org/chemicalsafety/risk-assessment/ 37849783.pdf (accessed August 16, 2017). 5 European Chemicals Agency (2008) Guidance on information requirements and chemical safety assessment Chapter R.6: QSARs and grouping of chemicals. Guidance for the implementation of REACH. 6 European Chemicals Agency (2016) Practical guide How to use and report (Q)SARs Version 3.1 – July 2016. DOI: 10.2823/81818. 7 European Chemicals Agency (2016) Preparation of an inventory of substances suspected to meet REACH Annex III criteria Technical documentation. 8 Regulation (EC) No 1272/2008 of the European Parliament and of the Council of 16 December 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No 1907/2006, 2008. 9 Toxic substance control act, US Senate, as amended through P.L. 107–377, December 31, 2002. 10 OECD (1997) OECD Guideline Test No. 471: Bacterial Reverse Mutation Test. 11 Golbamaki Bakhtyari, N., Raitano, G., Benfenati, E. et al. (2013) k Comparison of in silico models for prediction of mutagenicity. J. Environ. k Sci. Health C Environ. Carcinog. Ecotoxicol. Rev., 31, 45–66. 12 Zhao, C., Boriani, E., Chana, A. et al. (2008) A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere, 73, 1701–1707. 13 Lombardo, A., Roncaglioni, A., Boriani, E. et al. (2010) Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish. Chem. Cent. J., 4 (Suppl 1), 1–11. 14 Gissi, A., Lombardo, A., Roncaglioni, A. et al. (2015) Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: the bioconcentration factor (BCF). Environ. Res., 137, 398–409. 15 OECD (2012) OECD Guideline Test No. 305: Bioaccumulation in Fish: Aqueous and Dietary Exposure. 16 European Chemicals Agency (2014) Guidance on Information Requirements and Chemical Safety Assessment Chapter R.7c: Endpoint specific guidance. Guidance for the implementation of REACH. 17 Guidance on Information Requirements and Chemical Safety Assessment Chapter R.11: PBT/vPvB assessment. Guidance for the implementation of REACH. European Chemicals Agency, 2014. 18 Dimitrov, S., Dimitrova, N., Parkerton, T. et al. (2005) Base-line model for identifying the bioaccumulation potential of chemicals. SAR QSAR Environ. Res., 16, 531–554.

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19 Benfenati, E., Belli, M., Borges, T. et al. (2016) Results of a round-robin exercise on read-across. SAR QSAR Environ. Res., 27, 371–384. 20 Benigni R., Bossa C., Jeliazkova N.G., Netzeva T.I., and Worth A.P. (2008) The Benigni/Bossa rulebase for mutagenicity and carcinogenicity – a module of toxtree. Technical Report EUR 23241 EN, European Commission – Joint Research Centre.

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Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures Jim E. Riviere and Jason Chittenden

Center for Chemical Toxicology Research; Pharmacokinetics Biomathematics Program, North Carolina State University, Raleigh, NC, USA

CHAPTER MENU

Introduction, 269 Principles of Dermal Absorption, 270 Dermal Mixtures, 274 Model Systems, 275 k Local Skin Versus Systemic Endpoints, 277 k QSAR Approaches to Model Dermal Absorption, 278 Pharmacokinetic Models, 281 Conclusions, 285

10.1 Introduction

The skin is a primary route of exposure for chemicals in environmental and occupational settings; it also serves as a portal for systemic drug delivery using transdermal patches or is useful for local therapy of dermatological diseases using topical formulations. Exposure may also occur with the use of cosmetics and many personal care products. The skin is also a target for toxicity by these same agents owing to secondary or direct chemical action or as a result of immunological detection with amplification due to previous chemical sensitization. Because of this almost universal exposure of the skin to chemicals and drugs and because the toxicity is manifested in very visible and noticeable reactions, a great deal of attention has been focused on the effects of chemical exposure to this organ. There are two major types of studies that comprise the field of dermatotoxicology: chemical absorption and irritation/sensitization. Computational approaches in this field have been focused on the former owing to its ability to generate quantitative data suitable for such an approach. This is also logical as

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a chemical must traverse the protective outer layer of the skin in order to gain access to its viable cells and exert a toxicological effect. Thus, computational approaches to predict toxicity are confounded by chemical properties that allow absorption to occur, focusing the best work at this point on models to predict dermal absorption. Additionally, absorption studies that investigate mechanisms and/or the toxicity involved have traditionally been conducted using single neat chemical exposure or binary mixtures, that is, a single drug/chemical delivered in a single vehicle. Such studies have demonstrated the significant effects that these vehicles may have on chemical absorption [1–4]. However, most topical chemical exposures, whether deliberate or accidental, are rarely in the form of a binary mixture - they are often as complex chemical mixtures consisting of penetrants, different vehicles, and/or chemical additives. Additional studies on vehicles with additives such as surfactants, alcohols, and solvents have confirmed that their presence may alter the barrier properties of the skin [5–8], yet limited work has been done to broadly incorporate the effect of complex mixture interactions [9, 10]. With the abundant presence of complex mixtures in cosmetic and pharmaceutical products, and through occupational and environmental scenarios, the ability to predict absorption is ahighlydesirableobjective.Thisisthefocusofthepresentchapter. k k 10.2 Principles of Dermal Absorption

The skin is composed of two primary layers, the epidermis which includes the outermost stratum corneum barrier and underlying viable keratinocytes, and the dermis [11] (Figure 10.1). The stratum corneum is relatively imper- meable to most aqueous solutions and ions; however, it may be permeable to more lipophilic compounds. This layer is nonviable and considered to be the rate-limiting barrier in drug and chemical percutaneous absorption. It

Figure 10.1 Light micrograph of normal human skin. SC, stratum SC corneum; E, epidermis; D, Dermis. —— = 50 μm.

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is axiomatic that a topically applied chemical must first traverse the stratum corneum barrier before it is capable of eliciting any toxicological or immunological effect on subsequent cell layers, making absorption both the primary factor in assessing the dermal effects of drugs and chemicals, as well as a confounding factor in all dermal computational toxicology models targeted toward intact skin. Chemical absorption pathways can hypothetically involve both intercellular and intracellular passive diffusion across the epidermis and dermis and/or transappendageal routes, via hair follicles and sweat pores. Most available research has concentrated on the stratum corneum as the primary barrier to absorption, although the viable epidermis and dermis may also contribute resistance to the percutaneous penetration of specific chemical classes, for example, when the true barrier to absorption is not diffusional but is rather metabolic. Barrier is thus an operational definition and can be related to either a physical structure (e.g., the stratum corneum) or alternatively a biological process (e.g., diffusional resistance, metabolism, and vascular uptake) that retards absorption of topically applied chemicals. The accepted hypothesis for dermal absorption is that the dominant pathway for chemicals to traverse the stratum corneum is through the intercellular lipids. Lipophilic compounds diffuse through this lipid milieu while polar k molecules traverse the aqueous region of the intercellular lipids. This inter- k cellular region, conceptualized as the mortar in the “brick and mortar” model of the stratum corneum [12], is now considered the most likely path for absorption of lipophilic drugs. Although this model is conceptually simple, the actual physical chemical environment of the intercellular lipids is complex. It is filled with neutral lipids (complex hydrocarbons, free sterols, sterol esters, free fatty acids, and triglycerides) that make up 75% of the total lipids, as well as other polar lipids [13, 14]. These intercellular lipids are also inextricably linked to the outer cellular membranes of the corneocytes, making a relatively complex and fluid structure that is often modeled as a simple homogeneous lipid pathway. Successive tape stripping, delipidization techniques, and use of epidermis through heat or chemical separation techniques, have been used by investigators to demonstrate the dominant influence that the stratum corneum and the lipid domain holds on penetration of hydrophilic and lipophilic chemicals. It is in this domain that many mixture and formulation components can modulate penetrating drugs. Percutaneous absorption through the intercellular pathway of the stratum corneum is driven by passive diffusion down a concentration gradient described at steady state by Fick’s law of diffusion [15], / Flux =[(D ⋅ PC ⋅ SA) H](Δx) where D = diffusion coefficient, PC = partition coefficient, and SA is the applied surface area, H is membrane thickness (or more precisely the

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convoluted intercellular path length) and Δx is the concentration gradient across the membrane. Since in vivo blood or in vitro perfusate concentrations after absorption are negligible compared to applied surface concentration, Δx reduces to the surface concentration (C) available for absorption. It is this relationship that allows the prediction of compound flux across the skin to be correlated to factors predictive of D and PC (e.g., octanol/water partition coefficients). Flux is expressed in terms of applied surface area, often normalized to cm2. This is an oversimplification of the complex transport processes that occur in dermal diffusion [16, 17]; however, it forms the basis for most computational toxicology modeling efforts in this field. The term (D ⋅ PC∕H) is compound dependent and is termed the permeability ⋅ ⋅ coefficient (K p), reducing the determination of flux to Kp ΔX or Kp C,a first-order pharmacokinetic equation (dx∕dt = kX).Rearrangementofthis equation yields the primary method used to experimentally calculate the value

of K p:

Kp = steady state ﬂux∕concentration

It must be stressed that both transdermal ﬂux and K p are not only chemical dependent but also tightly constrained by the membrane system studied, as well as the experimental design of the study used to estimate it (neat com- k pound, vehicle, length of experiment, etc.). It is at this phase that additional k mixture interactions may occur through altered solubility on the surface or in the stratum corneum lipids, which modulates the PC. The PC that is integral to

K p is the PC between the surface or applied vehicle and the stratum corneum lipids. Different vehicles will thus result in different PCs. Similarly, skin from different species may result in different PCs owing to differences in the stratum corneum lipids and surface characteristics (e.g., sweat, sebum). From the computational toxicology perspective, this translates into quantitative models whose parameters are very dependent upon experimental variables often not appreciated to be significant contributors to the process. Dermal absorption data is often expressed as percentage of the dose absorbed. This is conceptually correct if one assumes that the permeability is unchanged across the dose as it represents a first-order pharmacokinetic process. It is also appropriate when comparing experimental treatments (e.g., temperature, vehicle) using the same applied dose. However, in many cases, topical dosing results in applying substantial amounts of chemical compared to what can be absorbed across the skin. In the case of soils, thick layering wheremostofthesoilisnotincontactwiththeskin,andthusnotabletoreach its surface to partition into suggests that most of an applied dose is not actually available for absorption. In such studies, only a monolayer of soil is in reality in contact with the skin. Unlike in fluid or gel matrices, compounds generally do not diffuse through distinct soil layers not in contact with the skin, unless water is added as another vehicle. In these cases, accounting for the applied

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dose as total dose in a multilayer system, overestimates available dose that artiﬁcially reduces the calculated percentage of the dose absorbed. Similarly, it

overestimates the surface concentration used to estimate K p. Caking of heavy dermal formulations results in a similar layering phenomenon. The dose may also bind to the application device and not be available for absorption. In these cases, a large fraction of the dose may not be thermodynamically driving the diﬀusion process. Finally, when the dose is applied in solution, saturation may result in precipitation of chemical. Rapid evaporation of a volatile vehicle may precipitate it, thereby decreasing its availability for absorption. All of these factors lead to a phenomenon often seen in dermal absorption studies where the percentage of dose absorbed decreases with applied dose. Conducting a study at high applied doses may underestimate absorption of lower applied doses, and vice versa. Alternatively, for some chemicals dosed in volatile vehicles, evaporation increases thermodynamic activity of the penetrating solute through supersaturation and enhances absorption. This brief review illustrates that many experimental variables can aﬀect the

determination of K p, an essential metric in any topical computation toxicology exercise. Table 10.1 tabulates these relevant parameters that should be considered when designing a dermal penetration study or using literature date for a quantitative modeling study. Figure 10.2 illustrates both the anatomical k k

Table 10.1 Experimental variables that should be controlled or documented when conducting dermal absorption studies.

Species, age, and sex of animal Application site on body or where skin obtained from for in vitro studies Dose, vehicle concentration, vehicle volume Area of skin dosed Vehicle, impurities, other formulation additives Occlusion, dosing device Length of dosing Length of data collection Assay method

Experimental endpoints and how calculated (e.g., K p,AUC) Additional in vitro systems parameters: Type of diffusion cell system employed (static, flow-through) Skin pre-treatment Skin thickness Composition of perfusate Perfusate flow rate, temperature

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Surface Potential interactions Modeling approaches • Liquid • Solubility • Evaporated • Colloidal changes • Drug delivery device • Evaporation Q • Binding S P h Stratum corneum • Partitioning from vehicle A D R i a to lipid r • Intercellular lipid pathway • Solubility in lipids f f m • Dead corneocytes • Diffusion a • Binding to corneocytes u s c o Epidermis • Diffusion i o k • Uptake/binding to i • Viable cell layer keratinocytes n • Epidermal–dermal junction n • Partitioning into dermis e Dermis-capillaries • Capillary uptake t • Partitioning into blood i • Point of systemic exposure • Binding/uptake into c • Capillary uptake in vasculature fibroblasts s • Lymphatic uptake • Lymphatic uptake

Figure 10.2 Relationship between anatomical regions in skin where chemical mixture modulation of absorption can occur, physicochemical processes involved, and type of modeling employed. k basis where interactions may occur and its relationship to the computational k modeling approach used.

10.3 Dermal Mixtures

Although topical administration offers several advantages over traditional routes [18], formulation development often requires overcoming the barrier function of the skin [19]. This is often accomplished by the inclusion of specific components that have been as carefully chosen as the drug/chemical itself. Such components have functions relative to the delivery, stability, or activity of the active ingredient [20]. Examples of commonly used components include surfactants and compounds to solubilize lipids within the stratum corneum [21–23] and penetration enhancers, which may increase the diffusion coefficient of drugs in the stratum corneum by disrupting the barrier of the stratum corneum and hence increasing the effective concentration of the drug in the vehicle [24–29]. The effects of formulation or vehicle on the rate and extent of absorption have been noted to be far greater with topical drug delivery rather than with any other route of administration [30]. This is exemplified by the broad potency range (I-V) of various marketed 0.5% betamethasone dipropionate products [31]. Cosmetic mixtures have additional aesthetic requirements of the vehicle and drug/chemical. Such criteria include visual appearance, odor

k k

Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 275

and residual impression after application, all of which influence consumer acceptance and patient compliance [30]. In all topical formulations, other components may also be present for reasons totally unrelated to dermal penetration, yet may have effects on the stability or chemical partitioning of the formulations that would impact the penetration of the active ingredient. In contrast to pharmaceutical formulations, occupational and environmental exposures are often to chemicals, which, from a dermal penetration perspective, are not functionally associated with the penetrant of interest; these include such compounds as contaminants, additives, and solvents [20]. Such chemicals are either present sequentially because they are applied to the skin independently at different times for unrelated purposes (e.g., cosmetics followed by sunscreen lotion), functionally for specific purposes (e.g., lubri- cants), or coincidentally because they are simultaneously associated together as waste or environmental contamination.

10.4 Model Systems

Assessment of percutaneous absorption for any topically applied drug or chemical, can be classified on the basis of either a model’s level of biological k complexity (in silico, in vitro, in vivo) or the specific species studied (human, k laboratory rodent, monkey, pig). The goal of the research should also be taken into consideration. Is the work being conducted to study the mechanism of absorption (e.g., identify a specific mathematical model or assess the effect of a vehicle) or to quantitatively predict absorption in humans. Is the study designed to look at a local effect in the skin or a systemic effect after absorption? That is, are the skin concentrations the relevant metric or is flux of chemical across the skin important? Model systems and approaches in use today to assess dermal absorption have been extensively reviewed [20, 32]. The primary approach to assess dermal absorption in most computational toxicology studies is the in vitro diffusion cell. In this model, skin sections (full thickness, dermatomed to a specific thickness) are placed in a two-chambered diffusion cell where receptor fluid is placed in a reservoir (static cells) or perfused through a receiving chamber (flow-through cells) to simulate dermal blood flow. Chemical may either be dosed under ambient conditions neat or dissolved in a vehicle (vertical cells) or in water (side-by-side diffusion cells), resulting in finite versus infinite dosing conditions, respectively. This is a major variable in selecting the mathematical model to be used to derive the permeability constant. Selection of the receptor fluid (e.g., saline, albumin-based media) is also critical as absorption would only be detected if the penetrating compound were soluble in the receptor fluid. This is particularly important for hydrophobic penetrants. Many studies of pharmaceutical compounds use saline as the receptor fluid owing to the hydrophilic nature of many

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276 Computational Toxicology

drugs, a choice that would falsely suggest minimal absorption for lipophilic chemicals as they would not be soluble in the receptor fluid, and thus could not be detected as absorbed. Steady-state flux is measured in these models and permeability calculated using the methods described in the following. In addition to the perfusate composition, temperature of the perfusate is also controlled, with pharmaceutical investigators suggesting that studies be conducted at 35 ∘C to mimic the surface temperature of the skin. These techniques have been exhaustively reviewed elsewhere) [20, 32–34]. Our laboratory recently conducted a study assessing the effect of experimental factors including static versus flow through diffusion cells, finite versus infinite dose, albumin versus saline as receptor fluid, and saturated versus

unsaturated doses on observed flux and calculated K p across six compounds: caffeine, cortisone, diclofenac sodium, mannitol, salicylic acid, and testosterone [35]. The important factors identified were dose volume, saturation level, and vehicle. Notably, for these compounds, the experimental system used or receptor fluid composition had minimal effect. In a later study using the same compounds and using different 96-well PAMPA (parallel artificial membrane permeability assays) artificial membrane systems, it was found that the correlation between diffusion cells and PAPMA systems for these same compounds were best with saturated solutions (r2 ≈ 0.6) and improved when 2 k skin retention was compared to membrane retention (r ≈ 0.7) [36]. k The second major approach used to assess dermal absorption is in vivo.This is the primary approach used to assess drug absorption by all routes of administration. It is also the approach used in many toxicology disposition studies where full mass balance is attempted. The chemical is dosed on the surface of an animal and total excreta (urine, feces, expired air) collected and analyzed for parent compound or metabolites. Radiolabeled compounds are often employed in these studies. These data are usually expressed as percentage dose absorbed per unit of surface area exposed, and is well adapted to laboratory rodent models. However, the resulting metrics are not optimal for computational toxicology approaches. The dose may be applied occluded (evaporation of dose prevented) or non-occluded (dose site open to ambient environment). In calculating the absorbed dose, the chemical at the dose site must be segregated from other tissues that would reflect absorbed chemical. This usually involves gently washing the non-absorbed chemical with a soapy solution. When larger animals (e.g., pigs, primates) or humans are studied and total mass balance is not possible (e.g., feces and expired air cannot be collected), the fraction of a systemically absorbed compound excreted in the urine must first be determined using parenteral dosing. In some classic studies, this parenteral route correction factor was conducted in monkeys [37] under the assumption that systemic distribution, metabolism, and elimination of these pesticides are similar in man and primate. In pigs, separate parenteral injections have been similarly made to determine fractional excretion by other routes [38].

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Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 277

For many pharmaceutical compounds administered as transdermal drug delivery systems, absorption may be assessed by determining the area under the curve (AUC) of the plasma concentration-time profile, the peak plasma flux and time of peak flux, much as it is for determining bioavailability from oral and other routes of administration. These are classical metrics of biopharmaceutical bioequivalence studies and are extensively covered in other texts [39, 40]. If plasma concentration-time data are available from intravenous or immediate release formulations of the same compound, then various deconvolution methods can be employed to infer the absorption profile over time from the transdermal route. This gives a more complete picture of the rate of absorption and can be useful for analyzing time-dependent variations in the absorption rate [41, 42]. There are several perfused skin preparations with an intact functional microvasculature. The major advantage of such a perfused system is that subsequent systemic influences on absorbed chemical are not present, yet the tissue is fully functional with an intact microcirculation unlike simpler in vitro models. The perfused rabbit ear model, perfused pig ear model, in situ sandwich skin flap in athymic rats, and the hybrid rat-human sandwich flap have been developed [43], but each intuitively has severe limitations. The isolated perfused porcine skin flap (IPPSF) developed in our laboratory is a unique ex k vivo skin preparation that has an intact functional cutaneous microcirculation. k Predictions from IPPSF studies have correlated well with in vivo absorption data for several drugs and insecticides [44–46]. IPPSFs are physiologically and biochemically viable and therefore can be used to assess cutaneous toxicity of topically applied chemicals [47]. The latter is most important as cutaneous toxicity as well as dermal absorption of various pesticide formulations can be assessed simultaneously.

10.5 Local Skin Versus Systemic Endpoints

Before a quantitative model is developed, it is important to have some knowledge of precisely what endpoint is being modeled. If a kinetic study is being conducted, is the focus on predicting transdermal flux and serum concentration-time profiles or is the goal to determine how much chemical will reach target sites within the skin? The same experimental model systems may be used for both endpoints; however, different data may have to be collected. For transdermal flux, the perfusate is collected and either the

K p or AUC determined. However, if local skin deposition is the endpoint, then skin samples or biopsies must be collected to assess the amount of chemical retained in skin, not just the amount which traversed skin which would be estimated if only ﬂux were monitored. As discussed in the methods section above, very lipophilic molecules may not partition into perfusate,

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278 Computational Toxicology

further underestimating the quantity of chemicals exposed to the skin. Unlike perfusate ﬂux parameters, there are no well-established metrics for dose deposition into skin. Finally, cell culture studies are often used to assess direct cutaneous toxicity to skin cells such as keratinocytes. As discussed above, this can be used to deﬁne whether a potential topical chemical that penetrates the skin can cause epidermal cell dysfunction. However, systemically administered chemicals may also distribute to the skin and modify keratinocyte cell function. Damage to skin, especially if the mechanism of action is immunological, does not require topical exposure.

10.6 QSAR Approaches to Model Dermal Absorption

Since passive diﬀusion is the primary driving force behind dermal absorption, physicochemical factors such as molecular weight and structure, lipophilicity,

pK a, ionization, solubility, partition coefficients, and diffusivity can influence the dermal absorption of various classes of chemicals. In addition, penetration of acidic and basic compounds will be influenced by the skin surface, which is weakly acidic (pH 4.2–5.6), as only the uncharged moiety of weak acids and k bases is capable of diffusing through the lipid pathway. Several of these factors k (e.g., molecular weight and partition coefficients) have been used to predict absorption of various drug classes [48–50]. The first such relationship which is still widely used to assess chemical absorption is that of Potts and Guy [51]:

2 Log Kp = 0.71 log PCoctanol ∕ water − 0.0061 MW − 6.3 (R = 0.67) where MW is the molecular weight. This equation was subsequently modiﬁed

(Potts and Guy [48]) to relate K p to molecular properties of the penetrants as follows: 𝛼H 𝛽H 2 Log Kp = 0.0256 MV − 1.72 Σ 2 − 3.93 Σ 2 − 4.85 (R = 0.94) 𝛼H where MV is molecular volume, Σ 2 is the hydrogen-bond donor acidity, and 𝛽H Σ 2 is the hydrogen-bond acceptor basicity. The most promising approach is to further extend this rationale using linear free energy relationships (LFER) to relate permeability to the physical properties of the penetrant under deﬁned experimental conditions (dose, membrane selection, vehicle). Geinoz et al. [52] critically reviewed most of such quantitative structure-permeability relationships (QSPeR) applied to dermal absorption and their study should be consulted. Abraham’s LFER model is representative of the dermal QSPeR approaches presently available [53]. This model was selected as it is broadly accepted by the scientiﬁc community as

k k

Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 279

being descriptive of the key molecular/physiochemical parameters relevant to solute absorption across the skin. This basic model can be written as follows: 𝛼H 𝛽H H log kp = c + a Σ 2 + b Σ 2 + sπ2 + rR2 + vVx H where π2 is the dipolarity/polarizability, R2 represents the excess molar refractivity, V x is the McGowan volume and the other parameters are as described earlier. The variables c, a, b, s, r,andv are strength coefficients coupling the molecular descriptors to skin permeability in the specific experimental system studied. Our laboratory has focused significant research on the effects of chemical mixtures on dermal absorption of penetrant compounds. In order to incorporate mixture effects, our laboratory has been exploring using an additional term operationally called the mixture factor (MF) yielding the following: 𝛼H 𝛽H H log kp = c + mMF + a Σ 2 + b Σ 2 + sπ2 + rR2 + vVx The nature of the MF is determined by examining the residual plot

(actual–predicted log kp) generated from the base LFER equation based on molecular descriptors of the permeants, against a function of the physical chemical properties of the mixture/solvents in which they were dosed [20, 54].

Figure 10.3 depicts the improvement in prediction of kp when this approach k is used. This depiction also illustrates the great impact that a formulation has k

on kp (seen by the heights of the columns) around a speciﬁc penetrant when only the penetrant’s molecular properties are considered (inset a) versus when both penetrant and mixture properties are included (inset b). When similar

(a) (b) Column variance Mixture factor represents mixture included in model components effect

Observed parameter Slope represents penetrant permeability

k Physical chemical property of individual penetrant (e.g., Log p)

Figure 10.3 Relationship between a QSAR model without and with a mixture factor component included. Note that the overall slope of the QSAR is dependent upon the penetrant’s chemical properties, while mixture component eﬀects result in columns along the penetrant property unless mixture components based on the mixture properties are also included.

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280 Computational Toxicology

analyses were conducted across both diffusion cell as well as the perfused IPPSF, different mixture factors were needed for each model system [55]. Also noted was that the QSPR model could be reduced to four terms in each system; however, the four terms differed between the two biological systems. This suggests that the rate limiting process for a mixture’s effect on absorption differs between the two biological systems studied. These findings are encouraging and imply that a QSPR model is possible for estimating dermal absorption as a function of chemical mixture composition. This approach was further validated where melting point of the vehicle constituents described mixture effects across both saturated and unsaturated doses when all experimental variables were also accounted for [56]. A similar approach was useful to predict absorption of a wide variety of 56 agrochemicals across a diverse set of 150 formulations [57]. When the diffusion cell data of Riviere and Brooks [54] was revisited, using a pharmacokinetic model that accounted for transient processes in the absorption profiles, the MF approach resulted in a concise QSPR containing terms for log P and MW of the penetrant and an MF for refractive index of the mixture [58]. This was consistent with previous results while improving the model fit, largely due to revised permeability estimates that account for transient vehicle effects. Figure 10.4 shows the QSPR developed which was expressed as the k product of diffusivity and partitioning terms proportional to K p: k ⋅ ⋅ log Kp ∼ log K D = 25.36 − 0.0041 MW ⋅ 2 − 0.9063 log P − 29.57 MFRefrac (R = 0.91) This relationship included molecular weight MW and log P, similar to the Potts and Guy [51] model, but also includes an MF term for refractivity

(MFRefrac) to adjust for the properties of the dosing vehicle. In another eﬀort, application of a compartmental dermatopharmacokinetic model to the IPPSF data of Riviere and Brooks [55] allowed for estimation of

Figure 10.4 QSPR model fit of porcine skin diffusion cell data using diffusivity (D) and partition (K) coefficients estimated by a random process dermatokinetic model [59]. D

* 1e–07 K Observed 1e–09

1e–09 1e–07 QSPR predicted K*D

k k

Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 281

k P k P k P log p~log + MW log p~log + MW + CMA log p~log +MW+CMA+TPSAd

1e–03

1e–04 Estimated (cm/min) Estimated p k

1e–05

1e–04 1e–03 1e–04 1e–03 1e–04 1e–03 k p Predicted (cm/min)

Figure 10.5 QSPR model fit of permeability coefficients obtained from a dermatokinetic model of in situ perfused porcine skin flap [60].

permeability constants (as opposed to the previously used AUC) and regression

to a QSPR. A relationship relating K p to penetrant properties log P and MW and mixture factors for the Connolly molecular area (CMA) and total polar k surface area diﬀerence (TPSAd) [60] was developed (R2 = 0.77): k

log Kp =−7.25 − 0.68(log P − 2.91)+8.32(MW − 225) − 3.04(CMA − 75.3)−2.29(TPSAd − 27.3)−log 10 The consecutive improvement in model fit as each MF term is added to the QSPR is shown in Figure 10.5. The QSPR in IPPSF contained similar properties to that in diffusion cells and suggests that it may be possible to combine the data and develop a single QSPR that would account for penetrant and vehicle effects on permeability across the different membrane systems. The literature on QSPR is exhaustive and rapidly growing. The limitation of applying these approaches to chemical absorption is the lack of large and comparable databases of chemical dermal absorption, as well as the lack of availability of molecular descriptors for many compounds. As will be discussed in the following, data suitable for large-scale analyses must be rigorously controlled relative to the species studied, the nature of the experiments (in vitro vs in vivo), dose, surface area, vehicle, and method of sample collection and analyses.

10.7 Pharmacokinetic Models

The ﬁnal area of computational toxicology to be discussed in this chapter applied to dermal absorption is the general area of pharmacokinetic models. These are usually developed as extensions of whole-animal-based models using

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classic compartmental [55, 61] or physiologically based [62] approaches. These texts should be consulted for an overview of the basic assumptions inherent in each genre of pharmacokinetic modeling, as these are carried forward when applied to the skin. There are an enormous variety of approaches taken to develop dermatopharmacokinetic models. These applications to the skin have recently been well reviewed elsewhere [17, 63]. The primary purpose for most dermatopharmacokinetic models is to quantitate the linkage between anatomical and physiological properties of skin that play rate-limiting roles in absorption with target sites for which concentration-time profiles are needed, such as plasma. The complexity of the models is a function of the chemical being studied as well as the level of precision (concentration, time frame) required for the prediction. For example, models created to estimate total percentage of the dose absorbed are much simpler than those designed to predict the time and magnitude of peak plasma concentrations in a subject. Numerous other factors play a role in the structure and complexity of models employed. In general, the stratum corneum is assumed to be a homogeneous membrane into which the compound partitions from the surface. Some models attempt to take into account the tortuous nature of the intercellular pathway when defining actual diffusion path length. For volatile compounds, models k may specifically include evaporative loss from the surface of the skin or binding k to other surface sites (e.g., application device, hair). The next stage of potential complexity is whether metabolism occurs in the epidermis, a process that may dramatically increase model complexity. The drug then enters the dermis or is assumed to directly enter the blood. If a compound is vasoactive, dermal blood flow may be specifically modeled. At all three tissue levels, irreversible or very slow tissue binding may also be included to model the formation of so-called depots in the stratum corneum or fat. An example of a pharmacokinetic model used in our laboratory [64–66] is depicted in Figure 10.6 which also illustrates the concept of a fixed skin depot as well as how it can be used to link to systemic pharmacokinetic models for making in vivo predictions. Some dermatopharmacokinetic models attempt to develop linkages between chemical concentrations in a specific compartment with a toxicological effect based on mechanisms of action [63]. This work is in its infancy but holds promise should sufficient data to conduct these modeling exercises become available. Models of aggregated data, containing measurements for multiple compounds, vehicles, and/or experimental systems, present additional challenges. Often the goal of such models is to provide comparable estimates of permeability parameters for inclusion in a QSPR or other secondary analyses. This can be difficult when different treatments have different underlying mechanisms, and thus would necessitate using different models. The traditional approach is to model each experimental unit (e.g., flux profile)

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Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 283

4 K Skin 40 surface

K 42

1 K 23K 12 Mobile 23 Fixed Vascular K skin skin 21 tissue tissue J 15 Predicted plasma concentration time profile 65K 65 Peripheral Central K 56

Kel

Figure 10.6 Compartmental pharmacokinetic model linking skin absorption determined in an in vitro model to a systemic model to predict plasma concentration time profiles in vivo. k k independently, but often the non-identifiability of parameters describing mechanistic effects, such as binding or evaporative loss, degrade the estimates of interest (e.g., permeability). In such cases, it may be beneficial to model the data simultaneously. A simultaneous model allows for individual experimental units to estimate shared parameters, for instance, the time for an ethanol vehicle to evaporate. One particularly powerful method for combining data is through mixed effect modeling. In a mixed effects model each of the treatments share some parameters (fixed effects) and individual variations from those shared parameters are treated as random variables. The estimation technique finds the fixed effect values (typical values for the dataset) as well as the random effect values (specific variations) for each experimental unit. Additional fixed effects to describe relationships between chemical and physical properties can be added to the model. The structure of the model greatly reduces the number of parameters as compared to estimating parameters for individual experimental units, and hence increases the power of the analysis. A mixed model approach was used to evaluate models for 12 compounds in 24 vehicle mixtures in porcine skin and silastic membrane diffusion cells, where the random effects accounted not only for differences in diffusivity by treatment but also for time-varying dynamics of vehicle effects [58]. Further refinement of the models for the porcine skin diffusion cells allowed for incorporation of an embedded QSPR in the model [59]. The approach was also extended to

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284 Computational Toxicology

model a QSPR in the ex vivo IPPSF system for 12 compounds in 10 vehicle mixtures [60]. In each of these cases, the aggregated data allows for system-wide parameters to be influenced by data from multiple experimental units, which can help prevent statistical problems when the parameter is not readily identifiable in some of the experimental units. An example of this would be if ethanol evaporation is modeled. In a case where the penetrant diffuses slowly, the evaporation rate of ethanol would be visible in the measured flux. But if the penetration rate is high enough, the dose is absorbed before the ethanol evaporates and the effect of the evaporation, and the ability to estimate the rate of evaporation, is not attainable from the data. Many transdermal flux profiles exhibit time-dependent variation in model parameters, such as diffusivity or partitioning. This is particularly apparent when comparing between different dosing vehicles, where modulation of the membrane or evaporation of the vehicle produce transient effects. It is often difficult to directly model such dynamics because the mechanism is either uncertain or not estimable from available data. Stochastic differential equations have been used in pharmacokinetic models to account for unpre- dictable, time-dependent deviations from deterministic predictions [67–71]. Incorporation of such a random process in the model may help to account for k these transient effects and improve the precision of estimates of parameters of k

Model 1005 Model 2006

0.3 Z+EO Z+EO MNA + EtOH + TZ EtOH + AZ

0.2

0.1

0.0 15 MNA + Water + PN

Flux (%/h) 5

0 6

0 0 100 200 300 400 5000 100 200 300 400 500 Time (min)

Figure 10.7 A comparison model ﬁts with (Model 2006) and without (Model 1005) incorporation of a random process to account for transient changes in diﬀusivity [59].

k k

Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 285

interest. In one analysis, a random process was used to account for transient changes to apparent diffusivity to allow for improved estimates of permeability coefficients in various penetrant-vehicle mixtures, where mechanistic models of the vehicle dynamics were either unknown or computationally intensive [59]. The ability of the random process model to compensate forthe transient dynamics is illustrated in Figure 10.7, which compares the model fit for a few example profiles with and without the random process. The development of models and tools for random processes in pharmacokinetics, and applications to dermatological models, is an ongoing area of research.

10.8 Conclusions

The percutaneous absorption and dermatotoxicity of topically applied drugs and chemicals is a major concern in pharmacology, pharmaceutical and toxicological sciences. As research in these areas continue, and regulatory oversight is applied to more classes of substances, efforts have been made to develop predictive models to quantitate chemical exposure to the skin and systemic circulations. Model complexity increases with greater anatomical or physiological details. Before realistic predictive models can be constructed, k experimental data must be collected of sufficient quality to make such efforts k worthwhile.

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37 Feldmann, R.J. and Maibach, H.I. (1974) Percutaneous penetration of some pesticides and herbicides in man. Toxicol. Appl. Pharmacol., 28, 126–132. 38 Carver, M.P. and Riviere, J.E. (1989) Percutaneous absorption and excretion of xenobiotics after topical and intravenous administration to pigs. Fundam. Appl. Toxicol., 13, 714–722. 39 Chow, S.C. and Liu, J.P. (2000) Design and Analysis of Bioavailability and Bioequivalence Studies, 2nd edn, Marcel Dekker, New York. 40 Vergnaud, J.M. and Rosca, I.D. (2005) Assessing Bioavailability of Drug Delivery Systems: Mathematical Modeling, Taylor and Francis, New York. 41 Dressman, J.B. and Reppas, C. (2016) Oral Drug Absorption: Prediction and Assessment, CRC Press. 42 Pithavala, Y.K., Soria, I., and Zimmerman, C.L. (1997) Use of the deconvolution principle in the estimation of absorption and pre-systemic intestinal elimination of drugs. Drug Metab. Dispos., 25, 1260–1265. 43 Pershing, L.K. and Krueger, G.G. (1987) New animal models for bioavailability studies, in Pharmacology and the Skin: Skin Pharmacokinetics (eds B. Shroot and H. Schaefer), S. Karger AG, Basel, 57–69. 44 Riviere, J.E., Bowman, K.F., Monteiro-Riviere, N.A. et al. (1986) The isolated perfused porcine skin flap (IPPSF). I. A novel in vitro model for k percutaneous absorption and cutaneous toxicology studies. Fundam. Appl. k Toxicol., 7, 444–453. 45 Riviere, J.E., Monteiro-Riviere, N.A., and Williams, P.L. (1995) Isolated perfused porcine skin flap as an in vitro model for predicting transdermal pharmacokinetics. Eur. J. Pharm. Biopharm., 41, 152–162. 46 Wester, R.C., Melendres, J., Sedik, L. et al. (1998) Percutaneous absorption of salicylic acid, theophylline, 2,4-dimethylamine, diethyl hexyl phthalic acid, and p-aminobenzoic acid in the isolated perfused porcine skin flap comparedtomaninvivo.Toxicol. Appl. Pharmacol., 151, 159–165. 47 Monteiro-Riviere, N.A. (1993) The use of isolated perfused skin in dermatotoxicology. In Vitro Toxicol., 5, 219–233. 48 Potts, R.O. and Guy, R.H. (1995) A predictive algorithm for skin permeability: the effects of molecular size and hydrogen bond activity. Pharm. Res., 12, 1628–1633. 49 Cleek, R.L. and Bunge, A.L. (1993) A new method for estimating dermal absorption from chemical exposure. 1. General approach. Pharm. Res., 10, 497–506. 50 Bunge, A.L. and Cleek, R.L. (1995) A new method for estimating dermal absorption from chemical exposure: 2. Effect of molecular weight and octanol–water partitioning. Pharm. Res., 12, 88–95. 51 Potts, R.O. and Guy, R.H. (1992) Predicting skin permeability. Pharm. Res., 9, 663–669.

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52 Geinoz, S., Guy, R.H., Testa, B., and Carrupt, P.A. (2004) Quantitative structure-permeation relationships (QSPeRs) to predict skin permeation: a critical evaluation. Pharm. Res., 21, 83–92. 53 Abraham, M.H. and Martins, F. (2004) Human skin permeation and partition: general linear free-energy relationship analyses. J. Pharm. Sci., 93, 1508–1523. 54 Riviere, J.E. and Brooks, J.D. (2005) Predicting skin permeability from complex chemical mixtures. Toxicol. Appl. Pharmacol., 208, 99–110. 55 Riviere, J.E. and Brooks, J.D. (2011) Predicting skin permeability from complex chemical mixtures: dependency of quantitative structure permeation relationships on biology of skin model used. Toxicol. Sci., 119, 224–232. 56 Karadzovska, D., Brooks, J.D., and Riviere, J.E. (2013) Modeling the effect of experimental variables on the in vitro permeation of six model compounds across porcine skin. Int. J. Pharm., 443, 58–67. 57 Guth, K., Riviere, J.E., Brooks, J.D. et al. (2014) In silico models to predict dermal absorption from complex agrochemical formulations. SAR QSAR Environ. Res., 25, 565–588. 58 Chittenden, J.T., Brooks, J.D., and Riviere, J.E. (2014) Development of a mixed-effect pharmacokinetic model for vehicle modulated in vitro trans- k dermal flux of topically applied penetrants. J. Pharm. Sci., 103, 1002–1012. k 59 Chittenden, J.T. and Riviere, J.E. (2015) Quantification of vehicle mixture effects on in vitro transdermal chemical flux using a random process diffusion model. J. Control Release, 217, 74–81. 60 Chittenden, J.T. and Riviere, J.E. (2016) Assessment of penetrant and vehicle mixture properties on transdermal permeability using a mixed effect pharmacokinetic model of ex vivo porcine skin. Biopharm. Drug Dispos., 37, 387–396. 61 Gibaldi, M. and Perrier, D. (1982) Pharmacokinetics,MarcelDekker, New York. 62 Reddy, M.B., Yang, R.S., Clewell, H.J., and Andersen, M.E. (2005) Physiological Based Pharmacokinetic Modeling, Wiley, New York. 63 McDougal, J.N., Zheng, Y., Zhang, Q., and Conolly, R. (2006) Biologically based pharmacokinetic and pharmacodynamic models of skin, in Dermal Absorption Models in Toxicology and Pharmacology (ed. J. Riviere), Taylor and Francis, New York, pp. 89–112. 64 Williams, P.L., Carver, M.P., and Riviere, J.E. (1990) A physiologically relevant pharmacokinetic model of xenobiotic percutaneous absorption utilizing the isolated perfused porcine skin flap. J. Pharm. Sci., 79, 305–311. 65 Williams, P.L. and Riviere, J.E. (1993) Model describing transdermal iontophoretic delivery of lidocaine incorporating consideration of cutaneous microvascular state. J. Pharm. Sci., 82, 1080–1084.

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66 Riviere, J.E., Williams, P.L., Hillman, R.S., and Mishky, L.M. (1992) Quantitative prediction of transdermal iontophoretic delivery of arbutamine in humans with the in vitro isolated perfused porcine skin flap. J. Pharm. Sci., 81, 504–507. 67 Tornoe, C.W., Overgaard, R.V., Agerso, H. et al. (2005) Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations. Pharm. Res., 22, 1247–1258. 68 Overgaard, R.V., Jonsson, N., Tornoe, C.W., and Madsen, H. (2005) Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm. J. Pharmacokinet. Pharmacodyn., 32, 85–107. 69 Overgaard, R.V., Holford, N., Rytved, K.A., and Madsen, H. (2007) PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations. Pharm. Res., 24, 298–309. 70 Kristensen, N.R., Madsen, H., and Ingwersen, S.H. (2005) Using stochastic differential equations for PK/PD model development. J. Pharmacokinet. Pharmacodyn., 32, 109–141. 71 Kakhi, M. and Chittenden, J. (2013) Modeling of pharmacokinetic systems k using stochastic deconvolution. J. Pharm. Sci., 102, 4433–4443. k

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New Technologies for Toxicology, Future Perspectives

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Big Data in Computational Toxicology: Challenges and Opportunities Linlin Zhao1 and Hao Zhu1, 2

1Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA 2Department of Chemistry, Rutgers University, Camden, NJ, USA

CHAPTER MENU

Big Data Scenario of Computational Toxicology, 293 Fast-Growing Chemical Toxicity Data, 295 The Use of Big Data Approaches in Modern Computational Toxicology, 299 Challenges of Big Data Research in Computational Toxicology and Relevant k Forecasts, 303 k

11.1 Big Data Scenario of Computational Toxicology

In the past decade, along with the vibrant and rapid development of chemical synthesis and biological screening technologies, immense data were generated daily and most of these data are available to the public [1, 2]. Biological data generated from high-throughput screening (HTS) of large chemical libraries contains rich toxicology information that has the potential to be integrated into toxicity research [3]. Currently available toxicity data exist both in structured formats (e.g., deposited into PubChem and other data-sharing web portals) and as unstructured data (papers, laboratory reports, toxicity web-site information, etc.). These data, even in structured formats, become so large and complex that it is diﬃcult to process and use them with traditional database management, data processing, and modeling tools. The term “big data” describes this phenomena, which can be viewed as the biggest change in modern toxicology. Originally, the focus of big data was the development of advanced data storage and handling techniques, such as cloud-based computing or high-speed heterogeneous computational environments [4]. The concept of big data is gaining increasing recognition in clinical studies and other research areas driven by biological data, including the ﬁeld of toxicology [1–3, 5]. The daily updated toxicology big data landscape

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consists of large-scale datasets from various sources, which contain enormous number of chemical toxicity endpoints. The terms volume (scale of data), velocity (growth of data), and variety (diversity of sources) have been used to characterize big data and ﬁts to the current toxicity big data landscape. Besides these characteristics, owing to the nature of experimental protocols, one should also be aware of the veracity (uncertainty of data) of these datasets. Thus, the “four v’s” (namely, volume, velocity, variety, and veracity) ofbig data also represent the relevant challenges of the modern toxicology research (Figure 11.1) [6]. The volume and velocity of the data-driven research for toxicology have been well recognized by multiple screening and data-sharing projects, which are described in the next section of this chapter. However, the use of all these available data sources is still based on traditional computational approaches (e.g., quantitative structure–activity relationships, QSAR) and the issues of variety and veracity were rarely considered in most previous studies. This chapter also highlights the recent progress of data-driven research to answer the above challenges.

k k

Volume Variety Data scale Data form

The FOUR V’s of chemical toxicity big data

Velocity Veracity Data growth Data uncertainty

Figure 11.1 The “four V’s” of big data can be used to describe the properties of these fast growing chemical toxicity data. (See color plate section for the color representation of this ﬁgure.)

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11.2 Fast-Growing Chemical Toxicity Data

Since the NIH Roadmap for medical research was launched in 2004 [7], several molecular library screening centers have been funded [8] and several HTS projects have been performed to experimentally test large chemical libraries. The recent data generation efforts in the area of toxicology are toxicity forecaster (ToxCast) initiated by the US Environmental Protection Agency (EPA) [9] and Toxicity Testing in the twenty-first century (Tox21), which was launched by the National Toxicology Program (NTP), the National Institutes of Health (NIH) Chemical Genomics Center (NCGC), and EPA [10–12]. The direct results of these experimental screening method efforts, especially HTS, are the toxicity data currently public available, as summarized in Table 11.1. These databases construct the current toxicity big data landscape, which is described in detail in the following. The well-known chemical/biological data-sharing projects were not initially designed for toxicants but contain considerable amounts of toxicity-relevant data. For example, PubChem is a public repository for chemical structures and their biological data, including the toxicity data from the screening centers as described above [13, 14]. Figure 11.2 shows the yearly increase in the number of PubChem compounds and bioassays. Over the past eight years, the number k of PubChem compounds increased from 19 million in September 2008 [34] to k over 60 million in September 2015 [35]. During the same period, the number of bioassays that were used to test these compounds increased from 1197 in September 2008 [34] to over 1 million in September 2015, resulting in over five terabytes of data [35]. Another large reservoir of bioassay data is the ChEMBL database, which is a manually curated chemical database maintained by the European Bioinformatics Institute (EBI), of the European Molecular Biology Laboratory (EMBL)[15]. The EBI’s goal is to provide freely available data and bioinformatics services to the scientific community. As part of this goal, the ChEMBL database was constructed for experimental data of both chemical toxicity and absorption, distribution, metabolism, and excretion (ADME) properties. ChEMBL version 22 (ChEMBL_22) was released in 2016. It contains over 1 million compounds and over 14 million data points [16]. The databases to specifically share toxicity data also made considerable progress. The Aggregated Computational Toxicology Online Resource (ACToR) portal, which was developed by the US EPA’s National Center for Computational Toxicology (NCCT) program, aggregates toxicity data for over 800,000 compounds from thousands of public sources which include HTS, chemical exposure, sustainable chemistry (chemical structures and physicochemical properties), and virtual tissue data [17, 18]. Similarly, TOXNET, which was developed by the National Library of Medicines’ (NLM) Division of Specialized Information Services (SIS), contains 16 separate sub-databases of various toxicity categories for thousands of diverse chemicals. By grouping

k k https://pubchem.ncbi.nlm.nih.gov/ https://www.ebi.ac.uk/chembl/ https://actor.epa.gov https://toxnet.nlm.nih.gov/ http://www.reach.lu/mmp/online/ website/menu_hori/homepage/ index_EN.html https://www.dimdi.de/static/en/ index.html toxicity In vitro mutagenesis in vivo In vivo and k k in vitro data Toxicity, genomics, and literature data Drugs, drug-like small molecules, and their bioactivity data Both Toxicity, diseases, and literature data Data submitted in EU chemical legislation, machine-readable Animal toxicity databioassay on rodents (rat and mouse) (ISSCAN); (ii) Salmonella typhimurium mutagenesis http://www.seurat-1.eu/ (Ames test) (ISSSTY); (iii) (micronucleus test) (ISSMIC); (iv) Cell transformation Assays (ISSCTA); (v) Mutagenicity and carcinogenicity of biocides (ISSBIOC) 13 million data points 500,000 assays over 300,000 compounds and related toxicity data substances and 3600 study types compoundsinthecurrent COSMOS database web portal 1 million bioassays, over 13 billion data points Public available toxicity data resources (as of 10/22/2016). Database Size Data description Accessibility ChEMBL [15, 16]ACToR [17, 18] Over 1 million compounds, over TOXNET [19] Over 800,000 compounds, over REACH [20] Multiple sub-databases contain SEURAT-1 [21, 22] 816,048 studies for 9800 Over 5500 cosmetic-type PubChem [13, 14] Over 60 million compounds, over ISSTOX [23] Five sub-databases (i) Long-term carcinogenicity Table 11.1

k k http://www.nite.go.jp/en/chem/ qsar/hess-e.html http://fraunhofer-repdose.de/ https://www.ncbi.nlm.nih.gov/ geo/ http://toxico.nibiohn.go.jp/ english/ research/resources/databases/ cebs/index.cfm https://ctdbase.org/ drugmatrix/index.html cmap/

k k High curated results of repeat dose toxicity tests Repeat-dose study data for dog, mouse, and rat sequencing and other forms of high-throughput functional genomics data submitted by the research community Toxicity data, gene expression data, and metadata Gene expression dataCompound, gene, and disease relationships Gene expression data http://www.niehs.nih.gov/ Gene expression data https://ntp.niehs.nih.gov/ http://portals.broadinstitute.org/ over 2000 compounds and toxicity mechanism information of about 80 compounds were added recently 1018 studies which resulted in 6002 speciﬁc eﬀects dosages and time points from various sources 32,000 genes, over 6000 diseases 10,000 genes genes HESS DB [24]RepDOSE [25] Repeated-dose toxicity study of GEO [26] 364 chemicals investigated in Open TG-GATEs [27] Over 4000 sub-datasetsCEBS [28] 170 compounds at multiple CTD [29–31]DrugMatrix [32] Microarray, next-generation About 10,000 toxicity bioassays Connectivity Map [33] Over 13,000 compounds, over About 600 About drug 1300 molecules compounds and and 7000

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PubChem compound (in millions) 70 60 50 40 30 20 10 PubChem compound (in millions) PubChem

1-Sep-20081-Sep-20091-Sep-20101-Sep-20111-Sep-20121-Sep-20131-Sep-20141-Sep-2015

PubChem bioassay 1,200,000 1,000,000 800,000 600,000 400,000 200,000 k bioassay PubChem k 0

1-Sep-20081-Sep-20091-Sep-20101-Sep-20111-Sep-20121-Sep-20131-Sep-20141-Sep-2015

Figure 11.2 Increase in the number of compounds and bioassays recorded in PubChem within eight years (from September 2008 to September 2015).

these databases together, TOXNET allows users to access all toxicity data for target compounds from one query form [19]. Another famous example is that of the European Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) legislation which aims to collect comprehensive safety information for all substances available on the European market. The current REACH database contains more than 9000 unique substances and about 800,000 safety assessment documents [20]. Some other toxicity resources were designed to support data for one or several speciﬁc toxicity categories. The Safety Evaluation Ultimately Replacing Animal Testing (SEURAT-1) was launched by the European Commission and the European Cosmetics Trade Association, Cosmetics Europe in 2011 and it is composed of six complementary research projects [21, 22]. Among them, the COSMOS project was dedicated to the development of methods to predict the safety of cosmetic ingredients [36]. The COSMOS database web portal contains over 5500 unique cosmetic-relevant compounds with their respective in vivo toxicity data. Another European institute, Istituto

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Big Data in Computational Toxicology: Challenges and Opportunities 299

Superiore di Sanita (ISS) developed ISSTOX database [23] which includes five sub-databases, each relative to a different toxicological endpoint. There are several databases containing multidose toxicity data, such as the Hazard Evaluation Support System Integrated Platform Database (HESS DB) [24] and RepDOSE [25]. HESS DB is a knowledge database containing toxicity data of compounds being tested in various doses/concentrations and relevant toxicity mechanism information. The RepDOSE database consists of toxicity data for 400 chemicals investigated in about 1000 multidose studies [25]. Another type of data source are toxicogenomics (TGx) studies, which generate enormous amounts of “omics” data that are meant to predict toxicity or genetic susceptibility induced by chemicals. Many modern in vitro toxicity studies now address that genomics information is sensitive to toxicants and these findings can be translated into biomarkers that is useful for chemical toxicity assessments [37, 38]. Several publicly available TGx databases have been initiated, such as the Gene Expression Omnibus (GEO) [26], the Japanese Toxicogenomics Project (TGP) database [39], the Chemical Effects in Bio- logical Systems (CEBS) database [28], and the Comparative Toxicogenomics Database (CTD) [29–31]. GEO is a public repository that archives diverse forms of high-throughput functional genomic datasets and it contains a large quantity of gene expression data that can be used in computational toxicology k studies [26]. TGP data are available to the public through the Open TG-GATEs k database [27], which includes the toxicology data for 170 compounds. The CEBS database [28] developed by the NIEHS is now the public repository for all NTP conventional toxicology and carcinogenicity data as well as NCGC HTS data along with the CTD, which aims to promote comparative studies of genes and proteins across species [29–31]. The DrugMatrix [32] and the Connectivity Map [33] are similar efforts in toxicogenomics data curation, but with more specific research goals.

11.3 The Use of Big Data Approaches in Modern Computational Toxicology

The massive chemical toxicity data available to research communities poses a great opportunity for modern toxicology research. However, in the current big data era, traditional computational approaches are not suitable to deal with big data resources. The typical big data research in toxicology that has taken place over the past ﬁve years; along with the fast-growing data, this ﬁeld was assisted by “super” computers with powerful computational ability.

11.3.1 Profiling the Toxicants with Massive Biological Data With all these screening efforts, especially the HTS projects, as mentioned above, a significant number of “popular” compounds (e.g., well-known toxicants) have been tested multiple times. For example, Table 11.2 shows

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Table 11.2 Twenty human toxicants with their relevant PubChem bioassay responses.

No. of active No. of inactive Chemicals CAS responses responses

Hexachlorophene 70-30-4 366 721 Captan 133-06-2 212 198 Phenylmercuric acetate 62-38-4 203 115 Chlordecone (Kepone) 143-50-0 166 442 Hydroquinone 123-31-9 121 450 2-Chloroacetophenone 532-27-4 78 191 Pentachlorophenol 87-86-5 78 679 Tributyltin oxide (TBTO) 56-35-9 77 37 Endosulfan 115-29-7 75 189 Hexachlorocyclopentadiene 77-47-4 69 203 (HCCPD) Propachlor 1918-16-7 69 379 Sodium diethyldithiocarbamate 148-18-5 68 77 p,p′-Dichlorodiphenyl 72-54-8 66 170 dichloroethane (DDD) k Acetaldehyde 75-07-0 63 173 k Phenol 108-95-2 62 369 Bisphenol A 80-05-7 61 739 p,p′-Dichlorodiphenyl 50-29-3 61 279 trichloroethane (DDT) Naphthalene 91-20-3 61 697 Chlorobenzilate 510-15-6 59 62 α-Hexachlorocyclohexane 319-84-6 58 619 (α-HCH)

20 toxicants obtained from the Integrated Risk Information System (IRIS) database. On the basis of the search result on PubChem, these toxicants were reported to be tested in hundreds of PubChem bioassays. As shown in Table 11.2, hexachlorophene (CAS 70-30-4), a disinfectant banned from the market, showed active responses in 366 bioassays. Other toxicants have similarly rich response information in PubChem (Table 11.2). The rich biological data for these toxicants can be viewed as response profiles which are useful for computational toxicology studies. There have been some pioneering studies that use bioassay data to profile toxicants in the early stages. For example, the major goal of the ToxCast project is to use hundreds of bioassays [40–48] to profile the compounds which have been

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Big Data in Computational Toxicology: Challenges and Opportunities 301

tested for their animal toxicity as shown in the Toxicity Reference Database (ToxRefDB) [49–51]. The profiling studies of ToxCast tried to develop predictive models for toxic compounds using a set of in vitro assays and/or in silico predicted results, for example, ToxPi [52]. The disadvantage of this type of study is the selection of biological data for profiling; besides, prediction is arbitrary and only limited to in-house data. In the current big data era, all the public toxicity data (e.g., shown in Table 11.1) can be used for profiling toxicants. In 2014, we developed an automatic virtual profiling tool to evaluate potential animal toxicants using all PubChem bioassay data [53]. The core of this study is a scoring system to evaluate the relationship between PubChem bioassays and animal toxicity. The top-ranked bioassays were used to profile the compounds of interest and make predictions of potential toxicants. Recently, similar effort was reported by Helal et al. [54]. They used PubChem bioassays to create bioprofiles for more than 300,000 chemicals and showed that these bioprofiles can be used in toxicity model development. Meanwhile, bioassayR, which is distributed as an R package, can be used for simultaneous analysis of large numbers of biological® data, especially those obtained from HTS [55]. Using bioassayR, bioassays can be clustered with the same targets and compound-target information can also be generated for further analysis. k The direct benefit of these toxicant profiling efforts is to provide new chem- k ical toxicity mechanisms by analyzing the relevant biological data. Recently, we developed the virtual Adverse Outcome Pathway (vAOP) of oxidative stress by profiling hepatotoxicants [56]. In this study, four PubChem assays obtained from bioprofiles of hepatotoxicants were used to create a vAOP. If a new compound contains the initial chemical features described in this vAOP and shows active responses in any of these four assays, it will be predicted to cause liver damage in vivo through inducing oxidative stress. The ToxCast project generated several similar analyses [57–59]. For example, they studied estrogen receptor (ER)-binding potentials by profiling ER binders by diverse in vitro assays [57, 58] and gene expression data [59]. The AOP generated by these assay results can not only predict potential ER binders but also illustrate the relevant toxicity mechanisms.

11.3.2 Read-Across Study to Fill Data Gap Traditional read-across approaches of computational toxicology, which were widely used to ﬁll data gaps of new compounds without relevant toxicity data, are usually based on chemical similarity search [60, 61] or QSAR predictions. The basic hypothesis of this type of studies is “similar compounds have similar bioactivities” and is not suitable for most chemical toxicity phenomenon in vivo with complicated toxicity mechanisms. Only using chemical similarity to justify the read-across will be error-prone, especially when chemically similar

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compounds show dissimilar toxicity phenomena. For example, “activity cliffs” (i.e., similar compounds having different toxicity) result in prediction errors in many toxicity modeling studies [62–67]. Thus, in the big data era, the use of biological data besides chemical structural information adds extra strength to the read-across process. In some early studies, integrating biological data as biological descriptors into the QSAR modeling procedure was found to be beneficial to the resultant toxicity models. For example, we developed enhanced acute toxicity models by integrating external biological data as extra descriptors [68, 69]. The resulting hybrid models, based on the combination of the chemical and biological descriptors, showed better performance than traditional QSAR models. Research studies were carried out Low et al., who concluded that the read-across study should be based on both chemical and biological data [70]. Similar results were reported by Garcia-Serna et al. [71]. They stated that combining chemical and biological data could enhance the ability to assess the toxicity of small molecules with higher confidence than that using chemical data alone. Recent read-across studies have been reported to be performed by using various sources with massive amounts of toxicity data. Kleinstreuer et al. [72] used the US EPA’s ToxCast dataset to perform read-across for a uterotrophic k database collected from a large number of historical reports. The purpose of k this study was to evaluate estrogenic endocrine disruption of new compounds. Another example was given by Luechtefeld et al. [5, 20, 73–75], who collected all the available historic REACH toxicity data for 9801 compounds. They performed a read-across study of REACH compounds for their acute oral toxicity [73], Draize eye irritation testing results [74], and skin sensitization activity [75]. A review of using big data to perform read-across of chemical toxicity [5] and two other strategy papers [76, 77] were also published. Recently, to fill data gaps, we developed a new read-across portal using public large-scale chemical and biological data called the Chemical In vitro–In vivo Profiling (CIIPro)portal [78], which can automatically extract biological data from public resources (i.e., PubChem) for compounds of interest. The read-across analysis based on biosimilarity, which was defined on the extracted biological data of target compounds, showed higher predictivity for estrogen receptor binding agents [79].

11.3.3 Unstructured Data Curation Chemical toxicity data are rapidly increasing not only in structured data formats (e.g., those databases shown in Table 11.1), but also in various types of text documents such as scientiﬁc articles, patents, industry reports, and media reports, which can be classiﬁed as unstructured toxicity data. The use of unstructured toxicity data has motivated the development of text mining approaches in computational toxicity. Text mining refers to the automatic

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Big Data in Computational Toxicology: Challenges and Opportunities 303

processes of deriving high-quality information from text. It usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluating and interpreting the output [80]. In computational toxicity, challenges always come from the step of structuring input text. For example, in documents, chemicals are usually referenced in different ways: common names, systematic nomenclatures, database identifiers (e.g., CAS number), InChI strings, or even as structures shown in images. Thus, text mining studies of chemicals of specific interest (e.g., animal toxicants) are strongly dependent on the named entity recognition (NER), which was reviewed by Vazquez et al. [81]. Furthermore, for a popular word (e.g., a commonly used term), it can refer to different meanings under different contexts. For example, CYP can be used in names of gene, protein, mutation, drug, or an adverse event [82–85]. As a part of biomedical science, the application of text mining in computational toxicity was for pathway extraction and reasoning and pharmacogenomics [86]. Text mining approaches contribute to computational toxicity studies by bringing useful knowledge from literature, either extracted or curated, together with in-house biological datasets to identify relationships between genes, pathways, drugs, environmental contaminants and diseases [87, 88]. There have been several studies applying text mining methods within toxicogenomics studies [89–91]. k k 11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts

The rise of big data heralds a profound change in the way that toxicologists perform their research. The big data era brings not only big progress but also big challenges [1, 92, 93]. Although there are some preliminary studies, as described above, which successfully apply big data sources in computational toxicology studies, the urgent needs of new approaches in this area are described in the following. Experimental error is inevitable in public data sources. It is understandable that the quality of data may be vary on the basis of the nature of experimental protocols. Currently, the usefulness of public data sources is questionable owing to a lack of necessary quality control [94]. A general worry has been raised regarding irreproducible experimental data [95–97], which is relatively common in complicated biological testing (e.g., animal models). There is also a golden rule in computational modeling studies, which is the “trash in, trash out” principle [5]. For this reason, the veracity of big data, represented by the potential data quality of public data resources, is a critical issue that aﬀects all relevant studies. There have been many studies [98–100] which have tried to address the incorrect chemical structure information. However, studies to automatically correct biological data errors are rare [101].

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Although the current data growth (i.e., velocity of big data) is exceptional and there are many available data for well-known toxicants, the missing data (i.e., lacking of necessary toxicity data for target compounds) is still a common issue. As described above, read-across studies can be used to fill the data gap in some cases. However, a good read-across practice can only be performed when an “unknown” compound has reliable predictions from its nearest neighbors [5]. For the “outliers” that are excluded because they are out of the applicability domain (AD) of available models [102, 103], extra experimental testing is still necessary. For this reason, a well-defined and applicable AD is critical for any chemical risk assessment studies. Currently, the AD is normally defined by chemical similarity between the test set and modeling set compounds. To make the AD more applicable in big data studies, new methods need to be developed, such as the biosimilarity confidence that we have recently reported [78]. Toxicology research becomes more complicated when various types of data (i.e., a variety of big data) are used in a single study. This is the ultimate challenge of computational toxicology and new computational approaches are always needed to realize this goal. In the above section, we described hybrid models and new computational approaches to use various types of toxicity data in the computational toxicity field (e.g., vAOP studies). However, this type of work is far from achieving final success. The current bioinformatics and k cheminformatics modeling approaches and data analysis methods that have k been developed in the past decade are not suitable for the requirements of big data analysis. Big data research will be one of the major efforts of modern toxicology in the future. With all these challenges, there is an urgent need for novel techniques in data mining/generation, curation, and analysis to fulfill the requirements of big data research in computational toxicity. The recent progress in computational toxicology described in this book can be viewed as leading in this direction. The success of data-driven studies will assist toxicologists by highlighting the value of the publicly available toxicity data and providing guidance for future experimental testing.

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling George van Den Driessche and Denis Fourches

Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA

CHAPTER MENU

Introduction, 313 Human Leukocyte Antigens, 314 Structure-Based Molecular Docking to Study HLA-Mediated ADRs, 322 Perspectives, 334 k k 12.1 Introduction

Adverse Drug Reactions (ADRs) are mild-to-serious undesired effects a patient can undergo after taking a particular medication. ADRs can even be life threatening for certain subpopulations of patients. Overall, ADRs present a major financial burden to the US healthcare system [1]. One important challenge for accurately monitoring both the occurrences and financial burdens associated with ADRs is mainly the lack of a cohesive definition used to track such events [2]. According to the FDA Adverse Event Reporting System (FAERS), there were over 1.2 million adverse events reported in 2014 compared to only 400,000 cases in 2006 [3]. For all the aforementioned reasons, there is a strong need to develop computational models that could accurately predict a drug’s likelihood of causing ADR. In fact, models reliably predicting such ADR for a subpopulation of patients are especially in high demand for future precision medicine protocols. There are two main different types of ADRs referred toas Type I and Type II [4, 5]. Type I ADRs are predictable pharmacological events induced by a drug, while Type II ADRs are idiosyncratic events. Predictable ADRs occur owing to off-target binding events resulting from the drug’s polypharmacology. A drug’s metabolism may also be the source of ADRs often subcategorized into pharmacokinetic and pharmacodynamics [6]. Pharmacokinetic drug properties are

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identified by studying the drug metabolism from absorption to excretion and identifying when the drug response actually occurs [6]. Pharmacodynamics ADRs are less studied and require a greater understanding of drug interactions with ion channels, protein receptors, enzymes, and nucleic acids for clearly understanding how the drug response is triggered [6]. Idiosyncratic ADRs (iADR) are extremely difficult to predict. They mostly occur when a drug triggers an undesired immune system response and may actually result from a drug’s pharmacodynamic properties [4–6]. The major biological pathway sought to trigger iADRs is the interactions of drugs with the human leukocyte antigen (HLA) proteins. To date, the most well-known example of such a type of HLA-drug interaction is that of abacavir (ABA) with the HLA-B*57:01 variant. Abacavir is used in the treatment of HIV; if treated patients express the HLA-B*57:01 variant, they are likely to experience the abacavir hypersensitivity syndrome (AHS). In this chapter, we recapitulate the relationships between HLA variants and drug associations resulting in ADRs. First, we explore the diversity of HLA proteins, their expression pathways, and highlight the structural differences of Class I versus Class II HLAs. Next, we discuss a few examples of known HLA-drug complexes and highlight some specific research efforts on identifying HLA-drug associations via population-wide studies and odds ratios (OR) k followed by a brief summary of the HLA-drug binding mechanisms. Finally, we k describe how structure-based molecular docking could be employed to screen for HLA-active drugs and the clear limitations associated with molecular docking. To conclude, we present perspectives for HLA-mediated ADR events.

12.2 Human Leukocyte Antigens

12.2.1 HLA Proteins In humans, the major histocompatibility complex (MHC) is encoded by the HLA, which is responsible for determining cell similarity and signaling to T-cells for immune system activation against pathogens. HLA proteins can be categorized into two classes: Class I and Class II. To date, the IMGT/HLA database (accessible at www.ebi.ac.uk/ipd/imgt/hla/) has over 15,000 HLA allele sequences with 11,000 Class I alleles and about 4,000 Class II alleles [7]. In general, Class I and Class II HLA perform the same function by presenting peptides to T-cells for immune system activation [8]. However, beyond their functions, the two classes of proteins become quite distinct. One major distinction is that Class I HLA presents a peptide to CD8+ T-cells; whereas Class II HLA presents a peptide to CD4+ T-cells [8]. Another major diﬀerence between the two classes occurs due to the source of peptide. The normal peptide source for Class I HLA occurs from an endogenous antigen, while the

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 315

normal peptide source for Class II HLA comes from an exogenous antigen [9]. However, under particular conditions these peptide sources can be reversed. In the case of cross-presentation, Class I HLA will present an exogenous peptide [10] and when autophagy protein degradation occurs, Class II HLA can present an endogenous peptide [11]. There are also notable diﬀerences in the structures of Class I and Class II HLA which are presented in Figure 12.1. Even though T-cell signaling occurs at the cell surface, the formation of Class I HLA occurs inside the cell in the endoplasmic reticulum (ER) [8]. The Class I heterodimer is assembled in the ER consisting of a 270 amino acid residue peptide binding domain and a 90–100 amino acid residue

β2-microglobulin light chain. Interestingly, it is not stable until after an 8–10 amino acid residue endogenous peptide is translocated into the ER to stabilize the HLA protein [8, 12]. The C- and N-termini of the peptide form H-bonds that anchor the peptide in the Class I pocket [8, 12]. After the HLA-peptide complex is formed, it is released from the ER and presented at the cell surface [9]. A more detailed exploration of the pathway can be found in the excellent review of MHC Class I and II pathways by Neefjes et al. [8]. The peptide binding domain of Class I HLA can be subdivided into three

90-residue α-domains. Two of these α-domains (α1 and α2) are directly involved in stabilizing the co-binding peptide between two α-helical walls connected by k a β-pleated ﬂoor [12]. Once Class I HLA is presented at the cell surface, the k

third α-domain (α3)andtheβ2-microglobulin chain provide anchoring sites to the cell surface [12]. Interestingly, even though there are over 11,000 Class I alleles, the overall protein structure is highly conserved [7]. A schematic of the structure for Class I HLA variants is provided in Figure 12.1.

Peptide α 1 Peptide B1 α α 2 1 α α 2 B 3 2 B2-m

HLA-Class I variants HLA-Class II variants 8–10 A.A. peptides 12–24 A.A. peptides

Figure 12.1 Structure of HLA-variants Class I and Class II. (See color plate section for the color representation of this ﬁgure.)

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316 Computational Toxicology

Class II HLAs share a lot of similarities with Class I HLAs such as the polymorphism, three-dimensional structures, genetic location, and function [8, 12]. However, there are several notable differences between the two classes beginning with the fact that there are significantly fewer known sequences of Class II HLA compared to Class I (4,000 known Class II alleles) [7]. Two other major differences concern the tissue distribution and the antigenic peptides between the Classes. Class I HLA is ubiquitously expressed in most cells, whereas Class II HLA is expressed in specific cells (APCs, DCs, macrophages, and B-cells) [8]. As discussed earlier, Class I antigenic peptides are 8–10 residues in length, while Class II antigenic peptides can be 12–24 residues in length [8, 12]. Antigenic peptide presentation in Class II HLA begins in the ER by the formation of an α-andaβ-heterodimer that are then stabilized by an invariant chain (Ii) [8]. Then the Ii-Class II HLA is transported from the ER to the MHC Class II Compartment (MIIC) where the Ii chain is digested to form a residual Class II-associated Ii peptide (CLIP) [8]. This CLIP peptide is typically 83–107 residues in length [13]. Finally, the CLIP peptide is replaced by a 12–24 mer antigenic peptide degraded from proteins in the endocytic pathway [14], before the Class II HLA protein is translocated from the MIIC to the cell surface. Again, a more thorough analysis can be found in the very detailed review by k Neefjes et al. [8]. k Structurally, Class II HLA consists of an α and a β heterodimer, with each section containing 90–100 residues [12]. Each of these regions can

then be subdivided into two subdomains that are membrane-distal (α1 and β1) and membrane-proximal (α2 and β2). The membrane-distal region is where peptide binding occurs, while the membrane-proximal region holds Class II HLA to the cell surface. Comparing the structure of Class II with Class

I, it becomes apparent that the α1 and β2 subdomains of Class II correspond to the peptide binding pocket α1 and α2 subdomains of Class I. As such, the membrane-distal region of Class II is composed of two alpha helical walls with β-pleated strands forming the peptide binding pocket ﬂoor. However, instead of antigenic peptides non-covalently binding in anchor fashion (as in Class I), Class II peptides undergo H-bonds between the pocket and center of the peptide which allows the C- and N-termini to hang past the edges of the peptide pocket (allowing for longer peptides to bind Class II HLA) [8, 12]. Continuing the comparison between Class I and Class II, it is known that the

α3 and β2-m subdomains of Class I correspond to the membrane-proximal subdomains of Class II (α2 and β2). An overview of the structure of Class I and II HLA alleles is provided in Figure 12.1.

12.2.2 ADR–HLA Associations Idiosyncratic ADRs can occur anytime a drug binds with an HLA variant and activates either CD4+ or CD8+ T-cells. Upon T-cell activation a patient can

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 317

suffer from a multitude of ADRs such as drug-induced liver injury (DILI), Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), hypersensitivity, drug reaction with eosinophilia and systemic symptoms (DRESS), malignant pleural effusion (MPE), or agranulocytosis/granulocytosis to name the most well-known examples. Table 12.1 provides examples of drugs and their reported OR for a particular HLA-variant; interestingly, some drugs, such as carbamazepine, are listed as being active at multiple variants [18–20]. The OR is a population-based statistical measure that assesses the likelihood of an exposure (drug-HLA variant interaction) to cause a particular outcome (ADR event occurs) [31]. This measurement is obtained using the following equation: (No. of exposed cases)×(No. of unexposed non-cases) OR = (No. of exposed non-cases)×(No. of exposed cases) (12.1) There are three possible outcomes in Equation 12.1; first, OR = 1inwhich case it can be concluded that exposure does not affect the odds of the outcome [31]. Second, if OR > 1, then the exposure is associated with a higher odds of the outcome, and finally, if OR < 1, then the exposure is associated with a lower odds of the outcome [31]. The recently built HLADR database compiles k all the known drug-HLA associations with their respective ORs [32]. k Let us take a real world example using ORs to determine a potential drug-HLA relationship. Clindamycin is an antibiotic that causes either delayed maculopapular exantherma or SJS/TEN in patients expressing the HLA-B*51:01 variant [21]. However, Yang et al. found that there were three other variants that had OR greater than one, indicating that the HLA variants B*15:27 (OR: 55), B*51:01 (OR: 24), C*14:02 (OR: 9), and C*15:02 (OR:19) could all be binding clindamycin to trigger an ADR event [21]. These results raise several questions: Do all of these alleles result in ADR events? Are drug-HLA interactions more favorable for a particular variant compared to the others? Will drug-HLA binding be strong enough to induce the ADR? Clearly the limitations of using OR to diagnose drug-mediated ADR events have been reached and alternative techniques are needed to analyze and elucidate drug-HLA complexes. In this particular case, Yang et al. attempted to perform a molecular docking study using a HLA-B*51:01 crystal (PDB:1E27) and homology models for HLA-B*15:27 (PDB Template:1XR8), HLA-C*14:02, and -C*15:02 (PDB Template: 1EFX) to determine which drug-HLA combination was most likely [21]. From their docking results, Yang et al. concluded that HLA-B*51:01 was most likely responsible for inducing ADR (GLIDE docking

score DSXP =−10.1 kcal/mol) [21]. Unfortunately, the docking results reported by Yang et al. indicated that docking at all four variants was actually possible and predicted as favorable (all docking scores < −7 kcal/mol) [21]. Addition- ally, the authors did not mention how the co-binding peptide was analyzed

k k [22] [24] [26] [15]; [18] [16] [20] [23] [17] [21] [25] et al. et al. [19] et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. a) a) a) Chung 900 Martin > B*50:02B*51:01 N/A C*14:02B(158T) 9 6 3.3 Goldstein DQB1 15.6 Legge B*15:27 55 Yang

k k irritation SJS/TENgranulocytosis C*15:02 6 DRESS exantherma BipolarDisorder DRESS A*31:01 33 12 Ozeki Epilepsy B*15:11 31 Shi List of drug-HLA associations with their reported odds ratios. Drug Indication ADR HLA variant OR References Allopurinol Hyperuricemia SJS/TEN B*58:01 580 Hung Ciproﬂoxacin Antibiotic Gastrointestinal FenoﬁbrateLamotrigine High cholesterol Anticonvulsant AGEP SJS/TEN A*33:01 B*15:02 N/A 3 Che ung Clozapine Schizophrenia Agranulocytosis/ Dapsone Antibiotic DRESS B*13:01 20 Tohyama AbacavirAtorvastatin HIVCarbamazepine High cholesterol Anticonvulsant N/A AHS SJS/TENClindamycin Antibiotic B*15:02 DRB1*10:10 B*57:01 Delayed maculopapular N/A Flucloxacillin 895 Antibiotic Chung DILI B*57:01 80 Daly Table 12.1

k k et al. [30] 2010 2010 [27] [29] et al. et al. et al. et al. et al. a) a) a) a) [28]

k k Epilepsy Seizures Oxacarbazepine Anticonvulsant SJS/TEN B*15:02 80 Hung Pazopanib Anticancer DILI B*57:01 N/A Xu Sertraline PTSD/OCD Serotonin syndrome A*33:01 N/A Phenytoin Anticonvulsant SJS/TEN B*15:02 33 Hung MethazolamideMethyldopaMinocycline GlaucomaNevirapine Antihypertensive Antimicrobial SJS/TEN N/A HIV Thyroid hyperplasiaDrugSimvastatin B*35:02 B*59:01 DRESS/MPETiclopidne A*33:01 N/A 1974 High cholesterol Indication N/A Thrombotic stroke Yang B*35:05 Myalgia Arthralgia Agranulocytosis ADR 19 B*13:02 Chantarangsu A*33:01 N/A HLA 36 Hirata OR References a) Putative data, unpublished.

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or taken into account in their docking procedure despite the fact that the peptide has a significant role in HLA stability [8, 12]. The role of the co-binding peptide with HLA-B*57:01 and abacavir will be discussed in greater detail in Section 12.3.2. Another well-studied example is the drug carbamazepine. Carbamazepine is used to treat epilepsy and bipolar disorder, but in patients who have one of the following HLA-variants, SJS/TEN may occur: HLA-B*15:02 (OR: 895), HLA-B*15:11 (OR: 31), or HLA-A*31:01 (OR:33) [18–20]. In 2012, Wei et al. confirmed carbamazepine’s affinity for HLA-B*15:02 through the use of T-cell proliferation and cytotoxic assays in addition to a HLA-B*15:02 peptide-specific assay [33]. Interestingly, the assay results also indicated that HLA-B*15:08, -B*15:11, and B*15:21 would have some binding affinity with carbamazepine [33]. Furthermore, based on the basis of their site-directed mutagenesis work and homology model (using HLA-B*15:01 as a template, PDB: 1XP8), Wei et al. were able to identify ARG62 and GLU63 as two critical amino acids for carbamazepine binding [33]. Using this previous work, two interesting studies have recently emerged. First, Schotland et al. used molecular analysis of side effects (MASE) to data-mine the FAERS database for drug-HLA associations for SJS/TEN and found that patients taking acetominophen (APAP) and carbamazepine k showed a reduced chance of SJS/TEN [34]. Using the homology model k developed by Wei et al. [33], Schotland et al. tested the binding affinities of carbamazepine and APAP and found that APAP had a higher binding affinity with HLA-B*15:02 [34]. The second study used the same homology model, but now involved performing molecular dynamic (MD) simulations to further understand the T-cell signaling pathway [35]. Zhou et al. [35] have proposed an interesting signaling pathway that is highlighted in Section 12.2.3. Thus far, only interactions between Class I HLA and drugs have been discussed. It is important to remember that drug-HLA interactions are also observed with Class II HLA. Clozapine is used for the treatment of schizophrenia, but in certain cases may result in agranulocytosis or granuloc- topenia. In 2007, Dettling et al. reported significant OR values for clozapine with HLA-B*57 (OR:22) and HLA-DRB5*02:01 (OR: 22) [36] Then in 2014, Goldstein et al. reported a possible connection for clozapine binding with HLA-B*57:01 or HLA-DQB1*05:02 variants [22]. Most recently, Legge et al. confirmed clozapine’s HLA-dependence with HLA-DQB1*05:02 with a reported OR of 15 [23]. Remarkably, these reports indicate that clozapine may bind with either Class I or Class II HLA [22, 23, 36]. The use of ORs provides evidence for a correlation between an ADR event and specific HLA variant(s), but it does not imply a significant binding affinity between a particular drug and HLA-variant. In some instances, (as with carbamazepine) binding assays are available to confirm drug-HLA associations, but even in vitro assays are limited in their ability to elucidate information

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 321

about specific drug and HLA binding pocket interactions. The best method to visualize HLA-drug binding interactions is through the use of NMR or X-ray crystallography. The association between abacavir and HLA-B*57:01 is the best understood one, notably owing to the availability of three X-ray crystals identified in 2012 by Illing et al. (PDB: 3VRI and 3VRJ) [37] and Ostrov et al. (PDB: 3UPR) [38]. These three crystals not only confirmed the HLA-drug complex previously reported has OR values (greater than 900) [15, 39] but also provided accurate starting structures for molecular docking and dynamics [40–42]. Recently, Metushi et al. conducted a virtual screening of the ZINC database to identify potential HLA-B*57:01-active compounds with a high similarity to abacavir [41]. After performing a 2D/3D similarity search, they next used a combination of pharmacophore and molecular docking (using PDB: 3UPR from their previous work) to identify several chemicals for an HLA-binding assay and a peptide-elution assay [38, 41]. Using this protocol, Metushi et al. identified acyclovir as a potential HLA-B*57:01-active compound and subjected it to a CD8+ T-cell response assay [41, 43]. Using the T-cell response assay, they were able to confirm that acyclovir does not mediate a T-cell response (as expected, this is due to historical lack of ADRs associated with acyclovir) [44–46]. Interestingly, in each of the HLA-drug interaction examples discussed above, k molecular docking was used as the main cheminformatics tool to explore k the binding modes of drugs in the HLA pocket [21, 22, 33–35, 40–42]. Best practices and limitations when performing molecular docking are discussed in Section 12.3.1. This is followed by an in-depth analysis of our own work in developing a virtual screening protocol at the HLA-B*57:01 variant using molecular docking, in Section 12.3.2 [42].

12.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms In order for a drug to cause an adverse event, it must signal to T-cells (CD8+ for Class I and CD4+ for Class II) after forming an HLA-drug-peptide complex. Currently, there are three proposed mechanisms for HLA-T-cell signaling: altered repertoire, pharmacological interaction (p. i.) complex, and the hapten complex [4, 47]. An adaptation of these signaling modes from Illing et al. [48] is provided in Figure 12.2. The altered repertoire signaling mechanism occurs when a drug non-covalently binds in the peptide pocket beneath the peptide as shown in Figure 12.2a. A p. i. complex is based on non-covalent interactions, but forms with the peptide surface outside of the binding pocket as shown in Figure12.2b.Thehaptencomplexistheonlyoccurrencewherethedrugiscova- lently bonded to the peptide and can occur either inside or outside the binding pocket as shown in Figure 12.2c. Interestingly, the altered repertoire and hap- tencomplexcanbothformeitheratthecellsurfaceorintheERduringHLA formation, whereas the p. i. complex forms solely on the cell surface [4, 47, 48].

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HLA Co-binding Cellular expressed iADR += +=Drug variant peptide HLA + peptide mechanism

Altered repertoire p.i. Complex Hapten complex (a) (b) (c)

Figure 12.2 HLA-drug binding mechanism adapted from Illing et al. [48] for T-cell activation (a) Altered repertoire (non-covalent). (B) p. i. complex (non-covalent). (C) Hapten complex (non-covalent). The non-covalent T-cell interaction is not shown.

These three signaling mechanisms have been suggested (and/or observed) for abacavir, carbamazepine, and flucloxacillin [35, 37, 38, 48, 49]. To date, the only structurally verified signaling mode is for HLA-B*57:01-abacavir-peptide in an k k altered repertoire complex (PDB: 3VRI, 3VRJ, 3UPR) [37, 38]. Carbamazepine is believed to form a p. i. complex with HLA-B*15:02-peptide-carbamazepine [48], but recent MD simulations by Zhou et al. [35] suggests that carbamazepine may non-covalently bind to a CD8+ cell before non-covalently binding to the peptide surface as shown in Figure 12.3. Without an NMR or crystal structure, it will be difficult to confirm this supposed mechanism. Flu- cloxacillin is believed to form a hapten complex due to the nature of β-lactams and their ability to form covalent bonds with lysine residues [48–50]. Without NMR structure or crystals of HLA-drug complexes, the use of molecular docking and molecular dynamics offers an opportunity to identify drugs that favor altered repertoire or p. i. complexes with HLA. Unfortunately, molecular docking and dynamics will be unable to identify hapten complexes owing to their use of classical force fields; a quantum mechanical technique would be needed to identify these types of complexes due to the formation of a covalent bond.

12.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs

Owing to the serious health implications of ADRs, the ability to predict whether a drug can bind with HLA and induce an immune system response

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 323

Alternative p.i. complex

CD8+

HLA-B*15:02

Classic p.i. complex

Figure 12.3 Alternativep.i.complexCD8+ T-cell signaling pathway for carbamazepine k binding with HLA-B*15:02. Source: Adapted from Zhou et al. (2016) [35]. k

is extremely important. However, solely relying on experimental assays to identify all possible drug-HLA relationships is perhaps not the best strategy. Currently, there are over 15,000 HLA-variants listed in the IMGT/HLA database [7]. If an assay were developed for every single variant and an experimental screening of DrugBank were performed (currently experiments performed. The screening of larger libraries such as ZINC database (over 15 million compounds) would simply become infeasible. That number does not even account for experimental errors and replicates in the different assays. Clearly, brute-force experimental screening for HLA-drug interactions is hardly possible if many variants and a large library of compounds are considered. Meanwhile, in silico virtual screening offers a comparatively quick alternative for identifying potentially HLA–active drug compounds. The use of similarity searching, pharmacophore, quantitative structure-activity relationships (QSAR), and molecular docking could all provide valuable insight into the identification of HLA-active compounds. However, owing to very limited structural

information about HLA-drug binding aﬃnities (K i), statistical techniques such as QSAR modeling cannot be conducted at this time. In contrast, molecular docking represents the most appropriate cheminformatics technique to perform a virtual screening tool for HLA owing to the availability of crystal structures (e.g., three structures for HLA-B*57:01-abacavir crystal structures 3VRI,

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3VRJ, 3UPR) and the possibility to build additional homology models for the other variants.

12.3.1 Structure-Based Docking Molecular docking consists of virtually inserting a ligand into the binding pocket of a target (e.g., protein) and then scoring the actual molecular interactions between the ligand and protein according to the binding mode of the ligand and a scoring function. Docking scores can be used to rank a set of ligands on the basis of their predicted binding affinities toward the target, and thus help identifying and prioritizing those ligand(s) with the most interesting interactions with the target. A classical docking procedure can be performed in three ways: rigid ligand-rigid protein, flexible ligand-rigid protein, and flexible ligand-flexible protein. Rigid-rigid docking is considered to be the least accurate and is only used for screening extremely large databases as a first and rapid initial step. Flexible-rigid docking is the most commonly used technique and is generally considered to produce reliable binding modes and binding affinities between ligands and proteins. In theory, the best approach would be to use fully flexible-flexible docking as this would account for induced conformational changes in proteins upon ligand binding, but this technique is k still rarely usedowing to the large computational cost. The rapid development k of ensemble docking (i.e., docking flexible ligands in many conformations of the protein) and GPU-accelerated molecular dynamics will accelerate the transition to flexible-flexible docking. When performing flexible-rigid molecular docking, the starting structure for the protein comes from an experimentally validated 3D structure (X-ray or NMR). Using this known structure, two approaches can be used: (i) docking is performed directly on that structure or (ii) docking is performed by developing a homology model built from this initial 3D structure. In the case of homology modeling, the residue sequence for a protein is known (for example HLA-B*15:02) but the 3D structure is unknown. However, there may be an NMR or crystal structure available of a closely related allele (such as HLA-B*15:01). Using the 3D structure of this closely related variant as a template, a theoretical homology structure can be built. This technique was used in the study of HLA-B*15:02 with carbamazepine [33–35]. Homology modeling is usually followed by several cycles of full-atom molecular dynamics to optimize the overall geometry of the protein before conducting molecular docking. In some instances, the actual binding mode of a drug with a given protein is directly known from NMR or X-ray crystallography. If this is the case, as with HLA-B*57:01 and abacavir, then molecular docking may be performed directly on that structure after in-depth preprocessing steps have been completed. First, unessential counter ions and waters are removed (the ones in the

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 325

binding site can be preserved if necessary) and explicit hydrogens are added. Then, missing side chains are identified and corrected. Protonation states and H-bonding are optimized. The overall structure is minimized [51–55]. Inour group, we are notably relying on the Protein Preparation Wizard available in the Schrödinger Suite (www.schrodinger.com). Protein Preparation Wizard affords the easy removal of waters, salts, and H-bond optimization [51], while utilizing PRIME to generate missing side chains [52, 53] and protonation states are generated using EPIK at physiological pH of 7 [54, 55]. Finally, protein minimization is performed using the OPLS3 force field [56–60]. After the protein structure has properly been curated, it is important to perform self-docking (sometimes referred to as “native docking”) of the parent ligand from the original crystal. Self-docking is a critical step to verify whether the docking software can accurately retrieve the native binding mode of the ligand. After successful completion of self-docking, the curated and prepared compounds of interest can be docked. Chemical curation is an important step to ensure the correctness of chemical structures to be docked [61, 62]. The curated set of ligands should then be optimized (either with classical- or quantum mechanics) in addition to generating active tautomerized states at physiological pH. Our group performs ligand preparation using LigPrep available in the Schrödinger suite which generates tautomeric states with EPIK [54, 55] and k minimizes conformers using the OPLS3 force field [56–60]. k With the properly curated and preprocessed ligand database, molecular docking can be performed. There are numerous docking software available: Autodock Vina [63], GOLD [64], FLEXX [65], and GLIDE [66–69]. Our group relies on Schrodinger’s GLIDE for docking owing to the availability of multiple scoring functions, HVSP, SP, XP, and SP-PEP [66–69], as well as the overall prediction performances of the software [67]. Briefly, GLIDE operates by docking in three main steps: (i) performs initial screening of ligand conformers in the active site of the receptor, (ii) minimizes the energy according to a force field (e.g., OPLS3), and (iii) scores the molecular interactions to assess the estimated free energy of binding of the ligand [66]. The GLIDE DS estimates the free energy of binding of a ligand in the binding pocket and is formally the sum of the Glide score and the EPIK state penalty occurred from generating ligand tautomers [51, 66]. The eModel (eM) score is the sum of the Glide score, ligand-receptor interaction energy, and the ligand strain energy [66]. When studying HLA, we used two empirical thresholds to identify HLA-active compounds. A drug was considered HLA binder or “active” when it was found to afford a DS below −7 kcal/mol and an eM score below −50 kcal/mol. Recently, we developed a virtual screening molecular docking procedure for the HLA-B*57:01 variant using three crystals of HLA-bound abacavir (PDB: 3VRI, 3VRJ, 3UPR) [42]. Interestingly, each structure had a different co-binding peptide which allowed us to explore the importance of that peptide in HLA-drug binding. In this work, we did not use ensemble docking

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as we wanted to develop a fast virtual screening procedure targeting the HLA-B*57:01 variant and the fact that abacavir is found in an altered repertoire complex led us to hypothesize that there is little variation in the protein structure.

12.3.2 Case Study: Abacavir with B*57:01 Recently, we developed and tested a virtual screening protocol for identifying HLA-B*57:01-active drugs using flexible-ligand and rigid-protein molecular docking [42]. The HLA-B*57:01 variant was selected owing to the availability of three X-ray crystals consisting of HLA-B*57:01-abacavir and a co-binding peptide in an altered repertoire complex (PDB: 3VRI, 3VRJ, 3UPR) [37, 38]. Owing to the availability of these three crystals with a confirmed drug-binding mechanism and known HLA-B*57:01-drug associations resulting in ADR events (AHS and DILI), we decided to use these structures as a starting point for developing a virtual screening protocol. A schematic of our protocol is provided in Figure 12.4. As described in Section 12.3.1, we started by curating the three protein structures using the Protein Preparation Wizard available in the Schrodinger Suite [51–55]. Once the three proteins were preprocessed, we conducted a protein alignment to identify the differences between the three binding pockets, the k three units of abacavir, and the three co-binding peptides. The protein pocket k was found to be in the same conformation for all three crystals with measured root mean square Deviations (RMSDs) less than 0.6 Å and overlay similarities

Adverse drug reaction?

Figure 12.4 Schematic for using molecular docking to perform virtual screening at HLA-B*57:01 variant. (See color plate section for the color representation of this ﬁgure.)

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 327

greater than 75% [42]. Furthermore, we found that aside from ring puckering in abacavir’s cyclopropyl group or orientation of the hydroxyl functional group, the structure of abacavir in the pocket was universal among all three crystals [42]. The largest variation between crystals was in the co-binding peptide as each peptide had a unique amino acid sequence for 3VRI (P1: RVAQLEQVYI), 3VRJ (P2: LTTKLTNTNI), and 3UPR (P3: HSITYLLPV) [42]. Interestingly, when a peptide backbone alignment was performed, it was found that the backbone of each peptide was in a similar orientation with a measured RMSD of less than 1.8 Å [42]. On the basis of alignment results, we moved forward with the self-docking abacavir. In a molecular docking experiment with a known binding mode between a protein and a drug, self-docking is an essential step in validating the ability of molecular docking to retrieve that particular binding mode with very high accuracy. In the case of HLA, self-docking is even more important owing to the presence of a co-binding peptide. When a flexible-ligand/rigid-receptor protocol is used, GLIDE fixes the entire protein in place. Importantly, in the case of HLA complexes, this includes the co-binding peptide. As such, we conducted docking in the absence and presence of the co-binding peptide using the SP and XP scoring functions of GLIDE [66–68] in order to understand the specific role of the peptide in docking [42]. These results are provided in Figure 12.5. TheSP k and XP scoring functions were both able to reliably reproduce the native bind- k ing pose of abacavir in the absence and presence of peptide P1 with measured RMSD of 1.21-1.28 Å provided in Figure 12.5a [42]. Interestingly, the differences between structures occurred as a result of ring strain in the cyclopropyl moiety and the hydroxyl functional group. The observed protein-drug interactions between HLA-B*57:01 and abacavir were identical between the native crystal and the docking-generated binding modes except with respect to the hydroxyl group [42]. Strangely, the hydroxyl group was found to participate in the H-bond with three different amino acid residues as shown in Figure 12.5b [42]. Native abacavir from 3VRI has H-bonding between the hydroxyl group and the TYR74 residue, while SP docking without peptide found the hydroxyl group to H-bond with the TYR99 residue, and SP docking with peptide found that the hydroxyl group H-bonds with carbonyl backbone of ALA3 from P1 (Figure 12.5b) [42]. Owing to the proximity of TYR74, TYR99, and ALA3, it is possible that in a dynamic system, the hydroxyl group actually rotates and H-bonds with all three residues. In order to validate this hypothesis, molecular dynamic simulations still need to be performed. We also explored the role of the co-binding peptide in stabilizing the HLA-B*57:01-drug complex. It was observed that all three peptides (P1, P2, and P3) lowered the DS by 1–2 kcal/mol (Figure 12.5c) and that the eM score was lowered by 12-17 kcal/mol (Figure 12.5d) [42]. Next, a small test set of 14 drugs and abacavir were docked at 3VRI, 3VRJ, and 3UPR in the absence and presence of peptides P1, P2, and P3, respectively [42].

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328 Computational Toxicology

Scoring Peptide RMSD function P1 (Å)

(–) 1.21 SP (+) 1.27

(–) 1.21 XP (+) 1.28 (a)

Native abacavir SP (–) P1 SP (+) P1

(b) k k HLA-B*57:01 variant HLA-B*57:01 variant 3VRI 3VRJ 3UPR 3VRI 3VRJ 3UPR 0.0 0.0 –2.0 –0.5 –4.0 –6.0 –1.0 –8.0 –10.0 –1.5 –12.0 eM (kcal/mol) DS (kcal/mol) –2.0 –14.0 Δ Δ SP XP –16.0 SP XP –2.5 –18.0 (c) (d)

Figure 12.5 (a) Self-docking of abacavir alignment, (b) binding modes of abacavir, (c), (d) docking results. Source: Van Den Driessche and Fourches (2016) [42]. https://jcheminf. springeropen.com/articles/10.1186/s13321-017-0202-6. Licenced under CC BY 4.0. (See color plate section for the color representation of this ﬁgure.)

The test set consisted of abacavir, allopurinol, atorvastatin, carbamazepine, ciprofloxacin, clozapine, fenofibrate, flucloxacillin, methyldopa, minocycline, pazopanib, sertraline, simvastatin, and ticlopidine [42]. Associated ADRs and indications of these drugs can be found in Table 12.1, while the structures are provided in Figure 12.6. The test set of compounds were docked using both SP and XP scoring functions using protein structures from 3VRI, 3VRJ, and 3UPR in both the absence and presence of peptides P1, P2, and P3 [42]. Heat maps of

k k

HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 329

F N N N O H HN N N N OH N OH HO N N HO OH

OH N NH CH3 2 H3C

Abacavir Allopurinol Atorvastatin

CI H3C H2N O N N N N N HN O N NH OH F O

Carbamazepine Ciprofloxacin Clozapine

O CH OH 3 O H3C H3C O O O CI N CH3 O NH OH CH CI CH3 2 H3C O 3 S F NH HO k N k O O CH 3 HO Fenofibrate Flucloxacillin Methyldopa

H3C CH3 H3CCH3 CH3 CI N N NH2 NH OH H3C HN S CH O CI 3 O NH N N H3C N OH N N OH O OH O OH CH3 Minocycline Pazopanib Sertraline

HO O

O O N H3C O S H3C CI CH3 H3C

H3C Simvastatin Ticlopidine

Figure 12.6 Chemical structures of the 14 drugs used to construct a virtual screening molecular docking model. Source: Van Den Driessche and Fourches (2016) [42]. https:// jcheminf.springeropen.com/articles/10.1186/s13321-017-0202-6. Licenced under CC BY 4.0.

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330 Computational Toxicology

the DS and eM scores are provided in Figures 12.7 and 12.8, respectively, where green boxes indicate the lowest measured DS or eM score (most favorable), red boxes indicate the highest measured DS or eM score (least favorable), and white boxes indicate that no favorable binding pose between drug and HLA-B*57:01 were obtained. Interestingly, the role of peptide becomes quite apparent when viewing either Figure 12.7 or 12.8: there are three drugs that have favorable DS scores (less than or equal to −7 kcal/mol) and favorable eM scores (less than or equal to −50 kcal/mol) in the absence of P1, P2, or P3, but do not pass the thresholds when peptide is present [42]. Interestingly, fenofibrate in the presence of P1 passes both thresholds, but fails both thresholds when docked in the presence of P2 or P3. For the test set of drugs DS values range from −1.8 (flucloxacillin, XP + P2) to −10.3 (fenofibrate, XP + P1); however, there is significantly more variance in the eM scores. The lowest measured eM score is −91.6 (pazopanib, XP – P1), while the highest eM score is positive 112.2 (simvastatin, XP + P1) [42]. Intriguingly, there are four drugs that exhibit positive eM scores when the XP scoring function is employed in the presence of either P1, P2, or P3 (clozapine, fenofibrate, flucloxacillin, and simvastatin) [42]. When these positive eM

3VRI 3VRJ 3UPR k SP XP SP XP SP XP k DS (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (kcal/mol) P1 P1 P1 P1 P2 P2 P2 P2 P3 P3 P3 P3 Abacavir Allopurinol Atorvastatin Carbamazepine Ciprofloxacin Clozapine Fenofibrate Flucloxacillin Methyldopa Minocycline Pazopanib Sertraline Simvastatin Ticlopidine Scale –11 –10 –9 –8 –7 –6 –5 –4 –3 –2 –1 0

Figure 12.7 Heat map of DS for full set. Green spaces represent the most favorable docking scores (DS < −7 kcal/mol), while spaces transition from orange to red represent drugs that have nonfavorable interactions with HLA-B*57:01 (DS > −7 kcal/mol). White spaces indicate that GLIDE was unable to identify a best binding mode between drug and HLA-B*57:01. Source: Van Den Driessche and Fourches (2016) [42]. https://jcheminf.springeropen.com/ articles/10.1186/s13321-017-0202-6. Licenced under CC BY 4.0. (See color plate section for the color representation of this ﬁgure.)

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 331

3VRI 3VRJ 3UPR SP XP SP XP SP XP eM (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (kcal/mol) P1 P1 P1 P1 P2 P2 P2 P2 P3 P3 P3 P3 Abacavir Allopurinol Atorvastatin Carbamazepine Ciprofloxacin Clozapine Fenofibrate Flucloxacillin Methyldopa Minocycline Pazopanib Sertraline Simvastatin Ticlopidine Scale –96 –84 –72 –60 –48 –36 –24 –12 0 19 76 114

Figure 12.8 Heat map of eM scores for the full test set. Green spaces represent the most favorable docking scores (eM < −50 kcal/mol) while spaces transition from yellow to red represent drugs that have nonfavorable interactions with HLA-B*57:01 (eM > −50 kcal/mol). White spaces indicate that GLIDE was unable to identify a best binding mode between drug k and HLA-B*57:01. Source: Van Den Driessche and Fourches (2016) [42]. https://jcheminf. k springeropen.com/articles/10.1186/s13321-017-0202-6. Licenced under CC BY 4.0. (See color plate section for the color representation of this ﬁgure.)

scores are observed, it is expected that the corresponding DS would also be high;however,insomeinstancesthisisnotthecaseandinfactthecorre- sponding DS for fenoﬁbrate, ﬂucloxacillin, and simvastatin are rather negative. For example, when simvastatin was docked using XP + P1, the resulting DS was −10.3 kcal/mol, while the eM score was 112.2 kcal/mol. Simvastatin’s DS indicates that the drug forms extremely favorable interactions with the binding pocket, but the eM scores indicate that this pose will be unfavorable. It seems unlikely that both situations can be true to such an extreme extent. These ambiguous results indicate a clear limitation of applying molecular docking to a tripartite complex such as HLA-drug-peptide, where the dynamic interactions between drug and peptide are extremely important. Therefore, molecular dynamics simulations will be needed to further evaluate, analyze, and understand these interactions. Overall, our model was able to reproduce the binding mode of crystallized abacavir and successfully docked a test set of drugs with the HLA-B*57:01 variant. A full summary of these results are provided in Figure 12.9, where green cells represent drugs that passed both threshold criteria using SP and XP functions, yellow cells represent drugs that only passed using the XP function, orange cells represent drugs that only passed using the SP

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332 Computational Toxicology

3VRI 3VRJ 3UPR (–) P1 (+) P1 (–) P2 (+) P2 (–) P3 (+) P3 Abacavir Allopurinol Atorvastatin Carbamazepine Ciprofloxacin Clozapine Fenofibrate Flucloxacillin Methyldopa Minocycline Pazopanib Sertraline Simvastatin Ticlopidine XP/SP XP pass/ XP fail/ XP/S pass SP fail SP pass fail

Figure 12.9 Full docking summary combining SP and XP results. Green represents compounds that passed both DS (DS < −7 kcal/mol) and eM (eM < −50 kcal/mol) thresholds for SP and XP scoring functions; yellow represents compounds that passed the thresholds for XP but failed using SP; orange represents compounds that failed the XP thresholds but passed SP; and red represents the compounds that failed the thresholds for both XP and SP scoring functions. Source: Van Den Driessche and Fourches (2016) [42]. https://jcheminf. k springeropen.com/articles/10.1186/s13321-017-0202-6. Licenced under CC BY 4.0. (See color k plate section for the color representation of this ﬁgure.)

function, and red cells represent drugs that failed both SP and XP scoring functions. Furthermore, our model was able to successfully distinguish active pazopanib as an HLA-B*57:01-active compound. Interestingly, our model failed to identify flucloxacillin as an HLA-B*57:01-active compound. This result may occur because flucloxacillin is suspected to form a hapten complex with lysine containing peptides [49, 50] and molecular mechanics cannot create covalent bonds. Although there are some clear limitations with applying molecular docking as a virtual screening procedure with HLA variants, we feel confident that our model will be able to identify previously unknown HLA-B*57:01-active compounds when we conduct a screening of the DrugBank and Tox21 databases.

12.3.3 Limitations Developing a virtual screening protocol that can quickly and accurately identify HLA-drug associations in order to reduce the number of drug-related adverse events is extremely important for patient safety and reducing treatment-related costs. However, with a lack of experimentally veriﬁed HLA-drug associations

(such as IC50 or pK i values), the use of traditional virtual screening techniques

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 333

(e.g., QSAR, pharmacophore) is extremely difficult. Our group developed a virtual screening protocol using molecular docking targeting the HLA-B*57:01 variant [42]. Our model was able to successfully reproduce the binding mode of crystalized abacavir with three co-binding peptides. Furthermore, we successfully identified the stabilization energy provided by the co-binding peptide for abacavir (DS is stabilized by ∼1–2 kcal/mol and the eM score is stabilized by ∼12–17 kcal/mol). However, there are some clear limitations when applying molecular docking to such a three-part system. First, our model employs a flexible-ligand/ rigid-receptor molecular docking approach. In systems without a co-binding peptide, this technique can accurately be used to screen compounds for activity. However, in that model the co-binding peptide is also treated as rigid. In reality, the co-binding peptide will undergo conformational changes in response to the binding of different drugs. The next phase of our research will attempt molecular dynamic simulations of HLA-B*57:01 variant with different co-binding peptides and three active drugs in order to better understand the dynamic relationships between HLA, drug, and peptide. This limitation of a rigid peptide is clearly demonstrated for clozapine, fenofibrate, flucloxacillin, and simvastatin when the XP scoring function is used and a positive eM score is measured (Figures 12.7–12.9). Identifying the cause of this k shortcoming may prove difficult. However, the range of this limitation may be k explored by conducting multiple docking studies, performing self-docking, and employing molecular dynamic studies. Finally, this problem may be minimized by allowing amino acid side chains to rotate freely. Lastly, there is one major challenge when attempting any molecular docking study of HLA-drug binding. There are three mechanisms for HLA-drug signaling mechanisms for T-cell activation: altered repertoire, p. i. complex, and hapten complex (Figure 12.3). If a drug forms an altered repertoire, then generating a docking grid in the protein pocket (or beneath a peptide) should allow us to accurately screen for HLA actives; but the peptide binding pockets are extremely large (8–12 amino acid residues in length for Class I). However, if a drug forms a p. i. complex with HLA and peptide, then a docking protocol built using an altered repertoire complex (similar to our model) may not accurately forecast these compounds as active, because the model was designed using a different binding. Instead, an ensemble docking approach will be needed where molecular dynamics are used to first identify, where a drug non-covalently binds to the surface of HLA-peptide and then a molecular docking procedure is employed. In the event that a drug forms a hapten complex (such as flucloxacillin), then molecular docking and molecular dynamics will both fail to identify whether a drug is HLA active. This limitation is the direct result of hapten complexes created from the formation of a covalent bond, which limits these complexes to studies by quantum mechanics. One technique that could be used to identify these compounds would be the use

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334 Computational Toxicology

of quantum mechanics/molecular mechanics (QM/MM), where large systems are treated with MM while the bond formation site of the complex is treated at the QM level.

12.4 Perspectives

The dramatic cost reduction of genetic sequencing is fueling the development of personalized medicine opportunities. Indeed, DNA sequencing can now be conducted for less than $1,000 per individual which is a drastic drop from the reported value of $4,000 from mid-2015 [70]. Therefore, it is likely plausible to envision a future where all patients are genotyped as part of their routine lab work. Such highly specific patient-gene profiling could enable new strategies for prescribing drugs to individuals by taking into account their actual gene alleles and/or gene expression profiles. This scenario is already in place for certain types of tumors: for instance, BRCA1 was identified as a carrier for breast cancer in 2002 [71], which enabled a famous actress (and thousands of other women) to undertake a preventive double mastectomy to reduce her chances of breast cancer in 2013 [72]. Undoubtedly, we can imagine the same type of precision medicine strategy for limiting ADRs. The frequency of a particular HLA variant is very different from one k k variant to another, from one population to another. A study conducted in 2001 by Cao et al. [73] studied the frequency of multiple HLA-variants in African–Americans (n = 251), Caucasians (n = 265), Hispanics (n = 234), North American Natives (n = 187), and Asians (n = 358) from the United States. In Figure 12.10, three HLA-variants are highlighted (HLA-A*31:01, -B*15:02, and -B*57:01) with the percentage occurrence by population plotted. Interestingly, the occurrence of a variant changes with the ethnicity of the studied population. For instance, in African–Americans it was observed that there

African–American Caucasian 10 Hispanic North American ntatives Asians 8

By population (%) 2

0 HLA-A*31:01 HLA-B*15:02 HLA-B*57:01

Figure 12.10 Population distribution by percentage for three select HLA-variants: HLA-A*31:01, HLA-B*15:02, and HLA-B*57:01. The ethnicities studied were African-American (n = 251), Caucasian (n = 265), Hispanic (n = 234), North American Natives (n = 187), and Asians (n = 358) from the United States. Please refer to Cao et al. 2001 [73] for further details.

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HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities 335

was 2.4% occurrence of the HLA-B*57:01 variant, whereas the HLA-A*31:01 and -B*15:02 variants occurred in less than 1% of African–Americans [73]. Furthermore, the occurrence of the HLA-B*15:02 variant was not observed in Caucasian, Hispanic, or North American Natives, but was observed in those populations for the HLA-A*31:01 and HLA-B*57:01 variants [73]. Clearly, the changing distribution of HLA-variants by ethnicity presents another obstacle to the challenge of forecasting specific drug-HLA inducing events. Thus, new experimental assays need to be developed that aim to identify in patients the most common variants and/or the ones known to be responsible for the most life-threatening ADRs. Meanwhile, HLA-mediated ADRs could be better assessed using the cheminformatics and computational chemistry techniques we presented in the previous sections of this chapter. Modeling results recapitulated in this chapter demonstrate that strong HLA binders can be detected using molecular docking. However, there are limitations to the use of molecular docking as a tool to forecast drug-HLA associations that should be considered when employing the model. With the advent of more experimental assays capable of determining the binding affinities between drugs and HLA-variants, more robust cheminformatics models will use those experimental points and become more accurate. The use of several different cheminformatics models would greatly increase the likelihood of identifying k true HLA-active compounds. k Onepossibletechniqueforimprovingtheaccuracyofanymoleculardocking model could be the development of a virtual HLA Pocketome. A virtual HLA Pocketome would enable fast and automatic screening of lead compounds and drug candidates toward a panel of HLA variants. Protein binding site libraries already exist for protein kinases, cytochrome P450, and nuclear hormone receptor families to give a few examples (www.pocketome.org). If the drug candidate or any other chemical would be predicted to hit a list of key HLA variants with a binding affinity predicted to be significant, then alerts could be made for this chemical. Since HLA-mediated ADRs are rarely seen during clinical trials, such in silico predictions would facilitate their detection and thus help preventing such life-threatening events.

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46 Mir-Bonafé, J.M., Román-Curto, C., Santos-Briz, A. et al. (2013) Eczema herpeticum with herpetic folliculitis after bone marrow transplant under prophylactic acyclovir: are patients with underlying dermatologic disorders at higher risk? Transpl. Infect. Dis., 15 (2), E75–E80. 47 Pichler, W.J., Beeler, A., Keller, M. et al. (2006) Pharmacological interaction of drugs with immune receptors: the p–i concept. Allergol. Int., 55 (Novem- ber 2005), 17–25. 48 Illing, P.T., Mifsud, N.A., and Purcell, A.W. (2016) Allotype specific interactions of drugs and HLA molecules in hypersensitivity reactions. Curr. Opin. Immunol., 42, 31–40. 49 Monshi, M.M., Faulkner, L., Gibson, A. et al. (2013) Human leukocyte antigen (HLA)-B*57:01-restricted activation of drug-specific T cells provides the immunological basis for flucloxacillin-induced liver injury. Hepatology, 57 (2), 727–739. 50 Jenkins, R.E., Meng, X., Elliott, V.L. et al. (2009) Characterisation of flucloxacillin and 5-hydroxymethyl flucloxacillin haptenated HSA in vitro and in vivo. PROTEOMICS - Clin. Appl., 3 (6), 720–729. 51 Madhavi Sastry, G., Adzhigirey, M., Day, T. et al. (2013) Protein and lig- and preparation: Parameters, protocols, and influence on virtual screening k enrichments. J. Comput. Aided. Mol. Des., 27 (3), 221–234. k 52 Jacobson, M.P., Friesner, R.A., Xiang, Z., and Honig, B. (2002) On the role of the crystal environment in determining protein side-chain conformations. J. Mol. Biol., 320 (3), 597–608. 53 Jacobson, M.P., Pincus, D.L., Rapp, C.S. et al. (2004) A hierarchical approach to all-atom protein loop prediction. Proteins Struct. Funct. Genet., 55 (2), 351–367. 54 Greenwood, J.R., Calkins, D., Sullivan, A.P., and Shelley, J.C. (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J. Comput. Aided. Mol. Des., 24 (6–7), 591–604. 55 Shelley, J.C., Cholleti, A., Frye, L.L. et al. (2007) Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J. Comput. Aided. Mol. Des., 21 (12), 681–691. 56 Banks, J.L., Beard, H.S., Cao, Y. et al. (2005) Integrated modeling program, applied chemical theory (IMPACT). J. Comput. Chem., 26 (16), 1752–1780. 57 Harder, E., Damm, W., Maple, J. et al. (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput., 12 (1), 281–296. 58 Jorgensen, W.L., Maxwell, D.S., and Tirado-Rives, J. (1996) Development and testing of the OLPS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc., 118 (15), 11225–11236.

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59 Jorgensen, W.L. and Tirado-Rives, J. (1988) The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc., 110 (6), 1657–1666. 60 Shivakumar, D., Williams, J., Wu, Y. et al. (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J. Chem. Theory Comput., 6 (5), 1509–1519. 61 Fourches, D., Muratov, E., and Tropsha, A. (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 50 (7), 1189–204. 62 Fourches, D., Muratov, E., and Tropsha, A. (2015) Curation of chemogenomics data. Nat. Chem. Biol., 11 (8), 535. 63 Trott, O. and Olson, A.J. (2009) AutoDock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 31 (2), 455–461. 64 Jones, G., Willett, P., Glen, R.C. et al. (1997) Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 267 (3), 727–748. 65 Rarey, M., Kramer, B., Lengauer, T., and Klebe, G. (1996) A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 261 (3), 470–489. k 66 Friesner, R.A., Banks, J.L., Murphy, R.B. et al. (2004) Glide: a new approach k for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 47 (7), 1739–1749. 67 Halgren,T.A.,Murphy,R.B.,Friesner,R.A.et al. (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 47 (7), 1750–1759. 68 Friesner, R.A., Murphy, R.B., Repasky, M.P. et al. (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem., 49 (21), 6177–6196. 69 Tubert-Brohman, I., Sherman, W., Repasky, M., and Beuming, T. (2013) Improved docking of polypeptides with glide. J. Chem. Inf. Model., 53 (7), 1689–1699. 70 National Human Genome Research Institute (NHGRI) (2016) The Cost of Sequencing a Human Genome. 71 van ’t Veer, L.J., Dai, H., van de Vijver, M.J. et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415 (6871), 530–536. 72 Jolie, A. (2013) My Medical Choice. New York Times. 73 Cao, K., Hollenbach, J., Shi, X. et al. (2001) Analysis of the frequencies of HLA-A, B, and C alleles and haplotypes in the five major ethnic groups of the United States reveals high levels of diversity in these loci and con- trasting distribution patterns in these populations. Hum. Immunol., 62 (9), 1009–1030.

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Open Science Data Repository for Toxicology Valery Tkachenko1, Richard Zakharov2,andSeanEkins3

1 Rockville, MD, USA 2 Rockville, MD, USA 3Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA

CHAPTER MENU

Introduction, 341 Open Science Data Repository, 342 Beneﬁts of OSDR, 344 k Technical Details, 351 k Future Work, 353

13.1 Introduction

Tools for collaboration predominantly revolve around desktop computer applications [1, 2] and use “software as a service” as a business model [1–3]. These desktop software have also become more widely accessible in academia and small companies (e.g., CDD Vault, Science Cloud). Such applications are useful for secure sharing of data with collaborators in which retention of intellectual property (IP) is important. However, we are increasingly seeing a shift to more companies, institutes, and researchers openly sharing data, regardless of IP. While this has been predominantly in the neglected disease space (GlaxoSmithKline, Novartis, and St. Jude’s sharing malaria and tuberculosis data) [4–8] this is starting to broaden, for example, GlaxoSmithKline sharing of kinase data [9] and AstraZeneca sharing their Absorption, Distribution, Metabolism and Excretion (ADME) data on ChEMBL [10]. Alongside this, there are increasing eﬀorts from researchers to publish in open access journals and release data into open or free databases [11, 12]. In addition, there are an increasing number of online tools that are viable for storing science-related content, for example, FigShare (Digital Science), SlideShare (Microsoft),

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Mendeley (Elsevier), Dropbox, and so on and consortia to coordinate and make data accessible, such as OpenPHACTS [13, 14], as well as a myriad of other initiatives to free up data [15–17]. There are also repositories such as Open Software Foundation (OSF), Dryad, Dataverse, DataHub, Kaggle, and others which may or may not owned be by publishers or other commercial organizations and some cases which are open source. This raises the question how can we possibly connect all this predominantly open data? Social media themselves can be useful for sharing data, such as blogs and wikis in which open scientists can describe their scientiﬁc methods and results, link to other content (uploaded images, graphs, and models), and beyond [11, 12]. We are also seeing the beneﬁts of investments over the last decade in high-throughput screening that has resulted in large structure-activity datasets entering public and open databases. For example the openly available ChEMBL [10, 18] database has already been assembled by careful curation, and provides the necessary raw data to kick-start literally thousands of groups of structure–activity datasets, of various sizes. Furthermore, the massive PubChem database is frequently used by scientists to deposit raw data in its original, machine-readable form as an adjunct to publication. The EPA Tox21 measurements against hundreds of targets [19] represents a third large dataset. However, the quality of the data for some might still be questionable. While all k these resources are great sources of information they are still prone to a series k of fundamental weaknesses which were introduced by design and cannot be incrementally eliminated. We therefore envisage in future a massive growth in open science and we predict the need for an open science data repository (OSDR) which would be particularly valuable for sharing curated data in real time and making models accessible, including those based on toxicology data.

13.2 Open Science Data Repository

We believe an OSDR will be useful for connecting scientists and sharing data for many types of projects. This represents a potential knowledge base for real-time management of various scientific data. OSDR fully adopts FAIR data principles (data fully supporting the FAIR - findable, accessible, interoperable, reusable - principle for research data findable, accessible, interoperable, reusable [20]). All data in OSDR are assigned a globally unique identifier, besides which data parts can be assigned a Digital Object Identifier (DOI). All data are described with rich metadata. Data and metadata become immediately searchable on creation or update. Metadata exposed through RESTful Application Program Interface (API) conform through “maturity level 3” and contain data identifiers. Both data and metadata are accessible through RESTful API. In fact, the whole OSDR is built as a swarm of microservices communications among one another and with User Interface (UI) through

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RESTful APIs. Data and metadata layers in ODSP are separated and have various levels of persistence, guaranteeing that metadata remain accessible even when data are not available anymore. Both data and metadata that persist in OSDR are exposed through RESTful API endpoints supporting a standard (extensible) set of ontologies and standard formats and protocols. Metadata use controlled vocabularies where possible, but OSDR also supports a process of building custom-controlled vocabularies. RESTful API supports “maturity level 3.” The OSDR data model is organized as a set of arbitrary hierarchical entities with the ability to associate metadata documents with the necessary plurality at each level of hierarchy. All OSDR entities are associated with the license or the license can be derived from constituent parts. All entities are associated with their provenance pointing to the source of the data record. Data and metadata in OSDR are grouped into domain-specific and ontology-driven groupings. The technology therefore can be readily used for sharing toxicology data and models (similar to those described in other chapters of this book). We believe open sharing of data with OSDR will also facilitate scientific insights and the discovery of new therapeutic approaches when used for drug discovery applications. OSDR provides capabilities for depositing and managing general data, it has a unique architecture that allows to connect new chemistry-intelligent modules to the existing data processing k and curation pipeline. To provide some perspective, some comparisons with k other databases and repositories are discussed in the following. PubChem [21] is a database built to deposit, process, and provide public access to chemical data, but its data-deposition pipeline is rigid and there are no real-time user curation capabilities. ChEMBL [10] and ChEBI [22] are highly curated chemical databases with heavy ontological information content, but without online curation. Figshare [23] is a general-purpose data repository with a heavy emphasis on scientific content and with one of its key features being the ability to generate and assign DOI to the data. Mendeley’s [24] data repository provides similar capabilities to those of Figshare. OpenPHACTS [14] was proposed as a semantic web knowledge base to provide private/public access to a variety of chemical and biological information. In reality, integration of data from multiple data sources is so challenging that data updates can be made once every few months at best, rather than in real-time as was expected. Open- PHACTS has also provided very valuable outcomes which we have built on, namely, its (i) data collections (ii) theoretical basis (e.g., ontologies), and (iii) practical experience. Our goal therefore is to address all these deficiencies accumulated in other systems and make OSDR the de-facto gold standard for open science. Such a tool will then enable improved data sharing and collaboration with potential impact across many scientific areas. The value proposition for the use of OSDR can be summarized as follows. The project is intended to bring together open and prepublication data and

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then facilitate research around the data. By connecting focused groups of disparate individuals and organizations scattered around the country or globe, ample opportunity exists to both gather and disseminate important information to a highly relevant target audience. This includes information about new scientific developments of interest. Team members will be able to borrow and reuse a growing collection of existing data. This should result in new technology which would benefit the toxicology, neglected, rare disease communities (as examples) as well as far beyond. We are making the OSDR open source [25], will provide support for the platform, offer custom development services, and license the API. This is a viable and well-established business model for open source software which will be leveraged.

13.3 Beneﬁts of OSDR

We are pioneering key innovations in the prototype OSDR which will likely be of value to customers (Figure 13.1) with a broad array of features and applications (Figures 13.2–13.4).

13.3.1 Chemically and Semantically Enabled Scientific Data k Repository k There is a clear disconnect between chemical databases, publishers’ data repositories, and semantic web knowledge bases. OSDR provides a basic chemistry data processing pipeline, including validation and standardization of chemical representation, but unlike PubChem, ChemSpider [26, 27], and others, it extends the list of supported types to reactions, crystals, and analytical data. Because of the ability to read and interpret chemical formats, OSDR provides chemical-indexing capabilities (the content can be searched by chemical structure, reaction, etc.) on top of regular searches by various alphanumeric properties. OSDR allows real-time data curation and will support ontology-based property assignment with subsequent complex searches. OSDR’s deposition pipeline includes the data mining stage which, for example, allows text mining and chemical names to chemical structures conversion on the fly when a new document is deposited. The OSDR security model supports private, shared, and public data. Statistical models are usually built on versatile data collected from various data sources. It is widely known that the quality and domain of applicability of models is defined by those of the training datasets (the primary sources of such data are, e.g., PubChem and ChEMBL). OSDR, by incorporating a data mining and curation pipeline on top of integration with multiple data sources, provides a platform for the rapid composition of training datasets for immediate modeling. It was shown in multiple publications [28] that the quality of quantitative structure–activity relationship (QSAR) predictions highly

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Figure 13.1 (1) Examples of the OSDR prototype to date showing bidirectional integration with various cloud drives allows seamless data transfers between cloud storage and OSDR; (2) web user interface also allows intuitive data deposition using drag and drop; (3) concise ﬁlter system provides a convenient way of navigating information stored or indexed in OSDR; (4) hierarchical presentation of information allows one to arrange the data based on organization or research structure; (5) standard CMS (content management system) operations are supported; (6) various view modes allow representing complex information in a visual and concise manner; (7) user interface based on modern web frameworks provides an excellent user experience. (See color plate section for the color representation of this ﬁgure.)

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Figure 13.2 Examples of the OSDR prototype to date showing OSDR tabular data entry. Mapping columns from a CSV file (1) to their semantic meaning (2) allows to resolve entries in real-time into a set of public database identifiers (3, ChemSpider, ChEMBL, PubChem), create a chemical structure from provided information (4), and calculate conversion confidence value based on a set of mappings (e.g., chemical name, InChI, SMILES).

depends on how the data used for model training was prepared as well as the type of descriptors used in model training and does not depend that much on the machine learning method.

13.3.2 Chemical Validation and Standardization Platform The Chemical Validation and Standardization Platform (CVSP) [27] is an open source application developed for OpenPHACTS for chemical data validation and standardization. The original rules set was taken from the FDA Substances Registry System user manual [29] and extended with IUPAC rules. CVSP is already a part of the OSDR data processing pipeline and will be extended further, beyond chemical structures, into other types of data.

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(a) k k Figure 13.3 Examples of the OSDR prototype to date. (a) Document browse mode with thumbnail previews. (b) Document view mode with a larger preview and other information arranged into infoboxes.

13.3.3 Format Adapters While recent standardization attempts will eventually result in a set of adopted formats for data representation, historically (and especially before the appearance of structured and schema-driven formats such as XML and JSON), a number of various data formats were developed and adopted. Notable efforts include the development of SPL (stands for structured product labeling and is an HL7 standard for medical information exchange and a future format for application submission into United State Food and Drug Administration’s Substance Registry System), ISA-tab and recently JSA-JSON, National Center for Biotechnology Information ASN.1, and others. On the other hand, while publishers mainly operate and accept ChemDraw CDX files with publications, major public databases such as PubChem and ChemSpider accept MOL and SDF formats, whereas for those involved with chemistry-related data science, the most convenient format is SMILES, InChI, InChIKey, or a set of internal or external identifiers. Some of these identifiers (e.g., InChIKey) are not reciprocally convertible, hence require existence of specific registries (e.g., InChI Resolver). Proprietary and in-house developed identifiers impose

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(b)

Figure 13.3 (Continued)

a similar challenge, but with an even more complicated situation as custom identifiers are never supported by publicly available registries. This quickly leads to the situation of creating multiple discrepancies on the level of data reformatting and exchange requiring a major effort to prepare data for deposition into PubChem or other databases. OSDR already supports various formats and is able to convert between them seamlessly on the fly at the time of import or export. The situation will only become more complicated when the FDA will start accepting applications for broad classes of chemicals in SPL format. With the experience gained by us in developing SPL representation of various classes of chemicals (substances, mixtures, proteins, structurally diverse substances, polymers, DNAs, RNAs, and various conjugates), OSDR already provides the ability to convert popular structure representations into

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Figure 13.4 Examples of the OSDR prototype to date. Built in preview mode showing diﬀerent ﬁle types (1, word; 2, excel; 3, powerpoint; 4, PDF).

SPL. Another problem is that most chemical data exist as CSV files with k chemical structures encoded as CAS numbers, chemical names, SMILES, or k InChIs and InChIKeys (in some cases). Chemical modeling software as well as other applications expect chemical structure to be represented as a connection graph which, for example should be able to extract/calculate fingerprints. Such a conversion may be trivial for SMILES and InChIs, but is problematic for chemical names, CAS and other registry numbers, InChIKeys, and other types of chemical identifiers. OSDR has the built in capability to load CSV files, specify column mapping to semantic type, and run real-time conversion with results being visually controlled. Once information is imported into a system, other operations such as export in various formats and modeling become possible. Another case publishing use is again connected to the fact that majority of electronic supplementary information exists as ChemDraw CDX files. There are multiple issues concerned with CDX files, including those with poor quality of drawing and misrepresentation of chemicals and reactions. OSDR reads and converts CDX files on the fly at deposition time and is able to flag erroneous chemistry using the built-in CVSP [27]. OSDR also allows export of all such imported/converted data in the SDF format, which is accepted by most public databases. Such functions can facilitate data depositions into those public databases which otherwise would require small businesses to buy costly software and resort to manual data preparation and curation, which is prohibitively expensive.

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13.3.4 Open Platform for Data Acquisition, Curation, and Dissemination Data are essential in science and data repositories well-supported by National Science Foundation (NSF) and other grants have been around for decades; yet they consist of merely file stores. The missing traits are (i) format awareness (the ability to visualize, index, and convert numerous domain-specific formats), (ii) secure data sharing, (iii) an ontological layer, (iv) real-time data quality checks (e.g., structure validation using CVSP), (v) integration with social networks (share, reuse, and build-on existing public profiles, including ORCID), and (vi) browser integration for ease of use and data submission. OSDR was designed with all these missing traits in mind and with proper implementation, it can create an ecosystem for the data market. This is a concept that rests on the assumption that should any goods (including information) become a commodity and proper tools operating on those commodities become available, then there are ample opportunities for new businesses to thrive on such a commodities market supplying various services around the supply–demand pipeline. For the data market, OSDR could help to provide chemicals for synthesis, chemical waste/by-product utilization, experimental data measurements, algorithm/model development (e.g., toxicology models for environmental or consumer product uses), and many others k applications. k

13.3.5 Dataledger We will implement DataLedger, which represents an open source framework based on the approach and technology used in Blockchain [30] and Hyper- ledger [31], which will allow creating a secure, open, and distributed chain of data, metadata, and provenance events associated with information stored in OSDR. Such technology allows tracking and deredundifying data, operations on data, and authentic results, which will help to eliminate data plagiarism, a tremendous challenge for scientific research. By various estimates only 3–5% of collected experimental data ever find their way into publication and supplementary information, while the rest gradually decay in private collections or institutional data vaults. The methodology of proper research data sharing was developed as a part of FORCE11 effort, but technical implementation based on FAIR data principles [20] is lagging behind. We believe that the concept of an OSDR supported by Dataledger technology will greatly facilitate creation of the Science Data Market. The latter is based on the assumption that should any goods including intellectual goods become a commodity and if an unobstructed flow of such commodities is facilitated by respective systems and tools then a new opportunity for free trade becomes possible. There is clearly no such case for scientific data which are produced in great volumes and normally funded by taxpayers money and at best find their way into open access or closed access

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publications with no way to reproduce the original results. Yet liberating such scientific data would have a tremendous impact on scientific progress in general. If the process of data creation (e.g., experimental measurement or data mining) becomes fully attributable to those who conduct it and if the system facilitates proper data licencing and attribution at all subsequent stages, then scientific data become commodities and those who participate in every stage of this process should be allowed to at least gain an attribution or at most be rewarded intellectually and financially. We think that this could be incredibly valuable in making data relevant to toxicology available in this way.

13.4 Technical Details

OSDR represents a knowledge base for real-time management of various scientific data. It recognizes various formats including the most popular cheminformatics formats (MOL and SDF [32] for representing chemical substances, RXN and RDF [33] for representing reactions and metabolic pathways, CIF [34] for representing crystallographic data, PDB [35] for representing proteins, InChI [36], SMILES [37], CML [38] for representing various types of chemical information), office formats (Microsoft Word, Excel and PowerPoint), PDF, k analytical data formats (JCAMP [39] and FID for spectral data), as well as gen- k eral tabular data (CSV format, containing information about chemicals such as SMILES, InChI, or chemical name along with general identifiers, properties and other text and numerical data). The flexible and extensible architecture allows it to connect new plugins for handling additional data types. The architecture of the system follows a regular pattern for deposition gateways into large chemical databases and is built on the assumption that raw data being deposited into a system have to undergo a series of parameterized transformations which in general are represented by a workflow consisting of various elements including blocks providing domain-specific functionality (e.g., format conversion, data import and export, automated chemical validation, and standardization [27]). The OSDR workflow supports extension of the core functionality with implementation of functional blocks which are connected into a system using.NET reflection [40] and MEF framework [41]. The architecture of the system is a result of our approximately 20 years of experience in building similar platforms for pharma companies and the broad scientific community (PubChem [42], ChemSpider [43], OpenPHACTS [13], National Chemistry Database Service [44]). OSDR follows the Agile development approach that allows rapid development-deployment-customer feedback iterations. The system is being developed as a set of loosely coupled independent services. This approach, called microservices [45], Figure 13.5, as of today, is broadly accepted and considered to be suitable for development of systems with a wide range of complexity.

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Typical microservice

Security

Cache (optional) Web API

Processing service A

Processing service B

Monitoring Storage

Figure 13.5 OSDR microservice overview.

The microservices approach allows adding new functionality to the existing k system without affecting the rest of the system’s stability. New modules and k services therefore can be developed and added to the system by either the developers or the open source software/scientific community as the need for such arises. Another important consideration of this architectural approach is the ability to build technically heterogeneous systems that can use any programming language or framework given the precondition that they follow the industry-standard RESTful application communication conventions. RESTful services are build using the regular HTTP protocol that is universally supported and does not need additional hardware or any additional software libraries to support [46]. This feature allows disparate teams to build extension services using the programming expertise they have with the tools they are most comfortable with. OSDR is also built with a security-first principle in mind. While the whole system comprises a set of micro-services and APIs, access to everything is controlled on the basis of cryptographically strong and secure network protocols. They support not just control for the data access but also sharing of data between collaborating parties. Integration with various cloud drives (currently GoogleDrive, DropBox, and Box) allows bidirectional secure exchange with information stored elsewhere and seamless import and export in various formats. An intuitive user interface provides ample capabilities for organizing the data in a way that corresponds to the organizational structure, field of research, ease of representation, and perception. The latest web frameworks make the system extremely lightweight,

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scalable, and able to run on all devices (desktops, tablets, handheld devices). The information presentation can be adjusted for the field of study, particular projects, and collaboration. Documents are represented with thumbnails with associated snippets of data and support full-screen previews. The document mode (Figure 13.3) supports various property views and allows the entering of additional arbitrary or templated information. The built-in preview mode (Figure 13.4) allows reading of rich text documents directly into OSDR. Large sets of supported formats allow OSDR to store various data files that are usually used and produced in research next to each other. With the integrated format conversion ability, one needs to pay very little attention to a particular storage format, focusing instead on the information nature, provenance, and analysis. OSDR supports import from various external systems and websites including ChemSpider, Wikipedia, and arbitrary web pages with subsequent text and data mining. Complementary chemical conversion restores concise hierarchy representing complex areas of study – chemicals, syntheses, materials, and so on. Records and file information are organized into a set of info-boxes with simple and complex fields. Complex fields allow OSDR to store, visualize, and index virtually any datatypes which can be hierarchically organized. The ability to associate licenses allows it to “mix and match” both open, embargoed, and closed data to produce k derivatives and share derived knowledge, still keeping track of all operations k involved in data manipulations. The ability to assign keywords based on controlled vocabularies allows OSDR to navigate data in various alternative ways. Custom and standard property editors and visualizers then allow creation and representation of complex knowledge in a clear and concise manner.

13.5 Future Work

The work on OSDR was started in November 2015 with an early prototype created in the summer of 2016. It was then realized that to keep the platform capable of responding to a modern informatics world challenges, the architecture of the system had to be completely changed and this has taken almost another year to implement. OSDR therefore currently consists of several major subsystems and a number of connected services (Figure 13.6). A staging area is a subsystem that supports real-time data management operations. CVSP (described earlier) is one of the microservices. Other services perform data import, export, conversion, processing, curation, and other functions. Information entry and editing is done in the user interface and conveyed to the main part of the system only through the RESTful Web API. Security control is currently implemented on top of IdentityServer. All system components are decoupled and interact either through RESTful API or using a system bus. Angular2 is used as a client framework, NET platform (currently a mix of .NET Framework 4.6.2 and .NET

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API Search

Security Bus control

Staging area CVSP Services Models

Machine Datasets Cheminformatics libraries learning libraries libraries

Figure 13.6 Logical architecture of OSDR with cheminformatics and machine learning modules. k k Core) is used for the server-side and MongoDB is used as the main database engine, although some services have their own specialized backup stores (e.g., Redis and ElasticSearch) (Figure 13.7). Microservice architecture is commonly associated with containerization technologies such as Docker [47] and Win- dows Containers [48] (Figure 13.8).

Messaging bus

Staging area CVSP ML

Community Community module 1 module 2

Text search Chemical search

Figure 13.7 OSDR microservice-oriented architecture.

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Jenkins Cl Developers Github

Docker Containerized Store Service

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Figure 13.8 OSDR development workﬂow.

Each OSDR module, or service, is packaged as a container image that requires minimum conﬁguration when deployed. The containers can be deployed using on-premises datacenters, on individual computers, or in a cloud services such AWS, Azure, or Google, using the same images that can published in the Docker Store repository, that similarly to Github, allows version control and convenient storage of the container images. Since OSDR, as a platform, consists of self-contained containerized services, the performance of the overall system can be adjusted by simply adding more container instances oftheservicesthatimpedethewholesystem’sperformance.Thecontainerized services can be dynamically spun up during peak hours or brought down to save costs by container orchestration mechanisms such as Docker Swarm [49] or Google Kubernetes [50]. These mechanisms observe the system’s usage, or load, and automatically add or remove service instances, allowing for system

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elasticity. This way, any scale desired for the current installation and/or current scientific research process can be achieved. OSDR is built using the modern CI/CD (continuous integration/continuous delivery) development paradigm to significantly shorten the software delivery time while ensuring the quality of the system. This is achieved through a succession of steps. Static code analysis tools are used at the development time to aid with the code checks while it is being written. Requiring that most, if not all, of the source code is covered by unit tests helps to ensure that the building blocks of the system are designed properly and function properly without hitches. The unit tests are run by a continuous integration server, such as Jenkins, at the time of each code check-in, so that the developer breaking any module would be notified immediately of any broken tests. A set of BDD (behavior-driven development) tests is being developed by the quality assurance team. The BDD tests describe the specific end-user scenarios. The tests can be written in a language closely resembling human-readable use cases. These tests are run automatically as well as part of the CI/CD pipeline with detailed reports indicating any potential problems.

13.5.1 Implementation of Ontology-Based Properties k k The properties editor in OSDR allows in-place operations on associated data types and values, including hierarchical ones which are used not only to capture a value of the property but also associated metadata, for example, provenance data, associated conditions, and references which is essential for data quality assessment and control. Such property values are stored in JSON and are imported from external sources generated during processing or created as a part of the curation process. Almost the same JSON representation is being synchronized with ElasticSearch that provides an out-of-the-box rich and fast full text and property search experience. To validate and enforce value correctness, we will allow it to connect externally controlled vocabularies whose content is usually available as plain text, XML, or JSON dumps. We will also provide an editor for controlled vocabularies as a part of OSDR. The OSDR properties data model allows it to associate property type with its semantic value and by associating semantic type with one or more controlled vocabularies, we will enforce data validation. To improve user experience and make the curation process more powerful we will develop specialized editors for complex properties. For example, chemical structure, which is usually stored as SMILES or MOL, will be conveniently edited in the chemical sketcher. We also provide a way to organize property groups into “info-boxes” based on their semantic meaning. The info-box will have a “schema” (JSON schema) associated with it and we already have code under development which allows generating view/edit forms based on the JSON schema.

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13.5.2 Implementation of an Advanced Search System Currently, OSDR allows data acquisition from numerous sources and formats, including many domain-specific formats and office documents, and even capturing data and metadata from web pages. But as the number of supported formats, their complexity, and the size of the data in OSDR grows it becomes essential to provide an alternative navigation system based on domain-specific information (such as chemical substructure, similarity, clustering), associated keywords, powerful free-text query variants, ontological terms, complex property values, and so on. We will implement an advanced search system for all information in OSDR and provide alternative navigation capabilities. Current searching in OSDR is based on ElasticSearch which is being populated with a replica of JSON representing respective objects in the staging area. We will expand search capabilities in the following ways. First, we will generate search forms based on the JSON schema associated with various info-boxes thus allowing one to use particular search criteria based on the area of research interest. Secondly, we will provide a set of “orthogonal” searches to slice data collections based on field types associated with particular data types. Combination of such searches with previous full-text searches will allow navigation of all data not just in hierarchical manner (which is connected to how the data are arranged in a system) but create data slices based on specific k k criteria. Third, we will implement chemical structure and reaction searches using third party open source software (Bingo from EPAM).

13.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework We will extend basic authentication and integration with social networks by developing an OSDR Scientist Profile which extends the one used by ORCID [51] and other publishing user profiles. To facilitate collaboration between scientists as well as their groups using data stored in OSDR, we will complete implementation of the data sharing and security layers which will fully support fine-grained access to the data on all levels. We will let scientists invite and des- ignate collaborators, expose data and send new data sharing requests, provide data quality ranking and usage statistics, and provide a comprehensive notification framework. To achieve this, we will start by extending the OSDR user profile with information specific to OSDR. We will then add a configuration layer on top of the OSDR basic schema to allow specification of access rules which can be enforced at organization and user levels. When respective access to such particular functions or area of OSDR is requested, we will use OAuth protocol to solicit such access from the resource owner. We will also implement a simple notification framework to notify resource owners of concerned actions of other system users.

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Further, we will work on integration with various social networks, cloud storage systems, and existing data repositories. We plan to expand the properties model by choosing and adapting taxonomies and ontologies relevant for biomedical information for example (BAO [52, 53]). We will also implement a full secure data sharing and collaboration mechanism that is based on the public key infrastructure [54], OpenID, and OAuth2. As we expect OSDR to grow and start gathering more information, the scientific data in such a system will become a commodity and as such will create an opportunity to further develop a “data market.” OSDR presents an opportunity for structure-activity datasets relating to toxicology to be accumulated. In turn, the standardization will provide datasets already in a format for descriptor generation and enable machine learning model construction. Such models could hence be stored in OSDR and potentially shared with collaborators. The benefits of using such open data repositories for academics could be that it will facilitate data sharing in a cost-effective manner. For small companies, it will provide industry-quality technology that is readily accessible. If we are to leverage the growth in toxicology-related datasets in recent years, we need them to be accessed for machine learning. OSDR will provide this capability. k k References

1 Ekins, S., Hohman, M., and Bunin, B.A. (2011) Pioneering use of the cloud for development of the collaborative drug discovery (CDD) database, in Collaborative Computational Technologies for Biomedical Research (eds S. Ekins, M.A.Z. Hupcey, and A.J. Williams), John Wiley & Sons, Inc., Hoboken, NJ. 2 Hohman, M., Gregory, K., Chibale, K. et al. (2009) Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. Drug Discov. Today, 14, 261–270. 3 Bost, F., Jacobs, R.T., and Kowalczyk, P. (2010) Informatics for neglected diseases collaborations. Curr. Opin. Drug Discov. Dev., 13, 286–296. 4 Cressey, D. (2011) Traditional drug-discovery model ripe for reform. Nature, 471, 17–18. 5 Guiguemde, W.A., Shelat, A.A., Bouck, D. et al. (2010) Chemical genetics of Plasmodium falciparum. Nature, 465, 311–315. 6 Gamo, F.-J., Sanz, L.M., Vidal, J. et al. (2010) Thousands of chemical starting points for antimalarial lead identiﬁcation. Nature, 465, 305–310. 7 Fidock, D.A. (2010) Drug discovery: priming the antimalarial pipeline. Nature, 465, 297–298.

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8 Ekins, S. and Williams, A.J. (2010) Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs. MedChemCommun, 1, 325–330. 9 Drewry, D.H., Willson, T.M., and Zuercher, W.J. (2014) Seeding collaborations to advance kinase science with the GSK published kinase inhibitor set (PKIS). Curr. Top. Med. Chem., 14, 340–342. 10 Bento,A.P.,Gaulton,A.,Hersey,A.et al. (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res., 42, D1083–D1090. 11 Bradley, J.-C., Guha, R., Lang, A. et al. (2009) Beautifying data in the real world, in Beautiful data (eds T. Segaran and J. Hammerbacher), O’Reilly Media Inc., Sebastopol, CA. 12 Bradley, J.C., Lancashire, R.J., Lang, A.S., and Williams, A.J. (2009) The spectral game: leveraging open data and crowdsourcing for education. J. Cheminform., 1,9. 13 http://www.openphacts.org/. OpenPHACTS, http://www.openphacts.org/ (accessed August 14, 2017). 14 Azzaoui,K.,Jacoby,E.,Senger,S.et al. (2013) Scientific competency questions as the basis for semantically enriched open pharmacological space development. Drug Discov. Today, 18 (17–18), 843–852. 15 Hunter, J. (2011) Precompetitive collaboration in the pharmaceutical indus- k try, in Collaborative computational Technologies for Biomedical Research k (eds S. Ekins, M.A.Z. Hupcey, and A.J. Williams), John Wiley & Sons, Inc., Hoboken, NJ, pp. 55–84. 16 Hunter, J. and Stephens, S. (2010) Is open innovation the way forward for big pharma? Nat. Rev. Drug Discov., 9, 87–88. 17 Hunter, A.J. (2008) The innovative medicines initiative: a pre-competitive initiative to enhance the biomedical science base of Europe to expedite the development of new medicines for patients. Drug Discov. Today, 13, 371–373. 18 ChEMBL, http://www.ebi.ac.uk/chembldb/index.php (accessed August 14, 2017). 19 Huang,R.,Xia,M.,Sakamuru,S.et al. (2016) Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat. Commun., 7, 10425. 20 Anon, The fair data principles, https://www.force11.org/group/fairgroup/ fairprinciples (accessed August 14, 2017). 21 Kim, S., Thiessen, P.A., Bolton, E.E. et al. (2016) PubChem substance and compound databases. Nucleic Acids Res., 44, D1202–D1213. 22 Degtyarenko, K., de Matos, P., Ennis, M. et al. (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res., 36, D344–D350. 23 Anon, Figshare, http://figshare.com/ (accessed August 14, 2017).

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24 Anon (2017) Mendeley, https://data.mendeley.com/ (accessed August 14, 2017). 25 Tkachenko, V. (2017) OSDR, https://github.com/scidatasoft/OSDR (accessed August 14, 2017). 26 Pence, H.E. and Williams, A.J. (2010) ChemSpider: an online chemical information resource. J. Chem. Educ., 87, 1123–1124. 27 Karapetyan, K., Batchelor, C., Sharpe, D. et al. (2015) The chemical validation and standardization platform (CVSP): large-scale automated validation of chemical structure datasets. J. Cheminform., 7, 30. 28 Fourches, D., Muratov, E., and Tropsha, A. (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 50, 1189–1204. 29 FDA (2007) Food and Drug Administration Substance Registra- tion System Standard Operating Procedure, https://www.fda.gov/ downloads/ForIndustry/DataStandards/SubstanceRegistrationSystem- UniqueIngredientIdentifierUNII/ucm127743.pdf (accessed August 14, 2017). 30 Anon, Blockchain, https://en.wikipedia.org/wiki/Blockchain_(database) (accessed August 14, 2017). 31 Anon, Hyperledger, https://www.hyperledger.org/ (accessed August 14, 2017). k 32 Anon, Chemical table file, https://en.wikipedia.org/wiki/Chemical_table_file k (accessed August 14, 2017). 33 Anon, MDL MOLfiles, RGfiles, SDfiles, Rxnfiles, RDfiles formats. https:// docs.chemaxon.com/display/docs/MDL+MOLfiles,+RGfiles,+SDfiles,+ Rxnfiles,+RDfiles+formats (accessed August 14, 2017). 34 Anon, Common intermediate format, https://en.wikipedia.org/wiki/ Common_Intermediate_Format (accessed August 14, 2017). 35 Anon, File format, http://www.wwpdb.org/documentation/file-format (accessed August 14, 2017). 36 Anon, Inchi, https://iupac.org/who-we-are/divisions/division-details/inchi/ (accessed August 14, 2017). 37 Anon, Simplified molecular-input line-entry system, https://en.wikipedia .org/wiki/Simplified_molecular-input_line-entry_system (accessed August 14, 2017). 38 Anon, Chemical markup language, https://en.wikipedia.org/wiki/Chemical_ Markup_Language (accessed August 14, 2017). 39 Anon, JCAMP, http://www.jcamp-dx.org/ (accessed August 14, 2017). 40 Anon, Reflection in the.NET framework, https://msdn.microsoft.com/en- us/library/f7ykdhsy%28v=vs.110%29.aspx?f=255&MSPPError=-2147217396 (accessed August 14, 2017). 41 Anon, Managed extensibility framework (MEF), https://msdn.microsoft .com/en-us/library/dd460648(v=vs.110).aspx (accessed August 14, 2017).

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42 Anon, The PubChem Database, http://pubchem.ncbi.nlm.nih.gov/ (accessed August 14, 2017). 43 ChemSpider, http://www.chemspider.com (accessed August 14, 2017). 44 Anon, National Chemistry Database Service, http://cds.rsc.org/ (accessed August 14, 2017). 45 Fowler M. (2014) Microservices, https://martinfowler.com/articles/ microservices.html (accessed August 14, 2017). 46 Anon, RESTful services, https://en.wikipedia.org/wiki/Representational_ state_transfer (accessed August 14, 2017). 47 Anon, Docker containers, https://www.docker.com/what-docker (accessed August 14, 2017). 48 Microsoft Windows Containers (2016) https://docs.microsoft.com/en-us/ virtualization/windowscontainers/about/ (accessed August 14, 2017). 49 Anon, Docker Swarm, https://www.docker.com/products/docker-swarm (accessed August 14, 2017). 50 Anon, Google Kubernetes, https://kubernetes.io/ (accessed August 14, 2017). 51 Anon (2017), ORCID, https://orcid.org/ (accessed August 14, 2017). 52 BAO, http://bioassayontology.org (accessed August 14, 2017). 53 Visser,U.,Abeyruwan,S.,Vempati,U.et al. (2011) BioAssay Ontology k (BAO): a semantic description of bioassays and high-throughput screening k results. BMC Bioinformatics, 12, 257. 54 Anon, Public key infrastructure. PKI, https://en.wikipedia.org/wiki/Public_ key_infrastructure (accessed August 14, 2017).

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Developing Next Generation Tools for Computational Toxicology Alex M. Clark1, Kimberley M. Zorn2, Mary A. Lingerfelt2,andSeanEkins2

1Molecular Materials Informatics, Inc., Montreal, Quebec, Canada 2Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA

CHAPTER MENU

Introduction, 363 Developing Apps for Chemistry, 364 Green Chemistry, 364 Polypharma and Assay Central, 374 k Conclusion, 382 k

14.1 Introduction

Public sources of open data from repositories like ChEMBL, PubChem, ToxCast, and so on can represent an ideal starting point for drug discovery and computational toxicology efforts and increasingly these datasets may also be useful for absorption, metabolism, excretion and toxicity (ADMET) modeling. However, this manually curated data is not in a form that is immediately accessible for computational model building. Being able to use transparent computational models simultaneously for visualizing activity trends for multiple targets (for diseases and ADMET) removes the burden of curation or purchasing and maintaining expensive software, and drastically simplifies the addition of new data. It also represents a new frontier of drug discovery as a world of small, agile distributed R&D organizations has access to valuable public datasets that can inform their research. The effort required to preprocess, filter, merge, validate, and normalize the structure and activity data requires a great deal of software expertise and medicinal chemistry domain knowledge, which are key skill sets that are rare and expensive to combine within one team. In this chapter, we will outline some of our efforts to make software accessible for cheminformatics as well as our most recent efforts which aims to address

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the lack of accessibility of public screening datasets for machine learning models. We have also recently discussed in more detail the need for a shift in the way individual scientists translate their chemistry and biology data into digital form such that it can be consumed by machine learning algorithms [1].

14.2 Developing Apps for Chemistry

Chemistry apps for mobile phones and tablets are available for a variety of workflows that involve chemical structures. One of the early challenges was coming up with user interface principles that made sketching viable on a small touchscreen [2], but once this was achieved, a generous subset of the universe of cheminformatics became viable on ultraportable devices. Mobile cheminformatics is exemplified by the Mobile Molecular DataSheet (Figure 14.1), which built on the core sketching capabilities to provide content creation for collections of molecules, reactions, and data. The ability to create, send, and receive chemical data on a mobile device was novel, and was supplemented by a growing suite of built-in calculations and web-service integration, such as the ability to search public databases. We are now using the expertise from creating such tools with collaborators like Collaborations k Pharmaceuticals, Inc. to build desktop and web applications to expand the k reach of these tools. Examples of products and descriptions are shown in Table 14.1. The following chapter describes some of our efforts to develop software applications that could be relevant to computational toxicology. While we have focused initially on mobile applications, these efforts will ultimately transition to the desktop in web applications.

14.3 Green Chemistry

Green chemistry is “the utilization of a set of principles that reduces or eliminates the use or generation of hazardous substances in the design, manufacture and application of chemical products” [14]. There have been recent eﬀorts to associate the green chemistry properties of molecules (such as stability, aquatic toxicity, etc.) with the molecular features that enables prediction [15–17] as well as the computational design of chemicals [18]. Much of the focus has been on optimizing material and energy use, preventing and minimizing waste, increasing use of renewable materials and energy, having safe processes, and eliminating or minimizing the use of hazardous chemicals. Solvent selection is key to determining the sustainability of future commercial production as solvents can make up more than 50% of materials used in the manufacture of bulk active pharmaceutical ingredients [19]. Solvent use is just a part of a chemical reaction that has to be considered in order to determine the overall

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Figure 14.1 Screenshots of the Mobile Molecular DataSheet.

greenness of a process. A reaction can be considered to be “greener” to the extent that it uses starting materials based on renewable feedstocks, avoids toxic or diﬃcult to dispose of reagents, favors catalysts rather than stoichiometric reagents, minimizes use of solvents, and telescopes multiple reaction steps into a single procedure in order to reduce isolation and puriﬁcation steps [14]. A number of quantitative metrics are useful to help optimize for

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Table 14.1 Mobile apps for chemistry developed by Molecular Materials Informatics, Inc.

App App name and descriptions

The Mobile Molecular DataSheet (MMDS) is the progenitor app [3], which provides features for drawing molecular structures and reactions, organizing datasheets, sharing data, access to web services, and so on. The functionality in this app is the foundation of a library, MMDSLib. The apps described below are all based on this library MolPrime is a simpliﬁed app [4] which allows a structure to be drawn, then used in several ways: reviewed for basic properties, launched with other apps, searched with Mobile Reagents or ChemSpider, copied to the clipboard or sent by e-mail

Green Solvents [5] presents a list of solvents, categorized by functional group. Each of the solvents comes with information about its properties with regard to environmental hazards and disposal. They can easily be looked up in ChemSpider and Mobile Reagents

ChemSpider Mobile [6] provides the most eﬀective way to search ChemSpider for chemical structures from a mobile device. The app was commissioned by the Royal Society of Chemistry, and is free to use

SAR Table enables the user to readily construct structure-activity k relationship tables and publish them [7] k

TB Mobile is a free app which combines cheminformatics (Bayesian models, clustering) and bioinformatics data for >800 small molecules that have known targets in Mycobacterium tuberculosis [8, 9]

Chemical Valence is a purely educational app designed to teach Lewis octet bonding theory to high school/freshman chemistry students, using intuitive animations and touchscreen interaction to introduce the correspondence between bonding electrons and chemists’ line notation [10]

The Green Lab Notebook is used to capture chemical syntheses in full detail, providing a variety of supporting calculations, archiving and database lookups [11]

Approved Drugs provides a convenient list of ∼1300 FDA-approved drugs that can be browsed by structure or viewed (2D and 3D) in clustering mode. Custom structures can be drawn and evaluated in the context of existing drugs [12]

PolyPharma provides access to a collection of models for targets and oﬀ-targets which can be evaluated for individual molecules [13] using a simple tap and swipe interface

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desirable properties, such as percentage yield, atom-economy, process mass intensity (PMI), and E-factor [20]. As well as being important ways to reduce the impact of the chemical industry on the environment, these considerations map directly to economics once a reaction is scaled up beyond the milligram quantities generated in a laboratory, particularly given that the externalities of chemical disposal are increasingly being passed on to industry.

14.3.1 Green Solvents and Lab Solvents To illustrate our preliminary work in green chemistry, we now describe our work on green solvents. This involved curation of public data and development of a novel interface [5]. As solvents can make up a large percentage of materials used in the manufacture of bulk active pharmaceutical ingredients, if we are to make synthetic processes “greener” it would be ideal to select greener solvents [21]. This has stimulated several companies to implement their own guides regarding the importance of solvent selection [21]. A consortium organized by the American Chemical Society called the Green Chemistry Institute (ACS GCI) Pharmaceutical Roundtable [22] currently involves 14 pharmaceutical® companies, and has developed a solvent selection guide publicly available on their website [19]. The ACS GCI Pharmaceutical Roundtable Solvent Selection k Guide lists the 60 solvents by chemical name and rates the solvents against k safety, health, air, water, and waste categories with scores from 1 (few issues) to 10 (most concern) with additional color coding (green, yellow, and red). In our opinion, the availability of this guide is not widely known because, to our knowledge, it has not been extensively publicized and its utility may be hamperedbecauseitonlyexistsasadocumentinPDFformatthatisonthe ACS GCI website (that also requires registration). The limitations in access and utility encouraged us to recast the content in a novel manner to greatly enhance its visibility and availability to practicing chemists. We have also used the solvent classification data for enabling predictions for solvents outside the guide. Mobile devices such as smartphones and tablet computers have seen rapid uptake in recent years and the associated app stores include a growing number of chemistry software apps [23]. These apps generally perform one or two functions and can be thought of as individually packaged features rather than the relatively heavyweight programs commonly used in desktop computing. However, such apps can usedatainterchangeandbeusedintheworkflowto increase the productivity of chemists [24]. We have used the ACS GCI Pharma- ceutical Roundtable Solvent Selection Guide data as a starting point to develop the first mobile app for green chemistry called Green Solvents (Figure 14.2) that is currently freely available for iPhone, iPod, and iPad. The advantages of developing such software is that it makes the solvent guide available at the bench or whenever the chemist is away from a desktop computer. The development and uses of this app include its value as an educational tool for students with

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Figure 14.2 The Green Solvents app. (a) Molecule overview. (b) Molecule details list scores (good = 1, bad = 10) for safety, health, ﬂammability, environment, waste, reactivity, and lifecycle criteria. (See color plate section for the color representation of this ﬁgure.)

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(b)

Figure 14.2 (Continued)

the goal being to foster an understanding of green chemistry by understanding which solvents are “greener.” Additional solvents not in the Green Solvents app were extracted from the GSK solvent selection guide [21] and have been used as a test set for predictive modeling [5]. Creating the Green Solvents app also motivated the addition of the PMI calculation [20] which is another green chemistry feature, into the Green Lab Notebook (GLN) app [25]. A version of the app for Android called Lab Solvents included the GSK data as well and was also developed. In addition to being made available as free mobile apps, we have also made the data on green solvents available in a general web format [26], and anyone can download the raw data in a variety of formats (e.g., MDL SDfile). The Green Solvents app has buttons along the top that correspond to categories. Turning each of them on or off makes it act like a filter. The three buttonsontheright(flame,skullandcrossbones,anddeadfish)areasummary of the seven different environmental properties assigned to the solvents. They are used on the solvent glyphs themselves along with a color code, to give

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a rough idea at a glance which ones are particularly unpleasant. Tapping on the corresponding buttons along the top panel causes the solvents to be sorted by best first, worst last; so if you want to know which solvents are most green in every sense, turn them all on. The only solvents that are completely green are water and carbon dioxide. The primary deliverable of the app is still about as simple as it gets: tapping on a solvent brings up a detail view, with all of the specifics shown: The specific environmental categories are shown listed out, and below them a variety of useful reference information. CAS registry numbers are given for each solvent, along with handy links to external databases such as ChemSpider, PubChem,andCompTox. Experimental physical properties of most relevance – melting and boiling points and density – are also included. We keep in mind, of course, that these data are guidelines.

14.3.2 Green Lab Notebook Requiring chemists to study the green chemistry literature and continually cross reference their experimental procedures with a field that they would not necessarily follow is a bottleneck to the adoption of greener synthetic procedures. While there are several databases for searching for chemical reactions (e.g., CAREACT, SyntheticPages, ORGSYN, Beilstein, SPRESI, k etc.), there is a lack of a single easily searchable repository of green chem- k istry reactions with highly favorable green chemistry properties. This is an important issue to address. A selective and thorough curation effort should be performed in a way that not only includes references to the relevant literature, but also includes detailed markup such that each reaction is represented as a transform. Such properly represented data can be used by an algorithm to map qualifying reactants or products, and apply the transformation to an arbitrary user-proposed molecular structure, either for creating new products (synthesis) or working backward from proposed products (retrosynthesis) [27, 28]. We hypothesize that if the process of designing a green reaction were as simple as drawing a reactant and/or product, and submitting it to a web service to find all the applicable ways of carrying out the reaction, the rate of adoption of green chemistry could be significantly accelerated. The benefits of thiswouldaccruetonotonlytheorganizationsdevelopingthechemicalsbut also the environment in general. There are significant pressures from regulations and regulators such as California’s Green Chemistry Legislation and from the market, driven by increasing consumer awareness, to identify alternatives to chemicals of concern. From a business standpoint, it makes sense to start moving away from these chemicals and materials as soon as possible. It is important in today’s commercial environment to be seen as green. This means an understanding of the chemicals in a product and their effect on the environment. Preferably companies should consider environmental impact, human health, ecotoxicity,

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and social metrics for a variety of endpoints. It would be ideal if one could bring in these metrics earlier, before the molecule is synthesized for the first time. This is highly relevant to the pharmaceutical industry that uses vast amounts of solvents, catalysts, stoichiometric reagents, and other materials when synthesizing new drugs. While there are millions of total reactions in some commercial databases such as SPRESI (3.9 million) and Beilstein (>22 million), it is likely that the list of known reaction transforms can be reduced to several hundred representative instances by raising the bar sufficiently high for green metrics. Such a list could be considered as the ideal first choice for synthetic planning. We propose focusing on recent publications in which researchers have devised greener transformations and in particular those efforts focused on pharmaceuticals [29–31]. This is achievable because there are several thousand known drugs [32]. By delivering an electronic web service, we envision that it could be ultimately used with computational toxicology and computational chemistry tools to predict properties relevant to green chemistry. Current approaches in this realm include using large datasets and molecular descriptors to build statistically validated models [33] which may be more useful than relying on quantum chemical approaches that use chemical reactivity alone [17]. The societal impact of this work is a more comprehensive adoption of environmentally benign syntheses, k resulting in greener and safer products delivered to the consumer. This will k result in chemists no longer having to study the green chemistry literature and continually cross reference their experimental procedures with a field that they would not necessarily follow. By removing this bottleneck to the adoption of greener synthetic procedures, we may accelerate the use of green chemistry. As a first step toward this, we have developed the GLN which is a tool that is designed primarily for capturing chemical reactions (Figure 14.3). It is structure centric and divides the reaction into individual components (reactants, reagents, and products) in order that they can be classified and balanced. Quantities such as mass or volume can be entered for these components, and are combined with structure, stoichiometry, density, concentration, and yield in order to automatically calculate related quantities whenever possible. Green chemistry metrics are always displayed for a reaction whenever there is enough information to calculate them. The app has strong information-sharing capabilities, including importing and exporting in a variety of formats, both of the informatics and graphical varieties. A number of customizable supporting data collections are provided, such as common solvents (with green properties), sketcher templates, and a molecular scratch sheet. The central unit used by the GLN is the datasheet, which can be thought of as a single file, which contains some number of chemically relevant data, such as molecules, reactions, experiments, and so on. An individual entry in a datasheet is referred to as a row,and a collection of datasheets is referred to as a folder.Mostofthepanelsshown on the main menu represent either a single datasheet, or a group of datasheets

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Figure 14.3 Examples of green reactions from the Green Lab Notebook app.

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(folder). In each of these cases, the graphical summary shown on the main screen shows the first few molecules or reactions within the underlying data. Tapping on the main area will open the corresponding editor, while tapping on the icon at the top left will bring up a high-level action menu that is specific to that panel. There are two panels that do not correspond to existing data: the information panel contains icons which are intended to be helpful, such as about and feedback.Thecreate experiment panel has the sole purpose of prompting for the creation of a new experiment datasheet, which can then be populated with data. The most important kind of datasheet used by the GLN is the experiment. Each entry in an experiment datasheet contains a chemical reaction (which can be multistep), corresponding properties and quantities, and other metadata. Most of the action takes place by editing and viewing experiment datasheets. The solvents folder is a collection of datasheets that contain information about laboratory solvents. These are initially populated with information from the green solvents data (see earlier). They can also be customized by editing, deleting, and adding new solvents. The scratch sheet is a collection of molecular structures that operates like a chemical clipboard. Structures can be conveniently copied back and forth between experiments. The template folder is a group of datasheets each of which has a structural fragment theme. These fragments play an important role in drawing new chemical k structures, as they can be fused and grafted onto a current drawing. This is k particularly useful for large fragments or anything that is tedious to redraw frequently, or whose structure is hard to remember. The list can be customized according to personal needs. The GLN allows the drawing and management of chemical reaction experiments. In summary, the app is structure centric, and takes care to represent reactions in a way that is meaningful to both human scientists and machine algorithms, which makes it suitable both for creating graphical output and for storing and using within informatics systems. The always-on automatic calculation of green chemistry metrics (atom economy, PMI, and E-factor), and convenient availability of common solvents with green properties, encourages consideration of environmental effects of chemistry. By removing this bottleneck to the adoption of greener synthetic procedures, we may accelerate the use of green chemistry. Chemists tend to make compounds using methods they know well and therefore encouraging them to explore new techniques which may be more efficient and greener is a challenge. While there are already databases of reactions, these do not explicitly help the chemist create greener products, and so this is still a trial and error process. We can make the process of designing a green reaction simpler by having the chemist use software to simply draw a reactant and/or product, have it find all of the applicable reactions with highly favorable green chemistry metrics. While the GLN app is a fully capable content creation device that runs on iPhones and iPads, the general idea is to position it as a tool that brings unique valuewhenitisactuallyusedatthebenchtop.Theplanningandanalysiscan

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be done using the same tool, or it can be done elsewhere: as long as the content can be easily passed back and forth between them, there is a real opportunity to further reduce the use of pen and paper, and correspondingly increase the fraction of science that is accessible to software.

14.4 Polypharma and Assay Central

We are now seeing the benefits of investments over the last decade in high-throughput screening that is resulting in large structure–activity datasets entering public and open databases. Model building based on such structure–activity data is underutilized in drug discovery. The next step is to solve the “data problem,” to a significant extent, for a very broad variety of disease targets and ADMET properties as present in the public domain. This will make the data and models accessible to academia and industry alike and level the playing field. This is a plausible objective for a particular reason: the openly available ChEMBL [34, 35] database has already been assembled by careful curation, and provides the necessary raw data to kick-start literally thousands of groups of structure–activity datasets, of various sizes. The ChEMBL project has done an excellent job of quality-controlled curation k [35–37] and machine-friendly annotation of activity values, which has been k widely cited as a breakthrough because it provides both quality and quantity, the combination of which was hitherto elusive in the open data realm. How- ever,becauseithasnotgonesofarastoorganizethisdatainawaythatis ready for computational (machine learning, quantitative structure–activity relationship, etc.) model building, [38] a huge opportunity exists to fill this gap by designing an automated script to sort through and arrange the existing content in a way that can be fed directly into modeling algorithms. At the present time, there are a number of very effective machine learning and other modeling techniques that are simply not used by a large fraction of the industry. Over the past ten years or so, we have applied naïve Bayesian approaches to ADME and drug discovery datasets [7, 39–55]. We are particularly interested in advancing the use of Bayesian models based on structure-derived fingerprints, because this technique is very well validated for over a decade and has been used successfully for many drug discovery projects. We have also been involved in making the constituent technologies available as open source software [8, 40, 41], which has recently eliminated the barrier of expensive proprietary software [56]. Perhaps most importantly, the method is transparent and robust: it is fast and works well for large and small datasets, and can be applied in a “turn-key” fashion with minimal expertise required of the operator. Like all modeling techniques, however, it is only as good as the data that is fed into it. Bayesian modeling of structure–activity data is a well-solved problem, but gathering high-quality data for specific projects on

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Developing Next Generation Tools for Computational Toxicology 375

a large scale is very far from solved. Most commonly in small drug discovery efforts, either the effort of gathering the data is off-putting, or the results of the modeling exercise are inadequate because the data is of poor quality. By ensuring that the data is available and well validated, both of these barriers will be removed, which means there will be few reasons for scientists to pass over the opportunity to include modeling in their drug discovery efforts. By creating a streamlined build system for assembling the data, we will be well positioned to expand the content. Visualization of huge numbers of models is unusual in contemporary cheminformatics (scientists are used to looking at one at time) and most research efforts focus on a small handful of properties to model at any one time. By making use of our recently published method for calibrating the Laplacian-corrected variation of the Bayesian technique [40] that is most suited to structure-derived fingerprints, we can present huge numbers of predictions using a color-coding scheme that is consistent across different series. The relationship between structure-derived fingerprints and their contributions to a Bayesian model have been exploited, using an algorithm that assigns a weighting to individual atoms, which allows users to visualize which part of a molecule contributes to higher or lower activity within the model. As well as using coloring to provide many predictions for many compounds k in a compressed space, we have further developed another technique for k color-coding individual atoms based on individual fingerprint contributions for a molecule being evaluated within a Bayesian model (Figure 14.4). This technique has been demonstrated recently as a proof of concept within several mobile apps (Mobile Molecular Datasheet, MMDS). The TB mobile 2.0 app [8,9]usesBayesianmodels(basedonECFP6fingerprints)topredictactivities against a number of different Mycobacterium tuberculosis targets, allowing surprisingly sophisticated drug discovery efforts to be done on an iPhone or iPad [40, 41]. The same ECFP6 fingerprints were used to implement a novel interactive clustering display, also within this app [8]. In general, we have obtained a considerable amount of experience in structure-centric visualization methods [57–59] and manipulations such as scaffold analysis [60, 61], much of which is currently implemented within a cheminformatics library (com.mmi from Molecular Materials Informatics, written in Java) which is being used for this project. Mostrecently,wehavebeenactivelyconstructingBayesianmodelsfor ADME properties such as aqueous solubility, mouse liver microsomal stability, and Caco-2 cell permeability [40], which complements our earlier ADME machine learning work [39, 42–44, 48, 51, 53–55]. This has led to models with good acceptable receiver operator characteristic scores >0.7 [40]. In particular, we have created a proof-of-concept extraction system using ChEMBL v20, originally intended for creating datasets for use in method development, and

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376 Computational Toxicology

(a) (b)

Figure 14.4 Example of preliminary work. (a) Highlighting molecules using Bayesian k models for various ADME/Tox properties. (b) Clustering molecules using ﬁngerprint k descriptors. (See color plate section for the color representation of this ﬁgure.)

testing a new algorithm for automated determination of cut-off thresholds for active/inactive classification [41] as illustrated for hERG data (Figure 14.5). There have been few other publicly discussed efforts to rearrange the ChEMBL data to create thousands of model-ready datasets, besides our own [41]. We intend to build upon this work in order to be able to create commercial products that can explore thousands of predictions per molecule, as well as provide an increasingly effective technology base for integrating newly acquired data, keeping our content ahead of the open data curation curve for selected disease targets and ADME/Tox properties [40]. The use of ChEMBL represents an advance in scientific terms and being relatively easy for us to consume in this project, it becomes the basis for generalizing to other databases. Furthermore, the massive PubChem database is frequently used by scientists to deposit raw data in its original, machine-readable form as an adjunct to publication. We have demonstrated the use of EPA Tox21 measurements against 29 targets (predominantly nuclear hormone receptors) [62] in the PolyPharma app, all of which were extracted directly from Pub- Chem (Figure 14.6). The objective of this project is to develop a web-based tool called “Assay Central” to compile a comprehensive collection of datasets for structure–activity data for a broad variety of disease targets and ADMET

k k

553 compounds >7.89 1. 0 test model 37 active desirability 516 inactive

0.5

ECFP6 (0.983) FCFP6 (0.987) 0369120.0 0.5 1.0 Selected Inactive Molecules N Cl O F HO O N

N N F H H N O HN Cl N HN H Cl N N S NN N H N O H N N S O O O k N O k NH2 O N N H Selected Active Molecules F O N N HN F H H N H H N N N O NH N N N N S S N N N N N N N H N N N H H HO H N H H NH N N S

Figure 14.5 Visualization of data cut-oﬀs, ROC plots, and active and inactive molecules for hERG Ki data from ChEMBL.

k k

(a) (b)

k k

Figure 14.6 Preliminary work using open datasets and computed models for (a) EPA Tox21 data used to make predictions that are visualized in the PolyPharma mobile app. (b) Novel visualization and prediction methods in PolyPharma showing atom highlighting for each model and clustering. http://itunes.apple.com/app/polypharma/id1025327772. (See color plate section for the color representation of this ﬁgure.)

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Developing Next Generation Tools for Computational Toxicology 379

properties, in a form that is immediately ready for model building and other forms of analysis using informatics methods. We will also provide these models and an interface for using them. This could also serve as a collaborative tool to bring researchers together to share data for model creation which itself would be a considerable achievement to increase access to open data. By removing barriers to making the data available within easy-to-use software products, the opportunities for cheminformatics to assist in discovering new drugs (and also toxicity liabilities) will be greatly increased. Besides specific datasets being maintained with convenience and quality, the quantity property opens up many new possibilities. While a research group may undertake data acquisition and model building for the immediate target of interest, being able to present model results for thousands of targets has potentially very exciting implications for serendipitous discoveries and drug repurposing or repositioning [63–66]. While the ChEMBL and other database teams have not made the data available in a form that is ready to drop into a model-building algorithm, we will provide this “final mile” and add additional layers of validation and processing. For PubChem data, we will need to collate and process submissions corresponding to selected assay protocols on a case by case basis, as with the EPA Tox21data [62]. We have already created a proof-of-concept extraction system using k ChEMBL v20, originally intended for creating datasets for use in method k development and testing a new algorithm for automated determination of cut-off thresholds for active/inactive classification [41]. This work will serve as the basis for the project. We have also done a significant amount of work with Bayesian modeling and underlying technology, which exists as institutional knowledge and functioning prototype code. Since we designed, built, and submitted an open source implementation of the ECFP6/FCFP6 fingerprints [8], and followed this up with Bayesian model-building modules [40, 41, 67], we have all of the pieces in place ready to be deployed as part of a production system. We also have experience in working with ontologies, in particular the BioAssay Ontology (BAO) project [68], which was recently used for hybrid machine/human parsing of assay descriptions, which is highly relevant for recording provenance for assays, and also overlaps significantly in terms of applied technology. We have also recently weighed in on the importance of data quality to the field of cheminformatics, and the methods that we are preparing to develop to ensure that our data collections are in excellent shape, have been long in the planning phase [1]. Content sourced from public databases commonly lacks provenance and is often in a very disreputable state (ChEMBL [34, 35] is one of the rare excep- tions). User-supplied data is often in problematic data formats, such as Excel spreadsheets (which have unlimited degrees of freedom for data entry styles), comma-delimited text files (which are often ambiguous and underspecified), or MDL SDfiles with little context (e.g., missing units) and inconsistent structure

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380 Computational Toxicology

representation styles. The amount of effort required to convert these data sources into a consistent and rigorous definition, and merge them together is very high and usually requires considerable manual labor, as it is impossible by definition to automate an import process from a format that has unlimited degrees of freedom. In too many cases, data quality issues are largely ignored. In spite of these issues, modeling is still often effective, but not ideal. If we canremovetheeffortrequiredtoassemblethedataandimprovethequality, we will also greatly increase the value of modeling to small research and development groups. Another core hypothesis is that it is better to start with small batches of high-quality data and grow them slowly with well-validated content than to attempt to grab everything that is available, even if much of it is junk. Using smaller, high-quality datasets also has the advantage that anyone involved in the modeling process can hope to examine the data that went into the model and gain an appreciation of its size, domain, and quality. For moderately large datasets, clustering methods can be used to support this goal.

14.4.1 Future Efforts with Assay Central We have started to develop Assay Central by incorporating more recently k published datasets for Chagas, Ebola, Leishmaniasis, and Zika, as well k as toxicity datasets such as hERG. Structures, activity data, and bioassay information are first curated in XMDS (Molecular Materials Informatics, http://molmatinf.com/xmds.html). Datasets are then processed in Assay Central, while Bayesian models are constructed from identical assay types and endpoints. This represents a first step to develop Assay Central. In future, the ChEMBL-extracted targets and assays will be annotated in more detail using semantic web terms from the BAO in a way that is compatible with our recent work [68], starting with links provided within the ChEMBL database itself, and using manual annotation where necessary. This will allow us to compare targets (e.g., by properties in Uniprot [69, 70]) or by the specifics of the assay measurements, which is essential for considering the addition of subsequent assay data. We started to supplement the original data with more data that we had previously curated from open collections, for instance solubility, bubonic plague, ebola, tuberculosis, malaria, and Chagas disease sets, as well as several smaller collections for other diseases (Figure 14.7). We will also acquire selected datasets from PubChem, for which we have verified the provenance and quality. These datasets will either complement existing groups of data from ChEMBL or be used to define new collections. We will apply best-of-breed methodology for checking and correcting structure–activity data [71] which errs on the side of caution for problems with non-obvious solutions, so that we can manually identify problems and either apply patches or data-source-specific automated corrections. As more data sources are added from diverse sources, the structure

k k

Developing Next Generation Tools for Computational Toxicology 381

k k

Figure 14.7 Assay Central schematic.

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382 Computational Toxicology

processing becomes increasingly important, as standard representation and de-duplication is essential for high-quality machine learning model building. For each of the target-activity groups, we will create a Bayesian model using ECFP6 or FCFP6 ﬁngerprints and this will be one of the primary outputs from the project. Models will be evaluated using internal and external testing with receiver operator characteristic (ROC > 0.75), the integral of the true-negative-rate – true-positive-rate curve as well as the enrichment [72], Kappa value [73, 74], Youden’s J statistic [75], F1 Score [76], and positive predicted value [77]. We have already begun to explore preliminary visualization methods (Figures 14.4 and 14.6) using multiple models, but these have so far focused primarily on a handful of machine learning models selected from a very large list. New visualization techniques are required to summarize large matrices of data, for example, a list of proposed structures versus thousands of target models. Calling attention to interesting model outcomes, making it convenient to drill down on interesting regions and creating an interface that is interactive and performant requires considerable innovation. We will continue to expand the data by upgrading to newer ChEMBL releases, selectively incorporating screening runs from other databases (such as PubChem [78]), by manual curation, and by accepting curated high-quality data from customers and partners. The quality control process will be constantly reﬁned as more k diverse data sources are incorporated. k

14.5 Conclusion

As much as touchscreen devices represent an exciting frontier for all kinds of computing, the reality is that content creation still works better with a big screen and a keyboard. The objective of all this work with chemical reactions (GLN) and machine learning (PolyPharma, Assay Central) is to bring the informatics content out of the dark ages and that involves effort on a number of fronts, including the creation of a lot of content. By effectively prototyping many of our cheminformatics ideas as mobile apps, we can quickly and cost effectively try out datasets, algorithms, and data visualization approaches. In the process, we are able to focus on potential products that could be created and used by scientists. For example, the Green Solvents and Lab Solvents mobile apps have been downloaded tens of thousands of times, but more importantly have made previously inaccessible data freely available to all. Several mobile apps such as MMDS, TB Mobile, and PolyPharma have made machine learning models available to all. In the case of PolyPharma, many of the models are relevant to prediction of endocrine disruption, an important toxicology issue both for pharmaceuticals and consumer products [79]. What we have put into practice so far is but a small example of what may be possible in the area of computational toxicology.

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Developing Next Generation Tools for Computational Toxicology 383

Acknowledgments

We gratefully acknowledge NIH funding R43GM122196 from NIGMS and many discussions with Dr Antony Williams.

References

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k k

389

Index

a adverse drug reaction (ADR) 94, 313, 3A4 136 322, 326, 335 AB110873 135 adverse outcome pathways (AOPs) abacavir 314, 318, 321, 322, 324, 149, 214, 233, 237 326–330 agile development 351 abacavir hypersensitivity syndrome agonist 126 (AHS) 326 agranulocytosis 317 Abbott diagnostics 201 agrochemicals 280 k ABCG5/G8 146 agrregated computational toxicology k ab initio 35, 36 online resource (ACToR) 295 Abraham’s LFER model 278 aldosterone synthase 135 absorption 3, 271, 273, 274 alert 265 acetaldehyde 300 aliphatic epoxides 56 alkaloids 127 acetaminophen 320 allopurinol 318, 328–332 acetycholinesterase 43, 44 allyl acrylate 48, 55 acoustic 231 alprazolam 178 ACS 367 Altamira 229 activity cliﬀ 217, 222, 233, 302 alzheimer’s disease 131 ACToR 295, 296 AM1 37 acyclovir 321 AM-2201 197 AdaBoost decision tree 17, 18 American legislation Toxic Substances ADI 255–257 Control Act 252 ADME 7, 78, 99, 135, 163, 341 Ames mutagenicity 11 ADMEdata.com 4 Ames test 260, 261 ADMET 149, 160, 204, 214–218, 222, aminophylline 100 224, 231–234, 237, 363, 374, 376 amlexanox 105 ADME/Tox 3, 9, 16, 203, 376 AM/MC 42 AdmetSAR 78 amphetamines 178, 180, 181, 183, adrenergic receptor antagonists 128 195, 196, 198, 201 adrenoceptors 130 AM1 semiempirical method 41

k k

390 Index

anabolic steroids 175, 192 ATP8B1 146, 148 analysis of dynamic adaptations in Autodock Vina 44, 325 parameter trajectories autophagy 315 (ADAPT) 162 autoxidation 234 anandamide 197 AWS 355 androgen receptor (AR) 130, 131 Azure 355 5𝛼-androstanedione 133 5-androstene-3B-17B-diol 133 b 4-Androstene-3,17-dione 133 bacterial reverse mutation assays 253 Angular2 353 bagging 75 Annex III criteria 250 BAO see bio assay ontology (BAO) Annex XI 248, 255 barbiturates 180 ANTARES 250 Bayer pharmaceuticals 231 antibodies 202 Bayesian 7, 15, 158, 375 antibody-based assays 175 Bayesian models 11, 235 antigenic peptide 316 Bayesian modeling 374, 379 antitargets 130 BCF see bioconcentration factor (BCF) antitarget screening 125 BCRP see breast cancer resistance applicability domain (AD) 74, 219, protein (BCRP) 230, 231, 248, 251, 259, 304 behavior-driven development 356 k applicability domain (AD) index 255 Beilstein 370, 371 k approved drugs 366 Benigni-Bossa rules 265 apps 364 benign prostatic hyperplasia (BPH) aquatic toxicity 62, 220 129 aqueous solubility 7, 375 Benoxacor 228, 229 area under the curve (AUC) 18, 162, benzodiazepines 178, 180–182, 186, 277, 281 201, 203 aristolochic acid I 107, 109 benzophenone-1 135 Aro 131 benzophenones 134 aromatase 131 benzoylecgonine 178, 184 arrhythmia 202 benzyloxazole 64 artiﬁcial neural networks (ANN) 13, benzylpenicillin 100 75 benzylpiperazine 196 aryl chloride 81 Berkeley Carcinogenic Potency aryl hydrocarbon receptor (AhR) 125 Database 224 ASBT 150, 153, 159 Bernoulli naïve Bayes (BNB) 17 assay central 374, 376, 380–382 betamethasone dipropionate 274 astemizole 72 big data 5, 293, 294, 299, 304 atom-based 47 bile acid derivatives 159 atorvastatin 318, 328, 332 bile salts 147 ATPase-aminophospholipid bile salt export pump (BSEP) transporter 148 146–150, 152, 155–157, ATP7B 148 160–161

k k

Index 391

bilirubin 146, 147 c binding mode 331 2C19 136 bioaccumulative 259 Caco-2 7, 9, 11 bio assay ontology (BAO) 358, 379 Caco-2 cell permeability 375 bioassayR 301 CAESAR 255–257 bioavailability 46, 277 caﬀeine 188, 276 biochemical and organic simulation calcium 127 system (BOSS) 38 CALEIDOS 250 biochemical pathways 62 camphor 134 bioconcentration factor (BCF) 251, canicular transporters 148 255, 257, 258 cannabinoids 176, 183, 184, 195, 199 bioinformatics 237, 304 cannabinoid receptors 130 biological descriptors 223 cannabipiperidiethanone 197 biopharmaceutical bioequivalence cannabis 178, 195 captan 300 277 CAR see constitutive androstane BIOVIA 5 receptor (CAR) bisphenol A 300 carbamazepine 188, 318, 320, 322, blackbox 79 328–332 Blockchain 350 9-carboxy-THC 198 blood 187 carcinogenic 252 k blood-brain-barrier 129, 145 k carcinogenicity 299 blood brain barrier permeability 11 cardiac toxicity 82 B3LYP 36 cardiomyocyte 72 B3LYP/6–3l +G(d) 40 cardiotoxicity 97 B3LYP method 35 cardiovascular toxicity 127 BMK 35 CAREACT 370 Boltzmann factors 41 CAS see chemical abstracts service bond enthalpies 53 (CAS) boosting tree 97 CAS 623152–11–4 41 BOSS see biochemical and organic CAS number 136 simulation system (BOSS) CAS registry 232 Box 352 catalysts 365 BPH see benign prostatic hyperplasia CaV1.2 128 (BPH) CBRA 226–228 BRCA1 334 CD4+ 314, 316 breast cancer 334 CD8+ 314, 316, 321 breast cancer resistance protein CDC 236 (BCRP) 99, 146, 148, 150, 153, CDD 4, 7, 11, 14, 158 157, 158 CDD models 10 bromocriptine 156 CDD vault 341 BSEP see bile salt export pump (BSEP) CDK see chemistry development kit bubonic plague 11, 380 (CDK)

k k

392 Index

CDX 347, 349 chemical validation and CEBS see chemical effects in biological standardization platform systems (CEBS) (CVSP) 346, 349, 350, 353 CEDIA 201 cheminformatics 214, 304 cell-free assays 124 chemistry apps 364 central nervous system (CNS) chemistry development kit (CDK) 7, 128–130 8, 10, 75 CFTR see cystic fibrosis chemogenomics 235 transmembrane conductance chemoinformatics 321 regulator (CFTR) Chemotyper 229, 230 Chagas 380 ChemSpider 9, 235, 344, 351, 353, 370 Chagas disease 4, 5, 11 ChemSpider Mobile 366 charge model 51 Chinese hamster ovary (CHO) 72 ChEBI 343 Chinese herbal medicine (CHM) 96 CHELPG 51 Chinese Pharmacopeia 95 ChemAxon 9 chlordecone 300 ChemBench 220 2-chloroacetophenone 300 ChEMBL 4, 75, 76, 79, 123, 149, 204, chlorobenzilate 300 215, 295, 296, 341, 343, 363, chlorthiazide 103 374–377, 379, 380, 382 CHM see Chinese herbal medicine k ChemDraw 347, 349 (CHM) k chemical abstracts service (CAS) 215, CHO see Chinese hamster ovary 216, 255, 349, 370 (CHO) chemical-biological read across 226 cholera 11 chemical effects in biological systems cholestatic 161 (CEBS) 297, 299 cholesterol 162 chemical electrophilicity 46 chromatography/mass spectroscopy chemical engineering appsuite 235 175 chemical exposure 236 2C-I 196 chemical fragment descriptors 155 CIF 351 chemical genomics center 295 CIIPro 302 chemical hardness 46 Cimicifugic acid 110 chemical in vitro-in vivo profiling 302 CiPA 72 chemical mixtures 279 ciprofloxacin 100, 318, 328–332 chemical potential 46, 47 cisapride 72 chemical softness 46 classic machine learning (CML) 16, chemical structure descriptors 228 17, 351 chemical subgraphs and reactions classification labeling and packaging markup language 230 (CLP) 252, 262 chemical subgraphs and reactions CLIA 177 markup language (CSRML) CLIA’88 177 230 clindamycin 317, 318 chemical valence 366 clonazepam 178

k k

Index 393

clozapine 318, 320, 328–332 constitutive androstane receptor (CAR) CLP see classiﬁcation labeling and 125, 126 packaging (CLP) copper transporting P-type ATP-ase clustering 376 148 CM5 51 CORAL 76 CMA see Connolly molecular area CORINA 229 (CMA) corneocytes 271 CML see classic machine learning cortisol 131, 175, 191, 193 (CML) cortisone 131, 276 CNS see central nervous system (CNS) cosmetics 136, 137, 270 CNTK 14 COSMOS 298 cocaine 178 Coulomb electron-electron energies cocaine metabolite 184 34 codeine 178 Coulomb potential 52 Cohen’s Kappa 157 Coulomb’s law 33, 36 collaboration 341 counterions 232 Collaborations Pharmaceuticals, Inc. coupled perturbed Hartree-Fock 45 covalent bond 333 364 covalent host-guest interactions 56 collaborative drug discovery 4, 235 covalent reactivity 54 collaborative drug discovery (CDD) cross reactivity 177, 188, 189, 193, k database 158 k 194 collaborators 357 crystal structure 159, 322 Combi-QSAR 217 CSRML see chemical subgraphs and comparative toxicogenomics database reactions markup language (CTD) 297, 299 (CSRML) compartmental 282 CSV 349, 351 complete basis set 34 CTD see comparative toxicogenomics complex mixture 270 database (CTD) complex models 160 curation 344 comprehensive in vitro proarrhythmia CVSP see chemical validation and assay 72 standardization platform CompTox 370 (CVSP) CompTox Mobile 235 cyclosporin A 100 computational chemistry 33 cyclosporine 188, 189 computational models 60 CYP 135 computational toxicology 21, 95, 271, CYP1A2 136 275, 293, 382 CYP3A4 9, 214, 217 conﬁdence interval 257 CYP3A time-dependent inhibition Connectivity Map 297, 299 160 Connolly molecular area (CMA) 281 CYP11B 135 consensus 223 CYP2C9 9 consensus model 261 CYP2C19 97

k k

394 Index

CYP2D6 9, 97, 136 dermato-toxicology 269 cystic ﬁbrosis transmembrane dermis 270, 271 conductance regulator (CFTR) descriptors 8, 31, 32, 45, 222 148 designer drugs 176, 195 cytotoxicity 9, 160 desipramine 182 dextromethorphan 180, 203 d 2D free energy map 59 2D6 136 DFT see density functional theory dapsone 318 (DFT) databases 124 DHEA see dehydroepiandrosterone data curation 231 (DHEA) DataHub 342 diazepam 178, 181–183 Dataledger 350 1.2-dichloroethane 44 data market 358 2,4-dichlorophenol 52 data mining 218, 229, 344 2,2-dichloroxirane 56 Dataverse 342 2,3-dichloroxirane 56 DDD 300 diclofenac 276 DDI 99, 100 2,2-diﬂuorooxirane 56 2D/3D similarity 321 digitoxigenin 132 DDT 300 digoxin 100, 202 𝛼 k decision trees (DT) 5, 75, 79 5 -dihydrotestosterone 133 k deconvolution 277 DILI see drug induced liver injury decoys 123, 124 (DILI) deep learning (DL) 13–15 DILIps see drug induced liver injury Deeplearning4j 14 prediction system (DILIps) deep neural networks (DNNs) 16, 17, 1,4-dimethylnapthalene 255 19–21 dipole 43 dehydroepiandrosterone (DHEA) dipole-dipole interactions 43 133, 191 discovery studio 5 dehydroepiandrosterone sulfate 191 distributed structure-searchable delta 9-tetrahydrocannabinol 197 toxicity (DSSTox) 216 de novo 31 distributed structure-searchable density functional theory (DFT) toxicity (DSSTox) databasae 34–36, 48, 49, 56 216 depressive disorders 131 distribution 3 dermal absorption 271, 272, 275, 278, diuretic 103 279 DL see deep learning (DL) dermal formulations 273 DMSO 224 dermal mixtures 274 DNA 55, 348 dermatological diseases 269 DNA sequencing 334 dermatological models 285 DNNs see deep neural networks dermatopharmacokinetic 282 (DNNs) dermatotoxicity 285 DOA/Tox 177–179, 181, 186

k k

Index 395

Docker 354, 355 electron density 49 docking 48, 122, 137 electronegativity 47 docking scores 324, 333 electronic 121 dond dissociation energy 53 electronic health records (EHRs) 82 dopachrome 57 electronic parameters 47 dopamine D2 128 electrophilicity 46, 47, 49 dopamine receptors 130 electrostatic potential 43 Dotmatics Ltd 235 elemental 235 DropBox 342, 352 EMBL see European Molecular Biology DrugBank 96, 132, 154, 158, 323, 332 Laboratory (EMBL) drug induced liver injury (DILI) 9, EMIT see enzyme-multiplied 160, 161, 317, 326 immunoassay technique drug induced liver injury prediction (EMIT) system (DILIps) 149 endocrine disruption 130, 382 DrugMatrix 297, 299 endocrine disruptors 133, 135 drug metabolites 175 endoplasmic reticulum 315 drug of abuse 175 endosulfan 300 drug reaction with eosinophilia and energy minimization 33 systemic symptoms 317 enrichment 124 drugs 136 ensemble 77 k Dryad 342 ensemble docking 325 k DSSTox see Distributed ensemble prediction 219 Structure-Searchable Toxicity enterocytes 145 (DSSTox) envirnonmental protection agency dual event 5 295 dutasteride 135 environmental chemicals 134, 136 environmental protection agency (EPA) e 220, 221, 228, 295, 342, 376, 379 EBI see European bioinformatics enzyme-multiplied immunoassay institute (EBI) technique (EMIT) 201 Ebola 380 epidermis 270, 271 ECFP 75 EPIK 325 ECFP6 7, 9–11, 16, 20, 379, 382 EPISuite 254 ECGs 73 epoxides 57 ECHA see European chemical agency ER see estrogen receptor (ER) (ECHA) ER𝛼 agonist 16, 20 ECHA guidelines 255 estradiol 133, 175, 191 E factor 367, 373 estrogen receptor (ER) 125, 130, 131, Ehrlich 121 220, 302 EHRs see electronic health records estrone 133 (EHRs) ethanol 284 ElasticSearch 354, 356, 357 ethyl acrylate 55 electron 53 ethyl 2-bromobutanoate 262

k k

396 Index

ethyl crotonate 55 fluvastatin 105 eTOX 149 flux 272 European Bioinformatics Institute FMO see frontier molecular orbital (EBI) 295 (FMO) European chemical agency (ECHA) FMO theory 46 250, 254 food additives 136 European Molecular Biology food and drug administration (FDA) Laboratory (EMBL) 295 72, 136, 176, 221, 230, 313, 348 EU-ToxRisk 253 force field 101 evaporative loss 283 free activation energies 56, 57 excretion 3 free energy perturbation (FEP) 63 experimental error 303 frontier molecular orbital (FMO) 45, experimental variables 273 48 exposure 269 F1 score 382 Exposure Forecasting (ExpoCast) 236 Fukui function 48, 50, 53 Fukui index 49 f Fukui indices 50 Facebook 14 furosemide 132 FAERS see FDA Adverse Event FXR see farnesoid X receptor (FXR) Reporting System (FAERS) k FAIR 163, 350 g k farnesoid X receptor (FXR) 125, 126 GAMESS 35, 38, 51 FCFP6 7, 9, 10, 379, 382 gas-phase 59, 61 FDA see food and drug administration gauche-conformer 44 (FDA) Gaussian 35, 38, 51 FDA adverse event reporting system Gaussian-2 34 (FAERS) 313, 320 Gaussian-3 34 FDA recommendations 160 Gaussian functions 38 FDA substances registry system 346 Gaussian processes 7 fenofibrate 318, 328–332 GBM 75 fentanyl 183 GB/SA 60 FEP see free energy perturbation (FEP) GC/MS 176 ferulic acid 108, 109 gene expression omnibus (GEO) 297, FIC1 148 299 Fick’s law of diffusion 271 gene ontology 149 FID 351 generalized born/ surface area 60 FigShare 341, 343 generalized read-across (GenRA) 228 finasteride 135 GenRA see generalized read-across fingerprints 7, 202, 379, 382 (GenRA) first-order pharmacokinetics 272 gentamicin 188 flame retardants 125, 230 GEO see gene expression omnibus FLEXX 325 (GEO) flucloxacillin 318, 322, 328–333 Github 355

k k

Index 397

GlaxoSmithKline 341 Hartree-Fock 33 GLIDE 327 heart failure 202 GLIDE docking 317 hecogenin 132 GLN see GreenLabNotebook(GLN) HEK see human embryonic kidney Global electronic parameters 42, 48 (HEK) Global softness 47 hepatic transporters 146 glucocorticoid recetor 125, 131 hepatotoxic 95 glutathione 47 herbal compounds 203 glycyrrhizin 131 hERG 9, 11, 16, 20, 71–75, 77, 78, 80, GMTKN24 37 82, 83, 97, 127, 128, 135, 136, GMTKN30 database 35 376, 377, 380 GOLD 325 hERG blockage 81, 83 gold standard 220 hERG blocker 80 GoogleDrive 352, 355 hERG inhibitors 79 Google Kubernetes 355 heroin 178 gossypol 132 HESS DB 297, 299 GPCR 128, 130 𝛼-hexachlorocyclohexane 300 GPU-accelerated molecular dynamics hexachlorocyclpentadiene 300 324 hexachlorophene 300 GR 125, 130, 131 hidden layer 13, 19 k granulocytosis 317 high-throughput 178 k Green chemistry 364 high throughput screening (HTS) 3, Green Chemistry Institute 367 216, 221, 237, 293, 299 Green Chemistry legislation 370 Hirshfeld charges 51 The Green Lab Notebook 366 hispanolone 132 Green Lab Notebook (GLN) histamine receptor 130 369–373, 382 HIV 314 green solvents 366, 367, 369, 382 HIV-RT 64 green solvents app 368 HL7 347 grepaﬂoxacin 72 HLA 314–316, 320, 323, 327, 334 GSH 47, 50 HLA-drug associations 332 GSK PKIS 204 HLA-drug binding mechanism 322 GSK solvent selection guide 369 HLA-drug interactions 323 HLA-drug signaling mechanism 333 h HLA-mediated ADRs 335 hair follicles 271 HLM see human liver microsomal halides 57 (HLM) Hamiltonian operator 36 HOMO 39, 45–48 Hansch analysis 156 homology models 77, 317, 320 haptenation 32, 56 homology modeling 73 hapten complex 321, 333 HOMO-LUMO 62 haptophore 121 3𝛽-HSD 135 hard and soft acids and bases 46 11𝛽-HSD 133

k k

398 Index

11𝛽-HSD1 132 immunoassays 175, 177, 178, 180, 11𝛽-HSD2 132 183, 186–188, 191, 195 17𝛽-HSD 134, 135 immunological effect 271 17𝛽-HSD3 134 InChI 216, 303, 347, 349, 351 5-HT 129 InChIKey 347, 349 5-HT2B 11 InChI string 231 HTS see high throughput screening industrial chemicals 136 (HTS) inhalation toxicology 63 HU-210 197, 198 inhibition 9 human embryonic kidney (HEK) 72 innovative medicines initiative (IMI) human experts 251 149 human intrinsic clearance 11 insecticides 277 human leukocyte antigen 314 in silico 82, 249, 254, 266, 323 human liver microsomal (HLM) 7 in silico activity profiling 137 human metabolic stability 9 intellectual property (IP) 341 hybrid models 304 ion channel 72, 82, 127 hybrid modeling 221 iPad 375 hybrid QSAR 220 iPhone 375 hybrid QSAR model 226 IPPSF see isolated perfused porcine hybrid QSAR modeling 224 skin flap (IPPSF) k hydration models 59 irreproducibility 215 k hydrocodone 178 irreproducible experimental data hydrogen bonds 121 303 hydrogen bonding 52 irritant 136 hydrogen-bonding interactions 51 ISAC 218 hydrophobic 121 Isoferulic acid 110 hydrophobic cavity 64, 72 isolated perfused porcine skin flap hydrophobic penetrants 275 (IPPSF) 277, 280, 281, 284 hydroquinone 53, 54, 300 ISSTOX 296, 299 4-hydroxypyridine 60 IUPAC 74, 346 hyperbilirubinemia 146, 162 hyperledger 350 j hypersensitivity 317 Japanese toxicogenomics project 225 i Jarvis Patrick clustering 102 iADR 314 JCAMP 351 ibuprofen 132, 179, 180 JChem 9 IdentityServer 353 Jenkins 356 idiosyncratic ADRs 314, 316 JSON 347, 356, 357 IEF-PCM 61 Jupyter Notebook 15 IMI see innovative medicines initiative JWH-018 197–199 (IMI) JWH-073 197–199 immune system 322 JWH-250 197, 198, 200

k k

Index 399

k Liver Toxicity Knowledge Base 96 Kaggle 342 liver X receptor (LXR) 125, 126, 162 kappa 382 local descriptors 47 KCNH2 71 local electrophilicity 49 KCNQ1 11, 16, 20 local lymph node assay (LLNA) 50, KcsA 77 60, 234 Keras 19 local softness 49 keratinocyte 278 logBCF 259 ketamine 179, 180 logD 62 ketoconazole 132 log octanol-water 233 ketoprofen 105 logP 62 ketorolac 105 lonchocarpic acid 110 kidney 96, 100, 145 lorazepam 178 k-nearest neighbor (kNN) 156, 224, Lowdin 51 225, 227, 262, 263, 266 LSD see lysergic acid diethylamide k-nearest neighbors 5 (LSD) Kohn-Sham theory 34 LTKB-BD 149 LUMO 39, 45–48, 50 l LXR see liver X receptor (LXR) Lab Solvents 367, 369, 382 lysergic acid diethylamide (LSD) 178 k lamotrigine 188, 189, 318 k laplacian-adjusted 11 m LCAO-MO 50 MACCS 75, 230 LC/MS/MS 176 machine learning 6, 21, 137, 364, 374 lead optimization 230 maculopaular exantherma 317 leave-one-out (LOO) cross validation major histocompatibility complex 219 314 legal high 196 malaria 4, 11, 380 Leishmaniasis 380 malignant pleural eﬀusion 317 LFER see linear free energy manganese transporter 148 relationships (LFER) mannitol 276 licorice 131 marijuana 178, 195 lidoﬂazine 132 marrubiin 132 ligand-based 73, 159 MASE 320 ligand database 325 MATE1 146, 148, 150, 158 linear free energy relationships (LFER) MATE2-K 158 278 mathematical model 275 linear logistic regression 17, 18 maximum recommeded therapeutic linear models 57 dose 11 lithospermic acid 108, 109 MC 41, 63 liver 145 MC/FEP 58 liver injury 95 McGowan volume 279 Liver Toxicity 125 MC simulations 58, 61

k k

400 Index

MD 63 Metropolis method 41 MDBD 180, 183 MHC 314 MDEA 183 Michael acceptors 47, 50, 55 MDL 379 microservices 351, 352, 354 MDL public keys 188, 189, 192 Microsoft 14 MDMA/Ecstasy 180, 183, 195, 196, microvasculature 277 201 midazolam 178 MDPBP 196 migration inhibitory factor (MIF) 57, MDPV see 58 methylenedioxypyrovalerone mineralocorticoids receptor 131 (MDPV) minocycline 319, 328–332 MDR 157 MIP-DILI 149 MDR1 99, 148, 150, 152 mislabeled compounds 232 MDR3 146, 157 missing data 304 meclofenamic acid 105 mitochondrial membrane potential medicines and healthcare products disruption assay 230 regulatory agency 94 mitochondrial toxicity 160 medium effects 59 mixed effect modeling 283 mefenamic acid 105 mixed model 283 MEF framework 351 mixture factor 279 k melting point 9 MLM half life 8 k Mendeley 342, 343 MlogP 257, 259 meperidine 180 ML python library 17 mephedrone 195, 196 MM 39, 41 mephentermine 196 MMDS 11, 12, 235, 375, 382 metabolic stability 232 MMFF94x 101 metabolic syndrome 131 MNDO99 38 metabolism 3, 137 mobile applications 364, 366 MetaCost 162 mobile devices 235 methamphetamine 183, 196, 201, 202 mobile molecular datasheet 11, 235, Methanothermobacter 77 364–366, 375 methazolamide 319 mobile phone 5 methcathinone 196 modelability index 217 methylacrolein 55 “MODelability Index” (MODI) 218 methyl acrylate 48 MOE see molecular operating methyldopa 319, 328–332 environment (MOE) methylenedioxypyrovalerone (MDPV) MOL 347, 351 195, 196 molecular analysis of side effects 320 methylone 195, 196 molecular dipole 44 6-methylprednisolone 193 molecular docking 321, 323, 335 methyltestosterone 194 molecular dynamics 40 6-methyl testosterone 192 molecular dynamic simulations 320 methylxanthines 188 molecular materials informatics 235

k k

Index 401

molecular mechanics 39 named entity recognition (NER) 303 molecular modelers 231 nandrolone 192, 194 Molecular networks 230 naphyrone 196 molecular operating environment napthalene 300 (MOE) 101, 103 3-(1-napththoyl)indole 197 molecular orbitals 38 National Center for Computational molecular similarity 179 Toxicology (NCCT) 295 molecular weight 234 National Chemistry Database Service Moller-Plesset perturbation theory 351 34 National Health and Nutrition MolPrime 366 Examination Survey (NHANES) MongoDB 354 236 Monte Carlo 40 National Library of Medicine (NLM) MOPAC2009 38 295 morphine 178, 185, 202 National Toxicology Program (NTP) MOUE2D 7 295 mouse intrinsic clearance 11 natural charges 51 mouse liver microsomal stability 7, natural population analysis 51 375 natural product database 132 MRP1 146, 147, 151, 155 natural products 137 k MRP2 146–148, 150, 152, 160, 162 NaV1.5 128 k MRP3 146, 147, 150, 152, 155, 161, NCC 102 162 NCC2 102 MRP4 146, 147, 150, 152, 155, 161, NCCT see National Center for 162 Computational Toxicology MRP5 146, 147 (NCCT) MRP6 146 NCGC 295, 299 Mtb 5 neglect of diatomic diﬀerential overlap MthK 77 36 Mulliken 51 neighborhood analysis 232 multiscale models 161 nephrotoxicity 96 multispace optimization 223 NER see named entity recognition muscarinic receptors 130 (NER) Mus Musculus 77 NET platform 353 Mutagenic 252 NET reﬂection 351 Mutagenicity 55, 56, 136, 251, 253, neurons 13 260, 266 nevirapine 319 M062X 35 n-fold external validation 219 Mycobacterium tuberculosis 6, 375 NHANES see National Health and Nutrition Examination Survey n (NHANES) naïve Bayes 18, 157 NIEHS 299 naïve Bayesian 5, 10 NIEHS/NTP 221

k k

402 Index

NIH 295 OCT1 146, 150, 151, 154 NIH clinical collection 102 OCT2 99, 150, 151, 154, 155 NIH/NCATS 221 OCT3 146 NIH roadmap 295 octotiamine 132 NIH 3T3, 160 odds ratio 314 NLM see National Library of Medicine OECD see Organization for Economic (NLM) Co-operation and Development NMR see nuclear magnetic resonance (OECD) (NMR) OECD guideline 253, 255 Nobel Prize 93 OMI-3 37 nonlinear modeling methods 156 ontological terms 357 non-testing methods 248 ontology 356 norethindrone 192, 194 OpenID 358 Novartis 341 OpenPHACTS 342, 343, 346, 351 novobiocin 105 opensciencedatarepository(OSDR) NTCP 146, 150, 151, 154 14–16, 342–348, 350–358 NTP see National Toxicology Program Open Software Foundation 342 (NTP) open source 11, 374 nuclear magnetic resonance (NMR) Open TG-GATE 297, 299 122, 202, 322, 324 opiates 178, 185, 186 nuclear receptors 125 k opiod(s) 183, 195, 201 k nucleophile 56 opiod receptors 130 nucleophilic 49, 56 OPLS-AA 58 nutraceuticals 137 OPLS-DA 154 NWChem 35 OPLS3 force ﬁeld 325 o optimized potentials for liquid OAT 147 simulations 58 OAT1 100–104, 106, 110–114 OR 317 OAT2 146 ORCID 350 OAT3 104, 118 organic anion transporter 1, 99, 147 OAT7 146 organic solute transporter 147 OATP 150 Organization for Economic OATP-B 113 Co-operation and Development OATP1B1 98, 99, 146, 150–151, 154, (OECD) 73, 149, 233, 250 162 ORGSYN 370 OATP1B3 99, 146, 150, 151, 154, 162 OSDR see open science data repository OATP2B1 146, 151 (OSDR) OAuth2 358 OST𝛽 146 obesity 131 overﬁt 13 occupational 270 oxacarbazepine 319 OCHEM 76 oxaprozin 105 Ochem 220 oxycodone 178

k k

Index 403

p 4-phenyl-4-piperidino-cyclohexanol P450 111 180 PADEL 75, 76 phenytoin 319 PAHs see polycyclic aromatic phones 364 hydrocarbons (PAHs) p-hydroxyphenylpyruvate 57 PAH epoxides 56, 63 physical chemical property 279 parallel artificial membrane physicochemical 76 permeability assays (PAMPA) physicochemical descriptors 246, 251 276 physiologically based 282 parallelization 32 pioglitazone 125 parenteral injections 276 Pipeline Pilot 5, 9 partial atomic charges 51 plasma 187 passive diffusion 278 PLR 75 pazopanib 319, 328–332 PLS 156 PCM see polarizable continuum PM3 38 models (PCM) PM6 37 PCP 179, 186 PMI see process mass intensity (PMI) penetrant 284 PM6 method 50 pentachlorophenol 300 PNEC see predicted no effect concentration (PNEC) 3-penten-2-one 48, 55 Poisson-Boltzmann equation 43 k percutaneous absorption 270, 271, k polarizability 43, 45 275, 285 polarizable continuum models (PCM) perfused skin 277 60 permeability 7, 9, 11 polycyclic aromatic hydrocarbons permeability coefficient 272 (PAHs) 62, 111, 112 peroxisome proliferator-activated polygonium multifloram 94 receptor 125 polymers 348 personalized medicine 334 PolyPharma 235, 366, 374, 376, 378, Pfizer 4, 7 382 P-glycoprotein 136 porcine skin diffusion cells 283 P-gp 7, 146, 149, 156, 157 posaconazole 132 pharmacodynamics 97, 314 potassium channel 97, 127 pharmacokinetics 97, 201, 280, 285 Potts and Guy 278, 280 pharmacokinetic modeling 282 PPAR 125, 126 Pharmacokinetic models 281, 284 p,p’-dichlorodiphenyldichloroethane pharmacophores 74–76, 102–104, 300 112, 114, 121–124, 126, 128, p,p’-dichlorodiphenyltrichloroethane 130, 133, 136, 137, 156, 158, 300 186, 321, 323 PR 131 phencyclidine 179, 180, 185 Pravadoline 197 phenol 300 precision medicine 334 phenylmercuric acetate 300 Pred-HERG 79–82

k k

404 Index

predicted no effect concentration QM see quantum mechanics (QM) (PNEC) 252 QM calculations 32, 33 predictive ADMET 237 QM descriptors 42 predictive models 285 QM/MM 39, 40, 54, 56, 334 predictive toxicology 31, 41 QM/MM/MC 41, 58 prednisolone 193 QMPF 254 pre-filters 124 QMRF see QSAR model reporting pregnane X receptor (PXR) 9, 98, format (QMRF) 125 QPRF see QSAR prediction reporting pre-hapten 234 format (QPRF) PRIME 325 QSAR see quantitative probe-likeness 11 structure-toxicity relationship probenecid 100, 105 (QSAR) process mass intensity (PMI) 367, 373 QSAR model 79, 232 progesterone 175, 191 QSAR model reporting format progesterone receptor 130 (QMRF) 250, 251, 254 pro-hapten 234 QSAR prediction reporting format propargyl acrylate 48 (QPRF) 250, 251 propoachlor 300 QSAR workflow 219 propoxyphene 186 QSPeR see quantitative PROSIL 250 k structure-permeability k protein binding 9 relationships (QSPeR) protein data bank 163 QSPR see quantitative protein degradation 315 structure-property relationship protonation 325 (QSPR) pruning 8, 9 QT interval 71 PubChem 4, 123, 204, 215, 230, 293, quantitative modeling 273 295, 296, 298, 300–302, 342–344, 348, 351, 363, 370, quantitative structure-permeability 376, 379, 382 relationships (QSPeR) 278 PubChem BioAssay 8 quantitative structure-property PubMed 96 relationship (QSPR) 45, PW6B95 35 280–282, 284 PXR see pregnane X receptor (PXR) quantitative structure-toxicity PXR activation 11 relationship (QSAR) 14, PyMol 235 73–75, 77, 78, 81, 95, 97, 123, 4-pyridone 60 156, 159, 214, 216–222, 224–226, 230, 231, 233, q 245–249, 251, 255, 262, 279, Q-Chem 35 281, 294, 301, 302, 323, 344, 374 qHTS 224 quantitative toxicity 80 qHTS descriptors 221 quantum mechanics (QM) 39, 45, 56 QikProp 38 QuikProp 78

k k

Index 405

r rosmarinic acid 108, 109 radiolabelled 276 Rotor syndrome 146 random forest (RF) 8, 17, 18, 75, 96, Rulequest Cubist 7 156, 157 rules 251 random process model 285 RXN 351 RAR see retinoic acid receptor (RAR) rational design 61 s Rattus norvegicus 77 SALI 218 RCS-4 197 salicylates 188 RDF 351 salicylic acid 276 RDKit 9, 16 salvianolic acid A 107, 109 REACH 221, 234, 247–255, 260–262, SAR see structure-activity relationship 266, 296, 298, 302 (SAR) reaction energetics 60 SARpy model 264 reaction rates 55 SAR Table 366 read across 226, 248, 265, 301, 302, SASA see solvent-accessible surface 304 area (SASA) receiver operator characteristic (ROC) Schrodinger 235, 325, 326 5, 11, 18, 201, 377 Schrodingers equation 33 receptors 127 Science Cloud 341 k Redis 354 Scikit-learn 17 k 5𝛽-reductase 131 screening databases 137 regulators 136, 230 SDF 229, 347, 351, 379 regulatory 246, 247 secobarbital 183 RepDOSE 297, 299 self-consistent-field 34 reprotoxic 252 semantic web 343 RESTful API 343, 353 semiempirical molecular orbital (SMO) retinoic acid receptor (RAR) 36, 37, 162 125 sensitization potency 60 reverse-mutation assay 260 serotonin receptors 128 RF see random forest (RF) sertindole 72 RF classification 158 sertraline 319, 328–332 Rhein 107, 109, 113 serum 187 rifampicin 132 SEURAT-1 296, 298 rigid-rigid docking 324 S9 fraction 253 RMSE see root-mean-square error side-effect database 96 (RMSE) SIDER 96 RNA 348 similarity 203 ROC see receiver operator similarity analysis 191 characteristic (ROC) similarity index 228 ROC curve 10, 17, 158 similarity searching 323 Roothaan-Hall 34 simvastatin 319, 328–332 root-mean-square error (RMSE) 7 sinapinic acid 108, 109

k k

406 Index

single nucleotide polymorphisms stratum corneum 52, 270, 271 (SNPs) 163 Streptomyces lividans 77 singly occupied molecular orbital 45 structural alerts 79, 80, 251, 264 SIS see specialized information services structure-activity relationship (SAR) (SIS) 4, 248, 249 skin 234, 269, 270, 276, 282 structure-based 122 skin permeability 53 structure-based approaches 77 skin proteins 56 structure-based clustering 232 skin sensitization 233, 302 structure-based docking 322, 324 skin sensitizers 60 STVM 162 SLC30A10 148 styrene epoxides 56 SlideShare 341 support vector machines (SVM) 5, 7, SMARTS 7 17, 19, 21, 75, 96, 97, 155–159 SMD see solute electron density (SMD) surfactants 270 SMILES 216, 347, 349, 351 Swarm 355 SMO see semiempirical molecular sweat pores 271 orbital (SMO) SyntheticPages 370 SNPs see single nucleotide system dynamics 31, 40 polymorphisms (SNPs) social networks 357 t k sodium 127 tablets 364 k sodium diethyldithiocarbamate 300 tacrolimus 188 soils 272 Tanimoto coefficient 203, 222 solubility 9, 11, 16, 20 Tanimoto score 113 solute electron density (SMD) 60 Tanimoto similarity 227 solvation 59 target fishing 137 solvents 365 tautomeric equilibrium 60 solvent-accessible surface area (SASA) TB Mobile 235, 366, 375, 382 41 TBTO 300 SOMO 45 t-butyl hydroquinone 53, 54 Specialized Information Services (SIS) TCA 181 295 T-cell 314–316, 321, 333 SPL 347, 349 TCM see traditional Chinese medicine SPRESI 370, 371 (TCM) StarDrop 78 TCM-drug interactions 106, 111 statistical mechanics 52 TCM-renal 111 steady-state flux 276 TCM safety 94 steatosis 162 TDM see therapeutic drug monitoring stem cell 72 (TDM) steric 121 telmisartan 105 steroid hormone 175, 176, 191 TensorFlow 14, 17, 19 Stevens-Johnson syndrome 317 terfenadine 72 stochastic differential equations 284 tertiary amine 80, 81

k k

Index 407

T.E.S.T. 254 toxicology 33, 136, 175 testis 134 toxicophore 121 testosterone 133, 175, 191, 194, 195, toxins 203 276 TOXNET 96, 295, 296, 298 tetrabromobisphenol A 125 ToxPrint 229, 230 tetrahydrocannabinol 178 ToxRefDB 221, 301 THC 178, 195, 198, 200 TR 131 theobromine 188 traditional Chinese medicine (TCM) theophylline 188, 190 93–96, 98–102, 104, 106, 109, therapeutic drug 203 110, 112, 114 therapeutic drug monitoring (TDM) training set 251 175, 187 transappendageal 271 thermodynamics 53 transdermal 277 thermodynamic activity 273 transdermal flux profiles 284 thiazide 103, 112 transition state theory 55 3D structure 324 transporters 7, 145, 149 thyroid receptors 130 transporter informatics 163 ticlopidine 319, 328–332 trees 15, 75 tip-based 231 tributyltin oxide 300 TIP4P hydration model 41 trichlormethiazide 104 k tobramycin 188 2,2,3-trichlorooxirane 56 k topical administration 274 tricyclic 182 topical formulations 269, 275 tricyclic antidepressant 180 topiramate 188 1,2,3-trihydroxybenzene 52 topological 76 1,2,4-trihydroxybenzene 52 Torch 14 tuberculosis 4, 11, 380 Torsades de Pointes 71, 82 Tu, Youyou 93 tosylates 57 2D liver model 162 Tox21 149, 204, 221, 247, 249, 253, 2D MACCS 103 295, 332, 342, 376, 378, 379 2D methods 179 ToxCast 204, 220, 225, 228, 229, 295, 2D MOE 162 300, 363 2D similarity 137, 179, 200 Tox-database 75 toxicants 299 u toxic epidermal necrolysis 317 UCSF Chimera 44 toxicity 3, 46, 252 UCSF-FDA TransPortal 101 toxicity cliff 233 UGT1A 98 toxicity forecaster 295 UNC-Chapel Hill 204 toxicity in the twenty first century Uniprot 380 295 unstructured data curation 302 toxicogenomics 225, 299 unsupervised machine learning toxicogenomics project 299 157 toxicologists 230 uterotrophic 302

k k

408 Index

v WOE see weight-of-evidence (WOE) validation 246 WOMBAT-PK 75 valproic acid 190 workﬂow 21 valvular heart disease 128 VEGA 251, 254, 255, 261, 265 x virtual adverse outcome pathway Xenopus laevis 72 (vAOP) 301 XMDS 380 virtual heart 161 XML 230, 347, 356 virtual HLA pocketome 335 XO 72 the virtual liver network (VLN) 161 X-ray 122, 324 virtual screening 124, 128, 224, 326, X-ray crystallography 202 332, 333 X-ray crystals 321 visualization 375 X-ray structure 32 vitamins 176 y w Youden’s J statistic 382 web app 79 Y-randomization test 74 weight-of-evidence (WOE) 226, 262 z WEKA 162 Zebraﬁsh models 237 Wikipedia 353 Zika 380 k k WIN 55, 212–2, 197 ZINC database 321, 323 Windows containers 354 Zwanzig equation 63 WITHDRAWN database 72 zwitterions 74

k k

Figure 1.2 See text page 12 for full ﬁgure caption.

k k

O O O O O O N O O O N

Global minima in aqueous solution:

Lowest-energy structure in binding pocket (bottom):

Lowest-energy structure in binding pocket (top):

Figure 2.3 See text page 44 for full ﬁgure caption.

Asn212 1.7 Å Ile64 2.8 Å Tyr95

H2 2.4 Å H1 3.0 Å 3.4 Å k Pro1 k Lys32

(a) (b)

Figure 2.7 See text page 58 for full ﬁgure caption.

4.1 enolate intermediate TS 4.0 1. 7 Å 3.9 TS 1. 9 Å 2.3 Å 3.8

3.7

H-HPP + (Å) H-Pro1 Enolate intermediate 3.6 1. 8 Å 1. 8 Å 3.5 0.0 0.5 1.0 1. 5 2.0 2.0 Å H-HPP – H-Pro1 (Å) 2.3 Å (a)

(b)

Figure 2.8 See text page 59 for full ﬁgure caption.

k k

10 10

8 8

6 6

4 4 HOMO–LUMO gap (eV) HOMO–LUMO gap –2 0 2 4 6 –2 0 2 4 6

log Po/w log Do/w

Figure 2.9 See text page 62 for full ﬁgure caption.

Y623 Y623

S624 S624

Y652 Y652

F656 F656 k k

(a) (b)

Figure 3.1 See text page 78 for full ﬁgure caption.

Figure 3.2 See text page 80 for full ﬁgure caption.

k k

(a)

(b) k k

Figure 3.3 See text page 81 for full ﬁgure caption.

k k

V3 F3 Aro|Hyd

V6 F2 Aro

F1 Ani

V4 V1 V2

Figure 4.1 See text page 103 for full ﬁgure caption.

k k

(a) (b)

(e) (f)

(g) (h)

Figure 4.3 See text page 109 for full ﬁgure caption.

k k

* # § H HBD HBA Xvol *

Figure 5.1 See text page 122 for full ﬁgure caption.

k k

Figure 5.2 See text page 126 for full ﬁgure caption.

(a) (b) (c)

β1-Adrenoceptor Dopamine D3 receptor Histamine H1 receptor

(d) (e)

Figure 5.4 See text page 129 for full ﬁgure caption.

k k

BP-1 BP-2 BP-3 BP-8 IC50 = 1.05 μM IC50 = 18.1 μM 93% rest activity* 86% rest activity*

Figure 5.7 See text page 135 for full ﬁgure caption.

(a)

1000

100

Apparent cortisolApparent (ng/mL) 0.1 k 0.01 k Cortisol deficiency) deficiency) -hydroxylase Prednisolone β (21-hydroxylase 11-Deoxycortisol 11-Deoxycortisol 11-Deoxycortisol 21-Deoxycortisol 21-Deoxycortisol (11 (healthy controls) (healthy controls) (healthy 6-Methylprednisolone (metyrapon challenge) (b) 1

0.8

0.6

0.4 2D Similarity cortisol to 0.2

0 Cross-reactivity Strong Weak Very weak None (5% or greater) (0.5–4.9%) (0.05–0.49%) (<0.05%)

Figure 7.5 See text page 192 for full ﬁgure caption.

k k

(a) 1000

100

0.1

Apparent testosterone (ng/mL) testosterone Apparent 0.01

0.001 Males Females deficiency) Nandrolone Testosterone Norethindrone k (21-hydroxylase k Androstenedione Androstenedione (healthy controls) (healthy Methyltestosterone (b) 1

0.8

0.6

0.4 2D Similarity testosterone to 0.2

0 Cross-reactivity Strong Weak Very weak None (5% or greater) (0.5–4.9%) (0.05–0.49%) (<0.05%)

Figure 7.6 See text page 195 for full ﬁgure caption.

k k

** CAS#79-43-6 CAS#141-44-4 Cl O CAS#92-52-4 O Cl Cl Structurally different Cl OH CAS#116-06-3 Both toxic chemicals can be close in O Both non-toxic biological space N NH O S 12

10 Similar chemicals 8 can be distant in biological space 6 *** CAS#101-96-2 Toxic 4 NH

2 NH CAS#793-24-8 NH Space of chemical descriptors

Figure 8.2 See text page 222 for full ﬁgure caption. k k

Volume Variety Data scale Data form

The FOUR V’s of chemical toxicity big data

Velocity Veracity Data growth Data uncertainty

Figure 11.1 See text page 294 for full ﬁgure caption.

k k

Peptide α1 Peptide B1

α2 α1

α2 B α3 2 B2-m

HLA-Class I variants HLA-Class II variants 8–10 A.A. peptides 12–24 A.A. peptides

Figure 12.1 See text page 315 for full ﬁgure caption.

k k

Adverse drug reaction?

Figure 12.4 See text page 326 for full ﬁgure caption.

k k

Scoring Peptide RMSD function P1 (Å)

(–) 1.21 SP (+) 1.27

(–) 1.21 XP (+) 1.28 (a)

Native abacavir SP (–) P1 SP (+) P1

(b)

HLA-B*57:01 variant HLA-B*57:01 variant k 3VRI 3VRJ 3UPR 3VRI 3VRJ 3UPR k 0.0 0.0 –2.0 –0.5 –4.0 –6.0 –1.0 –8.0 –10.0 –1.5 –12.0 –14.0 eM (kcal/mol) DS (kcal/mol) –2.0 SP XP Δ Δ SP XP –16.0 –2.5 –18.0 (c) (d)

Figure 12.5 See text page 328 for full ﬁgure caption.

k k

3VRI 3VRJ 3UPR SP XP SP XP SP XP DS (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (kcal/mol) P1 P1 P1 P1 P2 P2 P2 P2 P3 P3 P3 P3 Abacavir Allopurinol Atorvastatin Carbamazepine Ciprofloxacin Clozapine Fenofibrate Flucloxacillin Methyldopa Minocycline Pazopanib Sertraline Simvastatin Ticlopidine Scale –11 –10 –9 –8 –7 –6 –5 –4 –3 –2 –1 0

Figure 12.7 See text page 330 for full ﬁgure caption.

3VRI 3VRJ 3UPR SP XP SP XP SP XP eM (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (–) (+) (kcal/mol) P1 P1 P1 P1 P2 P2 P2 P2 P3 P3 P3 P3 Abacavir Allopurinol Atorvastatin Carbamazepine Ciprofloxacin k Clozapine k Fenofibrate Flucloxacillin Methyldopa Minocycline Pazopanib Sertraline Simvastatin Ticlopidine Scale –96 –84 –72 –60 –48 –36 –24 –12 0 19 76 114

Figure 12.8 See text page 331 for full ﬁgure caption.

Figure 12.9 See text page 332 for full ﬁgure caption.

k k

Figure 13.1 See text page 345 for full ﬁgure caption.

k k

(a) k k

Figure 14.2 See text page 368 for full ﬁgure caption.

k k

k k (b)

Figure 14.2 (Continued)

(a) (b)

Figure 14.4 See text page 376 for full ﬁgure caption.

k k

(a) (b)

k k

Figure 14.6 See text page 378 for full ﬁgure caption.