DOCSLIB.ORG

Improving Drug Discovery Efficiency Via in Silico Calculation of Properties

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Improving Drug Discovery Efficiency Via in Silico Calculation of Properties Improving Drug Discovery Efficiency via In Silico Calculation of Properties D. Ortwine 1 16th North American Regional ISSX meeting , 10/18/09 Outline • Background: Why Calculate Properties? • Calculable properties • Modeling Methods and Molecule Descriptors • Reporting Results From Calculations • Available Commercial Software • Strategies for Implementation • A Real Project Example • The Future • Conclusions • References 2 16th North American Regional ISSX meeting , 10/18/09 Lead Optimization in Drug Discovery The Needle in the Haystack 3 16th North American Regional ISSX meeting , 10/18/09 Why Calculate Properties? They can be related to the developability of drugs! Most marketed oral drugs have defined “property profiles” Paul D. Leeson and Brian Springthorpe, “The Influence of Drug-Like Concepts on Decision-Making in Medicinal Chemistry”, Nature Reviews Drug Discovery, vol. 6, pp. 881-890, 2007. Mark C. Wenlock, et.al, “A Comparison of Physiochemical Property Profiles of Development and Marketed Oral Drugs”, J. Med. Chem, 2003, 46, 1250-1256. 4 16th North American Regional ISSX meeting , 10/18/09 Why Calculate Properties? They can also be related to the ADMET Profile M. Paul Gleeson. Generation of a Set of Simple, Interpretable ADMET Rules of Thumb. J. Med. Chem. (2008), 51(4), 817-834. 5 16th North American Regional ISSX meeting , 10/18/09 Why Calculate Properties? • Prioritize synthesis -> Generate virtual individual molecules or combinatorial libraries, calculate properties, map back to R groups • Build an understanding of SAR • Combine with docking scores in a multiparameter optimization paradigm • Assist HTS triage • Replace measurements • Guide the growth of the compound collection • Guide the subsetting of the compound collection 6 16th North American Regional ISSX meeting , 10/18/09 7 16th North American Regional ISSX meeting , 10/18/09 Calculable Properties Descriptive Binding, 3D Shape N+O, Donors, Rings, Docking scores, TPSA, Size, Similarity, Fit to a pharmacophore, Connectivity Shape overlap Physiochemical MW, Log P, Log D, pKa, Composite Solubility, Polarizability, Ligand Efficiency, Critical packing (crystallinity) Ligand Lipophilic Efficiency, Cellular Efficiency DMPK LM, Hep Stability, Accuracy Calculation Difficulty PPB, Permeability, High Easy Vdiss, Cyp inhibition, Moderate Medium Metabolic ‘hotspots’ Low Hard 8 16th North American Regional ISSX meeting , 10/18/09 Property Definitions TPSA Topological polar surface area <120 desirable; <80 for CNS drugs LE Ligand Efficiency = -1.4 logKi / # of heavy atoms 1 0.3 is a good hit; 0.35-0.5 is a good clinical candidate LLE Ligand Lipophilic Efficiency = -log(Ki) – logD 2 7-9 is a good clinical candidate CellE Cellular Efficiency = -log( in vitro Ki) – log(cellular EC50) 0 is goal, <1.5 is acceptable 1Andrew Hopkins, et.al., Drug Discovery Today, 2004, 9, 430-431. 2Paul Leeson and Brian Springthorpe, Nature Reviews Drug Discovery, 2007, 6, 881-890. 9 16th North American Regional ISSX meeting , 10/18/09 Calculated vs Measured pKa R2 = 0.82 n = 133 s = 1.5 (75 compounds) (intercept not signif.) Calculated (MoKa(*) v1.0.3) v1.0.3) (MoKa(*) (MoKa(*) Calculated Calculated (*) http://www.moldiscovery.com Measured 10 16th North American Regional ISSX meeting , 10/18/09 Calcd. (Volsurf+) vs Expt’l. Thermodynamic Sol’y. (970 compounds) s = 0.8 log units G. Cruciani, P. Crivori, P.-A. Carrupt, B. Testa. Journal of Molecular Structure: THEOCHEM 503, 17-30, 2000. Volsurf+ manual (http://www.moldiscovery.com). 11 16th North American Regional ISSX meeting , 10/18/09 It’s Not Just About Lipophilicity Calculated Solubility vs. cLogD7.4 cLogS, µµµM (Stardrop (*) software) (*) http://www.optibrium.com/ cLogD7.4 12 16th North American Regional ISSX meeting , 10/18/09 Methods for Deriving DMPK Models • Regression • Partial Least Squares • Neural Networks • Discriminant Analysis (ADAPT, SIMCA, Support Vector Machines) • Decision Trees (Random Forest) • Baysean Methods (probabilistic approaches) • Use of 3D Structure of Target (CYPs, Transporters, Efflux Pumps,...) Models are derived using a subset of compounds, then the property is predicted for the held-out compounds (prediction or validation set) 13 16th North American Regional ISSX meeting , 10/18/09 Molecule Descriptors Used to Derive DMPK Models • Molecular fingerprints (Pipeline Pilot, MOE, Unity) • General molecular descriptors (MDL keys, OEchem) • Calculated properties (AlogP, ClogP, TPSA, ...) • Connectivity descriptors (e-state keys from Molconn-Z) • Geometrically derived (pharmacophores, from 3D structure of target correlograms in MetaSite) 14 16th North American Regional ISSX meeting , 10/18/09 Reporting Results • Predicted value • Confidence in the prediction (std. error, probability, quality of prediction) • The nearest neighbor in the training set (Tc* is typically used) • The number of near neighbors in the training set (above a Tc threshhold) • Geometric fit score (docking, pharmacophore overlap, shape similarity) • Details (model version, date) *Tc = Tanimoto coefficient = difference in binary fingerprints between two compounds = 15 16th North American Regional ISSX meeting , 10/18/09 Reporting Results: Example (*) cLM_HRM = Calculated liver microsomal stability in Human, Rat, and Mouse Result, probability, and error are reported (*) Output from a property calculator used at Genentech 16 16th North American Regional ISSX meeting , 10/18/09 Combining 2D and 3D Worlds 17 16th North American Regional ISSX meeting , 10/18/0I. Aliagas9 Comparing Multiple Ligands 18 16th North American Regional ISSX meeting , 10/18/09 Commercial Software • Pipeline Pilot v7.5 Accelrys • Stardrop Optibrium • MoKa, MetaSite, Volsurf+ Molecular Discovery • ACDlabs Molecular Discovery Ltd. • ADME Boxes PharmaAlgorithms • ADMET predictor Simulations Plus (Now merged with Molecular Discovery Ltd.) • SARchitect Strand Life Sciences • Metabolizer ChemAxon • Spotfire (visualization) Tibco • Vortex (visualization) Dotmatics 19 16th North American Regional ISSX meeting , 10/18/09 From Molecular Discovery Ltd. 20 16th North American Regional ISSX meeting , 10/18/09 Strategies For Implementation Obtain commercial software or develop your own -> A full-featured ‘chemically aware’ graphing package is a must Try generating global models on DMPK endpoints first ->If this fails, try project- or chemotype- specific models Report probabilities and errors along with the calculated values Track calculated vs. measured values on a regular basis -> Continually check the models’ performance (predicted vs. measured) -> Update the model regularly with new data -> Regularly discuss results with chemists Add calculated properties to your compound database -> Facilitates searching, subsetting, and rank ordering of compounds 21 16th North American Regional ISSX meeting , 10/18/09 “Real Project” Example 22 16th North American Regional ISSX meeting , 10/18/09 Solubility: Calculated vs Measured (Kinetic) Rough correlation is evident: Used as a guideline/filter on proposed compounds Average cLogS, µµµM (from Stardrop software) # of cpds Measured LogS (kinetic) 23 16th North American Regional ISSX meeting , 10/18/09 Performance of Human Liver Microsome (HLM) Stability Model Strong correlation is evident: Used as a guideline/filter on proposed compounds cHLM CL_Hep Probability # of cpds Measured HLM CL_Hep (mL/min/kg) 24 16th North American Regional ISSX meeting , 10/18/09 Predictivity of HLM Stability Model >14.5 6.2-14.5 <6.2 ( mL/min/kg) Calculated HLM Probability 25 16th North American Regional ISSX meeting , 10/18/09 PPB Model Validation: Human, Rat, Mouse (Training set, 750 compounds; Test set, ~250 compounds) Human Rat Mouse Predicted PPB >95% Predicted PPB <95% 26 16th North American Regional ISSX meeting , 10/18/09 Calculated Solubility vs PK Rat F% Average cLogS, µµµM (from Stardrop software) # of cpds PK Rat F% 27 16th North American Regional ISSX meeting , 10/18/09 In Vitro vs. Cellular Potency Disconnects In Vitro IC50 vs Cellular EC50 R2 = 0.46 -log( in vitro IC50) What causes the shift? -log(cellular EC50) 28 16th North American Regional ISSX meeting , 10/18/09 In Vitro vs. Cellular Potency Disconnects Looking for Relationships With Calculated Properties # of cpds CellE In Vitro vs Cellular Potency 29 16th North American Regional ISSX meeting , 10/18/09 Calcd. Solubility vs. Cellular Potency Average cLogS, µµµM (from Stardrop software) # of cpds -log(Cellular EC50) 30 16th North American Regional ISSX meeting , 10/18/09 Increased Sol’y. Reduces In Vitro/Cellular Pcy. Disconnect Average cLogS, µµµM (from Stardrop software) # of cpds CellE (in vitro/cellular potency) 31 16th North American Regional ISSX meeting , 10/18/09 CellE In Vitro vs Cellular potency by R Group Average CellE 1000x 100x 10x 1x # of cpds 32 16th North American Regional ISSX meeting , 10/18/09 cLogD: Progress Over Time cLogD7.4 Compound Registration Date 33 16th North American Regional ISSX meeting , 10/18/09 Ligand Lipophilic Efficiency(*): Progress Over Time LLE Compound Registration Date (*) LLE = pIC50 – cLogD7.4 [Values of 7-9 Desirable] 34 16th North American Regional ISSX meeting , 10/18/09 Whole Blood Potency: Progress Over Time -log(EC50) 100nM 1 µM 10 µM 100 µM Compound Registration Date 35 16th North American Regional ISSX meeting , 10/18/09 Stability in Hepatocytes: Progress Over Time CLhep, mL/min/kg Compound Registration Date 36 16th North American Regional ISSX meeting , 10/18/09 Med Chem Prioritizes Based on Filters 7041 compound targets - 87% cHLMprob >0.6 884 67%
Load more

Recommended publications
  • FDA-Approved Drugs with Potent in Vitro Antiviral Activity Against Severe Acute Respiratory Syndrome Coronavirus 2
    pharmaceuticals Article FDA-Approved Drugs with Potent In Vitro Antiviral Activity against Severe Acute Respiratory Syndrome Coronavirus 2 1, , 1, 2 1 Ahmed Mostafa * y , Ahmed Kandeil y , Yaseen A. M. M. Elshaier , Omnia Kutkat , Yassmin Moatasim 1, Adel A. Rashad 3 , Mahmoud Shehata 1 , Mokhtar R. Gomaa 1, Noura Mahrous 1, Sara H. Mahmoud 1, Mohamed GabAllah 1, Hisham Abbas 4 , Ahmed El Taweel 1, Ahmed E. Kayed 1, Mina Nabil Kamel 1, Mohamed El Sayes 1, Dina B. Mahmoud 5 , Rabeh El-Shesheny 1 , Ghazi Kayali 6,7,* and Mohamed A. Ali 1,* 1 Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza 12622, Egypt; [email protected] (A.K.); [email protected] (O.K.); [email protected] (Y.M.); [email protected] (M.S.); [email protected] (M.R.G.); [email protected] (N.M.); [email protected] (S.H.M.); [email protected] (M.G.); [email protected] (A.E.T.); [email protected] (A.E.K.); [email protected] (M.N.K.); [email protected] (M.E.S.); [email protected] (R.E.-S.) 2 Organic & Medicinal Chemistry Department, Faculty of Pharmacy, University of Sadat City, Menoufia 32897, Egypt; [email protected] 3 Department of Biochemistry & Molecular Biology, Drexel University College of Medicine, Philadelphia, PA 19102, USA; [email protected] 4 Department of Microbiology and Immunology, Zagazig University, Zagazig 44519, Egypt; [email protected] 5 Pharmaceutics Department, National Organization for Drug Control and Research, Giza 12654, Egypt; [email protected] 6 Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas, Houston, TX 77030, USA 7 Human Link, Baabda 1109, Lebanon * Correspondence: [email protected] (A.M.); [email protected] (G.K.); [email protected] (M.A.A.) Contributed equally to this work.
    [Show full text]
  • Chemistrymedicinal Chemistry Series
    Methods and Principles in ChemistryMedicinal Chemistry Series CASEPROFESSIONAL STUDY SAMPLER SCIENCE SAMPLER INCLUDING Chapter 2:21: The GPR81 Role HTS of Chemistry Case Study in Addressingby Eric Wellner Hunger and andOla FoodFjellström Security from LeadFrom Generation:The Chemical Methods,Element: Chemistry’s Strategies, Contribution and Case toStudies Our Global edited Future, by Jörg Holenz First Edition. Edited by Prof. Javier Garcia-Martinez and Dr. Elena Serrano-Torregrosa Chapter 22: The Integrated Optimization of Safety and DMPK Properties Enabling Chapter 5: Metal Sustainability from Global E-waste Management Preclinical Development: A Case History with S1P1 Agonists by Simon Taylor from EarlyFrom MetalDrug Sustainability:Development: Global Bringing Challenges, a Preclinical Consequences, Candidate and toProspects. the Clinic edited by Edited by Reed M. Izatt Fabrizio Giordanetto. Chapter 13: A Two-Phase Anaerobic Digestion Process for Biogas Production Chapter 14: BACE Inhibitors by Daniel F. Wyss, Jared N. Cumming, Corey O. Strickland, for Combined Heat and Power Generation for Remote Communities Fromand Andrew the Handbook W. Stamford of Clean from Energy Fragment-based Systems, Volume Drug 1. Edited Discovery: by Jinhu WuLessons and Outlook edited by Daniel A. Erlanson and Wolfgang Jahnke 597 21 GPR81 HTS Case Study Eric Wellner and Ola Fjellström 21.1 General Remarks One of the key lead generation strategies to identify new chemical entities against a certain target is high-throughput screening (HTS). Running an HTS requires a clear line of sight regarding the pharmacodynamic (PD) and pharma- cokinetic (PK) profile of the compounds one is interested in. This means that there has to be a clear screening and deconvolution strategy in place to success- fully assess the hits from an HTS output.
    [Show full text]
  • A New Ligand-Based Approach to Virtual Screening and Profiling of Large Chemical Libraries
    A new ligand-based approach to virtual screening and profiling of large chemical libraries Elisabet Gregori Puigjané Memòria presentada per optar al grau de Doctor en Biologia per la Universitat Pompeu Fabra. Aquesta Tesi Doctoral ha estat realitzada sota la direcció del Dr. Jordi Mestres al Departament de Ciències Experimentals i de la Salut de la Universitat Pompeu Fabra Jordi Mestres Elisabet Gregori Puigjané Barcelona, Maig 2008 The research in this thesis has been carried out at the Chemogenomics Laboratory (CGL) within the Unitat de Recerca en Informàtica Biomèdica (GRIB) at the Parc de Recerca Biomèdica de Barcelona (PRBB). The research carried out in this thesis has been supported by Chemotargets S. L. Table of contents Acknowledgements ........................................................................................... 3 Abstract .............................................................................................................. 5 Objectives ........................................................................................................... 7 List of publications ............................................................................................ 9 Part I – INTRODUCTION .................................................................................. 11 Chapter I.1. Drug discovery ..................................................................... 13 I.1.1. Obtaining a drug candidate ....................................................... 14 I.1.1.1. Hit identification ..........................................................
    [Show full text]
  • Molecular Obesity, Potency and Other Addictions in Drug Discovery
    Molecular Obesity, Potency and other Addictions in Drug Discovery Mike Hann Bio-Molecular Structure MDR Chemical Sciences GSK Medicines Research Centre Stevenage, UK [email protected] The challenge of drug discovery – Patients are still in need of more effective drugs for many diseases. – Payers are increasingly only prepared to pay for innovative rather than derivative drugs. – Investors believe they can get a better return on investment elsewhere – Legislators are demanding that only the very safest possible drugs are licensed. – Researchers are equally frustrated – despite all the accumulated knowledge from the new technologies, the challenge of successfully navigating through everything to find novel drugs seems to get harder rather than easier. – the fruit is possibly not as low hanging as before – The consequence of all this is that the average cost is now $1.8bn*! – We owe it to patients and society to do better than this. – *How to improve R&D productivity: the pharmaceutical industry's grand challenge. S. M. Paul, D. S. Mytelka, C. T. Dunwiddie, C. C. Persinger, B. H. Munos, S. R. Lindborg and A. L. Schacht, Nat. Rev. Drug Discovery, 2010, 9, 203. Learning from our mistakes – the resurgence of reason based on metadata from big Pharma Emergence of rules of thumb as guidance – Permeability/solubility: Pfizer analysis of existing drugs and oral absorption profile – (Lipinski, Adv. Drug. Del. Revs. 1997, 23, 3) – Mol Wt <500, LogP <5, OH + NH count <5, O + N count <10: 90% of oral drugs do not fail more than one of these rules. – Lipinski Rule of 5 – Receptor Promiscuity: AZ analysis of 2133 compounds in >200 Cerep Bioprint® assays – (Leeson & Springthorpe, Nat.
    [Show full text]
  • Integrated Ligand and Structure Based Approaches Towards Developing
    bioRxiv preprint doi: https://doi.org/10.1101/2020.11.26.399907; this version posted November 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Integrated Ligand and Structure based approaches towards developing novel Janus Kinase 2 inhibitors for the treatment of myeloproliferative neoplasms Ambili Unni.Pa, Girinath G. Pillaia,b, Sajitha Lulu.Sa* aSchool of Biosciences and Technology, VIT, Vellore, India; bNyro Research India, Kochi, India Email: [email protected], Phone: +91 9944807641 Abstract Myeloproliferative neoplasms (MPNs) are a group of diseases affecting hematopoiesis in humans. Types of MPNs include Polycythemia Vera (PV), Essential Thrombocythemia (ET) and myelofibrosis. JAK2 gene mutation at 617th position act as a major causative factor for the onset and progression of MPNs. So, JAK2 inhibitors are widely used for the treatment of MPNs. But, increased incidence of adverse drug reactions associated with JAK2 inhibitors acts as a paramount challenge in the treatment of MPNs. Hence, there exists an urgent need for the identification of novel lead molecules with enhanced potency and bioavailability. We employed ligand and structure-based approaches to identify novel lead molecules which could act as JAK2 inhibitors. The dataset for QSAR modeling (ligand-based approach) comprised of 49 compounds. We have developed a QSAR model, which has got statistical as well as biological significance. Further, all the compounds in the dataset were subjected to molecular docking and bioavailability assessment studies.
    [Show full text]
  • Fragment Library Screening Reveals Remarkable Similarities Between the G Protein-Coupled Receptor Histamine H4 and the Ion Channel Serotonin 5-HT3A Mark H
    Bioorganic & Medicinal Chemistry Letters 21 (2011) 5460–5464 Contents lists available at ScienceDirect Bioorganic & Medicinal Chemistry Letters journal homepage: www.elsevier.com/locate/bmcl Fragment library screening reveals remarkable similarities between the G protein-coupled receptor histamine H4 and the ion channel serotonin 5-HT3A Mark H. P. Verheij a, Chris de Graaf a, Gerdien E. de Kloe a, Saskia Nijmeijer a, Henry F. Vischer a, Rogier A. Smits b, Obbe P. Zuiderveld a, Saskia Hulscher a, Linda Silvestri c, Andrew J. Thompson c, ⇑ Jacqueline E. van Muijlwijk-Koezen a, Sarah C. R. Lummis c, Rob Leurs a, Iwan J. P. de Esch a, a Leiden/Amsterdam Center of Drug Research (LACDR), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands b Griffin Discoveries BV. De Boelelaan 1083, Room P-246, 1081 HV Amsterdam, The Netherlands c Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QW, UK article info abstract Article history: A fragment library was screened against the G protein-coupled histamine H4 receptor (H4R) and the Received 2 May 2011 ligand-gated ion channel serotonin 5-HT3A (5-HT3AR). Interestingly, significant overlap was found Revised 27 June 2011 between H4R and 5-HT3AR hit sets. The data indicates that dual active H4R and 5 HT3AR fragments have Accepted 28 June 2011 a higher complexity than the selective compounds which has important implications for chemical Available online 2 July 2011 genomics approaches. The results of our fragment-based library screening study illustrate similarities in ligand recognition between H4R and 5-HT3AR and have important consequences for selectivity profiling Keywords: in ongoing drug discovery efforts on H R and 5-HT R.
    [Show full text]
  • Practical Application of Ligand Efficiency Metrics in Lead Optimisation
    Scott JS, Waring MJ. Practical application of ligand efficiency metrics in lead optimisation. Bioorganic & Medicinal Chemistry 2018 Copyright: © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license DOI link to article: https://doi.org/10.1016/j.bmc.2018.04.004 Date deposited: 11/04/2018 Embargo release date: 05 April 2019 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints - eprint.ncl.ac.uk Graphical Abstract To create your abstract, type over the instructions in the template box below. Fonts or abstract dimensions should not be changed or altered. Practical application of ligand efficiency Leave this area blank for abstract info. metrics in lead optimisation James S. Scotta and Michael J. Waringb, aMedicinal Chemistry, Oncology, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, United Kingdom bNorthern Institute for Cancer Research, Chemistry, School of Natural and Environmental Sciences, Bedson Building, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom Bioorganic & Medicinal Chemistry journal homepage: www.elsev ier.c om Practical application of ligand efficiency metrics in lead optimisation James S. Scotta and Michael J. Waringb, aMedicinal Chemistry, Oncology, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, United Kingdom bNorthern Institute for Cancer Research, Chemistry, School of Natural and Environmental Sciences, Bedson Building, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom ARTICLE INFO ABSTRACT Article history: The use of composite metrics that normalise biological potency values in relation to markers of Received physicochemical properties, such as size or lipophilicity, has gained a significant amount of Received in revised form traction with many medicinal chemists in recent years.
    [Show full text]
  • Part I Introduction to Lead Generation
    1 Part I Introduction to Lead Generation 3 1 Introduction: Learnings from the Past – Characteristics of Successful Leads Mike Hann Contemporary nodding sages in drug discovery will often be heard to say “Tut, tut, if I wanted to get there, I wouldn’tstartfromhere.” Such comments are based on their experience (aka insights from hindsight!) where failure of com- pounds in late lead optimization, preclinical, or clinical work can all too often be associated with poor chemical and physicochemical properties of the chemical series being pursued. It is, of course, one of the basic truisms of science that where we start an optimization process will likely have profound influences on where it ends up! If this is true then why does so much of medicinal chemistry, and hence drug discovery, still suffer from a lack of awareness of these facts? After all they can save enormous amounts of time and money that are spent on taking forward compounds that fall outside of “drug-like space” until they predictably failed. Is it (1) because people still do not believe in a drug-like space and thus ignore the fact that compounds invariably get bigger and more lipophilic as lead optimi- zation progresses in the search for potency? Or is it (2) because they believe they will be exceptional in their skills and that this will allow their project to be equally exceptional and succeed outside of received or accepted wisdom? Or is it (3) that they just cannot find a good starting point that will deliver or, possibly, they have not tried hard enough to find such a starting point? Or is it (4) that such a poor choice of target that finding a small molecule to effectively interact with it is nigh impossible? All or any of these can be crucial in determining what course a project takes, but one of the biggest confounding issues is that although it can be argued (see below) that a drug-like space exists, there are many good drugs that fall outside of this drug-like space.
    [Show full text]
  • Structure-Based Discovery of Potent and Selective Melatonin Receptor
    RESEARCH ARTICLE Structure-based discovery of potent and selective melatonin receptor agonists Nilkanth Patel1, Xi Ping Huang2,3, Jessica M Grandner1†, Linda C Johansson1, Benjamin Stauch1, John D McCorvy2,3‡, Yongfeng Liu2,3, Bryan Roth2,3,4, Vsevolod Katritch1* 1Department of Biological Sciences and Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, United States; 2Department of Pharmacology, University of North Carolina Chapel Hill Medical School, Chapel Hill, United States; 3National Institute of Mental Health Psychoactive Drug Screening Program, Department of Pharmacology, University of North Carolina Chapel Hill Medical School, Chapel Hill, United States; 4Division of Chemical Biology and Medicinal Chemistry, University of North Carolina Chapel Hill Medical School, Chapel Hill, United States Abstract Melatonin receptors MT1 and MT2 are involved in synchronizing circadian rhythms and are important targets for treating sleep and mood disorders, type-2 diabetes and cancer. Here, we performed large scale structure-based virtual screening for new ligand chemotypes using recently solved high-resolution 3D crystal structures of agonist-bound MT receptors. Experimental testing of 62 screening candidates yielded the discovery of 10 new agonist chemotypes with sub- *For correspondence: micromolar potency at MT receptors, with compound 21 reaching EC50 of 0.36 nM. Six of these [email protected] molecules displayed selectivity for MT2 over MT1. Moreover, two most potent agonists, including † 21 and a close derivative of melatonin, 28, had dramatically reduced arrestin recruitment at MT , Present address: Discovery 2 Chemistry, Genentech Inc, South while compound 37 was devoid of Gi signaling at MT1, implying biased signaling.
    [Show full text]
  • Multi-Parameter Optimisation in Drug Discovery
    Discovery Technology Multi-Parameter Optimisation in Drug Discovery Drug discovery activities are producing ever-larger volumes of By Matthew Segall at Optibrium Ltd complex data that carry significant levels of uncertainty; multi- parameter optimisation methods enable this data to be better utilised to quickly target compounds with a good balance of properties – but they all have their strengths and weaknesses. One of the major challenges of drug discovery is to a compromise on less important properties in order to identify a compound with a good balance of the achieve critical requirements. many physicochemical and biological properties necessary to become a successful, efficacious and safe The challenge of simultaneously optimising multiple drug. Having identified poor pharmacokinetics (PK) factors has been previously addressed in many fields, as a major cause of clinical failure in the later 1990s, from engineering disciplines such as automotive projects now routinely attempt to mitigate this risk and aeronautical design, to economics. The methods by assessing the absorption, distribution, metabolism used to achieve this are described as multi-parameter and elimination (ADME) properties of compounds in optimisation (MPO), multi-dimensional optimisation early drug discovery. However, more recently, safety has (MDO) or multi-objective optimisation (MOOP). For become a greater cause of failure in the clinic leading to simplicity, we will use the term MPO to refer to all of the assessment of toxicity earlier in the drug discovery these methods. We can learn from these approaches process. The result has been an increase in cost and to better use the data generated in drug discovery a reduction in the productivity of drug discovery, but in order to quickly design and select compounds unfortunately a corresponding increase in the success with a good balance of properties.
    [Show full text]
  • Research 1..10
    Article pubs.acs.org/jmc β ‑ Biophysical Fragment Screening of the 1 Adrenergic Receptor: Identification of High Affinity Arylpiperazine Leads Using Structure- Based Drug Design † † † † † John A. Christopher,*, Jason Brown, Andrew S. Dore,́James C. Errey, Markus Koglin, † ‡ ‡ § † Fiona H. Marshall, David G. Myszka, Rebecca L. Rich, Christopher G. Tate, Benjamin Tehan, § † Tony Warne, and Miles Congreve † Heptares Therapeutics Ltd., BioPark, Welwyn Garden City, Hertfordshire, AL7 3AX, U.K. ‡ Biosensor Tools LLC, Salt Lake City, Utah 84103, United States § MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, U.K. *S Supporting Information β ABSTRACT: Biophysical fragment screening of a thermostabilized 1-adrenergic β fi receptor ( 1AR) using surface plasmon resonance (SPR) enabled the identi cation of moderate affinity, high ligand efficiency (LE) arylpiperazine hits 7 and 8. Subsequent hit to lead follow-up confirmed the activity of the chemotype, and a structure-based design − β fi approach using protein ligand crystal structures of the 1AR resulted in the identi cation of several fragments that bound with higher affinity, including indole 19 and quinoline 20. In the first example of GPCR crystallography with ligands derived from fragment β screening, structures of the stabilized 1AR complexed with 19 and 20 were determined at resolutions of 2.8 and 2.7 Å, respectively. ■ INTRODUCTION Until recent years, in contrast to soluble protein classes such as enzymes, X-ray crystal structures of GPCRs had been lacking G protein-coupled receptors (GPCRs) form a large and fi important protein family with 390 members (excluding with only the structure of the visual pigment rhodopsin, rst 1 reported in 2000, being available to guide structure-based drug olfactory receptors) in the human genome.
    [Show full text]
  • On the Virtues of Automated QSAR  the New Kid on the Block
    On the Virtues of Automated QSAR The New Kid on the Block Marcelo T. de Oliveira*1,2, Edson Katekawa2 1 Instituto de Química de São Carlos, Universidade de São Paulo, Avenida Trabalhador são-carlense 400, São Carlos, SP 13566-590, Brazil 2 Instituto de Física de São Carlos, Universidade de São Paulo, Brazil *Author for correspondence: [email protected] Quantitative Structure-Activity Relationship (QSAR) has proved an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued. Keywords: Automated QSAR • kernel PLS • prediction • QSAR • validation Background Hansch’s seminal publication on the correlation of partition coefficients and biological activity evaluating the effect of substituents through the application of straightforward regression methods laid the foundations of the field we recognize as Quantitative Structure-Activity Relationship (QSAR) [1-2]. Over half a century later and thousands of papers disclosing applications in numerous areas including medicinal chemistry, [3-5] environmental, [6-9] food, [10-12] and material [13-16] sciences, as well as regulatory [17-19] across academy, industry, and government, QSAR has proved an invaluable tool for experts and non-experts alike in identifying patterns in datasets and assisting in the process of making quantitative predictions [20-21].
    [Show full text]