Prediction of Metabolic Stability and Bioavailability with Bioisosteric Replacements Alison Pui Ki Choy Clare College University of Cambridge Date of submission: September 2017 This dissertation is submitted for the degree of Doctor of Philosophy. Prediction of Metabolic Stability and Bioavailability with Bioisosteric Replacements Alison Pui Ki Choy Abstract Drug development is a long and expensive process. Potential drug candidates can fail clinical trials due to numerous issues, including metabolic stability and efficacy issues, wasting years of research effort and resource. This thesis detailed the development of in silico methods to predict the metabolic stability of structures and their bioavailability. Coralie Atom-based Statistical SOM Identifier (CASSI) is a site of metabolism (SOM) predictor which provides a SOM prediction based on statistical information gathered about previously seen atoms present in similar environments. CASSI is a real-time SOM predictor accessible via graphical user interface (GUI), allowing users to view the prediction results and likelihood of each atom to undergo different types of metabolic transformation. Fast Metabolizer (FAME)1 is a ligand-based SOM predictor developed around the same time by Kirchmair et al. In the course of the evaluation of CASSI and FAME performance, the two concepts were combined to produce FamePrint. FamePrint is a tool developed within the Coralie Cheminformatics Platform developed by Lhasa Limited. which can carry out SOM predictions, as well as bioisosteric replacement identification. Same as CASSI, this is available via the Coralie application GUI. The bioavailability issues caused by the metabolic enzyme, cytochrome P450 3A4, and transporter protein P-gylcoprotein are also investigated in this work, along with the potential synergistic relationship between the two systems. In silico classifiers to distinguish substrates against non- substrates of the two systems are produced and it was envisaged that these classifiers can be integrated into FamePrint as an additional layer of information available to the user when deciding on bioisosteric replacements to use when optimising a compound. i Preface This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text This dissertation does not exceed the word limit for the Degree Committee. ii Acknowledgements I would like to thank my supervisor Professor Robert Glen for providing me with the opportunity to undertake this study and his support throughout. I would also like to thank Professor Johannes Kirchmair for his guidance and making my time at the Centre for Molecular Informatics a memorable one. I would also like to thank Andrew Howlett for sharing this unforgettable journey with me! I would also like to thank Lhasa Limited for funding the study, along with all the help and support they have given me. I would particularly like to acknowledge Dr Thierry Hanser, who has been incredibly supportive. Thank you for all the stimulating discussions and making me feel so welcome every time I have visited Lhasa. Finally, the journey to complete this thesis has not been a smooth or easy one. I am very glad to finally have the chance to thank the friends and family who has been there to support me along the way. Thank you. iii TABLE OF CONTENTS List of Tables ....................................................................................................................................................................... ix List of Figures ................................................................................................................................................................. xii Glossary ............................................................................................................................................................................. xv List of Abbreviations ......................................................................................................................................................... xvi INTRODUCTION ..................................................................................................................................... 17 1.1 DRUG DISCOVERY PROCESS ........................................................................................................................ 17 1.2 METABOLISM IN DRUG DISCOVERY .............................................................................................................. 19 1.3 ORAL BIOAVAILABILITY .............................................................................................................................. 19 1.4 AIM OF THE STUDY ................................................................................................................................... 20 IN SILICO TOOLS FOR DRUG DISCOVERY ................................................................................................ 21 2.1 SITES OF METABOLISM PREDICTION ............................................................................................................. 21 2.1.1 Ligand-based Methods ............................................................................................................... 22 2.1.1.1 Reactivity-based Methods ..................................................................................................................... 22 2.1.1.2 Combined Methods ............................................................................................................................... 23 2.1.1.3 Machine-learning Methods .................................................................................................................... 25 2.1.1.4 Fingerprint-based Methods ................................................................................................................... 25 2.1.1.5 Summary ................................................................................................................................................ 26 2.1.2 Metabolite Prediction Methods .................................................................................................. 27 2.1.3 MetaPrint2D ............................................................................................................................... 30 2.1.3.1 Identification of Modified Atoms ........................................................................................................... 30 2.1.3.2 Selection of Transformation Data .......................................................................................................... 31 2.1.3.3 Sites of Metabolism prediction .............................................................................................................. 33 2.1.3.4 MetaPrint2D-React Extension ................................................................................................................ 33 2.1.4 FAst MEtabolizer ........................................................................................................................ 38 2.1.4.1 Data Preparation .................................................................................................................................... 38 2.1.4.2 Descriptors ............................................................................................................................................. 38 2.1.4.3 Sites of Metabolism Prediction .............................................................................................................. 39 2.1.4.4 Model Evaluation ................................................................................................................................... 40 2.1.5 Summary .................................................................................................................................... 41 2.2 BIOISOSTERISM ........................................................................................................................................ 42 2.2.1 Ligand-based Methods ............................................................................................................... 43 2.2.1.1 Similarity-based Approaches.................................................................................................................. 43 2.2.1.1.1. Physiochemical Property Methods ................................................................................................... 43 2.2.1.1.2. Pharmacophore Methods ................................................................................................................. 46 2.2.1.2 Knowledge-based Approaches ............................................................................................................... 49 2.2.2 Metabolic Stability & Bioiosteres ............................................................................................... 51 iv 2.2.3 Summary ...................................................................................................................................