The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry PDF
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The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry Edited by Stephanie Kay Ashenden Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-820045-2 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Andre Gerhard Wolff Acquisitions Editor: Erin Hill-Parks Editorial Project Manager: Samantha Allard Production Project Manager: Punithavathy Govindaradjane Cover Designer: Greg Harris Typeset by SPi Global, India Contributors Paul-Michael Agapow Oncology R&D Real World Evidence, AstraZeneca, Cambridge, United Kingdom Stephanie Kay Ashenden Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Aleksandra Bartosik Clinical Data and Insights, Biopharmaceuticals R&D, AstraZeneca, Warsaw, Poland Kaustav Bera Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH; Maimonides Medical Center, Brooklyn, NY, United States Krishna C. Bulusu Bioinformatics and Data Science, Translational Medicine, Oncology R&D, AstraZeneca, Cambridge, United Kingdom Fabiola Cecchi Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Gaithersburg, MD, United States Adam M. Corrigan Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Richard Dearden Digital Health, Oncology R&D, AstraZeneca UK Ltd, Cambridge, United Kingdom Glynn Dennis AI and Analytics, Data Science and Artificial Intelligence, Biopharma R&D, AstraZeneca, Gaithersburg, MD, United States Sumit Deswal Genome Engineering, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden Laura A.L. Dillon Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Gaithersburg, MD, United States Gabriela Feldberg Digital Health, Oncology R&D, AstraZeneca UK Ltd, Durham, NC, United States Jason Hipp Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Gaithersburg, MD, United States xi xii Contributors Faisal M. Khan AI and Analytics, Data Science and Artificial Intelligence, Biopharma R&D, AstraZeneca, Gaithersburg, MD, United States Sajan Khosla Oncology Data Science, AstraZeneca, Gaithersburg, MD, United States Natalie Kurbatova Data Infrastructure and Tools, Data Science and Artificial Intelligence, R&D, AstraZeneca, Cambridge, United Kingdom Anant Madabhushi Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Reserve University; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States Armin Meier Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Munich, Germany Stewart F. Owen Global Sustainability, AstraZeneca, Cambridge, United Kingdom Mishal Patel Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Günter Schmidt Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Munich, Germany Elizaveta Semenova Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Khader Shameer AI and Analytics, Data Science and Artificial Intelligence, Biopharma R&D, AstraZeneca, Gaithersburg, MD, United States Jason R. Snape Global Sustainability, AstraZeneca, Cambridge, United Kingdom Daniel Sutton Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Jim Weatherall Data Science and Artificial Intelligence, R&D, AstraZeneca UK Ltd, Macclesfield, United Kingdom Thomas White AI and Analytics, Data Science & Artificial Intelligence, Biopharma R&D, AstraZeneca, Cambridge, United Kingdom Contributors xiii Hannes Whittingham Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom Johannes Zimmermann Translational Medicine, Research and Early Development, Oncology R&D, AstraZeneca, Munich, Germany Preface This book aims to pull together the full pharmaceutical research and development process, from concept to patient. It explores how the data science field, particularly areas such as artificial intelligence and machine learning, aids this process, helping us to make better decisions faster. In addition, this book is a platform for those wanting to understand the who, why, where, when, and what of artificial intelligence, machine learning, and data science throughout the drug discovery process. xv Acknowledgments and conflicts of interest Stephanie Ashenden would like to thank Ian Barrett, Stan Lazic, Yinhai Wang, Delyan Ivanov, Afshan Ahmed, Ola Engkvist, Claus Bendtsen, and Aurelie Bornot for their tremendous support, feedback, and guidance. She would also like to thank Rory and her parents Paul and Tania. Finally, Stephanie would like to thank all the authors and Erin Hill-Parks, Samantha Allard, and Punithavathy Govindaradjane at Elsevier. Paul Agapow would like to thank Samit Kundu, Mansoor Saqi, and Paul Metcalfe for the comments and suggestions. Aleksandra Bartosik would like to thank Paweł Gunerka, Patryk Hes, Zbyszek Pietras and Tomasz Zawadzki for reviews as well as insightful discussions. Aleksandra is also grateful to Andreas Bender for computational toxicology inspiration. Stewart F. Owen and Jason R. Snape are employed by AstraZeneca, a global innovation-based biopharmaceutical company that discovers, develops, and markets pharmaceuticals. Necessarily, AstraZeneca conducts environmental risk assessments of its products and submits these as part of the registration process as well as making the summary data available on their website. S.F. Owen and J.R. Snape thank all their past and present partners from academic, NGO and industry scientists, and regulators who collaborated on a wide range of projects. In particular, the authors thank Dr. Tom Miller and Dr. Leon Barron at Kings College London for introducing them to this field of ML and AI. They thank Jack Owen for use of his photograph. J.R. Snape and S.F. Owen thank Simomics for their partnership on the Innovate UK, “National Centre for the Replacement, Refinement & Reduction of Animals in Research” (NC3Rs) funded project number 102519 “Virtual Fish EcoToxicology Laboratory.” S.F. Owen and J.R. Snape also acknowledge the support of colleagues on the European Union Innovative Medicines Initiative 2 Joint Undertaking “Prioritisation and Risk Evaluation of Medicines in the EnviRonment (PREMIER),” project number 875508. This Joint Undertaking receives the support from the European Union’s Horizon 2020 research and innovation program and EFPIA. Their time preparing this chapter represents a contribution toward this project. xvii CHAPTER 1 Introduction to drug discovery Stephanie Kay Ashenden Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom The drug discovery process The drug discovery and development process (here meaning the pharmaceutical research and development pipeline from concept to beyond the patient) is a long, expensive, and complex process1; only a small proportion of molecules that are identified as a candidate drug are approved as new drugs each year.2 It has been estimated that it costs approximately US$2.6 billion to develop a new treatment.3 In addition to creating a finished product costing over $1 billion, it can take up to 15 years.1 The cost of research and development continues to increase.4 Considering the long timelines, the increasing cost and complexity of the drug discovery process, efforts to aid in reducing these concerns are of interest. Having said this, new drug modalities have been explored beyond small molecules, which can result in new methods of treatment, patient stratification has potential to speed discovery as well as more focused development. In combination with experimental methodologies, artificial intelligence is hoped to improve the drug discovery process.5 Artificial intelligence can help beyond areas of research and develop, such as in finance but these areas are beyond the scope of this book. A general overview of a typical drug discovery process (Fig. 1) is split up into several different stages,1 namely: target identification and target validation (focusing on de-risking experiments), lead discovery, lead optimization, preclinical testing, and clinical testing.6 While there is no one definite way to arrive at a novel drug, depending on whether a specific target of interest in present, both target-led (target is known) or phenotypic (target is not known) screening methods7 can be used. Today, drug discovery is being led by techniques such as high-throughput screening and empirical screening which involves screening libraries containing chemicals against targets in a physical way. However virtual screening, which screens libraries computationally for compound chemicals that target known structures and having them tested experimentally, has become a leading method to predict new compound structures.8 Experimental testing confirms that interactions between the known target and the desired compound is therefore optimized to achieve desirable properties1 including biological activity, while reducing or eliminating negative properties (such as toxicity).6 Target identification Target identification is involved in the process of identifying targets that are hypothesized to be linked to a disease and will also be suitably druggable. Ultimately the purpose of assessing drugabillity is to The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry. https://doi.org/10.1016/B978-0-12-820045-2.00002-7 Copyright © 2021 Elsevier Inc. All rights reserved.
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