Table Of ContentThe 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
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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
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