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xii Contents
12 Recent Advances in PK/PD and Quantitative
Systems Pharmacology (QSP) Models for Biopharmaceuticals 307
Hardik Mody, Venkata Krishna Kowthavarapu, and Alison Betts
12.1 Introduction to PK/PD and QSP Modeling 307
12.1.1 PK/PD Modeling 308
12.1.2 QSP Modeling 309
12.1.3 Why Are PK/PD Modeling and QSP Modeling Important
for Biotherapeutics? 310
12.2 PK/PD Characteristics and Considerations for Biotherapeutics 311
12.2.1 Monoclonal Antibodies (mAbs) 311
12.2.2 Antibody‑Drug Conjugates (ADCs) 314
12.2.3 Cell Therapies 316
12.2.4 Gene Therapies 318
12.2.5 Vaccines 319
12.2.6 mRNA/siRNA/Oligonucleotide Therapeutics 320
12.3 Use of M&S Approaches in the Discovery and Development of Biotherapeutics and Novel Modalities 321
12.4 Case Studies 323
12.4.1 Application of Mechanistic PK/PD Models to Inform Early
Drug Discovery Decisions for Biotherapeutics 323
12.4.2 QSP Modeling of ADCs for Preclinical to Clinical
Translation and Optimization of Doses for Different Oncology Indications 324
12.4.3 A Translational Platform PBPK Model for Antibody
Disposition in the Brain 326
12.4.4 Empirical Model‑based Cellular Kinetic Analysis
of CAR‑Ts in Clinical Studies, Investigation of Dose‑exposure‑response Relationship, and Covariate Modeling 328
12.4.5 Mechanistic, Multiscale PK/PD and PBPK Modeling for
In Vitro to In Vivo Correlation (IVIVC) and Preclinical to Clinical Translation for CAR‑T Therapy 331
12.4.6 QSP Model for Preclinical to Clinical Translation of CD3
Bispecic Antibodies 333
12.4.7 QSP Model for mRNA Therapeutic Lipid Nanoparticle for
Preclinical to Clinical Translation 335
12.4.8 QSP Model to Gather Mechanistic Insights for Gene
Delivery in Sickle Cell Disease 335
12.5 Conclusions and Future Perspectives 337
References 338
Contents xiii
13 The Articial Intelligence Revolution: Transforming
the Design and Optimization of Multispecic Antibodies 344
Per Jr. Greisen, Ziwei Pang, and Fernando Garces
13.1 Introduction 344
13.2 AI/ML: A Game Changer for Antibody Design 346
13.3 Multispecic Antibody Design 347
13.4 Adapting AI to the Design of Multispecic Antibodies 352
13.4.1 Structure Prediction and Modeling 352
13.4.2 Developability Prediction and Optimization 352
13.4.3 Virtual Screening and Lead Identication 352
13.4.4 In Silico Modeling and Simulation 353
13.5 The Future: Beyond Optimization 355
13.5.1 Market Trends and Commercialization 355
13.5.2 Logic Gates, Biosensors, and De Novo Design 355
13.5.3 Challenges and Opportunities 356
13.6 Conclusion 356
Acknowledgments 357 References 357
Index 361

Foreword

The unparalleled versatility of monoclonal antibodies as therapeutics has inspired sci‑ entists for decades. As early as the 1980s, antibody engineers aimed to create novel ver‑ sions of antibodies that would be more potent and engage alternate biological pathways compared to antibodies that are naturally produced in humans. Now, some 40 years later, this aim has been achieved, and the creation of antibody therapeutics is currently industrialized into a global enterprise. The commercial clinical pipeline has grown from a few dozen in the 1980s to over 1300 by 2023, driven by technological advances, global distribution of relevant knowledge, substantial nancial investments, and, criti‑ cally, the successful approval and marketing of these biotherapeutic macromolecules. Current protein engineering methods now allow the creation of molecules with a wide range of shapes and sizes. Antibody therapeutics may be monospecic or they may engage two or more different antigens or different epitopes on the same antigens. They may be conjugated to a variety of other biologically active components, such as small molecule cytotoxic agents or steroids, interleukins, or non‑antibody protein‑binding domains. These therapeutics may be composed of only an antibody fragment that has been stabilized through protein engineering (e.g., single‑chain variable fragments) or they may be small antibody domains derived from non‑human species (e.g., VHH). Importantly, over 200 antibody therapeutics have been granted marketing approvals or are the subject of marketing applications undergoing review in at least one country (https://www.antibodysociety.org/antibody‑therapeutics‑product‑data/).
Despite the advances of the past decades, the discovery and development of antibody therapeutics remain inefcient, costly, and time‑consuming endeavors with a relatively low approval success rate. The eld of biopharmaceutical informatics, and in general the application of machine learning and articial intelligence to antibody discovery, holds great promise in its ability to reduce inefciencies in the discovery process, which now includes a substantial amount of experimental work. Typically, antibody discovery pro‑ grams involve the generation of hundreds or thousands of molecules that need to be evaluated for numerous desired properties (e.g. specicity, afnity, developability, phar‑ macology), followed by iterations to create derivatives with improved properties. The improved efciencies inherent in the ability to design, select, and further engineer mole‑ cules entirely in silico thus hold great appeal for the biopharmaceutical industry. If a dis‑ covery process that includes in silico work yields a higher percentage of t‑for‑ purpose molecules, then approval success rates may increase, which would yield substantial reductions in development costs. Currently, at least two‑thirds of commercially spon‑ sored antibody therapeutics that enter clinical studies, which is the most expensive stage of development, are terminated due to issues with safety, efcacy, or business reasons.
Considering the potential of the eld to transform antibody discovery and devel‑ opment, the publication of Biopharmaceutical Informatics: Learning to Discover
xiv
Foreword xv
Developable Biotherapeutics is timely. Editors Sandeep Kumar and Andrew Nixon, as well as the authors who contributed chapters, are renowned experts in the eld. The book provides comprehensive coverage of the applications of in silico methods to numerous aspects of antibody therapeutics discovery, including identication of tar‑ gets, antibody design strategies, structure‑function relationships, and developability. Biopharmaceutical Informatics: Learning to Discover Developable Biotherapeutics will be a valuable resource for scientists involved in antibody therapeutics discovery, biopharmaceutical executives interested in developing more efcient and effective dis‑ covery processes, and all those who seek to advance the current state of the art.
Janice M. Reichert, Ph.D.
Director of Business Intelligence, The Antibody Society, Inc.; Editor‑in‑Chief, mAbs.

Preface

May everyone be happy. May everyone be healthy. May everyone see. May there be no sorrow or misery.
A prayer by ancient sages of Rigveda.
Eons ago, our ancestors dreamed of a perfect world. They desired for everyone to be happy and healthy. Greek philosophers described being healthy and happy as eudai‑ monia and wished it for everyone. Unfortunately, this ancient endeavor remains largely unrealized, despite tremendous progress in human development over the course of many civilizations and millennia. The recent COVID‑19 pandemic is an example of serious challenges we must overcome to assure eudaimonia for everyone. To achieve and remain in eudaimonia requires that we continue to invent novel medicines to speedily address unmet medical needs as they emerge. However, novel medicines, particularly biologic medicines, are very expensive to discover, test, and make in large amounts. Moreover, biologic drug discovery and development projects take too long and fail too often, with manufacturers often passing on the costs to the payers–nations, governments, insur‑ ance companies, pharmacies, hospitals, and eventually to the patients and their families. Empirical processes rooted in experimental trial and error along with our incomplete understanding of biomolecular structure‑function and dynamics, the patient’s genetic background, physiology, and disease history are among the leading root causes that underpin the failure of drug discovery and development projects.
But there is hope! Arrival of the digital age is enabling scientists to go beyond the cardinal principles of scientic progress, namely, generating hypotheses and infer‑ ring from observations. Computation has enabled us to explore new realms of scientic research via modeling, simulation, machine learning, and articial intelligence. Digital transformation of human civilization started in the early 1970s and has revolutionized our lives since then. Many industries, such as banking, commerce, marketing, utilities, manufacturing, travel, shopping, entertainment and so on, have all beneted from digi‑ tal transformation. Perhaps the biopharmaceutical industry can also benet via greater availability of experimental data, modeling, simulation, machine learning, and arti‑ cial intelligence. This realization led us to the holistic vision of Biopharmaceutical Informatics a few years ago. Biopharmaceutical Informatics calls for synergistic use of experimentation and computation to reduce empiricism inherent to the discovery and development of biotherapeutics. Reducing the empiricism shall accelerate as well as improve the productivity of biotherapeutic drug discovery and development cycles along with reducing the costs associated with them. This book describes our rst attempt to capture the promise of Biopharmaceutical Informatics and the excitement around it. We hope that our efforts shall inspire readers to explore this eld further by launching their own investigations.
xvi
Preface xvii
We are grateful to all the chapter authors who took time out of their busy schedules to educate us on their pathbreaking research efforts aimed at making the discovery and development of biotherapeutics more efcient. Although most of the book focuses on therapeutic antibodies, the scientic concepts can be extended to other classes of bio‑ pharmaceuticals as well. This book wouldn’t have been feasible without the unwavering support and encouragement from the publisher, particularly Hilary Lafoe, Sukirti Singh, Varalika Kathuria and Karthik Orukaimani.
We are also grateful to our families for all the support over time and patience dur‑ ing the editing of this book. Modernity often reinvigorates antiquity as newer genera‑ tions rekindle our ancient endeavors. We hope this book will inspire readers to develop new concepts and technologies that make the most advanced medicines accessible to everyone.
To everyone involved with this book, thank you.
Sandeep Kumar, Ph.D.
Andrew E. Nixon, Ph.D.

About the Editors

Dr.Sandeep Kumar is currently a Distinguished Fellow (Executive Director) at the department of Computational Science in Moderna Therapeutics, Cambridge, MA where he leads Molecular Design and Modeling team. Sandeep Kumar holds a Ph.D. in Computational Biophysics and has over 25 years of experience researching protein structure–Function relationships. Sandeep Kumar has so far contributed towards more than 100 research articles, reviews, book chapters, and has previously edited a book entitled “Developability of Biotherapeutics: Computational Approaches”. Sandeep has been contributing towards discovery and development of numerous monoclonal anti‑ bodies, antibody drug conjugates, bispecic and multi‑specic modalities, as well as vaccines. Based on the insights gained from these experiences, Sandeep has been advo‑ cating for Biopharmaceutical Informatics, a strategic vision dedicated to synergistic use of computation and experimentation towards a cost effective and more efcient discov‑ ery and development of Biotherapeutics. More recently, he is promoting the concept of DAbI (Discovery of Antibodies in silico) where he sees an opportunity for generative AI to not only accelerate biopharmaceutical drug design but also to expand the antigen space druggable by antibody‑based biotherapeutics.
Dr. Andrew E. Nixon is currently the Senior Vice President & Global Head, Biotherapeutics Discovery at Boehringer Ingelheim Pharmaceuticals, Inc., Ridgeeld, CT, USA. He earned his Ph.D. in Physical Biochemistry from the University of London for studies completed at the MRC’s National Institute for Medical Research. He has over 20 years of experience in biologic drug discovery and has contributed to over 100 anti‑ body discovery programs resulting in numerous clinical candidates and approved bio‑ logics, including TAKHZYRO, a fully human antibody inhibitor of plasma kallikrein.
xviii

Contributors

Rahmad Akbar
University of Oslo and Oslo University
Hospital
Oslo, Norway
Alison Betts
Takeda Pharmaceuticals Boston, Massachusetts
Adrian Carr
Large Molecules Research, Sano Cambridge, Massachusetts
Charlotte M. Deane
Oxford Protein Informatics Group,
Department of Statistics University of Oxford Oxford, United Kingdom
Venkata G. Dhara
Pzer Andover, Massachusetts
Paweł Dudzic
Natural Antibody Szczecin, Poland
Andreas Evers
Antibody Discovery & Protein
Engineering Merck Healthcare KGaA Darmstadt, Germany
Tonya Frolov
LabGenius Ltd London, United Kingdom
Fernando Garces
BioMap Palo Alto, California
Victor Greiff
University of Oslo and Oslo University
Hospital
Oslo, Norway
Per Jr.Greisen
BioMap Palo Alto, California
M. Michael Gromiha
Protein Bioinformatics Lab, Department
of Biotechnology, Bhupat and Jyoti
Mehta School of Biosciences Indian Institute of Technology Chennai, India and India and International Research
Frontiers Initiative, School of
Computing Tokyo Institute of Technology Yokohama, Japan
Tushar Jain
Adimab LLC Lebanon, New Hampshire
Alexander Jung
Boehringer Ingelheim Pharma GmbH &
Co. KG Global Innovation and Alliance
Management Biberach, Germany
xix
xx Contributors
Venkata K. Kowthavarapu
University of Florida Orlando, Florida
Eric Krauland
Adimab LLC Lebanon, New Hampshire
Konrad Krawczyk
Natural Antibody Szczecin, Poland
Sandeep Kumar
Biotherapeutics Discovery Boehringer Ingelheim Inc. Ridgeeld, Connecticut
Daisuke Kuroda
Research Center of Drug and Vaccine
Development National Institute of Infectious Diseases Tokyo, Japan
Shipra Malhotra
Takeda Oncology Cambridge, Massachusetts
Hardik Mody
Genentech San Francisco, California
Daniel A. Nissley
Oxford Protein Informatics Group,
Department of Statistics University of Oxford Oxford, United Kingdom
Andrew E. Nixon
Biotherapeutics Discovery Boehringer Ingelheim
Pharmaceuticals, Inc. Ridgeeld, Connecticut
Ziwei Pang
BioMap Beijing, China
Ponraj Prabakaran
Large Molecules Research Sano Cambridge, Massachusetts
R. Prabakaran
Protein Bioinformatics Lab, Department
of Biotechnology
Bhupat and Jyoti Mehta School of
Biosciences Indian Institute of Technology Chennai, India and India and Emory University Atlanta, Georgia
Bianka Prinz
Adimab LLC Lebanon, New Hampshire
Yu Q iu
Large Molecules Research Sano Cambridge, Massachusetts
Puneet Rawat
University of Oslo and Oslo University
Hospital Oslo, Norway and Protein Bioinformatics Lab, Department
of Biotechnology Bhupat and Jyoti Mehta School of
Biosciences Indian Institute of Technology Chennai, India
Contributors xxi
Matthew I. J. Raybould
Oxford Protein Informatics Group,
Department of Statistics University of Oxford Oxford, United Kingdom
Anahita Rouyan
Natural Antibody Szczecin, Poland
Tadeusz Satława
Natural Antibody Szczecin, Poland
Melody Shahsavarian
Large Molecules Research Sano Cambridge, Massachusetts
Divya Sharma
Protein Bioinformatics Lab, Department
of Biotechnology Bhupat and Jyoti Mehta School of
Biosciences Indian Institute of Technology Chennai, India
Vanita D. Sood
Fable Therapeutics Boston, Massachusetts
Madhuresh Sumit
Genomic Medicine CMC Sano Great Boston, Massachusetts
Maximiliano Vásquez
GENEART Laboratory Regensburg, Germany
Jack Wa de
Technical University of Denmark Kongens Lyngby, Denmark
Thomas Wat k i n s
Large Molecules Research Sano Cambridge, Massachusetts
Maria Wendt
Large Molecules Research Sano Cambridge, Massachusetts
Amrinder Singh
MORGI, Centre for Cancer Cell
Reprogramming Institute of Clinical Medicine and Department of Molecular Cell Biology Institute of Cancer Research, Oslo
University Hospital University of Oslo Oslo, Norway
Eva Smorodina
University of Oslo and Oslo University
Hospital Oslo, Norway
Trevor Wilkinson
AstraZeneca Cambridge, United Kingdom
Wiktoria Wilman
Natural Antibody Szczecin, Poland
Leonard Wossnig
University College London, United Kingdom
Sonia Wróbel
Natural Antibody Szczecin, Poland