Biopharmaceutical Informatics Learning to Discover Developable Biotherapeutics

Foreword
Preface
About the editors
List of contributors

1. Biopharmaceutical Informatics: An Introduction - Andrew E. Nixon and Sandeep Kumar

2. Digital transformation in the biopharmaceutical industry: rebuilding the way we discover complex therapeutics - Tonya Frolov, Leonard Wossnig, and Alexander Jung

3. Computational protein design strategies for optimization of antigen generation to drive antibody discovery - Trevor Wilkinson

4. Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery - Melody Shahsavarian, Thomas Watkins, Ponraj Prabakaran, Adrian Carr, Maria Wendt, and Yu Qiu

5. Applications of Artificial Intelligence and Machine Learning Towards Antibody Discovery and Development - Anahita Rouyan, Paweł Dudzic, Wiktoria Wilman, Tadeusz Satława, Sonia Wróbel, and Konrad Krawczyk

6. From Deep Generative Models to Structure-Based Simulations: Computational Approaches for Antibody Design - Daisuke Kuroda

7. Computational biophysical analyses of antibody structure-function relationships with emphasis on therapeutic antibody-based biologics - Puneet Rawat, Eva Smorodina, Divya Sharma, R. Prabakaran, Jack Wade, Rahmad Akbar, Amrinder Singh, Sandeep Kumar, Victor Greiff, and M. Michael Gromiha

8. Use of molecular simulations to understand structural dynamics of antibodies - Daniel A. Nissley, Matthew I. J. Raybould, Charlotte M. Deane, and Sandeep Kumar

9. Considerations of developability during the early stages of antibody drug discovery and design - Maximiliano Vásquez, Bianka Prinz, Eric Krauland, and Tushar Jain

10. In Silico Approaches to Deliver Better Antibodies by Design – The Past, the Present and the Future - Andreas Evers, Shipra Malhotra, and Vanita D. Sood

11. Use of systems biology approaches towards target discovery, validation, and drug development - Madhuresh Sumit and Venkata Gayatri Dhara

12. Recent advances in PK/PD and Quantitative Systems Pharmacology (QSP) models for biopharmaceuticals - Hardik Mody, Venkata Krishna Kowthavarapu, and Alison Betts

13. The Artificial Intelligence Revolution: Transforming the Design and Optimization of Multispecific Antibodies - Per Jr. Greisen, Ziwei Pang, and Fernando Garces

Index.

Biography

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 antibodies, antibody drug conjugates, bispecific and multi-specific modalities, as well as vaccines. Based on the insights gained from these experiences, Sandeep has been advocating for Biopharmaceutical Informatics, a strategic vision dedicated to synergistic use of computation and experimentation towards a cost effective and more efficient discovery 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 Nixon is currently Vice President, Biotherapeutics Molecule Discovery at Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA. Andy earned his Ph.D. in Physical Biochemistry from the University of London for studies completed at the MRC’s National Institute for Medical Research. Andy has over 20 years of experience in biologic drug discovery and has contributed to over 100 antibody discovery programs resulting in numerous clinical candidates and approved biologics including TAKHZYRO, a fully human antibody inhibitor of plasma kallikrein.

The unparalleled versatility of monoclonal antibodies as therapeutics has inspired scientists for decades. As early as the 1980s, antibody engineers aimed to create novel versions 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 financial investments, and, critically, 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 monospecific 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 inefficient, costly, and time-consuming endeavors with a relatively low approval success rate. The field of biopharmaceutical informatics, and in general the application of machine learning and artificial intelligence to antibody discovery, holds great promise in its ability to reduce inefficiencies in the discovery process, which now includes a substantial amount of experimental work. Typically, antibody discovery programs involve the generation of hundreds or thousands of molecules that need to be evaluated for numerous desired properties (e.g. specificity, affinity, developability, pharmacology), followed by iterations to create derivatives with improved properties. The improved efficiencies inherent in the ability to design, select, and further engineer molecules entirely in silico thus hold great appeal for the biopharmaceutical industry. If a discovery process that includes in silico work yields a higher percentage of fit-for-purpose molecules, then approval success rates may increase, which would yield substantial reductions in development costs. Currently, at least two-thirds of commercially sponsored antibody therapeutics that enter clinical studies, which is the most expensive stage of development, are terminated due to issues with safety, efficacy, or business reasons.

Considering the potential of the field to transform antibody discovery and development, the publication of Biopharmaceutical Informatics: Learning to Discover Developable Biotherapeutics is timely. Editors Sandeep Kumar and Andrew Nixon, as well as the authors who contributed chapters, are renowned experts in the field. The book provides comprehensive coverage of the applications of in silico methods to numerous aspects of antibody therapeutics discovery, including identification of targets, 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 efficient and effective discovery 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.