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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5387_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Contents
- •Foreword
- •Preface
- •About the Editors
- •Contributors
- •References
- •2.3.4 Barriers to Automation Adoption
- •2.4 Core Ingredients for Successful Digital Transformation
- •2.1 Introduction
- •2.3.1 Operational Challenges
- •2.3.2 Cultural Challenges
- •2.4.2 Cloud Computing
- •2.5 Case Studies of Successful Digital Transformation
- •2.6 Conclusion
- •References
- •3. Computational Protein Design Strategies for Optimization of Antigen Generation to Drive Antibody Discovery
- •3.1 Introduction
- •3.3 Antigen Generation Strategies
- •3.4 Computational Methods
- •3.4.2 Computational Protein Structure Prediction
- •References
- •4. Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery
- •4.1 Introduction
- •4.6 Summary and Future Directions
- •Acknowledgments
- •References
- •5.1 Introduction
- •5.2 Databases
- •5.2.1 Databases in Machine Learning Approaches
- •5.2.2 Database Types
- •5.3 Applications of Machine Learning in Antibody Discovery and Development
- •5.3.1 Structure Prediction with Deep Learning
- •5.3.3 Developability
- •5.4 Antibody Generation and Design by Language Models
- •5.4.1 Antibody Representations
- •5.4.2 Representation Learning
- •5.4.3 Language Models
- •References
- •6.1 Introduction
- •6.2 Antibody Generation through Deep Generative Models
- •6.3.1 Sampling and Scoring
- •6.5 Conclusions and Perspectives
- •Acknowledgments
- •References
- •7.1 Introduction
- •7.2.3 Computational Approaches to Predict Antibody–Antigen Interaction
- •7.3 Conclusion
- •Competing Interests
- •Acknowledgments
- •References
- •8.2 Common Types of Molecular Simulations for Biomolecules
- •8.2.1 Molecular Dynamics (MD) Simulations
- •8.2.2 Monte Carlo (MC) Simulations
- •8.2.3 Challenges of Molecular Simulations
- •8.3.1 Periodic Boundary Conditions
- •8.4 Uses of Molecular Simulation in Antibody Drug Development
- •8.5 Conclusion
- •References
- •9. Considerations of Developability During the Early Stages of Antibody Drug Discovery and Design
- •9.1 Introduction
- •9.2 Historical Perspective
- •9.3 Clinical Antibody Data Set
- •9.5 Control Antibodies
- •9.7 Assessment of Chemical Liabilities
- •9.8 Conclusions and Future Perspectives
- •Acknowledgments
- •References
- •Abbreviations
- •10.1 Introduction
- •10.4.1 Conclusions and Outlook
- •Acknowledgments
- •References
- •11.8 Conclusions and Future Directions
- •References
- •12.1 Introduction to PK/PD and QSP Modeling
- •12.1.1 PK/PD Modeling
- •12.1.2 QSP Modeling
- •12.2.1 Monoclonal Antibodies (mAbs)
- •12.2.3 Cell Therapies
- •12.2.4 Gene Therapies
- •12.2.5 Vaccines
- •12.2.6 mRNA/siRNA/Oligonucleotide Therapeutics
- •12.4 Case Studies
- •12.5 Conclusions and Future Perspectives
- •References
- •13.1 Introduction
- •13.2 AI/ML: A Game Changer for Antibody Design
- •13.3 Multispecific Antibody Design
- •13.4 Adapting AI to the Design of Multispecific Antibodies
- •13.4.1 Structure Prediction and Modeling
- •13.4.2 Developability Prediction and Optimization
- •13.4.4 In Silico Modeling and Simulation
- •13.5 The Future: Beyond Optimization
- •13.5.1 Market Trends and Commercialization
- •13.5.2 Logic Gates, Biosensors, and De Novo Design
- •13.5.3 Challenges and Opportunities
- •13.6 Conclusion
- •Acknowledgments
- •References
- •Index

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
Bispecic 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 Articial Intelligence Revolution: Transforming
the Design and Optimization of Multispecic 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 Multispecic Antibody Design 347
13.4 Adapting AI to the Design of Multispecic 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 Identication 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 monospecic 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 inefcient, 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 articial intelligence to antibody discovery, holds
great promise in its ability to reduce inefciencies 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. specicity, afnity, developability, phar‑
macology), followed by iterations to create derivatives with improved properties. The
improved efciencies 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, efcacy, 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 identication 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 efcient 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 scientic progress, namely, generating hypotheses and infer‑
ring from observations. Computation has enabled us to explore new realms of scientic
research via modeling, simulation, machine learning, and articial 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 beneted from digi‑
tal transformation. Perhaps the biopharmaceutical industry can also benet 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 efcient. Although most of the book focuses on
therapeutic antibodies, the scientic 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, bispecic and multi‑specic 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 efcient 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., Ridgeeld,
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
Pzer
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.
Ridgeeld, 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.
Ridgeeld, 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
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