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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

8 • Antibody Structural Dynamics 221
antibodies passed below certain solubility thresholds (calculated with CamSol84) dur‑
ing temperature‑ramp simulations, in which the Fv was heated over the course of the
simulation from 300 to 540 K with a temperature increment of 10–20 K and 20 ns of
dynamics simulated at each temperature. In general, decreased solubility is observed as
temperature increases, indicating partial unfolding that increases the solvent exposure
of aggregation‑prone regions.
Compared to experimental data showing the percent change in aggregates detected by
size‑exclusion chromatography after 3 months of storage at 40°C (Figure8.3g), we see
that the all‑atom simulations are strongly predictive of aggregation. In the case of trastu‑
zumab, a tight transition to less soluble states is seen from ~480–500 K; these simula‑
tions suggest trastuzumab contains a stable Fv that should only begin to lose solubility
at high temperatures, consistent with the experimental data showing that trastuzumab
indeed has effectively zero aggregation change over a 3‑month period. In contrast, beva‑
cizumab has a broad solubility transition from ~400 to 470 K, and in size‑exclusion
chromatography experiments was found to have a ~3% increase in aggregation over
3 months. These results are consistent with analyses showing that the proportion of
trastuzumab in a β‑sheet conformation in the all‑atom simulations is effectively con‑
stant at 0.45 until 500 K, when it transitions sharply to an unfolded state by 525 K.
Bevacizumab, in contrast, begins to lose its secondary structure at around 400 K and
unfolds partially over the course of over 100 K before losing all structure by 525 K.
By correlating their simulation results with experimental assays on aggregation,
Berner etal. suggest an intriguing path forward using simple temperature‑ramp simula‑
tions to predict solubility. One of the greatest strengths of their method is its simplic‑
ity– temperature‑ramp simulations are easy to perform, and their analysis relies on
standard solubility methods and straightforward calculations of secondary structure
content. Future studies considering more of the overall antibody structure (i.e., full Fab
or even full‑length antibody sequences) in the simulations could improve the informa‑
tion content of such studies as computational speed continues to improve.
8.5 CONCLUSION
In this chapter, we have described the background and modeling philosophy for
simulations of antibodies and described some example case studies in more detail.
The applications of MD simulations for antibodies are quickly evolving, undergoing
developments in both all‑atom and CG methodologies as well as analysis techniques.
The set of studies highlighted here is a small fraction of the complete literature on
this topic. For example, in addition to the CG results we described for the predic‑
tion of mAb solution properties, all‑atom simulations have also been employed to
predict the inuence of pH and temperature stress on mAbs.
poised to enter the development pipeline as a prime tool to predict the behavior of
concentrated mAb solutions. All‑atom simulations, on the other hand, will likely be
slower to enter industrial use due to their slow speed and high computational cost.
85,86
CG models appear

222 Biopharmaceutical Informatics
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Considerations of Developability During the Early Stages of Antibody Drug Discovery and Design
Maximiliano Vásquez, Bianka Prinz,
Eric Krauland, and Tushar Jain
9
9.1 INTRODUCTION
Antibodies have become the main class of biotherapeutics, with over a 100 molecules
approved for use in the United States and other major markets. Successful antibody
therapies require potent and specic engagement of a disease‑relevant target. Turning
an antibody into a drug, however, demands a series of additional criteria to be met, col‑
lectively known as “developability.” This has been increasingly appreciated over the
last decade, as numerous studies have been published, illustrating various aspects of the
developability problem.
Early examples centered around specic case studies, such as the effort to generate
potent, afnity‑enhanced, versions of the anti‑Respiratory Syncytial Virus (RSV) anti‑
body palivizumab.
ment in afnity and a 44‑fold enhancement in neutralization of RSV compared to the
228
1,2
A rst attempt1generated an antibody with a 1500‑fold improve‑

9 • Developability in Antibody Discovery 229
parental antibody palivizumab. Subsequent work, however, showed that in vivo potency
enhancement was only about 2‑fold,2 which was attributed to unexpectedly poor phar‑
macokinetics in the form of very fast clearance, and traced to increased nonspecic
binding across multiple tissues. A re‑examination of the afnity maturation work led
to a modied candidate, motavizumab, which had much reduced polyspecicity and
showed high potency in vitro and in vivo.2 Motavizumab entered clinical development
progressing to phase III and Food and Drug Administration (FDA) submission after
having been administered to thousands of patients3; however, it was terminated in late
2010 upon request by the FDA for additional clinical data.
Other work concerned the description of novel assays aimed at evaluating character‑
istics of antibody therapeutics candidates: cross‑interaction chromatography (CIC)4 and
afnity‑capture self‑interactionnano‑spectroscopy (AC‑SINS)
5,6
are two such examples.
More recently, studies looking at substantial numbers of antibody samples using one or
more assays have been published; among them are reports on chemical degradation,
7–9
aggregation propensity,10 viscosity and pharmacokinetics,11multiple biophysical proper‑
ties,12 experimental correlates of viscosity and opalescence,13 and pharmacokinetics.
14,15
It is also worth mentioning papers describing computational metrics that correlate
with general developability qualities; representatives include the therapeutic antibody
proler,16 the combination of in silico and experimental properties predictive of favor‑
able pharmacokinetics,17 sequence‑based characterization of approved antibody ther‑
apeutics as metric to assess candidate antibody sequences,18 and charge calculations
as predictors of viscosity, isoelectric point, pharmacokinetics, and general antibody
proling.
19
Assessing developability earlier in the discovery process is ideal because there is
more opportunity to avoid choosing a poor molecule when down‑selecting to a lead.
However, the challenge with early developability assessment is two‑fold. First, speed,
throughput, and minimal use of samples are practical requirements during this stage,
where typically large panels of molecules are involved. Secondly, the context within
which the antibodies exist is very different than that during clinical development, and
therefore, there is a challenge to design assays and then understand and validate their
predictive ability. It is crucial to nd a balance between the practical considerations
and the ability of an assay, or set of such assays, to correlate with relevant metrics
important in development. Metrics of polyspecicity and others relating to pharma‑
cokinetics,
approximately “predict” viscosity,
14,15,20,21
or the validation at a larger scale that dynamic light scattering can
13,22
are examples of approaches described that can
potentially meet this challenge.
This chapter is organized into several sections as follows. First, we offer a historical
perspective of how ideas and processes emerged over the last decade in our organiza‑
tion and our collaborators, with reference to mostly published material. Second, we
review a set of 137 antibody samples constructed using sequences from clinical‑stage
molecules12 and revisit previously published results. Next, we examine developability
data for 349 antibodies isolated from human B cells,20 followed by an analysis of assay
data obtained on over 150 antibody samples constructed using sequences from the lit‑
erature and corresponding to molecules aimed at targets of biomedical interest. These
were compiled from antibody controls used across discovery campaigns against over 60

230 Biopharmaceutical Informatics
distinct targets. This is followed by a discussion on how these assays have been used in
recent antibody discovery campaigns. We also include a section about the assessment
of chemical liabilities via undesirable post‑translational modications. Lastly, we offer
conclusions and perspectives for future work.
9.2 HISTORICAL PERSPECTIVE
In early years, we dealt mostly, but not exclusively, with antibodies isolated from
our synthetic human‑like antibody diversities harbored by our engineered yeast host
as full‑length IgG molecules. Access to yeast‑produced, full‑length, IgGs was subse‑
quently extended to situations where the source of diversity was from human B cells,
or from tissues isolated from immunized animals. With IgG material in hand, we were
interested in nding assays and workows that could address developability concerns
earlier in the antibody discovery process than had been incorporated up to that point.
In 2013, we published our rst two articles on this general subject.
article,24 a polyspecicity assay was described for application in both screening and
selection. Briey, soluble membrane protein (SMP) and soluble cytosolic protein (SCP)
are generated from Chinese Hamster Ovary (CHO) cells and biotinylated; a mix of these
two constitutes the polyspecicity reagent (PSR), which is then used as a probe to assess
binding by a test antibody. In what would become customary in much of our subsequent
work, we included data on 24 control samples generated from variable region sequences
from clinical development candidates with international nonproprietary name (INN)
designations.
25–28
The results indicated that a small number of the 24 “clinical” anti‑
bodies had high readouts in the PSR binding assay. The PSR metric also showed a
good correlation with those obtained from the CIC4 and baculovirus particle (BVP)
binding15 assays previously described. This article also demonstrated the ability of the
PSR approach to serve as a tool during the selection process utilizing ow cytometry,
where millions of antibody variants can be assessed and sorted out of one pot. Lastly,
for a subset of the antibody samples studied, a close relation was observed for measure‑
ments using the original soluble membrane preparation (SMP) from CHO cells with
preparations of membrane or cytosolic proteins from insect Spodoptera frugiperda
(Sf9) cells.24 In the second article,23 a clone self‑interaction assay using bio‑layer inter‑
ferometry (CSI‑BLI) was introduced and, again, tested with a few named (and some
reported as mAb1, mAb2, etc.) antibody samples. The CNTO607 antibody, which had
been reported to show poor biophysical properties,
29,30
was used as a positive control,
and it showed a high response compared to other controls, such as a sample made with
the variable regions of adalimumab.
In later work,31 we collaborated with the Tessier group, then at Rensselaer
Polytechnic, in an adaptation of this group’s SINS and AC‑SINS assays.
named antibodies, including CNTO607, were considered. CNTO607 was conrmed to
give high self‑interaction in this assay, but, in addition, we observed that three samples
made using INN sequences (corresponding to the clinical‑stage antibodies ganitumab,
23,24
In the rst
6,32
Here, over 30
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