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X
- •Foreword
- •Preface
- •Acknowledgements
- •Contents
- •Contributors
- •About the Editors
- •1.2.2.3 Progeria
- •1. Bioprocessing, Bioengineering and Process Chemistry in the Biopharmaceutical Industry: Using Chemistry and Bioengineering to Improve the Performance of Biologics
- •1.1 Introduction
- •1.2.2.2 Cystic Fibrosis
- •1.3.2.1 ADC Drugs
- •1.4 Top 25 Best-Selling Drugs
- •1.5.1 An Overview
- •1.5.2 Synthetic Biology
- •1.5.8 Biopharmaceutical Regulatory CMC
- •1.5.9 Technology Transfer
- •References
- •2.1 What Is Synthetic Biology?
- •2.6 CAR-T Cell Therapies
- •2.7 Conclusion
- •References
- •3.1 Introduction
- •3.2.1 Oligonucleotide Synthesis
- •3.2.1.1 Early Synthetic Chemistries
- •3.2.2 Solid Supports
- •3.2.3 Modern Oligo Synthesis Platforms
- •3.3 Gene Synthesis
- •3.3.1 Early DNA Assembly Methods
- •3.3.2 Array-Based Gene Synthesis
- •3.4 New Discovery Bottleneck
- •3.4.1.1 Hybridoma Technology
- •3.4.1.2 Phage Display Technology
- •3.4.1.3 Synthetic Antibody Library Construction
- •Semi-Synthetic Libraries
- •Fully Synthetic Libraries
- •3.5 Perspectives
- •References
- •4.1 Introduction
- •4.2.1 Batch
- •4.2.2 Fed-Batch
- •4.2.4 Hybrid Processes
- •4.2.7 Dynamic Perfusion Processes
- •4.3.2 Glucose Limitation
- •4.4.1 N-1 Perfusion
- •4.4.3 Linked Bioreactors
- •4.5 Process Analytical Technology
- •4.6 Single-Use Bioreactors (SUBs)
- •4.7 Conclusions
- •References
- •5.1 Introduction
- •5.2.1 Molecular Format Considerations
- •5.2.1.1 The Charge-Based Electrostatic Approach
- •5.2.1.2 The Knob into Hole Approach
- •5.2.2.1 Stable CHO Host Cell Integration System—Random or Targeted?
- •5.2.2.2 Expression Vector Considerations
- •5.2.2.3 Cell Line Screening Strategy Considerations
- •5.3.1 Upstream Process Development
- •5.3.2 Downstream Process Development Considerations
- •5.3.2.1 Unique Impurity Challenges
- •5.3.2.2 Stability Concerns
- •5.5.2.1 H/H Removal
- •5.5.2.2 HMMS Removal
- •References
- •6.1 Introduction
- •6.2.1 N-Linked Glycosylation
- •6.2.2 O-Linked Glycosylation
- •6.2.3 Glycosaminoglycan Synthesis
- •6.3.1 Mannosylation
- •6.3.2 Fucosylation
- •6.3.3 Galactosylation
- •6.3.4 Sialylation
- •6.5 Glycoengineering
- •6.5.1 Manipulating Heterogeneity
- •6.5.2 Manipulating Sialylation
- •6.5.2.1 Increasing α-2,6 Sialylation
- •6.5.3 Manipulating Fucosylation
- •6.5.4 Manipulating Branching
- •6.6.1 Temperature
- •6.6.2 pH
- •6.6.3.2 Amino Acids
- •6.6.3.3 Glycosaminoglycan Production
- •6.6.4 Culture Additives
- •References
- •7.1 Introduction
- •7.1.1 AAV Gene Therapy
- •7.3.1 Humoral Immunity
- •7.3.2 Cell-Mediated Immunity
- •7.4 Conclusion
- •References
- •8.1 Introduction
- •8.2 mRNA Vaccines
- •8.2.1 Background
- •8.2.2 Production Process
- •8.2.2.2 Production
- •8.4.1 Background
- •8.4.2 Production Process
- •8.4.2.2 Production
- •8.4.2.3 Viral Inactivation
- •8.5 Protein-Based Vaccines
- •8.5.1 Background
- •8.5.2 Production Processes
- •8.5.2.1 NVX-CoV2373 (Novavax)
- •8.3 Viral Vectors
- •8.3.1 Background
- •8.3.2 Production Process
- •8.3.2.2 Production
- •8.4 Whole Inactivated Virus Vaccines
- •8.5.2.2 CoVLP (Medicago)
- •8.5.2.3 EpiVacCorona (Vector Institute)
- •8.7 Conclusions
- •References
- •9. CAR-T Bioprocessing
- •9.1 Introduction
- •9.2.1 Introduction
- •9.2.2 Lentiviral Vector Design
- •9.2.5 Upstream Bioprocessing
- •9.2.6 Downstream Bioprocessing
- •9.3 Cell Product Bioprocessing
- •9.3.1 End-to-End Systems
- •9.3.4 Activation
- •9.3.6 Cell Expansion
- •9.3.8 T-Cell Cryopreservation
- •References
- •10.1.1 What Is CRISPR?
- •10.1.4 Mechanism Behind CRISPR Gene Editing
- •10.2.1 Creating Gene Knockouts
- •10.2.2 Creating Gene Knock-Ins
- •10.2.4 CRISPR Screens
- •10.3.1 Derivative Technologies
- •10.4.2 Delivery Methods
- •10.6.2 TCR Engineered T Cell Therapy
- •10.6.3 Chimeric Antigen Receptor T Cell Therapy
- •10.9.2 Safety Considerations
- •References
- •11.1 Introduction
- •11.1.2 Categories
- •11.2 Current Status
- •11.2.1 Approved Products
- •11.2.2 Market
- •11.3 Design
- •11.3.1 Building Blocks
- •11.3.2 Linkers
- •11.3.3 Oligomerization
- •11.3.3.1 Monomer
- •11.3.3.2 Dimer
- •11.3.3.3 Trimer
- •11.3.3.4 Tetramer
- •11.3.3.5 Pentamer
- •11.3.3.6 Hexamer
- •11.3.3.7 Octamer
- •11.3.4 Orientation
- •11.3.5 Protein Engineering
- •11.3.6 Immunogenicity
- •11.4 Manufacturing
- •11.4.1 Upstream
- •11.4.2 Downstream
- •11.4.3 Glycosylation
- •11.4.4 Aggregation
- •11.4.5 Analytics
- •11.5 Therapeutic Concepts
- •11.5.1 Half-Life Extension
- •Albumin Fusions
- •Fc Fusions
- •Transferrin Fusions
- •Repetitive Peptide Fusions
- •Glycosylated Peptides
- •11.5.1.3 Aggregate Forming Peptides
- •11.5.2 Targeting Functions
- •11.5.3.1 Fc Domain Receptor-Mediated Toxicity
- •11.5.3.2 Toxins
- •11.5.3.3 Immunocytokines
- •11.5.3.4 Human Enzymes
- •11.5.3.5 Apoptosis Induction
- •11.6 Summary
- •11.7 Future Perspectives
- •References
- •12.1 Introduction
- •12.2 ADC History
- •12.3 Target Selection
- •12.4 Antibody Selection
- •12.6 ADC Technology
- •12.7 ADC Clinical Development
- •12.8.1 Mylotarg
- •12.8.2 Adcetris
- •12.8.3 Kadcyla
- •12.8.4 Besponsa
- •12.8.5 Polivy
- •12.8.6 Padcev
- •12.8.7 Enhertu
- •12.8.8 Trodelvy
- •12.8.9 Blenrep
- •12.8.10 Zynlonta
- •12.8.11 Tivdak
- •12.9 Concluding Remarks
- •References
- •13.1 Introduction
- •13.2 Gemtuzumab Ozogamicin
- •13.3 Gemtuzumab Antibody
- •13.4 Calicheamicin
- •13.7.3 Isolation of N-Acetyl Calicheamicin
- •13.10 Conclusions
- •References
- •14.1 Introduction
- •14.2.1 Antibody Generation
- •14.3.1 Structure Prediction
- •14.3.2 Biophysical Properties
- •14.3.3 Hydrophobicity
- •14.3.5 Isoelectric Point (pI)
- •References
- •15.1 Introduction
- •15.2 ADA Program Development
- •15.2.3 Project Approach
- •15.2.4 Model Library
- •15.3 Case Study
- •15.3.3 Hypothesis Generation
- •15.3.5 Feature Engineering Example
- •15.3.7 Model Insights
- •References
- •16.1 Introduction
- •16.1.1.1 United States
- •16.1.1.2 European Union
- •16.1.2 Global Markets
- •16.4.1 United States FDA
- •16.4.2 European Medicines Agency (EMA)
- •16.4.3 The World Health Organization
- •References
- •17.1 Introduction
- •17.3.1.2 Clone Selection

400
L. Letendre et al.
13.10 Conclusions
The story of the reintroduction of Mylotarg into the US market is one of looking
back at what was developed and what data were available at the time to support the
registration. This analysis provided Pzer the opportunity to apply the many scientic advances, which had occurred since the original ling. These advances touched
the science that is used in characterizing both processes and products and allowed
Pzer to provide a control strategy containing a deeper understanding of the production and control of Mylotarg than was available originally.
Re-developing/investigating a drug manufacturing process with a strictly dened
set of drug product attributes and, in this case, with a 17-year history, is different
than dening a process as a drug goes into a Phase 1 study and rening it during
clinical development. The Mylotarg drug substance is an antibody-drug conjugate;
however, it was the drug substance intermediates, gemtuzumab and activated calicheamicin derivative, that beneted from manufacturing optimization. The intermediates could be more easily modied to address their shortcomings while maintaining
the nal drug substance quality.
Just as Pzer had no experience returning a nearly 20-year-old drug to market,
neither did the regulatory agencies. They were receptive to the possibility of a more
advanced intermediate as a regulatory starting material than originally led for activated calicheamicin derivative. While the FDA and EMA did ultimately require
visibility of the intermediate’s manufacturing process back to the bacterial cell
bank, still other global health authorities accepted N-acetyl calicheamicin as a regulatory starting material.
The multiple functions that worked on all aspects of the re-registration of
Mylotarg ultimately succeeded and it was returned to the US market on September
1, 2017, over 17years after it was originally approved and almost 7years after it
was voluntarily withdrawn. Mylotarg continued the regulatory success with approvals throughout the world including Europe where it had been rejected in prior years.
Mylotarg’s re-introduction in the US and introduction in other major markets
offered a new alternative treatment to AML patients globally to manage this devastating disease.
Acknowledgements With a project that has spanned two decades, it is impossible to acknowledge all those who have contributed to bringing Mylotarg to patients. Similarly, the number of
colleagues who have contributed to just the effort to return Mylotarg to the market is also quite
large. We acknowledge the following colleagues while realizing that the credit goes well beyond
this list.
Pzer Global Medicine Team who shepherded the vast amount of clinical data that allowed the
rest of the team to pursue their chemical/biochemical specialties as their contributions.
Contributors to the calicheamicin process development portion of the project include: Nataliya
Bazhina, Eric Bortell, Lawrence Chen, Joe Collins, Robert Dugger, Brad Evans, Stephen Freese,
Xi Hu, Julius Lagliva, Mark Maloney, Jim Mo, Vimal Patel, Amar Prashad, Wesley Swanson, April
Xu, Chunchun Zhang, Sen Zhang.
Contributors to the antibody portion of the project include: Daniel Boisvert, William Daniels,
Mary Denton, Chris Gallo, Heyi Li, Joshua Ochocki, Mary Switzer.

13 Mylotarg: TheJourney toFDA Reapproval andBroad International Approval
401
Contributors to the drug substance portion of the project include: Eric Bortell, Brooke
Czapkowski, Brad Evans, Qingping Jiang.
Contributors to the drug product portion of the project include: Bakul Bhatnagar, Serguei
Tchessalov, Nathalie Hayduk, who led the joint Pharmaceutical Sciences and Pzer Global Supply
development teams.
Contributors to the calicheamicin, drug substance, and drug product manufacturing include:
Pzer Global Supply Pearl River—Technology team, QA team, QC team, Tok Han, Michael
Hripcsak, David Merkooloff, Sonal Shah, Paramanathan Guruparan.
Contributors to the antibody manufacturing include: Pzer Global Supply Andover Team.
Contributors to the CMC regulatory lings include: Jana Cook, Caroline Kinross, Jacqueline
Macaulay, Jacklyn Moxham, Pzer Global CMC team.
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antibody-drug conjugates for cancer therapy. Molecules 26:5847
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4. Hamann PR etal (2002) Gemtuzumab ozogamicin, a potent and selective anti-CD33 antibodycalicheamicin conjugate for treatment of acute myeloid leukemia. Bioconjug Chem 13:47–58
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and postconsolidation therapy in younger patients with acute myeloid leukemia. Blood
121:4854–4860
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de-novo acute myeloid leukaemia (ALFA-0701): a randomised, open-label, phase 3 study.
Lancet 379:1508–1516
7. Rowe JM, Löwenberg B (2013) Gemtuzumab ozogamicin in acute myeloid leukemia: a
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indium-111 radioimmunoconjugates by cancer cells. Cancer Res 56:2123–2129
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for acute myeloid leukemia. In: Accounts in drug discovery: case studies in medicinal chemistry. RSC, pp103–119
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of calicheamicin γ
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16:337–362

Part VII
Biopharmaceutical Informatics
and Analytics


Chapter 14
Biopharmaceutical Informatics:
AStrategic Vision forDiscovering
Developable Biotherapeutic Drug
Candidates
JoschkaBauer, SebastianKube, PankajGupta, andSandeepKumar
Abstract Biotherapeutics are rapidly emerging as a successful class of pharmaceu-
ticals, even though signicant challenges to their discovery and development
remain. In this book chapter, we establish a conceptual framework for potential
computational interventions at every stage of biologic drug discovery and early
development. We call this framework biopharmaceutical informatics. This chapter
provides a comprehensive overview of our strategic vision. This vision calls for
closer collaboration between drug discovery and development functions of biopharmaceutical industry by integrating the considerations of developability during early
stages of drug discovery. Computational tools already available to enable biopharmaceutical informatics are reviewed in this work. While our focus is on monoclonal
antibody-based biologics, the concepts discussed in this work are also applicable to
novel formats such as multispecic biologics.
Keywords Biotherapeutics · Drug design · Drug development · Developability ·
Function · Computation · In silico · Bioinformatics
J. Bauer · S. Kube
Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG,
Biberach/Riss, Germany
P. Gupta
In Silico Team, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc.,
Ridgeeld, CT, USA
S. Kumar (*)
Molecule Design and Modeling, Computational Science, Moderna Therapeutics, Cambridge,
MA, USA
e-mail: Sandeep.Kumar@modernatx.com
K. Gadamasetti, S. A. Kolodziej (eds.), Bioprocessing, Bioengineering
and Process Chemistry in the Biopharmaceutical Industry,
https://doi.org/10.1007/978-3-031-62007-2_14
405© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

406
J. Bauer et al.
14.1 Introduction
14.1.1 Growing Demand forImproved Developability
ofBiotherapeutics
Biotherapeutics, especially, the monoclonal antibodies (mAb) have now been industrialized. This success is reected in the fact that the load in the development pipelines of the biopharmaceutical industry is ever increasing with more than 800
antibody therapeutics in the early-stage and 115in the late-stage phases with an
average R&D cost to launch per project of about two to four billion US dollars [1].
While we celebrate rise of monoclonal antibodies as the fastest growing class of
biotherapeutics (or biologics as they are often referred as), one should, nevertheless,
recall that the chance for a monoclonal antibody to be approved for marketing is
about 5–10% [2]. Improved molecular understanding of the new drug and t to the
engineering and development platforms at preclinical R&D stages might increase
this chance of success [3] and therefore contribute towardseven greater success of
biologics. This desire to improve the rate of translation of biologic drug candidates
into drug products available in the market is often summarized under the term
“developability,” which describes the feasibility of a drug candidate to successfully
progress from discovery to clinical drug product development, and canbe traced as
early as the rst decade of the 2000s [4, 5]. The developability concept is based on
experience gained from previous Chemistry, Manufacturing, and Control (CMC)
development programs [6], along with the realization that several of the CMC issues
encountered during the development stages could have been mitigated via minor
primary sequence modications during discovery.
14.1.2 Developability Issues Arising fromUnfavorable
Biophysical Properties
In comparison to small molecule drug products, biologics are more complex and heterogenous in their three-dimensional structures and physicochemical properties,
which are highly dependent on their respective manufacturing process [7]. A subset of
those properties is relevant for drug efcacy and safety and therefore needs to be
monitored and controlled [8]. They are referred to as Critical Quality Attributes
(CQAs) [9]. As manufacturing processes for biologics are generally more complex
and have a greater inuence on the biophysical properties of the active pharmaceutical
ingredient (API), the regulatory bar for drug manufacturers to demonstrate their ability to make biologic drug products via well controlled, safe, and reproducible processes is much higher in comparison to that for the small molecule drug products [10].
In an ideal biologic drug product development program, an excellent mAb candidate with optimal biophysical properties and a perfect t to standard development
platforms would be reproducibly expressed and puried with a high yield and

14 Biopharmaceutical Informatics: A Strategic Vision for Discovering Developable…
product quality resulting in low production costs. Subsequently, the active pharmaceutical ingredient would be formulated in liquid form with high chemical, conformational, colloidal, and physical stability during shipping and storage over its shelf
life and would be easily administrable at the concentrations required for effective
dosing [11]. However, the real circumstances of the biopharmaceutical industry
deviate substantially from this idealized state, since the majority of lead candidates
experience development issues that are related to their unfavorable biophysical
properties, but the specic reasons behind a drug failure in the development phase
remain often poorly documented and therefore not fully understood [4]. Increasing
the complexity, next-generation drugs of multi-specic mAb derivatives tend to
have even greater CMC hurdles [12, 13]. Additionally, the quality target product
prole dening the key design requirements of the nal drug product is not fully
dened in the early development stages [14]. The therapeutic dose and regimen for
clinical studies are not yet dened and need to be established in preclinical experiments with the same material that is used in the clinical studies [15]. The anticipated
administration route might change during the development as well as the primary
packaging and/or the inclusion of a medical device [16]. All these design choices
bring different and additional requirements on different physicochemical properties
of the biologics and the formulation that can also modify the platform manufacturing processes. Examples are solution viscosity, resistance to shear stress, interaction
on air: water and interfaces, compatibility to a liquid or lyophilized formulation,
compatible excipients, and active pharmaceutical ingredient (API) concentration.
Therefore, the biopharmaceutical industry aspires to meet the increasing demand
for modern biotherapeutics by improving their developability via rational design.
Table 14.1 summarizes the most commonly occurring issues in formulation
development of a liquid formulation for parenteral application.
407
14.1.3 Biopharmaceutical Informatics: AnIntegrated
Approach toDiscovery andDevelopment
ofBiotherapeutics
As described above, biotherapeutic drug candidates often face several developability issues and therefore warrant greater scrutiny from the regulatory agencies.
Remarkably, however, each of the development risks described above can be traced
back to the intrinsic physicochemical properties of the drug candidate encoded in its
primary sequence and three-dimensional structure [6], both of which are specically targeted by the promising approach of biopharmaceutical informatics. Kumar
etal. previously dened this emerging eld as any computational effort geared to
advance efcient and cost-effective translation of biologic drug candidates into drug
products [17]. Figure14.1 describes the motivation for biopharmaceutical informatics. This interdisciplinary area explicitly covers both in silico tools alone and in
combination with experimental studies such as developability assessment.

408
Table 14.1 Major challenges and opportunities for application of computation at various stages of
antibody-based biotherapeutic drug discovery and development
Opportunities for computational
Stage Major challenge(s)
In vitro
production of
immunogen(s)
Antibody
generation
Hit selection
and lead
identication
(LI)
Lead
optimization
(LO)
Early stage
developability
assessment
1. Immunogen proteins may have
unknown structure with varying
degree of sequence homology
with proteins of known
structures
2. Protein insolubility and
aggregation leading to low
material yields
1. Antibody generation by
immunizing animals is costly,
time consuming, yields variable
results, and may require
humanization
2. Humanized mice do not capture
fully human immune repertoire
3. Phage and yeast display
technologies can rapidly
identify binders but their
developability may need further
optimization
1. Sequencing of the hits
2. Epitope mapping of the hits to
assure desirable therapeutic
effect in absence of structural
models for Ag:Ab complex
3. Experimental testing of several
hundreds of hits for function
and developability can be time
and resource consuming
Identied lead candidates may
require humanization (if from a
nonhuman source such as wild
type mouse), afnity optimization,
and removal of physicochemical
liabilities for improved
developability
1. Molecular stability and tness
of the drug candidate nominated
by drug discovery to platform
processes used in drug
development
2. Dynamic development targets
applications
1. Protein structure prediction
2. Sequence/structure-based optimization
for improved conformational stability
andsolubility can help improve quantity
as well as quality of material
availablefor immunization
1. Development of computational techniques
analogous tovirtual screening of human
antibody repertoires can lead to rapid
identication of binders. It can also
potentially alleviate the need to produce
immunogens in large quantities
2. Computational design of phage and
yeast display libraries for improved
developability
3. Computational redesign of antibodies
with good developability to binding
different specicities
4. In silico generation of antigen-specic/
antigen-agnostic antibody libraries via
articial intelligence and machine learning
1. Development of appropriate Sanger and
NGS sequencing pipelines
2. Computational prediction of epitopes
and paratopes
3. In silico assessments of hits for
developability and manufacturability
can guide selection of developable hits
and identify lead candidate(s) with good
developability
Structure-based modeling of the lead
candidates can guide their humanization,
afnity maturation, and identication of
potential sequence/structural motifs that
may drive their physicochemical
degradation. Availability of this information
can help guide protein engineering
strategies for lead optimization (LO)
1. Multiscale molecular and mathematical
simulations to predict t to platform and
understand molecular response to
stresses faced during manufacturing,
storage, and shipping
2. Development of predictive algorithms for
identifying the appropriate bioprocess
conditions and formulation ingredients
for drug product development
3. Decision-support systems
J. Bauer et al.
(continued)

14 Biopharmaceutical Informatics: A Strategic Vision for Discovering Developable…
409
Table 14.1
Stage Major challenge(s)
Process and
drug product
development
Clinical
development
All stages Numerous challenges related to
(continued)
1. Up-scaling
2. Comparability
3. Customization of processes for
manufacturing (bioprocess),
formulation development, and
analytical characterization
1. Trial design and criteria for
patient recruitment
2. Safety, efcacy, and
pharmacology
function and physicochemical
stability of the biotherapeutic drug
candidates
Opportunities for computational
applications
Machine learning and mechanistic
modelling techniques to model
bioprocesses conditions and yields, and
development of digital twins
1. Biostatics and genomics technologies
2. Immunogenicity predictions and their
clinical relevance
3. Safety and toxicology data capture and
analyses
4. Modeling and simulation of
pharmacology of drug candidates
1. Data capture into databases that can be
easily analyzedby following FAIR
(Findable, accessible, interoperable, and
recyclable) principles
2. Data analyses and creation of digital
tools to predict experimental outcomes
3. Biopharmaceutical informatics
Fig. 14.1 Motivation for biopharmaceutical informatics
The correlation of “macroscopic” experimentally determined attributes of a biologic with its “microscopic” sequence-structure aspects computed in silico is the
major unsolved problem in biopharmaceutical informatics. Depending on the
amount of data available, several statistical as well as machine learning approaches
can be applied to obtain mathematical models capable of predicting solution behaviors of monoclonal antibodies solely from their sequence-structure information
[18–26]. Below, we stitch together a diverse set of concepts in bioinformatics,
molecular modelling, and simulations along with the experiments to demonstrate
practical feasibility of our vision of biopharmaceutical informatics and Table14.2
lists several opportunities to apply computation at every stage of biopharmaceutical
drug discovery and development. However, the eld has not matured uniformly in
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