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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5366_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •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

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343


Chapter 12
Development ofAntibody-Drug
Conjugates
DavidY.Jackson
Abstract Antibody drug conjugates (ADCs) are a rapidly growing class of targeted
cancer drugs in which a highly toxic small molecule (payload) is conjugated to a
tumor-selective antibody. Over the past two decades, a total of 11 ADCs have been
approved by the FDA in the United States; including: gemtuzumab ozogamicin
(Mylotarg™), brentuximab vedotin (Adcetris™), ado-trastuzumab emtansine
(Kadcyla™), inotuzumab ozogamicin (Besponsa™), polatuzumab vedotin
(Polivy™), enfortumab vedotin (Padcev™), trastuzumab deruxtecan (Enhertu™),
sacituzumab govitecan (Trodelvy™), belantamab mafodotin (Blenrep™), loncastuximab tesirine-lpyl (Zynlonta™), and tisotumab vedotin-tftv (Tivdak™). The
path to commercial success for these ADCs has been challenging however, and new
ADC approvals were rare prior to 2017 when only three ADCs had been approved
by the FDA. Then in 2019 three more ADCs were approved, followed by two
approvals in 2020 and two more in 2021. Dozens of ADCs are now in late-stage
clinical trials and new approvals are expected to remain consistent for the near
future. Following closely behind are over a hundred new ADCs in early clinical or
preclinical development. This chapter will summarize the history of currently
approved ADCs with emphasis on the challenges that were overcome during development and new technology that likely contributed to their success. The safety and
efcacy of each ADC will be discussed from a critical but honest perspective based
on personal experience. My intention in writing this chapter is to encourage readers
to educate themselves about the real benets and risks of ADC therapeutics so that
informed decisions can be made by cancer patients in collaboration with their
doctors.
Keywords Antibody-drug conjugate · Toxic payload · FDA approved · Cancer
drug · oncology · Targeted therapy
D. Y. Jackson (*)
DYJ Pharma, San Mateo, CA, USA
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_12
345© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

346
D. Y. Jackson
12.1 Introduction
Antibody-drug conjugates (ADCs) are a rapidly growing class of targeted therapeutic agents for the treatment of cancer [1, 2]. Cancer is the second most prevalent
cause of death in the US and many other countries; only heart disease kills more
people. Most conventional drugs for the treatment of cancer are highly potent small
molecules that kill cells or inhibit cell growth and often have poor therapeutic windows due to their lack of selectivity for tumor cells [3]. In the last three decades
however, new monoclonal antibody (mAb) therapeutics that bind to specic antigens on tumor cells have demonstrated improved tumor selectivity and safety [4].
These mAbs, however, frequently lack sufcient potency for use as single-agent
drugs and are often used in combination with conventional chemotherapeutic agents
to improve patient treatment outcomes [5]. Although multidrug treatment regimens
have been moderately successful in treating some forms of cancer, the increased
costs associated with conventional chemotherapy, combined with complicated dosing schedules and potential toxicity due to adverse drug interactions, demonstrate a
need for safer more effective cancer drugs.
Antibody-drug conjugates combine the target specicity of a monoclonal antibody with the potency of a small molecule drug (payload) by connecting them into
a single ADC molecule that retains the properties of both [6]. The improved selectivity and potency of ADCs lead to superior safety and efcacy resulting in broader
therapeutic windows compared to conventional chemotherapeutic drugs (Fig.12.1).
In fact, ADCs have been described as the “smart bombs” of cancer therapy because
they deliver highly toxic payloads directly to tumor cells while minimizing “collateral damage” to the surrounding noncancerous or normal tissues [7]. The concept
is simple, but in reality, tumor cells are not easy to target because they are derived
from normal cells and express similar proteins on their cell membrane. Moreover,
cancer cells are usually very good at camouage by mimicking their surroundings;
making them difcult targets to hit, even for antibodies.
The key to developing an effective ADC therapeutic is to nd the optimal combination of components; target, antibody, linker, and payload for a specic cancer
Fig. 12.1 ADCs improve the safety (higher MTD) & efcacy (lower Rx) to broaden the therapeutic window of conventional cancer drugs

12 Development ofAntibody-Drug Conjugates
347
type. This chapter will discuss these components and their desired properties in
order to give the reader a better understanding of the benets and limitations of the
currently approved ADC therapeutics. Emerging new technologies such as sitespecic conjugation methods designed to improve the safety and efcacy of future
ADCs will also be discussed. ADCs do not yet offer a cure for cancer but they do
offer an alternative to conventional chemotherapeutic drugs and should continue to
be investigated as targeted therapeutic agents. Cancer may never disappear completely from the human condition, but each small step toward nding a cure might
eventually save the life of someone you care about.
12.2 ADC History
Most antibody-drug conjugates have experienced a rugged path to commercial success and have overcome numerous challenges along the journey. For example, the
rst ADC to reach the market (Mylotarg) was approved in 2000 butwas later discontinued due to lack of efcacy [8]. Nearly 10years elapsed before the second
ADC, Adcetris was approved in 2011; followed by a third ADC, Kadcyla, which
was approved in 2013. With only three approvals in the rst decade of ADC development, their approval rate was lower than that for small molecules and only a fraction of the approval rate for most antibody therapeutics [9]. Fortunately, this trend
was temporary and the rate of ADC approvals has increased dramatically in recent
years with seven new ADCs approved for cancer between 2017 and 2022 [10].
Given the rapid increase in approvals, one might conclude that the lessons
learned from early ADCs may have facilitated the development pathways of those
that followed. Closer analysis however, indicates that the increase in ADC approvals
likely reects the growing number of ADCs entering clinical trials, rather than new
breakthroughs in ADC technology. During the past decade (between 2011 and
2021), the number of clinical trials has steadily increased by nearly tenfold to over
300 trials in 2021 (Fig.12.2a), yet the number of new ADC approvals during the
same time period only doubled (Fig.12.2b). Although the number of new clinical
trials continues to grow each year, the discrepancy between the number of phase I/
II and phase III trials indicates that most ADCs fail to progress beyond the early
stages of clinical development.
The reasons for these clinical failures are difcult to assess and seldom reported,
even if they’ve been determined by the companies that sponsored them. Based on
the currently available information, however, most ADC researchers would probably agree that systemic exposures to toxic payloads and off-target toxicity are primary contributors to ADC failures in the clinic [11]. Methods for reducing ADC
toxicity would therefore be expected to improve the overall safety of ADCs and will
likely be a major focus for future research and development efforts.
In general, there are four major components (target, antibody, linker, and drug
payload) that impact the commercial success or failure of an antibody-drug

348
A)
# of trials
Year
2
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Year
D. Y. Jackson
300
250
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# of trials
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Fig. 12.2 (a) Number of ADC clinical trials by phase of development since 2001. (b) Number of
new clinical trials and approvals since 2001. (Source: clinical trials.gov)
Desired Properties
Target/antigen
Antibody
Linker
Payload
Fig. 12.3 Components and desired properties of an antibody-drug conjugate (ADC)
conjugate (Fig.12.3). Pharmaceutical companies focus signicant efforts on selecting components with optimal properties in order to improve the effectiveness of
ADCs. Ironically the component that likely has the greatest impact on the success
or failure of an ADC resides in the tumor and is not part of the ADC [12]. The selectivity of an ADC for killing tumor cells over normal adjacent tissues is almost solely
dependent upon the expression prole of the antigen target, so target selection is an
essential part of the ADC early development process.

12 Development ofAntibody-Drug Conjugates
349
12.3 Target Selection
ADC targets are often described as “tumor antigens,“ or they are said to be “overexpressed” on tumors, but these terms are misleading for a number of reasons. First,
there are currently over a hundred different antigens being targeted by ADCs in
clinical development, yet very few are differentially expressed on tumors at higher
levels than on normal tissues [13]. A majority of the data used to evaluate potential
tumor-associated antigens are derived from mRNA expression analysis, which is
known to be highly variable, irreproducible, and not suitable for accurate quantication of proteins. Researchers frequently use complimentary methods such as IHC to
conrm the expression of a selected antigen on tumor cells, but IHC also has signicant limitations for quantifying protein expression and the results are dependent on
the samples being tested.
Problems with cross-reactivity are common with IHC and other antibody-based
detection methods (ELISAs, FACs ...etc.), and heterogeneous protein expression in
tumors creates additional uncertainties when trying to analyze expression data.
Furthermore, the quantitation of specic antigens via protein isolation, purication,
and analysis is not practical in a clinical setting. In general, it is very difcult to
accurately measure protein expression in tumors because proteins and mRNA often
continue to degrade in tumor samples, even after they are removed from the patient,
Ideally, the expression levels of a specic antigen in tumors would be compared
with expression levels in normal tissue, and higher target expression in tumors than
in surrounding normal tissues would be considered desirable because it would
enable relatively more drug to be delivered to the tumors by the ADC.Unfortunately,
it is also very difcult to obtain truly normal tissue samples to use as controls, since
most people are reluctant to give away parts of their healthy organs. As a result, the
availability of normal tissue samples in the US relies on organs donated by people
who suffer untimely deaths.
The prevalence of target expression, or the number of tumors that express a specic target antigen also varies widely and is dependent on the type of cancer being
treated. To further complicate matters, target expression is often not uniform
throughout the tumor and is seldom restricted to specic organs or systems [14].
Consequently, the therapeutic windows of most ADCs are quite narrow, and selecting an optimal target is likely the most important step toward developing an effective ADC.
12.4 Antibody Selection
In addition to the target, the antibody component of an ADC can also signicantly
impact its commercial success (Fig.12.3). Factors such as afnity for the target, the
specic binding epitope, the internalization rate, and the antibody subtype can all
affect the success of an ADC.Processes used for antibody production, the species
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