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Kinetic Modeling of the Antibody Disulfide Bond Reduction Reaction With Integrated Prediction of the Drug Load Profile for Cysteine‐Conjugated ADCs

Weggen, Jan Tobias ORCID iD icon 1; González, Pedro 2; Hui, Kimberly; Bean, Ryan; Wendeler, Michaela; Hubbuch, Jürgen ORCID iD icon 1
1 Institut für Bio- und Lebensmitteltechnik (BLT), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract:

Antibody‐drug conjugates (ADC) constitute a groundbreaking advancement in the field of targeted therapy. In the widely
utilized cysteine conjugation, the cytotoxic payload is attached to reduced interchain disulfides which involves a reduction of
the native monoclonal antibody (mAb). This reaction needs to be thoroughly understood and controlled as it influences the
critical quality attributes (CQAs) of the final ADC product, such as the drug‐to‐antibody ratio (DAR) and the drug load
distribution (DLD). However, existing methodologies lack a mechanistic description of the relationship between process
parameters and CQAs. In this context, kinetic modeling provides comprehensive reaction understanding, facilitating the model‐
based optimization of reduction reaction parameters and potentially reduces the experimental effort needed to develop a robust
process. With this study, we introduce an integrated modeling framework consisting of a reduction kinetic model for the species
formed during the mAb reduction reaction in combination with a regression model to quantify the number of conjugated drugs
by DAR and DLD. The species formed during reduction will be measured by analytical capillary gel electrophoresis (CGE), and
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Verlagsausgabe §
DOI: 10.5445/IR/1000178018
Veröffentlicht am 14.01.2025
Originalveröffentlichung
DOI: 10.1002/bit.28899
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Bio- und Lebensmitteltechnik (BLT)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 03.2025
Sprache Englisch
Identifikator ISSN: 0006-3592, 1097-0290
KITopen-ID: 1000178018
Erschienen in Biotechnology and Bioengineering
Verlag John Wiley and Sons
Band 122
Heft 3
Seiten 579–593
Vorab online veröffentlicht am 17.12.2024
Nachgewiesen in Dimensions
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Scopus
Web of Science
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