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Related Experiment Video

Updated: Jun 20, 2026

Genetic Encoding of a Non-Canonical Amino Acid for the Generation of Antibody-Drug Conjugates Through a Fast Bioorthogonal Reaction
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Rapid and Efficient Antibody-Drug Conjugate Design Using Mechanistic Bottom-Up Modeling from In Vitro to Human.

Jan-Philip Kahl1, Judith Stein1, Tatu Lindroos1

  • 1Department of Discovery Technologies, The Healthcare Business of Merck KGaA, Darmstadt 64293, Germany.

Bioconjugate Chemistry
|June 19, 2026
PubMed
Summary

A new modeling strategy accurately predicts antibody-drug conjugate (ADC) efficacy and toxicity from lab tests to human trials. This approach aids in designing better ADCs and selecting appropriate patients, accelerating drug development.

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Area of Science:

  • Pharmacology and Toxicology
  • Biotechnology and Pharmaceutical Sciences
  • Computational Biology and Bioinformatics

Background:

  • Antibody-drug conjugates (ADCs) show promise but frequently fail in clinical trials due to challenges in translating preclinical data.
  • Existing methods struggle to accurately predict ADC efficacy and toxicity across different designs and species.

Purpose of the Study:

  • To develop a modular, bottom-up modeling strategy for translating ADC efficacy and toxicity from preclinical studies to clinical outcomes.
  • To guide the design of novel ADCs and improve candidate and patient selection for accelerated development.

Main Methods:

  • Employed mechanistic pharmacokinetic-pharmacodynamic (PK-PD) models to integrate ADC, payload, and systemic parameters.
  • Validated the models using six diverse ADCs, predicting in vitro potency, in vivo efficacy, and key toxicities like neutropenia and thrombocytopenia.
  • Scaled parameters from preclinical models (mouse) to humans to reproduce clinical efficacy trends.

Main Results:

  • Achieved 94.64% accuracy in predicting in vitro ADC potency within a 2-fold range.
  • Successfully predicted in vivo efficacy comparable to xenograft data and reproduced clinical efficacy trends in humans.
  • Accurately predicted hematologic toxicities (neutropenia, thrombocytopenia) for specific ADCs.
  • Demonstrated how the model could have identified issues with a clinically failed ADC (RN927C), suggesting design modifications.

Conclusions:

  • The developed modeling strategy provides accurate efficacy and toxicity translation from in vitro to human studies using accessible parameters.
  • This approach enhances the understanding of ADCs and their components, supporting improved ADC design, candidate selection, and patient stratification.
  • The strategy has the potential to significantly accelerate the development of novel antibody-drug conjugates.