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Physiologically Based Pharmacokinetic Modeling as a Tool to Predict Drug Interactions for Antibody-Drug Conjugates

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Abstract

Background and Objectives

Monomethyl auristatin E (MMAE, a cytotoxic agent), upon releasing from valine-citrulline-MMAE (vc-MMAE) antibody-drug conjugates (ADCs), is expected to behave like small molecules. Therefore, evaluating the drug–drug interaction (DDI) potential associated with MMAE is important in the clinical development of ADCs. The objective of this work was to build a physiologically based pharmacokinetic (PBPK) model to assess MMAE–drug interactions for vc-MMAE ADCs.

Methods

A PBPK model linking antibody-conjugated MMAE (acMMAE) to its catabolite unconjugated MMAE associated with vc-MMAE ADCs was developed using a mixed ‘bottom-up’ and ‘top-down’ approach. The model was developed using in silico and in vitro data and in vivo pharmacokinetic data from anti-CD22-vc-MMAE ADC. Subsequently, the model was validated using clinical pharmacokinetic data from another vc-MMAE ADC, brentuximab vedotin. Finally, the verified model was used to simulate the results of clinical DDI studies between brentuximab vedotin and midazolam, ketoconazole, and rifampicin.

Results

The pharmacokinetic profile of acMMAE and unconjugated MMAE following administration of anti-CD22-vc-MMAE was well described by simulations using the developed PBPK model. The model’s performance in predicting unconjugated MMAE pharmacokinetics was verified by successful simulation of the pharmacokinetic profile following brentuximab vedotin administration. The model simulated DDIs, expressed as area under the concentration-time curve (AUC) and maximum concentration (C max) ratios, were well within the two-fold of the observed data from clinical DDI studies.

Conclusions

This work is the first demonstration of the use of PBPK modelling to predict MMAE-based DDI potential. The described model can be extended to assess the DDI potential of other vc-MMAE ADCs.

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Acknowledgments

This study was funded by Genentech (a member of the Roche group). All authors were employees of Genentech when this work was carried out. They have no other conflicts of interest to declare. We would like to thank the DMPK ADME group for generating the in vitro data, Priya Agarwal for processing the CD22 pharmacokinetic data, Amita Joshi, John Prescott, and Yu-Waye Chu for reviewing the manuscript, and Anshin BioSolutions for editorial support.

Author contributions

Yuan Chen, Divya Samineni, Sophie Mukadam, Jin Yan Jin, and Chunze Li participated in the model design; Yuan Chen, Divya Samineni, Sophie Mukadam, Ben-Quan Shen, Dan Lu, and Chunze Li collected data and ran simulations; Yuan Chen, Divya Samineni, Sophie Mukadam, Harvey Wong, and Chunze Li performed the data analysis and wrote the manuscript; and Yuan Chen, Divya Samineni, Sophie Mukadam, Ben-Quan Shen, Harvey Wong, Jin Yan Jin, Dan Lu, Sandhya Girish, Cornelis Hop, and Chunze Li reviewed the manuscript and approved it for submission.

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Authors and Affiliations

  1. Drug Metabolism and Pharmacokinetics, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA

    Yuan Chen, Sophie Mukadam, Harvey Wong & Cornelis Hop

  2. Clinical Pharmacology, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA

    Divya Samineni, Dan Lu, Sandhya Girish, Jin Yan Jin & Chunze Li

  3. Preclinical and Translational Pharmacokinetics and Pharmacodynamics, Genentech Inc, South San Francisco, CA, USA

    Ben-Quan Shen

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  1. Yuan Chen
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  2. Divya Samineni
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  3. Sophie Mukadam
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  6. Dan Lu
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  7. Sandhya Girish
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  9. Jin Yan Jin
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  10. Chunze Li
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Correspondence to Yuan Chen or Chunze Li.

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Chen, Y., Samineni, D., Mukadam, S. et al. Physiologically Based Pharmacokinetic Modeling as a Tool to Predict Drug Interactions for Antibody-Drug Conjugates. Clin Pharmacokinet 54, 81–93 (2015). https://doi.org/10.1007/s40262-014-0182-x

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  • DOI: https://doi.org/10.1007/s40262-014-0182-x

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