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Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1

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Abstract

A mechanistic model of the immune response was evaluated for its ability to predict anti-drug antibody (ADA) and their impact on pharmacokinetics (PK) and pharmacodynamics (PD) for a biotherapeutic in a phase 1 clinical trial. Observed ADA incidence ranged from 33 to 67% after single doses and 27–50% after multiple doses. The model captured the single dose incidence well; however, there was overprediction after multiple dosing. The model was updated to include a T-regulatory (Treg) cell mediated tolerance, which reduced the overprediction (relative decrease in predicted incidence rate of 21.5–59.3% across multidose panels) without compromising the single dose predictions (relative decrease in predicted incidence rate of 0.6–13%). The Treg-adjusted model predicted no ADA impact on PK or PD, consistent with the observed data. A prospective phase 2 trial was simulated, including co-medication effects in the form of corticosteroid-induced immunosuppression. Predicted ADA incidences were 0–10%, depending on co-medication dosage. This work demonstrates the utility in applying an integrated, iterative modeling approach to predict ADA during different stages of clinical development.

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Acknowledgments

The authors wish to thank the patients from the early clinical trial for contributing valuable data to support the analysis. We would like to thank Xiaoying Chen, Tim Hickling, and Paolo Vicini who developed and published the Pfizer model for immunogenicity prediction and made their model code publically available, which enabled our work. The work was partially presented previously in poster form at the 6th American Conference on Pharmacometrics (ACoP) held in Crystal City, VA, USA, in October 2015, and published in the meeting proceedings as abstract T-018 (Article in Journal of Pharmacokinetics and Pharmacodynamics 42:S51-S51, October 2015).

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Authors and Affiliations

  1. Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Princeton, New Jersey, 08543, USA

    Lora Hamuro & Giridhar S. Tirucherai

  2. Bioanalytical Sciences, Translational Medicine, Bristol-Myers Squibb, Princeton, New Jersey, 08543, USA

    Sean M. Crawford & Renuka C. Pillutla

  3. Molecular Structure and Design, Bristol-Myers Squibb, Princeton, New Jersey, 08543, USA

    Akbar Nayeem

  4. Analytical Strategy and Operations, Product Development, Bristol-Myers Squibb, Princeton, New Jersey, 08543, USA

    Binodh S. DeSilva

  5. Quantitative Clinical Pharmacology, Bristol-Myers Squibb, Route 206 & Province Line Road, Princeton, New Jersey, 08543, USA

    Tarek A. Leil & Craig J. Thalhauser

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  1. Lora Hamuro
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Contributions

All authors contributed to the design of the research. CT and LH wrote the manuscript. SC, AN, CT, and LH performed the research and analyzed data. CT conducted the modeling and simulation.

Corresponding author

Correspondence to Craig J. Thalhauser.

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All authors were employees of Bristol-Myers Squibb at the time of this work.

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Hamuro, L., Tirucherai, G.S., Crawford, S.M. et al. Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1. AAPS J 21, 94 (2019). https://doi.org/10.1208/s12248-019-0361-7

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