Abstract
Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861–875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.
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Acknowledgements
This work was supported by NIH grant GM114179 to D.K.S and the Centre for Protein Therapeutics Consortium at University at Buffalo. The authors would also like to thank Dr. Wojciech Krzyzanski, Dr. Robert Bies, and Olivia Campagne for their technical advice while performing the modeling analysis for this paper.
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Supplementary Figure 1
Model Estimated tumor doubling time values for different tumor models. (GIF 99 kb)
Supplementary Figure 2
The effect of censoring model generated tumor profiles on PFS rates, evaluated for HER2 3+ expressing patients. Key: Black - the original PFS rates (no censoring); Red – PFS rates generated after censoring upper 10% tumor profiles; Green - PFS rates after censoring middle 10% tumor profiles; Purple - PFS rates after censoring lower 10% tumor profiles; Yellow - PFS rates after censoring 10% tumor profiles randomly. (GIF 90 kb)
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Singh, A.P., Shah, D.K. Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1). AAPS J 19, 1054–1070 (2017). https://doi.org/10.1208/s12248-017-0071-y
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DOI: https://doi.org/10.1208/s12248-017-0071-y