A PK-PD Modeling and Simulation Based Strategy for Clinical Translation of Antibody-Drug Conjugates: A case study with T-DM1

Successful clinical translation of Antibody-Drug Conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. In this webinar, we will present application of a PK-PD based strategy for successful bench to bedside translation of ADCs using T-DM1 as an example. A mechanistic cellular disposition model was developed incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Specific biomeasures and chemomeasures reported for T-DM1 in the literature were incorporated in the model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with an in vivo tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Later, our integrated preclinical PK-PD model was used to characterize tumor growth inhibition (TGI) data in multiple HER2+ mouse models with varying level of HER2 expression. Clinical pharmacokinetics of T-DM1 was predicted by allometric scaling of monkey PK parameters. Finally, 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 capable of performing clinical trial simulations. Our model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in 3 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 and this strategy has now been validated for all clinically approved ADCs.