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)

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.

Antibody–Drug Conjugates: Pharmacokinetic/Pharmacodynamic Modeling, Preclinical Characterization, Clinical Studies, and Lessons Learned

“…The authors state that this model suggested that a fractionated dosing regimen may provide improved efficacy with trastuzumab emtansine. It was concluded that this modeling and simulation strategy for ADC pharmacokinetics/pharmacodynamics was capable of predicting the clinical efficacy of ADCs a priori and the authors were able to retrospectively validate this strategy for all clinically approved ADCs [78]. …”

Evolution of the Systems Pharmacokinetics-Pharmacodynamics Model for Antibody-Drug Conjugates to Characterize Tumor Heterogeneity and In Vivo Bystander Effect

“…Going forward we plan to translate this systems PK-PD to predict bystander effect of ADCs in the clinic (Shah et al, 2012;Betts et al, 2016;Singh and Shah, 2017a).…”

“…The final systems PK-PD model was used to evaluate the effect of different dosing regimen and tumor composition on the bystander effect of ADC. The presented PK-PD model could be translated to predict the bystander effect of ADCs in the clinic using the strategies presented by us before (Shah et al, 2012;Singh and Shah, 2017a).…”

“…Both elimination pathways (CL ADC and K dec P ) generate free (unconjugated) MMAE, and hence become the formation rates for MMAE. The disposition of free MMAE is also characterized using a 2-compartment model with linear systemic clearance (CL Drug ) and distributional clearance (CLD Drug ) (Shah et al, 2012;Shah et al, 2014;Khot et al, 2015;Singh et al, 2015b;Singh et al, 2016a;Singh and Shah, 2017a;Singh and Shah, 2018).…”

“…To further enhance quantitative understanding of this phenomenon, here we present an in vivo investigation and development of a systems pharmacokinetic-pharmacodynamic (PK-PD) model to characterize in vivo bystander effect of ADCs. Development of such models can be crucial for identifying drug-and system-specific parameters that are important for accomplishing in vivo bystander effect (Singh et al, 2015a;Singh et al, 2015b;Singh and Shah, 2017a;Singh and Shah, 2018). In addition, these models could also be used to guide the development of nextgeneration ADCs (van Geel et al, 2015), evaluate druggability of novel targets, and facilitate preclinical-to-clinical translation of ADC efficacy.…”