Computational exploration of mechanistic determinants of antibody drug-conjugate pharmacokinetics using quantitative systems pharmacology modeling strategies.

Abstract

e14000 Background: The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules This discrepancy is often attributed to deconjugation (Kamath, 2014), however recent evidence suggests that the underlying mechanisms may be more complex. Methods: This work employs a computational quantitative systems pharmacology (QSP) approach to understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies. Results: Using QSP approaches, the model (1) describes the kinetics of individual DAR species and agrees well with typical ADC PK, individual DAR PK, and average DAR measurements in vivo; (2), quantitatively describes the trade-off between higher DAR and lower exposure; consequently, we predict that ADC2 is half as potent as ADC4 and ADC8, which are equipotent; (3) longer mAb half-life reduces payload delivery after multiple doses; and (4) ADC half-life affects the percent of payload delivered through different mechanisms. Conclusions: A QSP model describing mechanism is a useful tool to translate and understand PK from preclinical species to human, by acting as a central repository of data, knowledge, and hypotheses. It provided a rational basis to generate testable hypotheses and provide early insights into complex ADC PK data and established the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs.