Antibody drug conjugate (ADC) Pharmacokinetics

With an ever increasing focus on antibody drug conjugates (ADC), computational models are needed to better understand the complexity of the various design parameters and objectives of ADCs. The following case study is a computational exploration of mechanistic determinants of ADC pharmacokinetics using quantitative systems pharmacology (QSP) modeling strategies.



SITUATION

•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, however recent evidence suggests that the underlying mechanisms may be more complex

 

SOLUTION

•Created 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

•Established the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs

 

RESULTS

•The model described the kinetics of individual DAR species and agrees well with typical ADC PK, individual DAR PK, and average DAR measurements in vivo. 

•The model quantitatively describes the trade-off between higher DAR and lower exposure.

•Model simulations show that longer mAb half-life reduces payload delivery after multiple doses. ADC half-life affects the percent of payload delivered through different mechanisms, with deconjugation increasing with longer half-life.

 

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