Abstract
The aim of the analysis was to develop a phenomenological longitudinal population pharmacokinetics (PK)-anti-drug antibodies (ADA) model to enable an informed and quantitative framework for assessment of ADA influence. Data used were from seven clinical studies of avelumab across drug development phases in patients with several tumor types. ADA as covariate in a population PK model, and Markov models of ADA status (ADA+ or ADA-) were investigated. Finally, a joint PK-ADA model was developed. In the population PK models that evaluated ADA as a covariate, the clearance increase attributable to ADA+ status was 8.5% (time-varying ADA) to 19.9% (time-invariant ADA with inter-occasion variability in clearance). With a discrete-time Markov model (DTMM), tumor type was identified as a significant covariate on the probability of ADA- to ADA+ transition. When ADA time course predicted by the DTMM model was implemented as a covariate in the population PK model, an increase in avelumab clearance of 11-41% was estimated depending on tumor type. With a continuous-time Markov model (CTMM), in addition to tumor type, baseline ADA status was identified to significantly influence the ADA- to ADA+ transition rate constant. The joint PK-CTMM model estimated the maximal increase in CL due to ADA as 15% and a decrease in ADA- to ADA+ transition rate of up to 37% with increasing avelumab concentration, with 50% of the maximum decrease occurring at 349 µg/mL. The present work established a framework for the assessment of interactions between PK and immunogenicity for therapeutic proteins.