A Bayesian K-PD model for synergy: A case study

Coadministration of 2 or more compounds can alter both the pharmacokinetics and pharmacodynamics of individual compounds. While experiments on pharmacodynamic drug-drug interactions are usually performed in an in vitro setting, this experiment focuses on an in vivo setting. The change over time of a safety biomarker is modeled using an indirect response model, in which the virtual pharmacokinetic profile of one compound drives the effect of the other. Several experiments at different dose level combinations were performed sequentially. While a traditional frequentist analysis consists of estimating the model parameters based on all the data simultaneously, in this work, we consider a Bayesian inference framework allowing to incorporate the results from a historical dose-response experiment.

Publication year:2018

Keywords:Bayesian inference, coadministration, indirect response model, pharmacodynamics, pharmacokinetics

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:1

CSS-citation score:1

Authors from:Higher Education

Accessibility:Closed