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.

When the effect of interest is heterogeneous, a one-stage meta-analysis ignoring clustering gives biased estimates. Two-stage meta-analysis generates estimates at least as accurate and precise as one-stage meta-analysis. However, in a study using small databases and rare exposures and/or outcomes, a correct one-stage meta-analysis becomes essential.

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK‐PD) framework. While, at first sight, a Bayesian PK‐PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often‐overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.