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Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned
Authors:Fabiola La Gamba  Tom Jacobs  Helena Geys  Thomas Jaki  Jan Serroyen  Moreno Ursino  Alberto Russu  Christel Faes
Abstract: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.
Keywords:Bayesian inference  nonlinear hierarchical models  pharmacodynamics  pharmacokinetics  recursive  sequential
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