  In preclinical investigations, e.g. in in vitro, in vivo and in silico
studies, the pharmacokinetic, pharmacodynamic and toxicological characteristics
of a drug are evaluated before advancing to first-in-man trial. Usually, each
study is analyzed independently and the human dose range does not leverage the
knowledge gained from all studies. Taking into account the preclinical data
through inferential procedures can be particularly interesting to obtain a more
precise and reliable starting dose and dose range. We propose a Bayesian
framework for multi-source data integration from preclinical studies results
extrapolated to human, which allow to predict the quantities of interest (e.g.
the minimum effective dose, the maximum tolerated dose, etc.) in humans. We
build an approach, divided in four main steps, based on a sequential parameter
estimation for each study, extrapolation to human, commensurability checking
between posterior distributions and final information merging to increase the
precision of estimation. The new framework is evaluated via an extensive
simulation study, based on a real-life example in oncology inspired from the
preclinical development of galunisertib. Our approach allows to better use all
the information compared to a standard framework, reducing uncertainty in the
predictions and potentially leading to a more efficient dose selection.
