Bayesian Framework for Multi-Source Data Integration -- Application to Human Extrapolation From Preclinical Studies

TL;DR

This paper introduces a Bayesian framework that integrates multiple preclinical data sources to improve human dose predictions, reducing uncertainty and enhancing dose selection in drug development.

Contribution

The paper presents a novel Bayesian approach for combining preclinical data from various sources to better predict human pharmacological parameters, which was not previously available.

Findings

Improved accuracy in human dose predictions.

Reduced uncertainty compared to standard methods.

Validated through extensive simulation and real-life oncology example.

Abstract

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.