Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies

• DOI: 10.1177/09622802241231493
• Publication Date: 2024-03-06T19:16:05Z
• Authors: Sandrine Boulet; Moreno Ursino; Robin Michelet; Linda BS Aulin; Charlotte Kloft; Emmanuelle Comets; Sarah Zohar
• Keywords: FOS: Computer and information sciences; Hellinger distance; [SDV]Life Sciences [q-bio]; posteriors merging; translational; Posteriors conflict; Translational; 610; 500; Posteriors merging; posteriors conflict; 01 natural sciences; Commensurability; [STAT] Statistics [stat]; 3. Good health; [STAT]Statistics [stat]; [SDV] Life Sciences [q-bio]; Methodology (stat.ME); [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]; 0101 mathematics; [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]; Statistics - Methodology

In preclinical investigations, for example, 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 all preclinical data through inferential procedures can be particularly interesting in obtaining a more precise and reliable starting dose and dose range. Our objective is to propose a Bayesian framework for multi-source data integration, customizable, and tailored to the specific research question. We focused on preclinical results extrapolated to humans, which allowed us to predict the quantities of interest (e.g. maximum tolerated dose, etc.) in humans. We build an approach, divided into four 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. Our approach allows us 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.