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

Sandrine Boulet^1,2, Moreno Ursino^1,2,3, Robin Michelet⁴

¹Inria, HeKA, Paris, France.

Statistical Methods in Medical Research

|March 6, 2024

Summary

This summary is machine-generated.

This study introduces a Bayesian framework to integrate preclinical drug data, improving human dose prediction. The approach enhances data utilization, reduces uncertainty, and supports more efficient dose selection for clinical trials.

Keywords:

Commensurability Hellinger distance posteriors conflict posteriors merging translational

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Area of Science:

Pharmacology and Toxicology
Biostatistics and Data Science

Background:

Preclinical drug studies (in vitro, in vivo, in silico) assess pharmacokinetic, pharmacodynamic, and toxicological profiles.
Current methods often analyze preclinical studies independently, limiting knowledge integration for human dose prediction.

Purpose of the Study:

To propose a customizable Bayesian framework for multi-source preclinical data integration.
To improve the precision and reliability of human dose range prediction by leveraging all available preclinical information.

Main Methods:

A four-step approach involving sequential parameter estimation, human extrapolation, posterior distribution commensurability checking, and information merging.
Utilizing Bayesian inference for multi-source data integration.
Evaluation through extensive simulations based on an oncology example.

Main Results:

The proposed Bayesian framework effectively integrates diverse preclinical data sources.
Demonstrated reduction in prediction uncertainty compared to standard frameworks.
Potential for more efficient and reliable selection of human drug doses.

Conclusions:

The developed Bayesian framework offers a superior method for utilizing preclinical data in drug development.
This approach can lead to more informed and precise early-stage clinical dose selection.
Enhances the predictive power of preclinical studies for human trials.