A machine learning approach to population pharmacokinetic modelling automation.

ABSTRACT:

Background

Population pharmacokinetic (PopPK) models are crucial for understanding drug behaviour across populations, yet traditional development is often labour-intensive and slow. This study demonstrates an automated, out-of-the-box approach for popPK model development, leveraging optimisation algorithms implemented using pyDarwin to efficiently handle a diverse range of extravascular drugs.

Methods

We proposed a generic model search space for drugs with extravascular administration and developed a penalty function to discourage over-parameterisation whilst ensuring plausible parameter values. Optimisation within the model search space was conducted using pyDarwin, employing Bayesian optimisation with a random forest surrogate combined with exhaustive local search. This ap

SUBMITTER: Richardson S 

PROVIDER: S-EPMC12314194 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature