Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions.

ABSTRACT: Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound

SUBMITTER: Gill J 

PROVIDER: S-EPMC9835131 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature