Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.

ABSTRACT: Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA-M1 and PA-M2. PA-M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA-M2 had blood and urine-derived variables as predictors. Activity phenotypes for cytochrome-P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N-acetyltransferase-2 (NAT-2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n =

SUBMITTER: Bhat AG 

PROVIDER: S-EPMC11812938 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature