Generalized Pharmacometric Modeling, a Novel Paradigm for Integrating Machine Learning Algorithms: A Case Study of Metabolomic Biomarkers

Abstract

There is an unmet need for identifying innovative machine learning (ML) strategies to improve drug treatment regimens and therapeutic outcomes. We investigate Generalized Pharmacometric Modeling (GPM), a novel paradigm that integrates ML algorithms with pharmacokinetic and pharmacodynamic structural models, population covariate modeling, and "big data," and enables identification of patient-specific factors contributing to drug disposition. We hypothesize that GPM will enhance forecasting of drug outcomes in diverse populations. We assessed random forest regression in conjunction with Bayesian networks as the ML methods within GPM and used the National Health and Nutrition Examination Survey population-based study database. GPM was utilized to identify subject-specific factors associated with cholesterol dynamics. Our results demonstrate the utility of GPM to enhance pharmacometrics modeling and its potential for modeling drug outcomes in diverse populations.