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Generalized Pharmacometric Modeling, a Novel Paradigm for Integrating Machine Learning Algorithms: A Case Study of Metabolomic Biomarkers
Author(s) -
McComb Mason,
Ramanathan Murali
Publication year - 2020
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.1746
Subject(s) - machine learning , covariate , computer science , random forest , identification (biology) , population , logistic regression , artificial intelligence , data mining , medicine , biology , botany , environmental health
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.