Machine Learning-Augmented Precision Dosing: Integrating Pharmacokinetic Modeling With Clinical Decision Support.
- 1Department of Pharmacy, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- 2School of Medicine (Including School of Pharmacy), Macau University of Science and Technology, Macau, China.
- 3School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Abstract
Inter-individual variability in drug exposure can lead to ineffective therapy or toxicity, particularly in vulnerable patients. Routine model-informed precision dosing (MIPD) relies mainly on population pharmacokinetic (PopPK) models, Bayesian forecasting, and therapeutic drug monitoring (TDM). Physiologically based pharmacokinetic (PBPK) modeling has a complementary role in extrapolation to special populations, evaluation of drug-drug interactions, and simulation of unobserved scenarios, but it remains uncommon for bedside dose adjustment. Machine learning (ML) may provide additional support when critical illness, organ dysfunction, or polypharmacy creates complex nonlinear relationships. The increasing availability of electronic health records, real-world data, and high-dimensional biomedical datasets has accelerated interest in ML for precision dosing. ML can support exposure prediction, limited-sampling AUC estimation, outcome-risk stratification, and monitoring prioritization. Its clinical role depends on the decision target, the availability of concentration data, and whether pharmacological constraints are retained within the modeling architecture. This review covers direct prediction models, limited-sampling AUC estimators, hybrid pharmacometric-ML models, neural ordinary differential equation approaches, and reinforcement learning for sequential dose decisions. We examine data sources, feature engineering, validation, reporting, and governance across glycopeptides, beta-lactams, polymyxins, triazole antifungals, and immunosuppressants. We also describe a staged clinical workflow in which concentration measurements and mechanistic models are used to calibrate data-driven predictions, and we identify the evidence needed for clinical implementation.
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BibTeX
@article{pang2026machine,
title = {{Machine Learning-Augmented Precision Dosing: Integrating Pharmacokinetic Modeling With Clinical Decision Support.}},
author = {Yunmeng Pang and Bingqi Zhang and Zhiqiu He and Yuliu Li and Jiaxin Yu and Wei Zuo},
journal = {CPT: pharmacometrics \& systems pharmacology},
year = {2026},
month = oct,
doi = {10.1002/psp4.70354},
url = {https://europepmc.org/article/MED/42902867},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Machine Learning-Augmented Precision Dosing: Integrating Pharmacokinetic Modeling With Clinical Decision Support.
AU - Yunmeng Pang
AU - Bingqi Zhang
AU - Zhiqiu He
AU - Yuliu Li
AU - Jiaxin Yu
AU - Wei Zuo
PY - 2026
DA - 2026/10/30
JO - CPT: pharmacometrics & systems pharmacology
DO - 10.1002/psp4.70354
UR - https://europepmc.org/article/MED/42902867
AB - Inter-individual variability in drug exposure can lead to ineffective therapy or toxicity, particularly in vulnerable patients. Routine model-informed precision dosing (MIPD) relies mainly on population pharmacokinetic (PopPK) models, Bayesian forecasting, and therapeutic drug monitoring (TDM). Physiologically based pharmacokinetic (PBPK) modeling has a complementary role in extrapolation to special populations, evaluation of drug-drug interactions, and simulation of unobserved scenarios, but it remains uncommon for bedside dose adjustment. Machine learning (ML) may provide additional support when critical illness, organ dysfunction, or polypharmacy creates complex nonlinear relationships. The increasing availability of electronic health records, real-world data, and high-dimensional biomedical datasets has accelerated interest in ML for precision dosing. ML can support exposure prediction, limited-sampling AUC estimation, outcome-risk stratification, and monitoring prioritization. Its clinical role depends on the decision target, the availability of concentration data, and whether pharmacological constraints are retained within the modeling architecture. This review covers direct prediction models, limited-sampling AUC estimators, hybrid pharmacometric-ML models, neural ordinary differential equation approaches, and reinforcement learning for sequential dose decisions. We examine data sources, feature engineering, validation, reporting, and governance across glycopeptides, beta-lactams, polymyxins, triazole antifungals, and immunosuppressants. We also describe a staged clinical workflow in which concentration measurements and mechanistic models are used to calibrate data-driven predictions, and we identify the evidence needed for clinical implementation.
SN - 2163-8306
ER -