Machine Learning-Augmented Precision Dosing: Integrating Pharmacokinetic Modeling With Clinical Decision Support

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.