Optimized therapeutic drug monitoring: the role of machine learning models

Abstract

The future of TDM lies not in replacing mechanistic models, but in their convergence with ML. While promising, clinical translation requires overcoming critical barriers in data access, model interpretability, and workflow integration. The long-term trajectory points toward dynamic Digital Twins capable of forecasting patient-specific benefit-risk profiles. Ultimately, validated hybrid tools embedded in clinical decision support systems could establish proactive, individualized dosing as the new standard of care in personalized pharmacotherapy.

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