PREDICTIVE INTELLIGENCE FOR SAFER THERAPIES: A NEW ERA IN DRUG MONITORING WITH AI

Therapeutic Drug Monitoring (TDM) plays a critical role in optimizing drug efcacy and safety by maintaining plasma drug concentrations within a targeted therapeutic range. However, traditional TDM approaches are often limited by delayed feedback, interindividual variability, and the complexity of pharmacokinetic and pharmacodynamic interactions. This review examines the growing integration of Articial Intelligence (AI) in TDM, highlighting how machine learning, deep learning, and data-driven modelling techniques are transforming drug monitoring into a more precise, adaptive, and personalized practice. AI models offer the potential to predict drug concentration-time proles, automate dose adjustments, and incorporate multifactorial patient data, including genetic, demographic, and clinical variables. The review also examines current advancements, clinical applications, and the challenges surrounding data quality, model interpretability, and regulatory acceptance. Finally, it outlines future perspectives, advocating for AI-enhanced TDM systems that support individualized therapy and real-time clinical decision-making in diverse healthcare settings.