Artificial Intelligence, Pharmacokinetic/Pharmacodynamic Modeling, and Digital Twins in Clinical Pharmacology: A Convergence toward Model-informed Precision Dosing

Background: The integration of artificial intelligence (AI) with pharmacokinetic/pharmacodynamic (PK/PD) modeling and digital twin (DT) technologies is transforming the practice of clinical pharmacology. These technologies enable individualized, real-time therapeutic optimization, bridging the gap between simulation and bedside care. Objective: This narrative review synthesizes recent advances in AI-driven therapeutic drug monitoring (TDM), data-augmented PK/PD modeling, and patient-specific DT platforms. It highlights their role in supporting model-informed precision dosing across diverse clinical domains, including critical care, infectious diseases, and oncology. Methods: A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore, covering studies from 2013 to 2025. Keywords included “AI,” “machine learning,” “digital twin,” “PK/PD,” and “precision dosing.” Relevant peer-reviewed articles, clinical trials, and implementation studies were analyzed thematically. Findings: AI enhances TDM by predicting interindividual variability, enabling real-time dose adjustments, and supporting automated clinical decision systems. In PK/PD modeling, machine learning and deep learning techniques improve the modeling of nonlinear, patient-specific dose–response relationships. DTs extend these capabilities by simulating virtual therapeutic trials, continuously updating with multimodal inputs, and guiding personalized therapy. Clinical examples demonstrate improved outcomes in antibiotic dosing in intensive care units, sepsis management, oncology, and chronic disease remission. Conclusions: The convergence of AI, PK/PD models, and DTs presents a robust, adaptive framework for delivering individualized pharmacotherapy. While challenges persist – including data interoperability, regulatory frameworks, and clinician adoption – ongoing innovation promises to shift clinical pharmacology from reactive to anticipatory and patient-specific care. This paradigm enables safer, more effective treatments while advancing the principles of precision medicine.