Enabling Technologies for Pharmacometrics Driven by Digital Twins in Clinical Research and Workforce Development

Digital twins, pharmacometrics, artificial intelligence, individualized pharmacotherapy, and workforce development are becoming increasingly interconnected in clinical pharmacy and drug development. Digital twin-driven pharmacometrics integrates mechanistic modeling with machine learning to enable patient-specific drug therapy prediction, simulation, and optimization. Recent applications include explainable artificial neural network-based pharmacokinetic models, interpretable machine learning approaches for early safety predictions, and in silico clinical trial designs. Collectively, these approaches support efficient clinical research and advance individualized pharmacotherapy. The successful implementation of digital twins depends on technological innovation and workforce development, requiring professionals with integrated expertise in pharmacology, modeling, and data science.