Abstract
Digital twin technology represents an emerging paradigm in precision medicine that enables creation of dynamic computational replicas of individual patients integrating biological, clinical, and environmental data. In pharmacotherapy, digital twins combine mechanistic pharmacokinetic-pharmacodynamic modeling with artificial intelligence to simulate drug exposure and therapeutic response prior to treatment administration. This capability allows optimization of dosing, prediction of adverse drug reactions, and selection of individualized therapies.
Current applications include precision dosing of narrow therapeutic index drugs, pharmacogenomics-guided therapy selection, oncology treatment planning, cardiovascular risk prediction, antimicrobial optimization, and adaptive management of chronic diseases. In drug development, digital twins support in-silico trials, synthetic control arms, dose selection, safety assessment, and precision indication discovery. Regulatory initiatives in model-informed drug development are increasingly incorporating simulation-based evidence. Despite substantial promise, widespread clinical implementation remains limited by data integration challenges, validation requirements, computational demands, regulatory uncertainty, and ethical concerns related to privacy and algorithmic bias. At present, digital twins should be considered an advanced clinical decision-support approach rather than a replacement for clinician judgment.
Digital twin–guided pharmacotherapy represents a transition from reactive prescribing to predictive model-informed precision medicine and has potential to improve therapeutic efficacy, safety, and development efficiency as validation frameworks mature.