AI-DRIVEN SYSTEMS PHARMACOLOGY AND DIGITAL TWINS IN PRECISION MEDICINE: TRANSFORMING DRUG RESPONSE PREDICTION, TOXICITY PROFILING, AND THERAPEUTIC OPTIMIZATION

Abstract

AI-driven systems pharmacology combined with digital twin technology is emerging as a transformative framework in precision medicine, enabling highly accurate prediction of drug response, toxicity profiling, and therapeutic optimization. Traditional pharmacological approaches often fail to account for inter-individual variability in genetics, environment, and disease heterogeneity, leading to unpredictable therapeutic outcomes and adverse drug reactions. Systems pharmacology integrates multi-scale biological data, including genomic, proteomic, metabolomic, and clinical datasets, to model complex drug–target–disease interactions. The incorporation of Artificial Intelligence (AI), particularly machine learning, deep learning, and network-based modeling, enhances the predictive capacity of these systems by identifying nonlinear relationships within high-dimensional biological datasets. Digital twins-virtual, dynamic representations of individual patients-extend this paradigm by simulating patient-specific biological systems in silico. These models enable continuous monitoring and prediction of drug efficacy, toxicity risks, and disease progression in real time. AI-powered digital twins integrate real-world patient data with mechanistic and statistical models to optimize therapeutic strategies before clinical application. This review explores the convergence of AI, systems pharmacology, and digital twin technology in modern precision medicine. It highlights their applications in pharmacokinetics/pharmacodynamics (PK/PD) modeling, adverse drug reaction prediction, oncology, cardiovascular disease, and neuropharmacology. Additionally, it discusses challenges such as data heterogeneity, model interpretability, computational demands, and ethical concerns surrounding patient-specific digital modeling. The integration of AI-driven systems pharmacology with digital twins represents a paradigm shift from population-based medicine to individualized predictive therapeutics, paving the way for next-generation intelligent healthcare systems.