Digital Twin-Enabled Toxicology: Current Applications, Opportunities, and Future Directions

Toxicology has long depended on animal studies and static computational models to evaluate the safety of chemical substances and pharmaceutical compounds. These conventional approaches are increasingly critiqued for their limited translational accuracy, ethical concerns, and inability to capture real-time, individual-level physiological variation. Digital twin technology, virtual replicas of biological systems that are continuously updated with real-world data, has emerged as a promising paradigm that addresses many of these shortcomings. This review examines the current and potential applications of digital twin technology in toxicology, highlighting its utility across drug safety evaluation, environmental toxicology, occupational health, and personalised medicine. A narrative review framework was employed. Peer-reviewed literature published between 2015 and 2026 was retrieved from PubMed, Scopus, and Web of Science. Studies were selected based on relevance to the review objectives, methodological rigour, and contribution to the conceptual synthesis. Evidence indicates that digital twins are being applied in toxicology to simulate organ-level and whole-body responses to toxic exposures, predict dose-response relationships, and model individual susceptibility. Integration with omics data, wearable biosensors, and artificial intelligence further enhances their predictive fidelity. Regulatory bodies including the United States Environmental Protection Agency and the European Medicines Agency are beginning to acknowledge in silico approaches as complementary evidence in safety assessments. Digital twin technology offers a promising basis for advancing toxicological research and risk assessment, although much of this potential remains to be demonstrated. Challenges around data standardisation, model validation, and regulatory acceptance require concerted interdisciplinary efforts to overcome.