Digital Twins in Toxicology: Toward Predictive, Personalized and Real-Time Risk Assessment

Toxicology has accumulated many predictive tools - physiologically based pharmacokinetic (PBPK) models, quantitative systems pharmacology (QSP) frameworks organ-on-chip systems, quantitative structure-activity relationship (QSAR) models, adverse outcome pathway (AOP) networks and machine-learning approaches. Yet predictive practice remains fragmented. Each tool captures a slice of biology; few capture the whole; none captures the patient. We argue that the concept of a digital twin - a continuously updated, mechanistically grounded, data-driven digital representation of an individual biological system - offers the missing organisational layer. We first trace the origin of digital twins from aerospace engineering through biomedicine into toxicology, emphasising how the defining properties (dynamic data assimilation at the pace a decision requires, multi-scale integration, hybrid mechanistic-statistical architecture, personalisation, predictive simulation) map onto existing toxicological methods. We then position PBPK and QSP as mature proto-twin components and name the architectural gaps - continuous data flow, uncertainty quantification, feedback-driven recalibration and regulatory-grade validation - that separate today's best models from a working twin. A four-layer architecture is proposed: data, mechanistic, AI and simulation. Six published systems are tested against the five conditions; none satisfies all five. We examine applications in drug development, personalised risk assessment, mass-poisoning response, chemical-biological-radiological-nuclear (CBRN) preparedness and antidote discovery for unknown toxins. Finally, we confront the translational gap: data heterogeneity, validation standards, interpretability, regulatory acceptance and ethical custody of patient-level models. Toxicology, we conclude, has most of the ingredients needed for digital-twin implementation; what remains is the integration work, two unresolved scientific problems and the institutional willingness to address them.