In Silico Clinical Trials in Drug Development: Virtual Patients, Applications, and Regulatory Convergence.

Conventional clinical trials remain the benchmark for evaluating therapeutic safety and efficacy, yet they are constrained by escalating costs, withdrawal over the extended follow-up periods, recruitment difficulties, ethical limits, and a restricted ability to characterize heterogeneous populations. In silico clinical trials, which use computational models of patient physiology to simulate the effect of interventions across a virtual cohort, have emerged as a complementary paradigm that is efficient, scalable, and mechanistically informed. However, the maturity of in silico clinical trial applications varies considerably between physiological systems. This review pursues three aims. First, we introduce a common taxonomy for virtual patients and in silico trials, spanning levels of model personalization, from fully synthetic populations through hybrid cohorts to patient-specific digital twins, and levels of abstraction, from compartment-based models to whole-organ anatomically accurate models. Second, we survey applications across multiple organ systems, drawing on illustrative examples that expose markedly different degrees of modeling maturity, from comparatively established cardiac and hepatic safety and efficacy studies to areas where mechanistic models remain early in development. Third, we examine the regulatory landscape, tracing its evolution towards risk-informed credibility assessment, and the recent harmonization of model-informed drug development guidance. Although personalized modeling and regulatory pathways are evolving, they often do not converge within a unified framework. Their wider adoption will require robust evaluation against experimental and clinical evidence to demonstrate their predictive reliability.