Digital Twin Technology in Clinical Drug Research: Application Progress and Regulatory Strategy Analysis

Artificial intelligence (AI)-enabled digital twin (DT) technology, as an emerging technological tool, creates virtual replicas of real patients by accurately simulating their characteristics, offering new approaches to clinical drug research. This review systematically describes advances in the application of DT in clinical drug research: DT technology uses mechanism-driven quantitative pharmacology modeling and data-driven AI modeling as its core implementation methods, and can be applied to key scenarios such as trial protocol optimization, treatment response prediction, construction of virtual control groups, and assistance in participant recruitment. This review further analyzes and compares the relevant regulatory policies of major global regulatory agencies, points out that global regulation is characterized by "convergence in direction and localization in detail, " and advocates a risk-based regulatory framework. It also notes that the current application of DT technology in clinical drug trials still faces multiple challenges, including data quality control, model credibility assessment, and the formulation of regulatory standards, and it proposes feasible recommendations for practical application. This review provides important references for promoting the standardized application of DT in drug clinical research and informing the formulation of regulatory strategies. Overall, DT technology holds significant potential for improving clinical development efficiency and supporting research in special fields such as rare diseases and pediatric medicines; however, at the current stage, it remains only suitable as a tool to assist in trial design and decision-making in exploratory research and cannot yet fully replace confirmatory clinical trials.