Lixia Fu

This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.

This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.