Digital twins of ex vivo human lungs enable accurate and personalized evaluation of therapeutic efficacy

Digital twins are an emerging concept in healthcare that envisions integration of molecular, physiological, functional and clinical data to create computational models of biological systems such as cells, organs and individuals. However, the lack of large, multimodal datasets has so far precluded the realization of comprehensive digital twins in medicine. Ex vivo lung perfusion (EVLP) allows the study of human lungs outside the body under physiological conditions and generates multimodal data from imaging, physiologic monitoring and molecular assays. Here we report lung digital twins developed from the largest known clinical EVLP dataset. We show that the digital twin framework accurately models >75 parameters spanning lung physiology, biochemistry, radiography, transcriptomics, metabolomics and proteomics. Furthermore, direct comparison to experimental data on EVLP lungs treated with alteplase demonstrates that digital twins can precisely assess therapeutic efficacy. Together, these results establish human lung digital twins developed using EVLP as a data-rich approach to improve the evaluation of therapeutic effects.