A research team reported that digital twins built from ex vivo human lung perfusion (EVLP) data can model therapeutic effects with high fidelity. Using a large clinical EVLP dataset, the investigators report digital twin frameworks that cover more than 75 physiological, biochemical, imaging, and omics parameters. The study showed the twins could accurately assess efficacy when EVLP lungs were treated with alteplase, demonstrating direct comparison to experimental outcomes. Digital twins here refers to computational models that integrate multimodal measurements to simulate biological behavior. The results position EVLP-derived digital twins as a potential way to improve evaluation of candidate therapeutics before or alongside clinical decision-making.