Digital Twins of Ex Vivo Human Lungs Enable Personalized Therapeutic Evaluation
Researchers have successfully developed comprehensive digital twins of human lungs using data from ex vivo lung perfusion (EVLP), marking a significant advancement in personalized medicine. Published in Nature Biotechnology, the study utilizes the largest known clinical EVLP dataset, comprising over 1,000 cases, to create high-fidelity computational models. These digital twins integrate multimodal data, including physiology, biochemistry, radiography, transcriptomics, metabolomics, and proteomics, accurately modeling more than 75 parameters. The framework was validated by comparing simulation results with experimental data from lungs treated with alteplase, demonstrating its ability to precisely assess therapeutic efficacy. This approach overcomes previous limitations caused by the lack of large, multimodal datasets in healthcare. By simulating future biological functions without altering physical system settings, these digital lungs offer a robust, data-rich method for evaluating treatment effects. This innovation extends the concept of digital twins from engineering to biology, providing a powerful tool for preclinical and translational research to improve decision-making in organ transplantation and therapeutic development.
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Digital Twins of Ex Vivo Human Lungs Enable Personalized Therapeutic Evaluation
Researchers have successfully developed comprehensive digital twins of human lungs using data from ex vivo lung perfusion (EVLP), marking a significant advancement in personalized medicine. Published in Nature Biotechnology, the study utilizes the largest known clinical EVLP dataset, comprising over 1,000 cases, to create high-fidelity computational models. These digital twins integrate multimodal data, including physiology, biochemistry, radiography, transcriptomics, metabolomics, and proteomics, accurately modeling more than 75 parameters. The framework was validated by comparing simulation results with experimental data from lungs treated with alteplase, demonstrating its ability to precisely assess therapeutic efficacy. This approach overcomes previous limitations caused by the lack of large, multimodal datasets in healthcare. By simulating future biological functions without altering physical system settings, these digital lungs offer a robust, data-rich method for evaluating treatment effects. This innovation extends the concept of digital twins from engineering to biology, providing a powerful tool for preclinical and translational research to improve decision-making in organ transplantation and therapeutic development.
Nature Biotechnology