Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases
T. Linden, F. Hanses, D. Domingo-Fernandez, L. N. DeLong, A. T. Kodamullil, J. Schneider, M. J. G. T. Vehreschild, J. Lanznaster, M. M. Ruethrich, S. Borgmann, M. Hower, K. Wille, T. Feldt, S. Rieg, B. Hertenstein, C. Wyen, C. Roemmele, J. J. Vehreschild, C. E. M. Jakob, M. Stecher, M. Kuzikov, A. Zaliani, H. Froehlich, LEOSS study group,
Abstract
Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimers Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.
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