Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

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 Alzheimer’s 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.

Covid-19 Mortality Prediction Using Machine Learning Methods

“…The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive. The next study [10] experimentally verified that some anti-cancer drugs can be regarded as potential treatments against COVID-19. A broad panel of time-to-event machine learning models was implemented and compared, such as Elastic net penalized Cox proportional hazards regression and Weibull accelerated failure time regression, DeepSurv neural network approach, Random Survival Forests and XGBoost Survival Embeddings.…”