摘要：SummaryNon-alcoholic fatty liver disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combinesin vitrosteatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introducedin silicoapproach screened 20′000 compounds, while complementaryin vitroand proteomic assays were developed to test the efficacy of the 46in silicopredictions. This approach successfully identified six compounds, including the known anti-steatogenic drugs resveratrol and sirolimus. In short, gallamine triethiotide, diflorasone, fenoterol, and pralidoxime ameliorate steatosis similarly to resveratrol/sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds forin vivotesting.Graphical abstractDisplay OmittedHighlights•A computational and experimental drug-screening platform for NAFLD was created•This framework evaluatesin silicoand validatesin vitroa great number of compounds•20′000 compounds were screenedin silicoand 21 were selected for validation•Six compounds reversed fully or partially the steatotic phenotypePharmaceutical science; Computational bioinformatics; Complex system biology
