A network-based computational and experimental framework for repurposing compounds toward the treatment of non-alcoholic fatty liver disease.

Non-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 combines steatosis 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 introduced approach screened 20'000 compounds, while complementary and proteomic assays were developed to test the efficacy of the 46 predictions. 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 for testing.