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

Abstract

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 <i>in vitro</i> 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 <i>in silico</i> approach screened 20'000 compounds, while complementary <i>in vitro</i> and proteomic assays were developed to test the efficacy of the 46 <i>in silico</i> 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 <i>in vivo</i> testing.