1. A network-based computational and experimental framework for repurposing compounds toward the treatment of non-alcoholic fatty liver disease
- Author
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Georgios P. Fragulidis, Nikos Meimetis, Odysseas Chaliotis, Konstantinos Zeakis, Danae Zareifi, Maria Sofotasiou, George K. Matsopoulos, Eirini Pantiora, Antonis Vezakis, Nafsika Chala, and Leonidas G. Alexopoulos
- Subjects
Multidisciplinary ,Drug discovery ,business.industry ,In silico ,Fatty liver ,Resveratrol ,medicine.disease ,Bioinformatics ,Drug repositioning ,chemistry.chemical_compound ,chemistry ,In vivo ,Sirolimus ,medicine ,Steatosis ,business ,medicine.drug - 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 in vitro 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 in silico approach screened 20'000 compounds, whilst complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified 6 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 in vivo testing.
- Published
- 2021