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

Danae Stella Zareifi; Odysseas Chaliotis; Nafsika Chala; Nikos Meimetis; Maria Sofotasiou; Konstantinos Zeakis; Eirini Pantiora; Antonis Vezakis; George K Matsopoulos; Georgios Fragulidis; Leonidas G Alexopoulos

doi:10.1016/j.isci.2022.103890

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

iScience. 2022 Feb 9;25(3):103890. doi: 10.1016/j.isci.2022.103890. eCollection 2022 Mar 18.

Affiliations

¹ School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece.
² School of Electrical Engineering, National Technical University of Athens, 15780 Athens, Greece.
³ 2nd Department of Surgery, Aretaieio Hospital, University of Athens, School of Medicine, 11528, Athens, Greece.
⁴ ProtATonce Ltd, Patriarchou Grigoriou & Neapoleos Demokritos Science Park, Building#27, Agia Paraskevi GR15343, Greece.

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, while complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico 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 in vivo testing.

Keywords: Complex system biology; Computational bioinformatics; Pharmaceutical science.