A proposed computational workflow for bioinformatics-based drug repurposing: A case study of Canagliflozin

Small molecule drugs such as sodium/glucose co-transporter 2 (SGLT2) inhibitors are used as selective tools in the management of diseases. However, they often have many off-target effects which were commonly considered as unfavorable. Recent evidence showed that off-target effects can be harnessed for potentially novel therapeutic uses, which is referred to as drug repurposing. In silico analysis using publicly available datasets and various bioinformatic tools allows the prediction of novel targets and focuses the resources of experimental validation to only targets with high potential. Here, the presented study proposes a structured approach for a computational workflow using publicly available datasets for the repurposing of canagliflozin, a SGLT2 inhibitor, for the potential mitigation of cardiovascular complications through the modulation of perivascular adipose tissue (PVAT) inflammation. Looking for enrichment of pathway gene sets within predicted targets of canagliflozin revealed over-representation in the pathways of coagulation, hypoxia and inflammation. These pathways are correlated with remodeling of perivascular adipose tissue (PVAT) and have a cardioprotective role. Previous studies on PVAT alterations in response to high fat diet identified differentially expressed genes (DEGs) compared to PVAT expression profile in the normal diet group. These datasets were reanalyzed to screen for pathway enrichment of DEGs and explore a possible overlap with predicted targets of canagliflozin. Indeed, galectin 1 (LGALS1) gene was identified as possible common target. Finally, canagliflozin was docked on LGALS1 using molecular docking software to calculate the energy of interaction. Hence, this study suggests a computational workflow for drug repurposing using in silico tools to pave the way for shortened and targeted experimental studies.