In Silico Prediction of Potential Drug Combinations for Type 2 Diabetes Mellitus by an Integrated Network and Transcriptome Analysis

Abstract

Type 2 diabetes mellitus (T2DM) is a heterogeneous disorder, so achieving the desired therapeutic efficacy through monotherapy is tricky. Drug combinations play a vital role in treating multiple complex diseases by providing increased efficacy and reduced toxicity. Here, we adopted a computational framework to discover potential drugs and drug pairs for T2DM. Firstly, we collected T2DM‐associated genes and constructed the disease module for T2DM. Then, by quantifying the proximity between drugs and the disease module, we found out potential drugs. Based on the drug‐induced gene expression profiles, we further performed Gene Set Enrichment Analysis (GSEA) on these drugs and identified several potential candidates. In addition, through network‐based separation, potential drug combinations for T2DM were predicted. Results from this study could provide insights for anti‐T2DM drug discovery and rational drug use of existing agents. As a useful computational framework, our approach could also be applied in drug research for other complex diseases.

Network-Based Methods and Their Applications in Drug Discovery

“…Three of them, Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways, suggesting their potentials as promising inhibitors for COVID-19 . Moreover, several network-based methods, such as NBI and Path-based methods, were also applied in the drug repositioning for various diseases, including cancer, COVID-19, type 2 diabetes, and Alzheimer’s disease . In 2022, we proposed a new method, named NEDNBI (newly emerged disease via network-based inference), and applied it in the gene–disease–drug heterogeneous network .…”