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Updated: Oct 14, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
In Silico Prediction of Potential Drug Combinations for Type 2 Diabetes Mellitus by an Integrated Network and
Dan Teng1, Yuning Gong1, Zengrui Wu1
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.
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.
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