Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations. (August 2018)

• Record Type: Journal Article
• Authors: Munir, Anum; Elahi, Sana; Masood, Nayyer
• Is Part Of: Computational biology and chemistry. Volume 75(2018)
• Page Start: 24
• Page End: 31
• Publication Date: 2018-08
• Subjects: Clustering -- Drug–drug interaction -- EGFR -- Lipinski rule -- Repositioning; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.04.011
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The strong interactions among the drugs are deemed as synergistic. The network structure is usually portrayed by a high modularity. Modularity is directly connected to the links, which demonstrate the drug interactions. Toxic doses are frequently administered as LD50 values in mg/kg of the body weight. DDI networks are generated on the modularities to find effective drug Abstract: EGFRs are a vast group of receptor tyrosine kinases playing an important role in a number of tumors, including lungs, head and neck, breast, and esophageal cancers. A couple of techniques are being used in the process of drug design. Drug repositioning or repurposing is a rising idea that consists of distinguishing modern remedial indications for officially existing dynamic pharmaceutical compounds. Here, a novel approach of analyzing drug–drug interaction networks, based on clustering methodology is used to reposition effective compounds against mutant EGFR having G719X, exon 19 deletions/insertions, L858R, and L861Q mutations. Data about 2062 drugs are obtained, and mining is performed to filter only those drugs which fulfill Lipinski rule of five. Clustering is performed, and DDIs are built on the clusters to identify effective drug compounds. Only 1052 compounds fulfill Lipinski rule. 12 clusters are formed for 1052 drugs compounds. DDIs are developed for each cluster. Only 15 drugs are suggested to be more effective assuming strong interactions in a DDI.