Disease Pathway Cut for Multi-Target drugs

• Published in: BMC bioinformatics Vol. 20; no. 1; pp. 74 - 12
• Main Authors: Bang, Sunjoo, Son, Sangjoon, Kim, Sooyoung, Shin, Hyunjung
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13-02-2019; BioMed Central; BMC
• Subjects: Aging; Algorithms; Alzheimer's disease; Apoptosis; Bioinformatics; Biomarkers; Computer applications; Cyclin-dependent kinases; Dementia; Directed PPI; Disease pathway; Diseases; Drug discovery; Drugs; Gene expression; Gene set enrichment analysis; Genes; Genomes; Identification; Kinases; Literature reviews; Methodology; Methods; Min-cut algorithm; Mutation; Pathway network; Side effects; Studies; Target gene identification; Target recognition; Toxicity; Type 2 diabetes; South Korea; Directed PPI; Target gene identification; Min-cut algorithm; Disease pathway; Pathway network
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-2638-3

Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression.
In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug.
The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets.