Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

• DOI: 10.1371/journal.pcbi.1002503
• Publication Date: 2012-05-10T21:08:34Z
• Authors: Feixiong Cheng; Chuang Liu; Jing Jiang; Weiqiang Lu; Weihua Li; Guixia Liu; Weixing Zhou; Jin Huang; Yun Tang
• Keywords: Drug repositioning

Drug-target interaction (DTI) is the basis of drug discovery and design. It time consuming costly to determine DTI experimentally. Hence, it necessary develop computational methods for prediction potential DTI. Based on complex network theory, three supervised inference were developed here predict used repositioning, namely drug-based similarity (DBSI), target-based (TBSI) network-based (NBI). Among them, NBI performed best four benchmark data sets. Then a drug-target was created with based 12,483 FDA-approved experimental binary links, some new DTIs further predicted. In vitro assays confirmed that five old drugs, montelukast, diclofenac, simvastatin, ketoconazole, itraconazole, showed polypharmacological features estrogen receptors or dipeptidyl peptidase-IV half maximal inhibitory effective concentration ranged from 0.2 10 µM. Moreover, simvastatin ketoconazole potent antiproliferative activities human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated these could be powerful tools repositioning.