PLoS Computational Biology · 2012 · 837 citations · 47 references
Life SciencesBioinformaticsPharmacologyDrug DesignDrug DiscoverySystems PharmacologyComputational Drug DiscoveryDrug-target Interaction MappingDrug-target Interaction NetworksNetwork InferenceNetwork PharmacologyComplex Network TheoryCompound-target Interaction PredictionDrug-target Network ConstructionDrug-target Similarity NetworksEstrogen ReceptorsNetwork-based Drug-target PredictionSimilarity InferenceSimilarity-based Drug Repositioning
Drug‑target interaction is fundamental to drug discovery, yet experimental determination is time‑consuming and costly. The study aims to develop computational methods to predict potential drug‑target interactions. The authors created three supervised inference methods—drug‑based similarity inference, target‑based similarity inference, and network‑based inference—and applied the best‑performing NBI to build a drug‑target network from 12,483 FDA‑approved and experimental links, predicting additional DTIs. NBI outperformed the other methods on four benchmark datasets, and in vitro assays confirmed that five repurposed drugs—montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole—targeted estrogen receptors or DPP‑IV with IC50/EC50 values of 0.2–10 µM, while simvastatin and ketoconazole also inhibited MDA‑M‑231 breast cancer cells, demonstrating the methods’ utility for DTI prediction and drug repositioning.
Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.
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