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

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          Abstract

          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.

          Author Summary

          Study of drug-target interaction is an important topic toward elucidation of protein functions and understanding of molecular mechanisms inside cells. Traditional methods to predict new targets for known drugs were based on small molecules, protein targets or phenotype features. Here, we proposed a network-based inference (NBI) method which only used drug-target bipartite network topology similarity to infer new targets for known drugs. The performance of NBI outperformed the drug-based similarity inference and target-based similarity inference methods as well as other published methods. Via the NBI method five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, were identified to have polypharmacological effects on human estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration from submicromolar to micromolar by in vitro assays. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that the drug-target bipartite network-based inference method could be a useful tool for fishing novel drug-target interactions in molecular polypharmacological space.

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          Most cited references54

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          Network pharmacology: the next paradigm in drug discovery.

          The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
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            Network medicine: a network-based approach to human disease.

            Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
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              Drug repositioning: identifying and developing new uses for existing drugs.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                May 2012
                May 2012
                10 May 2012
                : 8
                : 5
                : e1002503
                Affiliations
                [1 ]Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
                [2 ]School of Business, East China University of Science and Technology, Shanghai, China
                Stanford University, United States of America
                Author notes

                Conceived and designed the experiments: F. Cheng, W. Zhou, Y. Tang. Performed the experiments: F. Cheng, C. Liu, J. Jiang, W. Lu, J. Huang. Analyzed the data: F. Cheng, C. Liu, W. Li, G. Liu, Y. Tang. Contributed reagents/materials/analysis tools: F. Cheng, Y. Tang. Wrote the paper: F. Cheng, C. Liu, Y. Tang.

                Article
                PCOMPBIOL-D-11-01829
                10.1371/journal.pcbi.1002503
                3349722
                22589709
                c6da9603-a9ce-46a8-8a56-1ac3d6866d4e
                Cheng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 6 December 2011
                : 19 March 2012
                Page count
                Pages: 12
                Categories
                Research Article
                Biology
                Biophysics
                Biomacromolecule-Ligand Interactions
                Biophysics Theory
                Computational Biology
                Systems Biology
                Chemistry
                Chemical Biology
                Medicinal Chemistry
                Mathematics
                Mathematical Computing
                Medicine
                Drugs and Devices
                Adverse Reactions
                Clinical Pharmacology
                Drug Information
                Drug Interactions
                Pharmacoeconomics
                Toxicology

                Quantitative & Systems biology
                Quantitative & Systems biology

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