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      A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus

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          Abstract

          The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.

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          Network pharmacology: the next paradigm in drug discovery.

          Andrew Hopkins (2008)
          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 pharmacology.

            Andrew Hopkins (2007)
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              Network-based global inference of human disease genes

              Xuebing Wu, Rui Jiang, Michael Zhang … (2008)
              Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome-wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome-wide prioritization of candidate genes for over 5000 human phenotypes, including those with under-characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Evid Based Complement Alternat Med
                Journal ID (iso-abbrev): Evid Based Complement Alternat Med
                Journal ID (publisher-id): ECAM
                Title: Evidence-based Complementary and Alternative Medicine : eCAM
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1741-427X
                ISSN (Electronic): 1741-4288
                Publication date (Print): 2013
                Publication date (Electronic): 10 October 2013
                Publication date PMC-release: 10 October 2013
                Volume: 2013
                Electronic Location Identifier: 203614
                Affiliations
                Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
                Author notes
                *Lirong Chen: lirongc@ 123456pku.edu.cn and

                Academic Editor: Francis B. Lewu

                Article
                DOI: 10.1155/2013/203614
                PMC ID: 3810496
                SO-VID: 4d1fcdee-3fae-4734-b64e-67d864601783
                Copyright © Copyright © 2013 Jiangyong Gu et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 25 June 2013
                Date accepted : 19 September 2013
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Complementary & Alternative medicine
                Data availability:
                ScienceOpen disciplines: Complementary & Alternative medicine

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