Literature DB >> 17516560

Network analysis of FDA approved drugs and their targets.

Avi Ma'ayan1, Sherry L Jenkins, Joseph Goldfarb, Ravi Iyengar.   

Abstract

The global relationship between drugs that are approved for therapeutic use and the human genome is not known. We employed graph-theory methods to analyze the Federal Food and Drug Administration (FDA) approved drugs and their known molecular targets. We used the FDA Approved Drug Products with Therapeutic Equivalence Evaluations 26(th) Edition Electronic Orange Book (EOB) to identify all FDA approved drugs and their active ingredients. We then connected the list of active ingredients extracted from the EOB to those known human protein targets included in the DrugBank database and constructed a bipartite network. We computed network statistics and conducted Gene Ontology analysis on the drug targets and drug categories. We find that drug to drug-target relationship in the bipartite network is scale-free. Several classes of proteins in the human genome appear to be better targets for drugs since they appear to be selectively enriched as drug targets for the currently FDA approved drugs. These initial observations allow for development of an integrated research methodology to identify general principles of the drug discovery process. Copyright (c) 2007 Mount Sinai School of Medicine.

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Year:  2007        PMID: 17516560      PMCID: PMC2561141          DOI: 10.1002/msj.20002

Source DB:  PubMed          Journal:  Mt Sinai J Med        ISSN: 0027-2507


  12 in total

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Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

2.  Quinidine preferentially blocks the slow delayed rectifier potassium channel in the rested state.

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Review 3.  Scale-free networks in cell biology.

Authors:  Réka Albert
Journal:  J Cell Sci       Date:  2005-11-01       Impact factor: 5.285

Review 4.  Toward predictive models of mammalian cells.

Authors:  Avi Ma'ayan; Robert D Blitzer; Ravi Iyengar
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5.  Tricyclic antidepressants and histamine H1 receptors.

Authors:  E Richelson
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Authors:  G Weitz-Schmidt; K Welzenbach; V Brinkmann; T Kamata; J Kallen; C Bruns; S Cottens; Y Takada; U Hommel
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Journal:  Cardiovasc Drugs Ther       Date:  1999-04       Impact factor: 3.727

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Authors:  H Moriya; Y Takagi; T Nakanishi; M Hayashi; T Tani; I Hirotsu
Journal:  Life Sci       Date:  1999       Impact factor: 5.037

9.  DrugBank: a comprehensive resource for in silico drug discovery and exploration.

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10.  The Gene Ontology (GO) project in 2006.

Authors: 
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

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  40 in total

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Review 2.  Network integration and graph analysis in mammalian molecular systems biology.

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Journal:  IET Syst Biol       Date:  2008-09       Impact factor: 1.615

Review 3.  Insights into the organization of biochemical regulatory networks using graph theory analyses.

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Journal:  J Biol Chem       Date:  2008-10-20       Impact factor: 5.157

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Review 6.  Network analyses in systems pharmacology.

Authors:  Seth I Berger; Ravi Iyengar
Journal:  Bioinformatics       Date:  2009-07-30       Impact factor: 6.937

Review 7.  Systems biology of kidney diseases.

Authors:  John Cijiang He; Peter Y Chuang; Avi Ma'ayan; Ravi Iyengar
Journal:  Kidney Int       Date:  2011-08-31       Impact factor: 10.612

8.  The dynamics of signaling as a pharmacological target.

Authors:  Marcelo Behar; Derren Barken; Shannon L Werner; Alexander Hoffmann
Journal:  Cell       Date:  2013-10-10       Impact factor: 41.582

9.  Local and global modes of drug action in biochemical networks.

Authors:  Jean-Marc Schwartz; Jose C Nacher
Journal:  BMC Chem Biol       Date:  2009-04-07

10.  Systems pharmacology and genome medicine: a future perspective.

Authors:  Aislyn D Wist; Seth I Berger; Ravi Iyengar
Journal:  Genome Med       Date:  2009-01-22       Impact factor: 11.117

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