Network analysis of FDA approved drugs and their targets. Mt Sinai J Med

Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, New York 10029, USA.
Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine (Impact Factor: 1.62). 04/2007; 74(1):27-32. DOI: 10.1002/msj.20002
Source: PubMed


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.


Available from: Avi Ma'ayan, Apr 10, 2014
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    • "An example of bipartite graph in the corporate world will be, company board networks, where the board members are linked to the companies they lead [48]. Examples of bipartite graph uses in genomics are comparative genomics [49] or gene-disease relationships [24, 25]. There is a lack of tools for the analysis of two-mode networks. "
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    Full-text · Article · Apr 2015 · PLoS ONE
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    • "Several studies have attempted to characterize drug targets from a theoretical point of view as such knowledge could be a tool to speed up the drug discovery process. Bioinformatics methods to characterize and predict drug targets have included: pathway and tissue enrichment, domain enrichment, number of exons and protein degree in an interaction network [2], GO enrichment [3], sequence similarity to known targets [4], side-effect similarity [5], physicochemical properties of the sequence of known drug targets [6], entropies of tissue expression and ratios of non-synonymous to synonymous SNPs [7], methods based on drug similarity, target similarity and network similarity [8,9], in addition to traditional text and data mining approaches [10]. These studies include network-based and non-network-based prediction methods, supervised and non-supervised, from those using the protein interaction space to those including chemical and pharmacological spaces, from single metrics to elaborated predictors with multiple features. "
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    • "Despite a significant and continuous increase in medical research spending, the annual number of new drugs approved and new drug targets identified has remained almost constant for the past 20-25 years, with about twenty new drugs and about five new targets per year. At this rate it will take more than 300 years to double the number of available drugs [7, 14]. However, there are several ways to address these burdens. "
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