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.

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Available from: Avi Ma'ayan, Apr 10, 2014
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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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    ABSTRACT: Background Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. Results Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. Conclusions These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.
    BMC Bioinformatics 11/2012; 13(1):294. DOI:10.1186/1471-2105-13-294 · 2.58 Impact Factor
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    • "In this field, the research focus is rapidly shifting from studying an individual entity (e.g., one disease, drug, or gene) to entire networks of many different biological entities. Computational analysis of the knowledge represented in biomedical networks can uncover important new relationships, generate new testable hypotheses and provide new insight into biological systems [5] [6]. Recent investigations use systems biology methods to examine drug responses, by utilizing a network-based view of the genes involved in complex drug responses [7] [8]. "
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    ABSTRACT: An important task in pharmacogenomics (PGx) studies is to identify genetic variants that may impact drug response. The success of many systematic and integrative computational approaches for PGx studies depends on the availability of accurate, comprehensive and machine understandable drug-gene relationship knowledge bases. Scientific literature is one of the most comprehensive knowledge sources for PGx-specific drug-gene relationships. However, the major barrier in accessing this information is that the knowledge is buried in a large amount of free text with limited machine understandability. Therefore there is a need to develop automatic approaches to extract structured PGx-specific drug-gene relationships from unstructured free text literature. In this study, we have developed a conditional relationship extraction approach to extract PGx-specific drug-gene pairs from 20million MEDLINE abstracts using known drug-gene pairs as prior knowledge. We have demonstrated that the conditional drug-gene relationship extraction approach significantly improves the precision and F1 measure compared to the unconditioned approach (precision: 0.345 vs. 0.11; recall: 0.481 vs. 1.00; F1: 0.402 vs. 0.201). In this study, a method based on co-occurrence is used as the underlying relationship extraction method for its simplicity. It can be replaced by or combined with more advanced methods such as machine learning or natural language processing approaches to further improve the performance of the drug-gene relationship extraction from free text. Our method is not limited to extracting a drug-gene relationship; it can be generalized to extract other types of relationships when related background knowledge bases exist.
    Journal of Biomedical Informatics 04/2012; 45(5):827-34. DOI:10.1016/j.jbi.2012.04.011 · 2.19 Impact Factor
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    • "Graph analysis has drawn much interest among bioinformatics researchers due to the rapid growth of publicly available high throughput data [5] [6] [7] [8] [9] [10] [11] [12]. Such data have provided linkages among 1532-0464/$ -see front matter Ó 2011 Elsevier Inc. "
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    ABSTRACT: Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse.
    Journal of Biomedical Informatics 02/2011; 44(4):536-44. DOI:10.1016/j.jbi.2011.01.010 · 2.19 Impact Factor
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