Article

Network analysis of FDA approved drugs and their targets

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: 1.56). 04/2007; 74(1):27-32. DOI: 10.1002/msj.20002
Source: PubMed

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.

0 Followers
 · 
135 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.
    Trends in Pharmacological Sciences 08/2014; 35(9). DOI:10.1016/j.tips.2014.07.001 · 9.99 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Gene expression is regulated by trans-acting transcription factors and microRNAs (miRNAs) through interactions with their respective cis-regulatory elements. The effects that drugs induce result from complex interactions in pathways downstream from their primary targets. These interactions, from gene regulatory apparatus and from drug-induced pathways, form a complex, multilayered network. Knowing that drugs can perturb miRNA expression profiles, a genomewide analysis of drug-induced intronic miRNA perturbations has been presented here. By comparative analysis of control and drugged data sets from 27 independent gene expression experiments, it was feasible to detect the effect of drugs on miRNA target genes. Signatures of 21 of 28 miRNAs, predicted to be influenced by drug action, were detected. This study demonstrates that the action of drugs on mRNA expression can be mediated through the combinatorial effects of miRNAs. In addition, transcription factors, through miRNAs within the introns of their target genes, can exert an indirect effect on the expression of distal mRNAs.
    Pharmacogenetics and Genomics 01/2015; DOI:10.1097/FPC.0000000000000111 · 3.45 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this perspective, we focus on new, systems-centric views of structure-based drug design (SBDD) that we believe will impact future drug discovery research and development. We will first discuss new ways to identify drug targets based on systems intervention analysis, and then introduce emerging SBDD methods driven by advancements in systems biology.
    Journal of the American Chemical Society 07/2014; 136(33). DOI:10.1021/ja504810z · 11.44 Impact Factor

Full-text (2 Sources)

Download
24 Downloads
Available from
May 17, 2014