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Drug—target network

Nature Biotechnology volume 25, pages 1119–1126 (2007)Cite this article

Abstract

The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein–protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration–approved drugs and proteins linked by drug–target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.

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Figure 1: Distribution of drugs and drug targets.
Figure 2: Drug–target network (DT network).
Figure 3: Target-protein network (TP network) and cellular component profiles.
Figure 4: Drug targets, protein interactions and coexpression.
Figure 5: Human Disease Network and drug targets.
Figure 6: Drug targets and disease genes on human PPI network.

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References

  1. Hopkins, A.L. & Groom, C.R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002).

    Article  CAS  Google Scholar 

  2. Imming, P., Sinning, C. & Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov. 5, 821–834 (2006).

    Article  CAS  Google Scholar 

  3. Overington, J.P., Bissan, A. & Hopkins, A. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).

    Article  CAS  Google Scholar 

  4. Russ, A.P. & Lampel, S. The druggable genome: an update. Drug Discov. Today 10, 1607–1610 (2005).

    Article  Google Scholar 

  5. Drews, J. Stategic trends in the drug industry. Drug Discov. Today 8, 411–420 (2003).

    Article  Google Scholar 

  6. Barabasi, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  Google Scholar 

  7. Han, J.-D. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93 (2004).

    Article  CAS  Google Scholar 

  8. Jeong, H., Mason, S., Barabasi, A.-L. & Oltvai, Z. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    Article  CAS  Google Scholar 

  9. Vidal, M. Interactome modeling. FEBS Lett. 579, 1834–1838 (2005).

    Article  CAS  Google Scholar 

  10. Goh, K.I. et al. The human disease network. Proc. Natl. Acad. Sci. USA 104, 8685–8690 (2007).

    Article  CAS  Google Scholar 

  11. Jimenez-Sanchez, G., Childs, B. & Valle, D. Human disease genes. Nature 409, 853–855 (2001).

    Article  CAS  Google Scholar 

  12. Peltonen, L. & McKusick, V. Dissecting human disease in the postgenomic era. Science 291, 1224–1229 (2001).

    Article  CAS  Google Scholar 

  13. Wishart, D.S. et al. Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).

    Article  CAS  Google Scholar 

  14. Paolini, G.V., Shapland, R.H., van Hoorn, W.P., Mason, J.S. & Hopkins, A.L. Global mapping of pharmacological space. Nat. Biotechnol. 24, 805–815 (2006).

    Article  CAS  Google Scholar 

  15. Roth, B.L., Sheffler, D.J. & Kroeze, W.K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353–359 (2004).

    Article  CAS  Google Scholar 

  16. Hopkins, A.L., Mason, J.S. & Overington, J.P. Can we rationally design promiscuous drugs? Curr. Opin. Struct. Biol. 16, 127–136 (2006).

    Article  CAS  Google Scholar 

  17. Mencher, S.K. & Wang, L.G. Promiscuous drugs compared to selective drugs (promiscuity can be a virtue). BMC Clin. Pharmacol. 5, 3 (2005).

    Article  Google Scholar 

  18. Newman, M.E. Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64, 016131 (2001).

    Article  CAS  Google Scholar 

  19. Cokol, M., Iossifov, I., Weinreb, C. & Rzhetsky, A. Emergent behavior of growing knowledge about molecular interactions. Nat. Biotechnol. 23, 1243–1247 (2005).

    Article  CAS  Google Scholar 

  20. Rual, J.-F. et al. Toward a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).

    Article  CAS  Google Scholar 

  21. Stelzl, U. et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 (2005).

    Article  CAS  Google Scholar 

  22. Eppig, J.T. et al. The Mouse Genome Database (MGD): from genes to mice–a community resource for mouse biology. Nucleic Acids Res. 33, D471–D475 (2005).

    Article  CAS  Google Scholar 

  23. Hartwell, L.H., Hopfield, J.J., Leibler, S. & Murray, A.W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    Article  CAS  Google Scholar 

  24. Ge, X. et al. Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics 86, 127–141 (2005).

    Article  CAS  Google Scholar 

  25. Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A. & McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).

    Article  CAS  Google Scholar 

  26. Butcher, E.C., Berg, E. & Kunkel, E. Systems biology in drug discovery. Nat. Biotechnol. 22, 1253–1259 (2004).

    Article  CAS  Google Scholar 

  27. Chanda, S.K. & Caldwell, J. Fulfilling the promise: drug discovery in the post-genomic era. Drug Discov. Today 8, 168–174 (2003).

    Article  CAS  Google Scholar 

  28. Searls, D.B. Pharmacophylogenomics: genes, evolution and drug targets. Nat. Rev. Drug Discov. 2, 613–623 (2003).

    Article  CAS  Google Scholar 

  29. van der Greef, J. & McBurney, R. Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat. Rev. Drug Discov. 4, 961–967 (2005).

    Article  CAS  Google Scholar 

  30. Lindpaintner, K. The impact of pharmacogenetics and pharmacogenomics on drug discovery. Nat. Rev. Drug Discov. 1, 463–469 (2002).

    Article  CAS  Google Scholar 

  31. Gershell, L.J. & Atkins, J. A brief history of novel drug discovery technologies. Nat. Rev. Drug Discov. 2, 321–327 (2003).

    Article  CAS  Google Scholar 

  32. Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    Article  CAS  Google Scholar 

  33. Capdeville, R., Buchdunger, E., Zimmermann, J. & Matter, A. Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug. Nat. Rev. Drug Discov. 1, 493–502 (2002).

    Article  CAS  Google Scholar 

  34. Babu, M.M., Luscombe, N.M., Aravind, L., Gerstein, M. & Teichmann, S.A. Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14, 283–291 (2004).

    Article  CAS  Google Scholar 

  35. Lee, T.-I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).

    Article  CAS  Google Scholar 

  36. Rodriguez-Caso, C., Medina, M.A. & Sole, R.V. Topology, tinkering and evolution of the human transcription factor network. FEBS J. 272, 6423–6434 (2005).

    Article  CAS  Google Scholar 

  37. Wagner, A. & Fell, D.A. The small world inside large metabolic networks. Proc. Biol. Sci. 268, 1803–1810 (2001).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank Andrew L. Hopkins, William G. Kaelin and the members of the M.V. and A.-L.B. laboratories and the Center for Cancer Systems Biology (CCSB), especially David E. Hill, for useful discussions. This work was supported by the Dana-Farber Cancer Institute Strategic Initiative (to M.V.), the W. M. Keck Foundation (to M.V.) and an National Institutes of Health (NIH) grant 2R01-HG001715 from the National Human Genome Research Institute and the National Institute of General Medical Sciences (to M.V. and Frederick P. Roth). K.-I.G. and A.-L.B. were supported by NIH grants IH U01 A1070499-01 and U56 CA113004 and National Science Foundation Grant ITR DMR-0926737 IIS-0513650.

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Authors and Affiliations

  1. Center for Cancer Systems Biology (CCSB), Harvard Medical School, 44 Binney St., Boston, 02115, Massachusetts, USA

    Muhammed A Yıldırım, Kwang-Il Goh, Michael E Cusick, Albert-László Barabási & Marc Vidal

  2. Department of Cancer Biology, and Department of Genetics, Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney St., Boston, 02115, Massachusetts, USA

    Muhammed A Yıldırım, Michael E Cusick & Marc Vidal

  3. Division of Engineering and Applied Sciences, Harvard University, 9 Oxford St., Cambridge, 02138, Massachusetts, USA

    Muhammed A Yıldırım

  4. Center for Complex Network Research and Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556, USA.,

    Kwang-Il Goh & Albert-László Barabási

  5. Department of Physics, Korea University, Seoul, 136-713, Korea

    Kwang-Il Goh

  6. and the Departments of Physics, Center for Complex Network Research, Computer Science and Biology, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, 02115, MA

    Albert-László Barabási

Authors
  1. Muhammed A Yıldırım
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  2. Kwang-Il Goh
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  4. Albert-László Barabási
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Corresponding authors

Correspondence to Albert-László Barabási or Marc Vidal.

Supplementary information

Supplementary Text and Figures

Supplementary Notes; Supplementary Figures 1–7 (PDF 1821 kb)

Supplementary Table 1

Curated Approved Drugs and Corresponding Targets from DrugBank database (as of March 29th 2006). (XLS 106 kb)

Supplementary Table 2

Curated Experimental Drugs and Corresponding Targets from DrugBank database (as of March 29th 2006). (XLS 121 kb)

Supplementary Table 3

Approved Drugs and Corresponding Disease and Disease Genes obtained from OMIM (as of December 21st 2005). (XLS 261 kb)

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Yıldırım, M., Goh, KI., Cusick, M. et al. Drug—target network. Nat Biotechnol 25, 1119–1126 (2007). https://doi.org/10.1038/nbt1338

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