Large Scale Ranking and Repositioning of Drugs with Respect to DrugBank Therapeutic Categories

  • Matteo Re
  • Giorgio Valentini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7292)

Abstract

The ranking and prediction of novel therapeutic categories for existing drugs (drug repositioning) is a challenging computational problem involving the analysis of complex chemical and biological networks. In this context we propose a novel semi-supervised learning problem: ranking drugs in integrated bio-chemical networks according to specific DrugBank therapeutic categories. To deal with this challenging problem, we designed a general framework based on bipartite network projections by which homogeneous pharmacological networks can be combined and integrated from heterogeneous and complementary sources of chemical, biomolecular and clinical information. Moreover, we propose a novel method based on kernelized score functions for fast and effective drug ranking in the integrated pharmacological space. Results with 51 therapeutic DrugBank categories involving about 1300 FDA approved drugs show the effectiveness of the proposed approach.

Keywords

Bipartite Network Therapeutic Category Recall Level Drug Repositioning Random Walk With Restart 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    DiMasi, J., et al.: New drug development in the United States from 1963 to 1999. Clinical Pharmacology and Therapeutics 69(5), 186–196 (2001)Google Scholar
  2. 2.
    Ashburn, T., et al.: Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews 3(8), 28–55 (2004)Google Scholar
  3. 3.
    DiMasi, J., et al.: The price of innovation: new estimates of drug development costs. Journal of Health Economics 22(2), 151–185 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Anand, G.: How drug’s rebirth as treatment for cancer fueled price rises: once-demonized thalidomide boosts celgene’s sales; patients see costs soar. The Wall Street Journal 15(A1) (2004)Google Scholar
  5. 5.
    Noeske, T., Sasse, B., Strak, H., et al.: Predictiong compound selectivity by self-organizing maps: cross-activities of metabotropic glutamate receptor antagonists. Chem. Med. Chem. 1, 1066–1068 (2006)Google Scholar
  6. 6.
    Wei, G., Twomey, D., Lamb, J., et al.: Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell 10, 331–342 (2006)CrossRefGoogle Scholar
  7. 7.
    Kotelnikova, E., Yuryev, A., Mazo, I., Daraselia, N.: Computational approaches for drug repositioning and combination therapy design. Journal of Bioinformatics and Computational Biology 8, 593–606 (2010)CrossRefGoogle Scholar
  8. 8.
    Li, J., Zhu, X., Chen, J.: Building disease-specific drug-protein connectivity maps from molecular interaction networks and pubmed abstracts. PLoS Computational Biology 5, e1000450 (2009)CrossRefGoogle Scholar
  9. 9.
    Lamb, J., et al.: The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795), 1929–1935 (2006)CrossRefGoogle Scholar
  10. 10.
    Iorio, F., Bosotti, R., Scacheri, E., Mithbaokar, P., Ferriero, R., Murino, L., Tagliaferri, R., Brunetti-Pierri, N., Isacchi, A., di Bernardo, D.: Discovery of drug mode of action and drug repositioning from transcriptional responses. PNAS 107(33), 14621–14626 (2010)CrossRefGoogle Scholar
  11. 11.
    Gottlieb, A., Stein, G., Ruppin, E., Sharan, R.: PREDICT, a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology 7, 496 (2011)CrossRefGoogle Scholar
  12. 12.
    Sirota, M., et al.: Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med. 96(3), 96–97 (2011)Google Scholar
  13. 13.
    Keiser, M., Setola, V., Irwin, J., et al.: Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009)CrossRefGoogle Scholar
  14. 14.
    Yamanishi, Y., Kotera, M., Kaneisha, M., Goto, S.: Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics (ISMB) 26, 246–254 (2010)CrossRefGoogle Scholar
  15. 15.
    Chiang, A., Butte, A.: Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86, 507–510 (2009)CrossRefGoogle Scholar
  16. 16.
    Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., Djoumbou, Y., Eisner, R., Guo, A., Wishart, D.: DrugBank 3.0: a comprehensive resource for ’omics’ research on drugs. Nucleic Acids Res. 39, D1035–D1041 (2011)CrossRefGoogle Scholar
  17. 17.
    Dudley, J., Desphonde, T., Butte, A.: Exploiting drug-disease relationships for computational drug repositioning. Briefings in Bioinformatics 12(4), 303–311 (2011)CrossRefGoogle Scholar
  18. 18.
    Smola, A.J., Kondor, R.: Kernels and Regularization on Graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 144–158. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Kuhn, M., von Mering, C., Campillos, M., Jensen, L., P. B.: STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 36, D684–D688 (2008)CrossRefGoogle Scholar
  20. 20.
    Gong, L., et al.: PharmGKB: an integrated resource of pharmacogenomic data and knowledge. Curr. Protoc. Bioinformatics 14(17) (2008)Google Scholar
  21. 21.
    Davis, A., et al.: The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res. 39, D1067–D1072 (2011)CrossRefGoogle Scholar
  22. 22.
    Nikolova, N., Jaworska, J.: Approaches to measure chemical similarity - a review. QSAR Comb. Sci. 22(9-10), 1006–1026 (2003)CrossRefGoogle Scholar
  23. 23.
    Weininger, D.: Smiles, a chemical language and information system. Journal of Chemical Information and Modeling 28(31) (1988)Google Scholar
  24. 24.
    Kuhn, M., Szklarczyk, D., Franceschini, A., Campillos, M., von Mering, C., Jensen, L., Beyer, A., Bork, P.: STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res. 38, D552–D556 (2010)CrossRefGoogle Scholar
  25. 25.
    Lovasz, L.: Random Walks on Graphs: a Survey. Combinatorics, Paul Erdos is Eighty 2, 1–46 (1993)MathSciNetGoogle Scholar
  26. 26.
    Lippert, G., Ghahramani, Z., Borgwardt, K.: Gene function prediction from synthetic lethality networks via ranking on demand. Bioinformatics 26(7), 912–918 (2010)CrossRefGoogle Scholar
  27. 27.
    Sandyk, R., Fisher, H.: L-tryptophan supplementation in parkinson’s disease. Int. J. Neurosci. 45((3-4), 215–219 (1989)CrossRefGoogle Scholar
  28. 28.
    MacDonald, R., Jeffery, L.: Benzodiazepines specifically modulate GABA-mediated postsynaptic inhibition in cultured mammalian neurones. Nature 271, 563–564 (1976)CrossRefGoogle Scholar
  29. 29.
    Hanson, S., Czajkowski, C.: Structural mechanisms underlying benzodiazepine modulation of the GABA A receptor. The Journal of Neuroscience 28(13), 3490–3499 (2008)CrossRefGoogle Scholar
  30. 30.
    Kuhn, M., Campillos, M., Letunic, I., Jensen, L., P., B.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(343) (2010)Google Scholar
  31. 31.
    Croft, D., O’Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., et al.: Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691–D697 (2010)CrossRefGoogle Scholar
  32. 32.
    Woollard, P., Mehta, N., Vamathevan, J., Van Horn, S., Bonde, B., Dow, D.: The application of next-generation sequencing technologies to drug discovery and development. Drug Discovery Today 16(11-12), 512–519 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matteo Re
    • 1
  • Giorgio Valentini
    • 1
  1. 1.DSI, Dipartimento di Scienze dell’ InformazioneUniversità degli Studi di MilanoMilanoItalia

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