Predicting Drug-Drug Interactions Through Large-Scale Similarity-Based Link Prediction

  • Achille Fokoue
  • Mohammad Sadoghi
  • Oktie Hassanzadeh
  • Ping Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

Drug-Drug Interactions (DDIs) are a major cause of preventable adverse drug reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. We present Tiresias, a framework that takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed approach and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs and among newly developed and existing drugs.

References

  1. 1.
    Apweiler, R., Bairoch, A., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al.: Uniprot: the universal protein knowledgebase. Nucleic Acids Res. 32(Suppl. 1), D115–D119 (2004)CrossRefGoogle Scholar
  2. 2.
    Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Suppl. 1), D267–D270 (2004)CrossRefGoogle Scholar
  3. 3.
    Brown, S.H., Elkin, P.L., Rosenbloom, S., Husser, C., Bauer, B., Lincoln, M., Carter, J., Erlbaum, M., Tuttle, M.: VA national drug file reference terminology: a cross-institutional content coverage study. Medinfo 11(Pt. 1), 477–481 (2004)Google Scholar
  4. 4.
    Chandel, A., Hassanzadeh, O., Koudas, N., Sadoghi, M., Srivastava, D.: Benchmarking declarative approximate selection predicates. In: ACM SIGMOD International Conference on Management of Data, SIGMOD 2007, pp. 353–364 (2007)Google Scholar
  5. 5.
    Chatr-aryamontri, A., Breitkreutz, B.J., Oughtred, R., Boucher, L., Heinicke, S., Chen, D., Stark, C., Breitkreutz, A., Kolas, N., O’Donnell, L., et al.: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43, D470–D478 (2014). doi:10.1093/nar/gku1204 CrossRefGoogle Scholar
  6. 6.
    Davis, A.P., Murphy, C.G., Saraceni-Richards, C.A., Rosenstein, M.C., Wiegers, T.C., Mattingly, C.J.: Comparative toxicogenomics database: a knowledgebase and discovery tool for chemical-gene-disease networks. Nucleic Acids Res. 37(Suppl. 1), D786–D792 (2009)CrossRefGoogle Scholar
  7. 7.
    Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)Google Scholar
  8. 8.
    Flockhart, D.A., Honig, P., Yasuda, S.U., Rosebraugh, C.: Preventable adverse drug reactions: A focus on drug interactions. Centers for Education & Research on TherapeuticsGoogle Scholar
  9. 9.
    Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P.: Predicting drug-drug interactions through large-scale similarity-based link prediction. http://researcher.watson.ibm.com/researcher/files/us-achille/adrTechreport.pdf
  10. 10.
    Fokoue, A., Hassanzadeh, O., Sadoghi, M., Zhang, P.: Predicting drug-drug interactions through similarity-based link prediction over web data. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016. ACM (2016)Google Scholar
  11. 11.
    Gottlieb, A., Stein, G.Y., Oron, Y., Ruppin, E., Sharan, R.: Indi: a computational framework for inferring drug interactions and their associated recommendations. Mol. Syst. Biol. 8(1), 592 (2012)Google Scholar
  12. 12.
    King, G., Zeng, L.: Logistic regression in rare events data. Polit. Anal. 9(2), 137–163 (2001)CrossRefGoogle Scholar
  13. 13.
    Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al.: DrugBank 3.0: a comprehensive resource for ‘comics’ research on drugs. Nucleic Acids Res. 39(Suppl. 1), D1035–D1041 (2011)CrossRefGoogle Scholar
  14. 14.
    Luo, H., Zhang, P., Huang, H., Huang, J., Kao, E., Shi, L., He, L., Yang, L.: Ddi-cpi, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic Acids Res. 42, W46–W52 (2014). doi:10.1093/nar/gku433 CrossRefGoogle Scholar
  15. 15.
    Skrbo, A., Begović, B., Skrbo, S.: Classification of drugs using the atc system (anatomic, therapeutic, chemical classification) and the latest changes. Medicinski arhiv 58(1 Suppl. 2), 138–141 (2003)Google Scholar
  16. 16.
    Tatonetti, N.P., Patrick, P.Y., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31 (2012)CrossRefGoogle Scholar
  17. 17.
    Vilar, S., Uriarte, E., Santana, L., Lorberbaum, T., Hripcsak, G., Friedman, C., Tatonetti, N.P.: Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat. Protoc. 9(9), 2147–2163 (2014)CrossRefGoogle Scholar
  18. 18.
    Vilar, S., Uriarte, E., Santana, L., Tatonetti, N.P., Friedman, C.: Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS ONE 8(3), e58321 (2013)CrossRefGoogle Scholar
  19. 19.
    Zhang, P., Agarwal, P., Obradovic, Z.: Computational drug repositioning by ranking and integrating multiple data sources. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part III. LNCS, vol. 8190, pp. 579–594. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Summits Transl. Sci. Proc. 2014, 132 (2014)Google Scholar
  21. 21.
    Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Label propagation prediction of drug-drug interactions based on clinical side effects. Scientific reports 5 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Achille Fokoue
    • 1
  • Mohammad Sadoghi
    • 1
  • Oktie Hassanzadeh
    • 1
  • Ping Zhang
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations