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Similarity-based modeling in large-scale prediction of drug-drug interactions

Abstract

Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5–7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.

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Figure 1
Figure 2: Example of some structural keys in the MACCS fingerprint for the drug diazepam.
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Figure 12: ROC curves showing the performance of the different DDI predictors in the DrugBank database (example provided in ANTICIPATED RESULTS with 9,454 true positives and 420,674 false positives).

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References

  1. Pirmohamed, M. & Orme, M.L. Drug Interactions of Clinical Importance. Chapman & Hall 1998.

  2. Tatonetti, N.P., Fernald, G.H. & Altman, R.B. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J. Am. Med. Inform. Assoc. 19, 79–85 (2012).

    Article  PubMed  Google Scholar 

  3. US Food and Drug Administration (FDA) http://www.fda.gov/ (accessed April 2013).

  4. Becker, M.L. et al. Hospitalisations and emergency department visits due to drug-drug interactions: a literature review. Pharmacoepidemiol. Drug Saf. 16, 641–651 (2007).

    Article  PubMed  Google Scholar 

  5. Bjornsson, T.D. et al. The conduct of in vitro and in vivo drug-drug interaction studies: a Pharmaceutical Research and Manufacturers of America (PhRMA) perspective. Drug Metab. Dispos. 31, 815–832 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Vilar, S. et al. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis. J. Am. Med. Inform. Assoc. 18, I73–I80 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Vilar, S., Harpaz, R., Santana, L., Uriarte, E. & Friedman, C. Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis. PLoS ONE 7, e41471 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Vilar, S. et al. Drug-drug interaction through molecular structure similarity analysis. J. Am. Med. Inform. Assoc. 19, 1066–1074 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Vilar, S., Uriarte, E., Santana, L., Tatonetti, N.P. & Friedman, C. Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS ONE 8, e58321 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Vilar, S., Uriarte, E., Santana, L., Friedman, C. & Tatonetti, N.P. State of the art and development of a new drug-drug interaction large-scale predictor based on 3D pharmacophoric similarity. Curr. Drug Metabolism. 15, in press (2014).

  11. Durant, J.L., Leland, B.A., Henry, D.R. & Nourse, J.G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280 (2002).

    Article  CAS  PubMed  Google Scholar 

  12. Liu, M. et al. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J. Am. Med. Inform. Assoc. 19(e1), e28–e35 (2012).

    Article  Google Scholar 

  13. Campillos, M., Kuhn, M., Gavin, A.C., Jensen, L.J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263–266 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Dixon, S.L. et al. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput. Aided Mol. Des. 20, 647–671 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Fowler, S. & Zhang, H. In vitro evaluation of reversible and irreversible cytochrome P450 inhibition: current status on methodologies and their utility for predicting drug-drug interactions. AAPS J. 10, 410–424 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hudelson, M.G. et al. High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods. J. Med. Chem. 51, 648–654 (2008).

    Article  CAS  PubMed  Google Scholar 

  17. Pang, K.S., Rodrigues, A.D. & Peter, R.M. (eds.), Enzyme- and Transporter-Based Drug-Drug Interactions: Progress and Future Challenges. Springer, 2010.

  18. Jonker, D.M., Visser, S.A.G., van der Graaf, P.H., Voskuyl, R.A. & Danhof, M. Towards a mechanism-based analysis of pharmacodynamic drug-drug interactions in vivo. Pharmacol. Ther. 106, 1–18 (2005).

    Article  CAS  PubMed  Google Scholar 

  19. Rahnasto, M., Raunio, H., Poso, A., Wittekindt, C. & Juvonen, R.O. Quantitative structure-activity relationship analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme. J. Med. Chem. 48, 440–449 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Afzelius, L. et al. Competitive CYP2C9 inhibitors: enzyme inhibition studies, protein homology modeling, and three-dimensional quantitative structure-activity relationship analysis. Mol. Pharmacol. 59, 909–919 (2001).

    Article  CAS  PubMed  Google Scholar 

  21. De Rienzo, F., Fanelli, F., Menziani, M.C. & De Benedetti, P.G. Theoretical investigation of substrate specificity for cytochromes p450 IA2, p450 IID6 and p450 IIIA4. J. Comput. Aided Mol. Des. 14, 93–116 (2000).

    Article  CAS  PubMed  Google Scholar 

  22. Percha, B., Garten, Y. & Altman, R.B. Discovery and explanation of drug-drug interactions via text mining. Pac. Symp. Biocomput. 2012, 410–421 (2012).

    Google Scholar 

  23. Tari, L., Anwar, S., Liang, S., Cai, J. & Baral, C. Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism. Bioinformatics 26, i547–i553 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Percha, B. & Altman, R.B. Informatics confronts drug-drug interactions. Trends Pharmacol. Sci. 34, 178–184 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. 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, 592 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hill, T. & Lewicki, P. Statistics Methods and Applications. A Comprehensive Reference for Science, Industry and Data Mining. StatSoft, 2006.

  27. Tatonetti, N.P., Ye, P.P., Daneshjou, R. & Altman, R.B. Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4, 125ra31 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Olvey, E.L., Clauschee, S. & Malone, D.C. Comparison of critical drug-drug interaction listings: the Department of Veterans Affairs medical system and standard reference compendia. Clin. Pharmacol. Ther. 87, 48–51 (2010).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by 'Plan Galego de Investigación, Innovación e Crecemento 2011-2015 (I2C)', by the European Social Fund (ESF), by the Angeles Alvariño program from Xunta de Galicia (Spain) (S.V.), by a training grant from the National Heart, Lung, and Blood Institute (NHLBI) T32HL120826 (T.L.), and by a Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation Research Starter Award (N.P.T.), as well as by grants R01 LM010016, R01 LM010016-0S1, R01 LM010016-0S2 and R01 LM008635 (C.F.).

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Authors

Contributions

S.V., C.F. and N.P.T. conceived and designed the experiments; S.V. performed the experiments; S.V. and N.P.T. analyzed the data; S.V., E.U., L.S., T.L., G.H., C.F. and N.P.T. contributed reagents/materials/analysis tools; and S.V., E.U. and N.P.T. wrote the paper.

Corresponding authors

Correspondence to Santiago Vilar or Nicholas P Tatonetti.

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The authors declare no competing financial interests.

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Vilar, S., Uriarte, E., Santana, L. et al. Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat Protoc 9, 2147–2163 (2014). https://doi.org/10.1038/nprot.2014.151

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