Abstract
There is an urgent need to discover or deduce drug-drug interactions (DDIs), which would cause serious adverse drug reactions. However, preclinical detection of DDIs bears a high cost. Machine learning-based computational approaches can be the assistance of experimental approaches. Utilizing pre-market drug properties (e.g. side effects), they are able to predict DDIs on a large scale before drugs enter the market. However, none of them can predict comprehensive DDIs, including enhancive and degressive DDIs, though it is important to know whether the interaction increases or decreases the behavior of the interacting drugs before making a co-prescription. Furthermore, existing computational approaches focus on predicting DDIs for new drugs that have none of existing interactions. However, none of them can predict DDIs among those new drugs. To address these issues, we first build a comprehensive dataset of DDIs, which contains both enhancive and degressive DDIs, and the side effects of the involving drugs in DDIs. Then we propose an algorithm of Triple Matrix Factorization and design a Unified Framework of DDI prediction based on it (TMFUF). The proposed approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. Moreover, it provides a unified solution for the scenario that predicting potential DDIs for newly given drugs (having no known interaction at all), as well as the scenario that predicting potential DDIs among these new drugs. Finally, the experiments demonstrate that TMFUF is significantly superior to three state-of-the-art approaches in the conventional binary DDI prediction and also shows an acceptable performance in the comprehensive DDI prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wienkers, L.C., Heath, T.G.: Predicting in vivo drug interactions from in vitro drug discovery data. Nat. Rev. Drug Discov. 4, 825–833 (2005)
Leape, L.L., Bates, D.W., Cullen, D.J., Cooper, J., Demonaco, H.J., Gallivan, T., Hallisey, R., Ives, J., Laird, N., Laffel, G., et al.: Systems analysis of adverse drug events. ADE Prevention Study Group. JAMA 274, 35–43 (1995)
Veith, H., Southall, N., Huang, R., James, T., Fayne, D., Artemenko, N., Shen, M., Inglese, J., Austin, C.P., Lloyd, D.G., Auld, D.S.: Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat. Biotechnol. 27, 1050–1055 (2009)
Huang, S.M., Temple, R., Throckmorton, D.C., Lesko, L.J.: Drug interaction studies: study design, data analysis, and implications for dosing and labeling. Clin. Pharmacol. Ther. 81, 298–304 (2007)
Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Label propagation prediction of drug-drug interactions based on clinical side effects. Sci. Rep. 5, 12339 (2015)
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, 2147–2163 (2014)
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, 46–52 (2014)
Cheng, F., Zhao, Z.: Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc.: JAMIA 21, e278–e286 (2014)
Koch-Weser, J.: Serum drug concentrations in clinical perspective. Ther. Drug Monit. 3, 3–16 (1981)
Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A.C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., Tang, A., Gabriel, G., Ly, C., Adamjee, S., Dame, Z.T., Han, B., Zhou, Y., Wishart, D.S.: DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091–D1097 (2014)
Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4, 125ra131 (2012)
Acknowledgement
This work was supported by RGC Collaborative Research Fund (CRF) of Hong Kong (C1008-16G), National High Technology Research and Development Program of China (No. 2015AA016008), the Fundamental Research Funds for the Central Universities of China (No. 3102015ZY081) and the Program of Peak Experience of NWPU (2016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shi, JY., Huang, H., Li, JX., Lei, P., Zhang, YN., Yiu, SM. (2017). Predicting Comprehensive Drug-Drug Interactions for New Drugs via Triple Matrix Factorization. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-56148-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56147-9
Online ISBN: 978-3-319-56148-6
eBook Packages: Computer ScienceComputer Science (R0)