Abstract
One of the biggest challenges in drug development is increasing costs of bringing new drugs to the market. Many candidate drugs fail during phase II and III trials due to unexpected side effects and experimental methods remain cost ineffective for large scale discovery of adverse effects. Alternatively, computational methods are used to characterize drug side effects, but they often rely on training predictors based on drug and side effect similarity. Moreover, these methods are typically tailored to the underlying data set and provide little mechanistic insights on the predicted associations. In this study, we investigate the role of network topology in explaining observed side effects of drugs. We show that the interactome based proximity can be used to identify side effects and we highlight a use case in which interactome-based side effect prediction can give insights on drug side effects observed in the clinic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allison, M.: Reinventing clinical trials. Nat. Biotechnol. 30(1), 41–49 (2012)
Hay, M., Thomas, D.W., Craighead, J.L., Economides, C., Rosenthal, J.: Clinical development success rates for investigational drugs. Nat. Biotechnol. 32(1), 40–51 (2014)
Tai-Yin, W., Jen, M.-H., Bottle, A., Molokhia, M., Aylin, P., Bell, D., Majeed, A.: Ten-year trends in hospital admissions for adverse drug reactions in England 1999–2009. J. R. Soc. Med. 103(6), 239–250 (2010)
Zhao, S., Li, S.: A co-module approach for elucidating drug-disease associations and revealing their molecular basis. Bioinformatics 28(7), 955–961 (2012)
Guney, E., Garcia-Garcia, J., Oliva, B.: GUILDify: a web server for phenotypic characterization of genes through biological data integration and network-based prioritization algorithms. Bioinformatics (Oxford, England) 30(12), 1789–1790 (2014)
Guney, E., Menche, J., Vidal, M., Barabási, A.-L.: Network-based in silico drug efficacy screening. Nat. Commun. 7, 10331 (2016)
Berger, S.I., Ma’ayan, A., Iyengar, R.: Systems pharmacology of arrhythmias. Sci. Signal. 3(118), ra30 (2010)
Brouwers, L., Iskar, M., Zeller, G., van Noort, V., Bork, P.: Network neighbors of drug targets contribute to drug side-effect similarity. PLoS ONE 6(7), e22187 (2011)
Berger, S.I., Iyengar, R.: Role of systems pharmacology in understanding drug adverse events. Wiley Interdiscip. Rev.: Syst. Biol. Med. 3(2), 129–135 (2011)
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(Database issue), D1091–1097 (2014)
Kuhn, M., Letunic, I., Jensen, L.J., Bork, P.: The SIDER database of drugs and side effects. Nucleic Acids Res. 44(D1), D1075–1079 (2016)
Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31 (2012)
Menche, J., Sharma, A., Kitsak, M., Ghiassian, S.D., Vidal, M., Loscalzo, J., Barabási, A.-L.: Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science (New York, N.Y.) 347(6224), 1257601 (2015)
Kuhn, M., Al Banchaabouchi, M., Campillos, M., Jensen, L.J., Gross, C., Gavin, A.-C., Bork, P.: Systematic identification of proteins that elicit drug side effects. Mol. Syst. Biol. 9(1), 663 (2013)
Guney, E., Oliva, B.: Exploiting Protein-protein interaction networks for genome-wide disease-gene prioritization. PLoS ONE 7(9), e43557 (2012)
Ji, Z.L., Han, L.Y., Yap, C.W., Sun, L.Z., Chen, X., Chen, Y.Z.: Drug Adverse Reaction Target Database (DART): proteins related to adverse drug reactions. Drug Saf. 26(10), 685–690 (2003)
Lounkine, E., Keiser, M.J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J.L., Lavan, P., Weber, E., Doak, A.K., Côté, S., Shoichet, B.K., Urban, L.: Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403), 361–367 (2012)
Mestres, J., Gregori-Puigjané, E., Valverde, S., Solé, R.V.: Data completenessthe Achilles heel of drug-target networks. Nat. Biotech. 26(9), 983–984 (2008)
Acknowledgements
The author is grateful to Dr. Patrick Aloy for providing computational resources for this study and the members of the lab for fruitful discussions. EG is supported by EU-cofunded Beatriu de Pinós incoming fellowship from the Agency for Management of University and Research Grants (AGAUR) of Government of Catalunya.
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
Guney, E. (2017). Investigating Side Effect Modules in the Interactome and Their Use in Drug Adverse Effect Discovery. In: Gonçalves, B., Menezes, R., Sinatra, R., Zlatic, V. (eds) Complex Networks VIII. CompleNet 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-54241-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-54241-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54240-9
Online ISBN: 978-3-319-54241-6
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)