Abstract
Adverse drug reaction prediction is important before releasing the drug into markets. It is one of the significant causes of failure in drug progression in the pharmaceutical industry. A post-marketing undetected adverse event can lead to severe health conditions or morbidity. This paper proposes a technique for prediction of drug-drug-adverse event. The prediction model learns chemical, phenotypic, and graph embedding features. We explored both machine learning and deep learning approaches. The model predicts the presence of adverse event for the co-prescribed drug with an Area Under Curve (AUC) score of 0.90 and the accuracy with the 0.83.
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References
Offsides and Twosides (2020). http://tatonettilab.org/offsides/. Accessed 20 Mar 2020
An, S.Y.: Vancomycin-associated spontaneous cutaneous adverse drug reactions. Allergy Asthma Immunol. Res. 3, 194–198 (2011)
Banda, J.M., Evans, L., Vanguri, R.S., Tatonetti, N.P., Ryan, P.B., Shah, N.H.: A curated and standardized adverse drug event resource to accelerate drug safety research. Sci. Data 3, 2052–4463 (2016)
Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, p. 2787–2795 (2013)
Ferdousi, R., Safdari, R., Omidi, Y.: Computational prediction of drug-drug interactions based on drugs functional similarities. J. Biomed. Inf. 70, 54–64 (2017)
Feucht, C., Patel, D.R.: Principles of pharmacology. Pediatr. Clin. North Am. 58(1), 11–19 (2011)
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)
Kastrin, A., Ferk, P., Leskosek, B.: Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning. PLoS One 13, e0196865 (2018)
Liu, R., AbdulHameed, M.D.M., Kumar, K., Yu, X., Wallqvist, A., Reifman, J.: Data-driven prediction of adverse drug reactions induced by drug-drug interactions. BMC pharmacol. Toxicol. 8 (2017)
Masnoon, N., Shakib, S., Kalisch-Ellett, L., Caughey, G.E.: What is polypharmacy? a systematic review of definitions. BMC Geriatr. 17, 230 (2017)
Routledge, P.A., O’Mahony, M.S., Woodhouse, K.W.: Adverse drug reactions in elderly patients. Br. J. Clin. Pharmacol. 57, 121 (2004)
Saha, A., Mukhopadhyay, J., Sarkar, S., Gattu, M.: BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials. Med. Biol. Eng. Comput. 58, 2339–2354 (2020)
Scheiber, J., et al.: Mapping adverse drug reactions in chemical space. J. Med. Chem. 52, 3103–3107 (2009)
Wang, Z., Clark, N.R., Ma’ayan, A.: Drug-induced adverse events prediction with the lincs 1000 data. Bioinformatics 32, 2338–2345 (2016)
Zheng, Y., Peng, H., Ghosh, S., Lan, C., Li, J.: Inverse similarity and reliable negative samples for drug side-effect prediction. BMC Bioinf. 19, 554 (2019)
Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34, i457–i466 (2018)
Acknowledgments
This work is supported by the project “Effective Drug Repurposing through literature and patent mining, data integration and development of systems pharmacology platform" sponsored by MHRD, India and Excelra Knowledge Solutions, Hyderabad.
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Saha, A., Mukhopadhyay, J., Sarkar, S., Gattu, M. (2024). Adverse Event Classification from Co-prescribed Drugs by Integrating Chemical, Phenotypic and Graph Embedding Features. In: Ghosh, A., King, I., Bhattacharyya, M., Sankar Ray, S., K. Pal, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2021. Lecture Notes in Computer Science, vol 13102. Springer, Cham. https://doi.org/10.1007/978-3-031-12700-7_35
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DOI: https://doi.org/10.1007/978-3-031-12700-7_35
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