Mining Drug-Drug Interaction Induced Adverse Effects from Health Record Databases

National Science Foundation Project

Award Number: 1622526, 1827472

Investigators: Li Shen, Xia Ning, and Lang Li

NSF Progarm: Smart and Connected Health

Abstract

Recent advances in large-scale electronic health record database techniques provide exciting new opportunities to the study of drug safety. Drug-drug interactions (DDIs), a major cause of adverse drug events (ADEs), are a serious global health concern, and a severe detriment to public health. The scale of DDIs involving three or more drugs (also called high-order DDIs) has posed a prohibitory challenge for its molecular pharmacology and clinical research, which motivates alternative strategies such as mining health record data. This project aims to develop large-scale computational strategies and effective software tools for mining high-order DDI effects from health record databases, in order to yield novel discoveries in drug safety, and ultimately to benefit national health and well being.

To achieve the above goal, this project is designed to complete four specific tasks. Task 1 aims to develop a novel statistical framework to discover high-order DDI signals associated with ADEs from health record databases. Task 2 aims to study a novel drug safety problem for mining directional DDI signals. Task 3 aims to develop an innovative approach for mining directional DDI patterns at the drug-group level. Task 4 is devoted to software development, evaluation and validation. The project applies these methods to analyze three independent databases, packages method implementations into a user-friendly software toolkit, and releases the toolkit to the public. This project not only facilitates the development of novel computational techniques in drug safety research, but also addresses emerging scientific questions in modeling, mining, and visual exploration of complex data such as the health record data. The project's educational activities include course development, student mentoring and advising, and involvement of minority and underrepresented students in research activities.

Products

Ning X, Schleyer T, Shen L, Li L. (2017) Pattern discovery from directional high-order drug-drug interaction relations. ICHI’17: The 5th IEEE International Conference on Healthcare Informatics, 9 pages, Park City, Utah, August 23-26, 2017.

Ning X, Shen L, Li L. (2017) Predicting high-order directional drug-drug interaction relations. ICHI’17: The 5th IEEE International Conference on Healthcare Informatics, 6 pages, Park City, Utah, August 23-26, 2017.

Chasioti D, Yao X, Zhang P, Ning X, Li L, Shen L. (2017) Mining directional drug interaction effects on myopathy using the FAERS database. PSB’17: Pac Symp Biocomput., poster #71, Big Island of Hawaii, January 3-7, 2017.

Chasioti D, Yao X, Zhang P, Quinney S, Ning X, Li L, Shen L. (2017) Mining and visualizing the network of directional drug interaction effects. NetSci’17: Int. School and Conf. on Network Science, Indianapolis, IN, June 21-23, 2017.

Ning X, Li L, Shen L. (2017) Pattern discovery from directional high-order drug-drug interaction relations. NetSci’17: Int. School and Conf. on Network Science, Indianapolis, IN, June 21-23, 2017.

Chiang C, Zhang P, Wang X, Wang L, Zhang S, Ning X, Shen L, Quinney S, Li L. (2018) Translational high-dimensional drug Interaction discovery and validation using health record databases and pharmacokinetics models. Clinical Pharmacology & Therapeutics, 103(2):287-95.

Wang X, Zhang P, Chiang C, Wu H, Shen L, Ning X, Zeng D, Wang L, Quinney SK, Feng W, Li L. (2018) Mixture drug-count response model for the high dimensional drug combinatory effect on myopathy. Statistics in Medicine, 37(4):673-86.

Chiang WH, Schleyer T, Shen L, Li L, Ning X. (2018) Pattern discovery from high-order drug-drug interaction relations. Journal of Healthcare Informatics Research, 2(3):272-304.

Chiang W, Shen L, Li L, Ning X. (2018) Drug-drug interaction prediction based on co-medication patterns and graph matching. International Journal of Computational Biology and Drug Design, in press.

Zhang P, Li M, Chiang CW, Wang L, Xiang Y, Cheng L, Feng W, Schleyer TK, Quinney SK, Wu HY, Zeng D, Li L. (2018) Three-Component Mixture Model-Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System. CPT Pharmacometrics Syst Pharmacol., 7(8), 499-506. doi: 10.1002/psp4.12294

He Y, Liu J, Ning X. (2018) Drug selection via joint push and learning to rank. IEEE Transactions on Computational Biology and Bioinformatics., in press. DOI: 10.1109/TCBB.2018.2848908

Peng B, Ning X. (2019) Deep learning for high-order drug-drug interaction prediction. BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, September 2019 Pages 197–206. DOI: 10.1145/3307339.3342136

Chasioti D, Yao X, Zhang P, Lerner S, Quinney SK, Ning X, Li L, Shen L. (2019) Mining directional drug interaction effects on myopathy using the FAERS database. IEEE J Biomed Health Inform, 23(5):2156-2163. doi: 10.1109/JBHI.2018.2874533

Yao X, Tsang T, Quinney S, Zhang P, Ning X, Li L, Shen L. (2019) Mining and visualizing high-order directional drug interaction effects using the FAERS database. ICIBM’19: Int. Conf. on Intelligent Biology and Medicine, Columbus, OH, USA, June 9-11, 2019. (To appear in BMC Medical Informatics and Decision Making)