Discovering Drug-Drug Associations in the FDA Adverse Event Reporting System Database with Data Mining Approaches

Weizhong Zhao, Huyen Le, James J. Chen, Hesha Duggirala, Richard Forshee, Taxiarchis Botsis, Henry Francis, Huixiao Hong, Weida Tong, Yi Ting Hwang, Wen Zou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Objective: To classify causal associations among drugs and adverse events by the identified drug safety signals from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).Material and Methods: FAERS reports were collected for a period between 2004 and 2014. Empirical Bayes Geometric Mean was applied to model associations between drugs and adverse events. Identified signals were evaluated using Reporting Ratio values and Chi-Square test. Based on the identified drug-associated adverse events, we constructed a drug-drug association network, and applied a random walk algorithm to find drug communities with similar adverse event patterns. We developed 14 clusters for comparison with the 14 main groups in the first level of the Anatomical Therapeutic Chemical (ATC) classification system to evaluate relationships between the two classification systems.Results: The retrieved FAERS dataset included 981 drugs and 16,179 adverse events, from which we identified 63,083 significant drug-adverse event pairs. We found new potential safety signals when comparing the drug-adverse event pairs with information from relevant sources. Network analysis of the constructed drug communities revealed connections among drugs, adverse events, and ATC codes, suggesting that drug adverse events may be used to predict the ATC codes of unclassified drugs. For the ATC-classified drugs, the network analysis revealed potential relationships among the drugs by calculating the similarities of the adverse events.Conclusion: The generated drug groups and the drug-drug associations derived from the network analysis might have predictive potential for adverse events, as well as provide information for drug review and development.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1197-1204
Number of pages8
ISBN (Electronic)9781665401265
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
Duration: Dec 9 2021Dec 12 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/9/2112/12/21

Keywords

  • FAERS
  • adverse events
  • data mining
  • drug-drug associations
  • drugs
  • network analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Information Systems and Management

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