TY - GEN
T1 - Discovering Drug-Drug Associations in the FDA Adverse Event Reporting System Database with Data Mining Approaches
AU - Zhao, Weizhong
AU - Le, Huyen
AU - Chen, James J.
AU - Duggirala, Hesha
AU - Forshee, Richard
AU - Botsis, Taxiarchis
AU - Francis, Henry
AU - Hong, Huixiao
AU - Tong, Weida
AU - Hwang, Yi Ting
AU - Zou, Wen
N1 - Funding Information:
This article reflects the views of the authors and does not necessarily reflect those of the U.S. Food and Drug Administration (FDA). This work and the publication were funded by FDA. Weizhong Zhao and Huyen Le acknowledge the support of a postdoctoral fellowship from the Oak Ridge Institute for Science and Education, administered through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - FAERS
KW - adverse events
KW - data mining
KW - drug-drug associations
KW - drugs
KW - network analysis
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U2 - 10.1109/BIBM52615.2021.9669460
DO - 10.1109/BIBM52615.2021.9669460
M3 - Conference contribution
AN - SCOPUS:85125190836
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 1197
EP - 1204
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -