Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events

Xiangmin Ji; Guimei Cui; Chengzhen Xu; Jie Hou; Yunfei Zhang; Yan Ren

doi:10.3389/fphar.2021.773135

Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events

Front Pharmacol. 2022 Jan 3:12:773135. doi: 10.3389/fphar.2021.773135. eCollection 2021.

Authors

Xiangmin Ji¹, Guimei Cui¹, Chengzhen Xu², Jie Hou³, Yunfei Zhang⁴, Yan Ren¹

Affiliations

¹ School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.
² School of Computer Science and Technology, Huaibei Normal University, Huaibei, China.
³ College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
⁴ Department of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos, China.

Abstract

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost. Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety. Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (IC_PNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the IC_PNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden's index. Results: Of the statistical approaches implemented, the IC_PNM showed the best performance (AUC, 0.8291; Youden's index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively. Conclusion: The proposed IC_PNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.

Keywords: FDA adverse event reporting system; adverse drug events; pharmacological network model; pharmacovigilance; signal detection algorithm.