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

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.

SUBMITTER: Ji X

PROVIDER: S-EPMC8762263 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Esketamine was approved for the treatment of treatment-resistant depression in 2019. After the approval of esketamine, numerous concerns have been raised regarding its long-term safety and tolerability. A previous systematic pharmacovigilance study on esketamine-related adverse events (AEs) was published in 2020; however, it has not been updated 2 years later. The primary aim of this study was to detect and characterize neurological safety signals of esketamine to partially update the knowledge in this field using the FDA pharmacovigilance database. Reporting odds ratio (ROR) was calculated for esketamine-related neurological AEs from 2019 to 2021 with a signal considered when the lower limit of the 95% confidence interval (CI) of ROR (ROR025) exceeded one. Severe and non-severe cases were compared using an independent samples t-test or chi-squared (χ2) test, and a rating scale was used to prioritize the signals. The database contained 720 cases of esketamine-associated neurological AEs, with 21 signals detected, ranging from a ROR025 of 1.05 (disturbance in attention) to 204.00 (sedation). 16 latest neurological AEs emerged in the second year of marketing approval of esketamine, with eight signals detected. The associations between esketamine and nervous system disorders persisted when stratifying by sex, age, and reporter type, whereas the spectrum of neurological AEs differed in stratification regimens. Esketamine dosage, antidepressant polypharmacy, or co-prescription with benzodiazepines affected AEs severity (t = 2.41, p = 0.017; χ2 = 6.75, p = 0.009; and χ2 = 4.10, p = 0.043; respectively), while age and sex did not (p = 0.053 and p = 0.397, respectively). Three signals were categorized as moderate clinical priority [i.e., sedation, dizziness, and dysgeusia (priority points 7, 5, and 5, respectively)], showing the same early failure type profiles. Notably, seven detected disproportionality signals were not previously detected in clinical trials. Although the majority of results were in line with those obtained in the previous study, there were discrepancies in the spectrum of neurological AEs and the effects of several risk factors on AEs severity among the two studies that should be recognized and managed early in clinical treatments.

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

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