Ontology-Based Classification and Analysis of Adverse Events Associated With the Usage of Chloroquine and Hydroxychloroquine

Abstract

Multiple methodologies have been developed to identify and predict adverse events (AEs); however, many of these methods do not consider how patient population characteristics, such as diseases, age, and gender, affect AEs seen. In this study, we evaluated the utility of collecting and analyzing AE data related to hydroxychloroquine (HCQ) and chloroquine (CQ) from US Prescribing Information (USPIs, also called drug product labels or package inserts), the FDA Adverse Event Reporting System (FAERS), and peer-reviewed literature from PubMed/EMBASE, followed by AE classification and modeling using the Ontology of Adverse Events (OAE). Our USPI analysis showed that CQ and HCQ AE profiles were similar, although HCQ was reported to be associated with fewer types of cardiovascular, nervous system, and musculoskeletal AEs. According to EMBASE literature mining, CQ and HCQ were associated with QT prolongation (primarily when treating COVID-19), heart arrhythmias, development of Torsade des Pointes, and retinopathy (primarily when treating lupus). The FAERS data was analyzed by proportional ratio reporting, Chi-square test, and minimal case number filtering, followed by OAE classification. HCQ was associated with 63 significant AEs (including 21 cardiovascular AEs) for COVID-19 patients and 120 significant AEs (including 12 cardiovascular AEs) for lupus patients, supporting the hypothesis that the disease being treated affects the type and number of certain CQ/HCQ AEs that are manifested. Using an HCQ AE patient example reported in the literature, we also ontologically modeled how an AE occurs and what factors (e.g., age, biological sex, and medical history) are involved in the AE formation. The methodology developed in this study can be used for other drugs and indications to better identify patient populations that are particularly vulnerable to AEs.

Adverse drug events in the prevention and treatment of COVID-19: A data mining study on the FDA adverse event reporting system

“…In our study, a number of expected ADE signals on the PT level were observed such as "creatinine renal clearance decreased" (95% CI of PRR = 137.80 ± 4.17) and "liver function test increased" (95% CI of PRR = 29.84 ± 1.23) of remdesivir, "acute generalized exanthematous pustulosis" (95% CI of PRR = 46.53 ± 1.77) and "torsade de pointes" (95% CI of PRR = 33.68 ± 1.62) of hydroxychloroquine, and "ventricular arrhythmia" (95% CI of PRR = 30.27 ± 2.43) and "cardiovascular insufficiency" (95% CI of PRR = 22.07 ± 2.28) of azithromycin (Drugs.com, 2021). Some of these signals were also consistent to other data mining research studies conducted before (Gérard et al, 2021;Ngai et al, 2022). In addition, for azithromycin and remdesivir, we noticed that all the top 3 signals are associated with the heart and liver.…”

“…Some data mining works also have been performed trying to figure out some meaningful signals. However, these works focused either only on some certain ADEs or only on some certain kinds of medicines ( Ngai et al, 2022 ; Wu et al, 2022 ; Zhao et al, 2022 ). Hence, a work providing a whole picture based on the FAERS might be imperative and more helpful.…”

Profiling COVID-19 Vaccine Adverse Events by Statistical and Ontological Analysis of VAERS Case Reports

“…Enriched (i.e., statistically significant) MedDRA terms from VAERS data analysis can be mapped to the OAE terms, which can be further analyzed using the more accurate OAE AE classification. Multiple studies have shown that the method of the MedDRA-OAE term mapping followed by OAE-based AE classification enables valuable AE classification and data analysis (Sarntivijai et al, 2012;Xie et al, 2016a;Xie et al, 2016b;Wang et al, 2017;Xie et al, 2018;Ngai et al, 2022).…”