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

Jamie Ngai; Madison Kalter; James Brian Byrd; Rebecca Racz; Yongqun He

doi:10.3389/fphar.2022.812338

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

Front Pharmacol. 2022 Mar 23:13:812338. doi: 10.3389/fphar.2022.812338. eCollection 2022.

Authors

Jamie Ngai¹, Madison Kalter², James Brian Byrd³, Rebecca Racz⁴, Yongqun He^{5

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Affiliations

¹ College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.
² College of Literature, Science, and Arts, University of Michigan, Ann Arbor, MI, United States.
³ Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
⁴ Division of Applied Regulatory Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.
⁵ Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
⁶ Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States.
⁷ Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States.

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.

Keywords: COVID-19; FAERS database; adverse event; chloroquine; drug; hydroxychloroquine; ontology.