Pharmacoepidemiology

Objectives: To evaluate reports of diabetic ketoacidosis (DKA) associated with antipsychotic drug (APD) use submitted to the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from January 2000 to December 2022. DKA cases were identified using the MedDRA preferred term “diabetic ketoacidosis” in reports listing antipsychotic drugs as suspect medications. Disproportionality analyses, including the proportional reporting ratio (PRR) and empirical Bayes geometric mean (EBGM), were used to assess reporting patterns. Multiple analyses were performed, including those restricted to primary suspect listed drugs only, expanded to incorporate secondary suspect drugs, and sensitivity analyses excluding reports submitted by legal professionals. Results: Among 19,961 DKA reports in FAERS, 2489 (12.5%) listed atypical antipsychotics as the primary suspect drug, whereas reports involving typical APDs were rare. The majority of reports were submitted by healthcare professionals (74.1%), and nearly half originated from the United States (45.4%). Hospitalization was a frequent outcome, reported in 74.3% of cases. Quetiapine and olanzapine were the most frequently reported atypical APDs, with disproportionality analyses demonstrating strong safety signals when compared to all other drugs in FAERS: olanzapine PRR 13.2 (95% CI: 12.4–14.2) and quetiapine PRR 11.8 (95% CI: 11.1–12.5). The findings remained consistent across multiple sensitivity analyses, including incorporating secondary suspect drugs, when the comparator group was restricted to only psychotropic drugs, and excluding reports submitted by lawyers. Conclusions: This pharmacovigilance analysis highlights a potential safety signal for DKA with atypical antipsychotic drugs, notably quetiapine and olanzapine. While these findings do not establish causality, they underscore the need for further investigation using clinical and epidemiological data.

Pharmacovigilance approaches have conventionally focused on the use of epidemiological data to detect emergent adverse drug reactions (ADRs). Recent advances in the use and availability of real-world data have expanded opportunities to detect ADR signals in medical records. We provide a limited review of pharmacovigilance practices and tools we have specifically considered implementing into our comprehensive medication management clinic and associated research programs. Use of pharmacogenomic variants has proven useful only on a limited scale as such data are reliant on low-dimensional approaches matching variants to drugs, often with small effect sizes. As such, most ADRs go unrecognized, undocumented, and unactionable. We posit that an idealized pharmacovigilance framework that relies on artificial-intelligence-assisted reporting with adjudication by pharmacovigilance experts and new models of ambulatory pharmaceutical practice would establish the following attributes: (1) all metadata relating to medication use would be available in the medical record in computable and interoperable data models, (2) digital surveillance tools would detect most ADR events with attributed pharmacological contributions, (3) all events would be characterized using standard adjudication rubrics, and (4) all events would iteratively inform an ADR knowledgebase and improve models to advance detection and prediction of ADR during the course of patient care with a focus on having the necessary tools for clinicians to prevent ADRs. This review provides a limited and focused framework for more systematic documentation of ADRs and tactics to mitigate the idiopathic nature of most ADRs.

Background/Objectives: Human immunodeficiency virus (HIV) continues to be a global public health concern. Several antiretroviral drugs have been approved for the treatment, post-exposure, and pre-exposure prophylaxis of HIV. Darunavir (DRV) is a protease inhibitor (PI) approved for the management of HIV globally. This study aims to generate safety signals for DRV through data mining and analysis of adverse events (AEs) reported to the United Kingdom (UK) Medicines and Healthcare products Regulatory Agency (MHRA) Yellow Card Scheme. Methods: Disproportionality analysis was conducted using reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) approaches to identify potential safety signals. Results: The MHRA database contained n = 779 reports (n = 1791 AEs) attributed to DRV. The majority of AEs were reported for males. Positive safety signals were identified at both the system organ class (SOC, n = 5) and preferred term level (PT, n = 95). At SOC level, endocrine disorders emerged as a signal of interest n = 33 cases (ROR: 8.17, 95% CI: 5.78–11.56; PRR:7.96, 95% CI: 5.68–11.15; and IC: 2.85, IC025: 2.51). Among the results, 40 new potential safety signals are not listed on the product labelling in the UK. These include serious AEs such as cerebrovascular accident, brain injury, thrombosis, and pregnancy, puerperium, and perinatal AEs. Conclusions: This study provides additional real-world safety data for DRV in the UK and paves the way for future observational studies to investigate the identified safety signals.