Lung Cancer Adverse Events Reports for Angiotensin-Converting Enzyme Inhibitors: Data Mining of the FDA Adverse Event Reporting System Database

Abstract

Because of contradictory evidence from clinical trials, the association between angiotensin-converting enzyme inhibitors (ACEIs) and lung cancer needs further evaluation. As such, the current study is to assess disproportionate reporting of primary malignant lung cancer among reports for ACEIs submitted to the FDA adverse event reporting system utilizing a pharmacovigilance approach. We conducted a disproportionality analysis of primary malignant lung cancer adverse events associated with 10 ACEIs by calculating the reported odds ratios (ROR) and information component (IC) with 95% confidence intervals (CI). ROR was adjusted for sex, age, and reporting year by logistic regression analyses. From January 2004 to March 2020, a total of 622 cases of lung cancer adverse event reports were identified for ACEIs users. Significant disproportionate association was found for ACEIs as a drug class (ROR: 1.22, 95% CI: 1.13–1.32; IC: 0.28, 95% CI: 0.17–0.39. adjusted ROR: 1.23, 95% CI: 1.02–1.49). After stratification based on gender, a subset analysis suggested that female patients exhibited a significant disproportionate association, while male patients did not. Sensitivity analyses that limited the data by reporting region, comorbidity, and reporting year also showed similar trends. Statistical significant lung cancer signals were detected among patients who received ACEI, especially female patients. The disproportionality analysis of the FAERS database suggests mildly increased reporting of lung cancer among ACEI users. Further robust epidemiological studies are necessary to confirm this relationship.

Association between angiotensin-converting enzyme inhibitors and the risk of lung cancer: a systematic review and meta-analysis

“…Based on the search strategy, 2400 records from the databases were screened, of which 392 duplicated records and 1976 records did not meet the inclusion criteria and were excluded. Finally, 11 clinical studies [12][13][14][23][24][25][26][27][28][29][30], including seven cohort studies [12,14,23,[26][27][28]30] and four case-control studies [13,24,25,29], conducted between 1988 and 2020 were included in the meta-analysis (Fig. 1).…”

“…One study [29] did not voluntarily report mean follow-up time, based on a post hoc analysis of the database it used, we found that its mean follow-up time was 1.73 years, but the study was not included in the follow-up time subgroup analysis because some follow-up data were missing from the database. The other included studies in the subgroup analysis voluntarily reported the follow-up time of 3.6-9 years.…”

“…The ACEI group was still considered the study group in all subgroup analyses. Compared with the two control subgroups-one [12][13][14][23][24][25][26][27][28][29][30] comprising ARBs users (21,493 of 1,239,877 [1.73%] vs 51,226 of 8,657,649 [0.59%]; OR 1.18; 95% CI 1.03-1.35; P = 0.02; I 2 = 97%) and the other [26] comprising patients neither with ACEIs nor ARBs (2509 of 253,348 [0.99%] vs 21,536 of 2,910,352 [0.74%]; OR 1.34; 95% CI 1.29-1.40), the ACEI group had a higher risk of lung cancer (Fig. 3).…”

Cutaneous Toxicity Associated With Enfortumab Vedotin: A Real-Word Study Leveraging U.S. Food and Drug Administration Adverse Event Reporting System

“…The FAERS database Quarterly Data Files (January 1, 2019, to November 4, 2021) were used. OpenVigil FDA, a validated pharmacovigilance tool, was adapted to access the FDA drug-event database with the additional openFDA drug mapping and duplicate detection functionality (18)(19)(20).…”

Peripheral Neuropathy During Concomitant Administration of Proteasome Inhibitors and Factor Xa Inhibitors: Identifying the Likelihood of Drug-Drug Interactions

“…Compared to other international databases for spontaneous AE reporting, FAERS has several distinctive characteristics, including the heterogeneous catchment area (to broaden the generalization of findings), the public access to raw data that can be downloaded in a format suitable for customized analysis ( Antonazzo et al, 2020 ). Data mining from the FAERS pharmacovigilance source can be utilized to evaluate drug safety, such as identifying rare or new AEs ( Meng et al, 2021 ) and quantitatively detecting drug-drug interactions (DDIs) ( Oshima et al, 2018 ; Antonazzo et al, 2020 ). The present study aimed to detect safety signals between the concomitant use of PI and FXaI (both as a drug class and as a single agent) and the appearance of PN.…”