Pozsgai Kevin, Szűcs Gergő, Kőnig-Péter Anikó, Balázs Orsolya, Vajda Péter, Botz Lajos, Vida Róbert György
Department of Pharmaceutics and Central Clinical Pharmacy, Faculty of Pharmacy, University of Pécs, Pécs, Hungary.
Institute of Bioanalysis, Medical School, University of Pécs, Pécs, Hungary.
Front Pharmacol. 2022 Sep 6;13:964399. doi: 10.3389/fphar.2022.964399. eCollection 2022.
The public health threat of substandard and falsified medicines has been well known in the last two decades, and several studies focusing on the identification of products affected and preventing consumption have been published. However, the number of these products reaching patients and causing health consequences and adverse drug reactions is not a well-researched area. Our aim was to identify and describe the characteristics of cases that are related to adverse drug reactions potentially originating from counterfeit medication using publicly available pharmacovigilance data. A descriptive study was performed based on pharmacovigilance data retrieved from Individual Case Safety Reports (ICSRs) identified in the European Medicines Agency's EudraVigilance and FDA Adverse Event Reporting System (FAERS) databases in April 2022 using selected MedDRA preferred terms: counterfeit product administered, product counterfeit, product label counterfeit, product packaging counterfeit, suspected counterfeit product, adulterated product, product tampering, and suspected product tampering. ICSRs were analyzed by age and gender, by year of reporting, region of origin, reporter's profession, and severity of the outcome. The disproportionality method was used to calculate pharmacovigilance signal measures. A total of 5,253 cases in the FAERS and 1,049 cases in the EudraVigilance database were identified, generally affecting middle-aged men with a mean age of 51.055 (±19.62) in the FAERS and 64.18% of the cases between 18 and 65 years, while the male to female ratios were 1.18 and 1.5. In the FAERS database, we identified 138 signals with 95% confidence interval including sildenafil ( = 314; PRR, 12.99; ROR, 13.04; RRR, 11.97), tadalafil ( = 200; PRR, 11.51; ROR, 11.55; RRR, 10.94), and oxycodone ( = 190; PRR, 2.47; ROR, 2.14; RRR, 2.47). While in the EV data 31, led by vardenafil ( = 16, PRR = 167.19; 101.71-274.84; 95% CI, RRR = 164.66; 100.17-270.66; 95% CI, ROR = 169.47; 103.09-278.60; 95% CI, < 0.001), entecavir ( = 46, PRR = 161.26, RRR = 154.24, ROR = 163.32, < 0.001), and tenofovir ( = 20, PRR = 142.10, RRR = 139.42, ROR = 143.74, < 0.001). The application of pharmacovigilance datasets to identify potential counterfeit medicine ADRs can be a valuable tool in recognition of potential risk groups of consumers and the affected active pharmaceutical ingredients and products. However, the further development and standardization of ADR reporting, pharmacovigilance database analysis, and prospective and real-time collection of potential patients with health consequences are warranted in the future.
在过去二十年中,不合格和伪造药品对公共卫生的威胁已广为人知,并且已经发表了几项专注于识别受影响产品和防止消费的研究。然而,这些产品到达患者手中并导致健康后果和药物不良反应的数量并不是一个研究充分的领域。我们的目的是利用公开可用的药物警戒数据,识别和描述可能源自假冒药品的药物不良反应相关病例的特征。基于从欧洲药品管理局的EudraVigilance和美国食品药品监督管理局不良事件报告系统(FAERS)数据库中检索到的个体病例安全报告(ICSR)中的药物警戒数据,于2022年4月进行了一项描述性研究,使用了选定的MedDRA首选术语:使用假冒产品、产品假冒、产品标签假冒、产品包装假冒、疑似假冒产品、掺假产品、产品篡改和疑似产品篡改。通过年龄和性别、报告年份、原产地区、报告者职业以及结果的严重程度对ICSR进行分析。使用不成比例法计算药物警戒信号指标。在FAERS数据库中识别出5253例病例,在EudraVigilance数据库中识别出1049例病例,这些病例一般影响中年男性,FAERS中平均年龄为51.055(±19.62),18至65岁之间的病例占64.18%,而男女比例分别为1.18和1.5。在FAERS数据库中,我们确定了138个具有95%置信区间的信号,包括西地那非( = 314;PRR,12.99;ROR,13.04;RRR,11.97)、他达拉非( = 200;PRR,11.51;ROR,11.55;RRR,10.94)和羟考酮( = 190;PRR,2.47;ROR,2.14;RRR,2.47)。而在EudraVigilance数据中,有31个信号,以伐地那非为首( = 16,PRR = 167.19;101.71 - 274.84;95% CI,RRR = 164.66;100.17 - 270.66;95% CI,ROR = 169.47;103.09 - 278.60;95% CI, < 0.001)、恩替卡韦( = 46,PRR = 161.26,RRR = 154.24,ROR = 163.32, < 0.001)和替诺福韦( = 20,PRR = 142.10,RRR = 139.42,ROR = 143.74, < 0.001)。应用药物警戒数据集来识别潜在的假冒药品药物不良反应,对于识别潜在的消费者风险群体以及受影响的活性药物成分和产品可能是一个有价值的工具。然而,未来有必要进一步发展和规范药物不良反应报告、药物警戒数据库分析以及对有健康后果的潜在患者进行前瞻性和实时收集。