检测 COVID-19 患者的药物-药物相互作用。
Detecting Drug-Drug Interactions in COVID-19 Patients.
机构信息
Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States.
Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
出版信息
Stud Health Technol Inform. 2022 Jun 6;290:330-334. doi: 10.3233/SHTI220090.
COVID-19 patients with multiple comorbid illnesses are more likely to be using polypharmacy to treat their COVID-19 disease and comorbid conditions. Previous literature identified several DDIs in COVID-19 patients; however, various DDIs are unrecognized. This study aims to discover novel DDIs by conducting comprehensive research on the FDA Adverse Event Reporting System (FAERS) data from January 2020 to March 2021. We applied seven algorithms to discover DDIs. In addition, the Liverpool database containing DDI confirmed by clinical trials was used as a gold standard to determine novel DDIs in COVID-19 patients. The seven models detected 2,516 drug-drug pairs having adverse events (AEs), 49 out of which were confirmed by the Liverpool database. The remaining 2,467 drug pairs tested to be significant by the seven models can be candidate DDIs for clinical trial hypotheses. Thus, the FAERS database, along with informatics approaches, provides a novel way to select candidate drug-drug pairs to be examined in COVID-19 patients.
患有多种合并症的 COVID-19 患者更有可能使用多种药物治疗 COVID-19 疾病和合并症。先前的文献已经确定了 COVID-19 患者中的几种药物相互作用；然而，还有许多其他的药物相互作用尚未被识别。本研究旨在通过对 2020 年 1 月至 2021 年 3 月的 FDA 不良事件报告系统（FAERS）数据进行全面研究，来发现新的药物相互作用。我们应用了七种算法来发现药物相互作用。此外，还使用包含临床试验证实的药物相互作用的利物浦数据库作为金标准来确定 COVID-19 患者中的新药物相互作用。这七种模型检测到 2516 对具有不良事件（AE）的药物-药物对，其中 49 对被利物浦数据库证实。其余 2467 对经七种模型检测到具有显著意义的药物对可作为临床试验假说的候选药物相互作用。因此，FAERS 数据库和信息学方法为选择在 COVID-19 患者中进行检查的候选药物-药物对提供了一种新方法。
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