Detecting Drug-Drug Interactions in COVID-19 Patients.
|アブストラクト||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.|
|ジャーナル名||Studies in health technology and informatics|
|投稿者||Jeong, Eugene; Person, Anna K; Stollings, Joanna L; Su, Yu; Li, Lang; Chen, You|
|組織名||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.;Medical Intensive Care Unit, Vanderbilt University Medical Center, Nashville,;Department of Computer Science and Engineering, The Ohio State University,;Columbus, Ohio, United States.;Department of Biomedical Informatics, College of Medicine, The Ohio State;University, Columbus, Ohio, United States.;Department of Electrical Engineering & Computer Science, School of Engineering,;Vanderbilt University, Nashville, Tennessee, United States.|