| アブストラクト | We improved a previous pharmacological target adverse-event (TAE) profile model to predict adverse events (AEs) on US Food and Drug Administration (FDA) drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating AEs from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific AE, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. TAE analysis continues to show promise as a method to predict adverse events at the time of approval. |
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| ジャーナル名 | Clinical pharmacology and therapeutics |
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| Pubmed追加日 | 2020/10/23 |
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| 投稿者 | Schotland, Peter; Racz, Rebecca; Jackson, David B; Soldatos, Theodoros G; Levin, Robert; Strauss, David G; Burkhart, Keith |
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| 組織名 | Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center;for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring,;Maryland, USA.;Molecular Health GmbH, Heidelberg, Germany.;Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research,;US Food and Drug Administration, Silver Spring, Maryland, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/33090463/ |
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