| アブストラクト | A vital aspect of the drug discovery and development process is the identification and filtering of drugs with high risk of dangerous adverse events. Torsade de Pointes (TdP) is one example of an adverse event that requires thorough in vitro and in vivo drug screening. This is because TdP, a tachycardic ventricular arrhythmia, can develop into fatal cardiac events if left unresolved and has been missed during drug development with profound consequences. These factors led to the development of pre-clinical screening guidelines based on the presence of drug induced QT prolongation (QTp), which has been linked to TdP. These guidelines have high sensitivity, but low specificity, as they tend to predict QTp, which precedes TdP events but does not always lead to TdP. Computational models have the potential to improve these prediction methods by bridging the gaps between preclinical and clinical data. This study proposes the use of adverse event reports obtained from the FDA Adverse Event Reporting System as a representation of clinical TdP risk. By incorporating these reports into computational models, a more accurate risk prediction may be developed. |
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| ジャーナル名 | Journal of pharmacological and toxicological methods |
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| Pubmed追加日 | 2019/8/10 |
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| 投稿者 | Ether, Nicholas; Leishman, Derek; Bailie, Marc; Lauver, Adam |
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| 組織名 | Michigan State University, East Lansing, MI 48824, United States.;Michigan State University, East Lansing, MI 48824, United States; Eli Lilly and;Co., Indianapolis, IN 46285, United States.;Michigan State University, East Lansing, MI 48824, United States. Electronic;address: lauverda@msu.edu. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/31398384/ |
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