Li Shihai, Xu Zili, Guo Mingkun, Li Menglong, Wen Zhining
College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.
College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China; Medical Big Data Center, Sichuan University, Chengdu, Sichuan 610064, China.
Drug Discov Today. 2022 Mar;27(3):831-837. doi: 10.1016/j.drudis.2021.10.009. Epub 2021 Oct 27.
Drug-induced prolongation of the QT interval is common in a variety of pharmaceutical treatments and can lead to serious clinical outcomes. Although substantial efforts have been made to prevent drug-induced QT interval prolongation, the lack of a centralized data source remains the main obstacle to further study of the underlying mechanism and the development of effective prediction strategies. To fill this gap, we propose a schema for stratifying the risk of marketed QT prolonging drugs based on US Food and Drug Administration (FDA)-approved drug labeling and developed a Drug-Induced QT Prolongation Atlas (DIQTA). Potential application of DIQTA was shown by precision dosing in off-label use and therapeutic strategy optimization, as well as the facilitation of artificial intelligence (AI)-based modeling in predictive toxicity.
药物引起的QT间期延长在多种药物治疗中很常见，并可能导致严重的临床后果。尽管已经做出了大量努力来预防药物引起的QT间期延长，但缺乏集中的数据源仍然是进一步研究潜在机制和开发有效预测策略的主要障碍。为了填补这一空白，我们提出了一种基于美国食品药品监督管理局（FDA）批准的药品标签对已上市QT延长药物的风险进行分层的方案，并开发了药物诱导QT延长图谱（DIQTA）。DIQTA在超说明书用药的精准给药和治疗策略优化中的潜在应用，以及在预测毒性方面对基于人工智能（AI）建模的促进作用得到了展示。
