一个心脏电生理学的多尺度计算模型,用于药物诱导的心律失常风险分层
Ana Rahma Yuniarti1,2, Aroli Marcellinus1, Ali Ikhsanul Qauli1,3
1Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Gyeongsangbuk Republic of Korea.
一个单细胞生物标志物 (qNet) 能够有效预测心律失常风险,其表现优于器官水平指标. 结合两种生物标志物为Torsades de Pointes风险分层提供了最小的额外好处.
科学领域:
- 心血管药理学心血管药理学
- 计算生物学 计算生物学
- 药物安全评估 药物安全评估
背景情况:
- 综合性体外前节律失常试验 (CiPA) 使用in silico模拟进行心脏安全.
- 单细胞模拟捕捉了离子变化,但错过了组织层面的复杂性,如导电性和异质性.
- 器官水平的节律失常受单细胞模型中不完全代表的因素的影响.
研究的目的:
- 评估一个多尺度分类器是否能改善Torsades de Pointes (TdP) 的风险分层.
- 为了比较一个结合的生物标志物方法 (单细胞qNet + 器官级模拟QT) 与单个指标.
- 为了确定单细胞与器官水平心脏安全生物标志物的预测值.
主要方法:
- 模拟28种CiPA药物在1-4×Cmax时.
- 从每种药物的2000个单细胞模拟中得出的平均qNet.
- 从3D组织模拟生成平均QT,并使用顺序逻辑回归分析.
主要成果:
- 与平均QT相比,平均qNet在TdP风险分层方面表现优越.
- 将平均QT添加到平均qNet中,在AUC,校准和错误率上提供了微不足道的改善.
- 平均qNet作为一个强大的初级生物标志物出现,多尺度模型只提供增量增益.
结论:
- 电脑心电图衍生QT不足以作为组织水平心律不规律性的唯一预测因素.
- 平均 qNet 是评估心脏安全性的可靠生物标志物.
- 未来的多尺度模型可能会从结合导电和分散特征以及更大的药物队伍中受益.
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