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在心脏风险评估模型中整合电生理和机械生物标志物

Lucia Priego¹, Maria Teresa Mora¹, Jordi Llopis-Lorente¹

¹Centro de Innovación e Investigación en Bioingeniería, Universitat Politècnica de València, Valencia, España.

Computer methods and programs in biomedicine

|June 12, 2025

概括

电生理学生物标志物可靠地预测心律失常风险. 机械生物标志物虽然不能改善心律失常的预测,但却揭示了药物诱导的心脏收缩率的显著变化,提供了关键的安全见解.

科学领域:

心血管药理学心血管药理学
计算毒理学计算毒理学
药物安全评估药物安全评估

背景情况:

心脏药物安全对于识别增加心律失常风险的分子至关重要,比如Torsade de Pointes (TdP).
目前的方法主要使用电生理学 (EP) 生物标志物,忽略了药物对心脏收缩性的影响.
整合机械和EP生物标志物可以提高早期心脏风险预测.

研究的目的:

整合电生理学和机械生物标志物,以改善心脏药物安全性评估.
评估组合生物标志物的预测能力,以评估前节律失常和异型风险.
评估诸如通道阻断剂 (CCB) 等化合物的对心脏功能的影响.

主要方法:

利用细胞和in silico 3D人口方法来评估39种化合物.
评估了对电生理学和机械生物标志物的影响,用于预节律失常和异型风险.
开发了使用单独EP生物标记物和与机械生物标记物结合使用的分类模型.

主要成果:

仅EP生物标志物就能提供对Torsade de Pointes (TdP) 风险的可靠预测.
机械生物标志物没有提高TdP风险分类准确性.
机械指标确定了CCB和负义无otropic 剂的显著收缩变化,与射出分数相关.

结论:

电生理学评估仍然有效地预测torsadogenic风险.
机械生物标志物提供了药物诱导的心脏收缩性变化的重要信息.
未来的研究应该增强具有更多数据和复杂几何学的in silico模型,以更好地实现翻译适用性.

关键词:

心脏收缩性是指心脏的收缩性.心脏建模的心脏建模药物安全药物安全模型的人口.有关节律失常的风险.这就是Torsade de Pointes的意义.

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