一个用于基因型特异性心脏毒性风险预测的机器学习平台,使用患者衍生的iPSC-CMs
Yun-Gwi Park1, Na Kyeong Park2, Youngsun Lee3
1Department of Animal Science and Technology, College of Biotechnology and Natural Resources, Chung-Ang University, Anseong, 17546, Republic of Korea.
Journal of advanced research
|July 24, 2025
概括
使用患者衍生的心肌细胞的机器学习模型预测药物诱导的Torsades de Pointes风险. 这个平台增强了药物安全性和精确医学,用于遗传性心脏病.
科学领域:
- 心血管药理学心血管药理学
- 干细胞生物学 干细胞生物学
- 计算生物学 计算生物学
背景情况:
- 药物诱导的Torsades de Pointes (TdP) 需要撤出市场,特别是对于遗传性心脏通道病症患者.
- 患者特异性诱导多能干细胞衍生心肌细胞 (iPSC-CMs) 为研究电生理脆弱性提供了一个模型.
研究的目的:
- 开发一种机器学习 (ML) 平台,用于疾病特异性心脏毒性评估.
- 将iPSC-CM与高通量微电极阵列 (MEA) 记录集成,用于药物查.
主要方法:
- 从长QT综合征 (LQTS) 和布鲁加达综合征 (BrS) 患者中生成和表征iPSC-CMs.
- 暴露iPSC-CM对28种化合物,通过MEA测量电生理反应.
- 使用交叉验证进行训练和验证的ML模型 (ANN,随机森林,XGBoost).
主要成果:
- 使用LQTS iPSC-CMs的人工神经网络 (ANN) 模型在预测TdP风险方面取得了高准确性 (AUC=0.94).
- 确定了不同的药物敏感性:BrS细胞对阻断剂,LQTS细胞对抑制剂.
- 根据疾病特异性概况重新分类的模两可的化合物,验证基因型特异性风险评估.
结论:
- 开发了一个可扩展的,个性化的心脏毒性查平台,使用患者衍生的iPSC-CMs.
- 该平台改善了药物安全预测和监管评估.
- 这种方法推进了对心律失常风险评估的精准医学.
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