药物引起的心脏毒性是药物失效的主要原因之一,这导致随后退出药物开发。因此,在药物开发的早期阶段确定潜在的毒性候选物非常重要.人诱导的多能干细胞衍生的心肌细胞(hiPSC-CM)是评估心律失常候选化合物的有用工具。然而,尚未完全建立使用hiPSC-CM预测尖端扭转型室性心动过速(TdP)风险的合适模型.本研究旨在建立基于hiPSC-CM的预测TdP模型。以体外心律失常综合试验(CiPA)推荐的28个化合物为训练集,在不同风险组建立模型,高风险和中等风险人群与低风险人群。随后,电生理反应的6个终点被用作潜在的模型预测因子.准确性,以灵敏度和曲线下面积(AUC)作为模型的评价指标,以已知TdP风险的7个化合物进行模型鉴别和校正。结果表明,在这七个模型中,逻辑回归和AdaBoost模型的AUC较高,在训练集和测试集上差异不大,表明判别能力和模型稳定性良好,分别。因此,这两个模型被当作子模型,配置相似的权重,并使用软投票策略构建新的TdP风险预测模型.分类的准确性,新模型在训练集上的灵敏度和AUC分别为0.93、0.95和0.92,分别和测试集上的所有1.00,在训练集和测试集上都表现出良好的辨别能力。在训练集和测试集上,风险阈值定义为0.50,预测结果和观察结果之间的一致性分别为92.8和100%。分别。总的来说,本研究建立了基于hiPSC-CM的TdP风险预测模型,该模型可作为复合诱发心律失常的有效预测工具.
Drug-induced cardiotoxicity is one of the main causes of drug failure, which leads to subsequent withdrawal from pharmaceutical development. Therefore, identifying the potential toxic candidate in the early stages of drug development is important. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a useful tool for assessing candidate compounds for arrhythmias. However, a suitable model using hiPSC-CMs to predict the risk of torsade de pointes (TdP) has not been fully established. The present study aimed to establish a predictive TdP model based on hiPSC-CMs. In the current study, 28 compounds recommended by the Comprehensive in vitro Proarrhythmia Assay (CiPA) were used as training set and models were established in different risk groups, high- and intermediate-risk versus low-risk groups. Subsequently, six endpoints of electrophysiological responses were used as potential model predictors. Accuracy, sensitivity and area under the curve (AUC) were used as evaluation indices of the models and seven compounds with known TdP risk were used to verify model differentiation and calibration. The results showed that among the seven models, the AUC of logistic regression and AdaBoost model was higher and had little difference in both training and test sets, which indicated that the discriminative ability and model stability was good and excellent, respectively. Therefore, these two models were taken as submodels, similar weight was configured and a new TdP risk prediction model was constructed using a soft voting strategy. The classification accuracy, sensitivity and AUC of the new model were 0.93, 0.95 and 0.92 on the training set, respectively and all 1.00 on the test set, which indicated good discrimination ability on both training and test sets. The risk threshold was defined as 0.50 and the consistency between the predicted and observed results were 92.8 and 100% on the training and test sets, respectively. Overall, the present study established a risk prediction model for TdP based on hiPSC-CMs which could be an effective predictive tool for compound-induced arrhythmias.