Molecular Devices LLC, Sunnyvale, CA 94089, USA.
Toxicol Appl Pharmacol. 2013 Dec 15;273(3):500-7. doi: 10.1016/j.taap.2013.09.017. Epub 2013 Oct 1.
Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes show promise for screening during early drug development. Here, we tested a hypothesis that in vitro assessment of multiple cardiomyocyte physiological parameters enables predictive and mechanistically-interpretable evaluation of cardiotoxicity in a high-throughput format. Human iPSC-derived cardiomyocytes were exposed for 30 min or 24 h to 131 drugs, positive (107) and negative (24) for in vivo cardiotoxicity, in up to 6 concentrations (3 nM to 30 uM) in 384-well plates. Fast kinetic imaging was used to monitor changes in cardiomyocyte function using intracellular Ca(2+) flux readouts synchronous with beating, and cell viability. A number of physiological parameters of cardiomyocyte beating, such as beat rate, peak shape (amplitude, width, raise, decay, etc.) and regularity were collected using automated data analysis. Concentration-response profiles were evaluated using logistic modeling to derive a benchmark concentration (BMC) point-of-departure value, based on one standard deviation departure from the estimated baseline in vehicle (0.3% dimethyl sulfoxide)-treated cells. BMC values were used for cardiotoxicity classification and ranking of compounds. Beat rate and several peak shape parameters were found to be good predictors, while cell viability had poor classification accuracy. In addition, we applied the Toxicological Prioritization Index (ToxPi) approach to integrate and display data across many collected parameters, to derive "cardiosafety" ranking of tested compounds. Multi-parameter screening of beating profiles allows for cardiotoxicity risk assessment and identification of specific patterns defining mechanism-specific effects. These data and analysis methods may be used widely for compound screening and early safety evaluation in drug development.
人诱导多能干细胞(iPSC)衍生的心肌细胞在药物早期开发的筛选中显示出前景。在这里,我们测试了一个假设,即在体外评估多个心肌细胞生理参数能够以高通量格式对心脏毒性进行预测和具有机制解释的评估。人 iPSC 衍生的心肌细胞在 384 孔板中以高达 6 个浓度(3 nM 至 30 μM)暴露于 131 种药物 30 分钟或 24 小时,这些药物对体内心脏毒性呈阳性(107 种)和阴性(24 种)。快速动力学成像用于通过与跳动同步的细胞内 Ca(2+)通量读数来监测心肌细胞功能的变化,并监测细胞活力。使用自动数据分析收集了许多心肌细胞跳动的生理参数,例如跳动率、峰形状(幅度、宽度、上升、下降等)和规律性。使用逻辑回归模型评估浓度-反应曲线,根据与 0.3%二甲基亚砜(DMSO)处理的细胞的估计基线相比,一个标准差的偏差,得出基准浓度(BMC)起始点值。BMC 值用于心脏毒性分类和化合物排序。跳动率和几个峰形状参数被发现是很好的预测因子,而细胞活力的分类准确性较差。此外,我们应用毒性优先指数(ToxPi)方法来整合和显示从许多收集参数中得出的“心脏安全性”测试化合物的排序数据。对跳动曲线的多参数筛选可用于心脏毒性风险评估和确定定义机制特异性作用的特定模式。这些数据和分析方法可广泛用于化合物筛选和药物开发中的早期安全性评估。