Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System
Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System
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DOI：
10.1016/j.bpj.2019.07.029
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发表时间：
2019-12-17
影响因子：
3.4
通讯作者：
Wang, Ken
中科院分区：
文献类型：
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作者：
Lei, Chon Lok;Clerx, Michael;Wang, Ken
预测药物如何影响心律是药物开发的关键一步，需要深入了解化合物对离子通道的作用。体外 hERG 通道电流记录是评估小分子致心律失常潜力的重要步骤，现在通常使用自动化高通量膜片钳平台进行。这些机器可以执行针对特定门控过程的传统电压钳协议，但完全表征电流所需的协议阵列通常太长，无法应用于单个单元。最近推出了具有此功能的较短的高信息协议，但它们通常与高吞吐量平台不兼容。我们提出了一种新的 15 秒协议来表征 hERG (Kv11.1) 动力学，适用于手动和高通量系统。我们通过将其应用于稳定表达 hERG1a 的中国仓鼠卵巢细胞，展示了其在 Nanion SyncroPatch 384PE（一种 384 孔自动化膜片钳平台）上的用途。根据这些记录，我们在 25 摄氏度下构建了 hERG 模型的 124 个细胞特异性变体/参数化。在每个细胞中运行另外八个独立的协议，用于验证模型预测。然后，我们使用分层贝叶斯模型组合实验记录，我们用该模型来量化模型参数的不确定性及其在细胞之间的变异性；我们使用这个模型来提出变异的原因。这项研究展示了一种测量和量化不确定性的稳健方法，并表明使用高通量系统定量快速捕获完整 hERG 通道动力学是可能且实用的。
Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug development and requires a deep understanding of a compound's action on ion channels. In vitro hERG channel current recordings are an important step in evaluating the proarrhythmic potential of small molecules and are now routinely performed using automated high-throughput patch-clamp platforms. These machines can execute traditional voltage-clamp protocols aimed at specific gating processes, but the array of protocols needed to fully characterize a current is typically too long to be applied in a single cell. Shorter high-information protocols have recently been introduced that have this capability, but they are not typically compatible with high-throughput platforms. We present a new 15 second protocol to characterize hERG (Kv11.1) kinetics, suitable for both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, by applying it to Chinese hamster ovary cells stably expressing hERG1a. From these recordings, we construct 124 cell-specific variants/parameterizations of a hERG model at 25 degrees C. A further eight independent protocols are run in each cell and are used to validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we use to quantify the uncertainty in the model parameters, and their variability from cell-to-cell; we use this model to suggest reasons for the variability. This study demonstrates a robust method to measure and quantify uncertainty and shows that it is possible and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly.