Beattie Kylie A, Luscombe Chris, Williams Geoff, Munoz-Muriedas Jordi, Gavaghan David J, Cui Yi, Mirams Gary R
Computational Biology, Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.
J Pharmacol Toxicol Methods. 2013 Jul-Aug;68(1):88-96. doi: 10.1016/j.vascn.2013.04.004. Epub 2013 Apr 25.
Drugs that prolong the QT interval on the electrocardiogram present a major safety concern for pharmaceutical companies and regulatory agencies. Despite a range of assays performed to assess compound effects on the QT interval, QT prolongation remains a major cause of attrition during compound development. In silico assays could alleviate such problems. In this study we evaluated an in silico method of predicting the results of a rabbit left-ventricular wedge assay.
Concentration-effect data were acquired from either: the high-throughput IonWorks/FLIPR; the medium-throughput PatchXpress ion channel assays; or QSAR, a statistical IC50 value prediction model, for hERG, fast sodium, L-type calcium and KCNQ1/minK channels. Drug block of channels was incorporated into a mathematical differential equation model of rabbit ventricular myocyte electrophysiology through modification of the maximal conductance of each channel by a factor dependent on the IC50 value, Hill coefficient and concentration of each compound tested. Simulations were performed and agreement with experimental results, based upon input data from the different assays, was evaluated.
The assay was found to be 78% accurate, 72% sensitive and 81% specific when predicting QT prolongation (>10%) using PatchXpress assay data (77 compounds). Similar levels of predictivity were demonstrated using IonWorks/FLIPR data (121 compounds) with 78% accuracy, 73% sensitivity and 80% specificity. QT shortening (<-10%) was predicted with 77% accuracy, 33% sensitivity and 90% specificity using PatchXpress data and 71% accuracy, 42% sensitivity and 81% specificity using IonWorks/FLIPR data. Strong quantitative agreement between simulation and experimental results was also evident.
The in silico action potential assay demonstrates good predictive ability, and is suitable for very high-throughput use in early drug development. Adoption of such an assay into cardiovascular safety assessment, integrating ion channel data from routine screens to infer results of animal-based tests, could provide a cost- and time-effective cardiac safety screen.
延长心电图QT间期的药物是制药公司和监管机构主要关注的安全问题。尽管进行了一系列检测以评估化合物对QT间期的影响,但QT间期延长仍是化合物研发过程中导致项目失败的主要原因。计算机模拟检测可以缓解此类问题。在本研究中,我们评估了一种预测兔左心室楔形检测结果的计算机模拟方法。
浓度效应数据通过以下方式获取:高通量的IonWorks/FLIPR;中通量的PatchXpress离子通道检测;或QSAR(一种hERG、快速钠通道、L型钙通道和KCNQ1/minK通道的IC50值统计预测模型)。通过根据每种测试化合物的IC50值、希尔系数和浓度,对每个通道的最大电导率进行因子修正,将药物对通道的阻断纳入兔心室肌细胞电生理的数学微分方程模型中。进行模拟,并根据来自不同检测的输入数据评估与实验结果的一致性。
当使用PatchXpress检测数据(77种化合物)预测QT间期延长(>10%)时,该检测的准确率为78%,灵敏度为72%,特异性为81%。使用IonWorks/FLIPR数据(121种化合物)时,也显示出类似的预测水平,准确率为78%,灵敏度为73%,特异性为80%。使用PatchXpress数据预测QT间期缩短(<-10%)时,准确率为77%,灵敏度为33%,特异性为90%;使用IonWorks/FLIPR数据时,准确率为71%,灵敏度为42%,特异性为81%。模拟结果与实验结果之间也存在明显的强定量一致性。
计算机模拟动作电位检测显示出良好的预测能力,适用于药物早期研发中的超高通量应用。将这种检测方法应用于心血管安全性评估,整合常规筛选中的离子通道数据以推断基于动物测试的结果,可以提供一种具有成本效益和时间效益的心脏安全性筛选方法。