Elkins Ryan C, Davies Mark R, Brough Stephen J, Gavaghan David J, Cui Yi, Abi-Gerges Najah, Mirams Gary R
Global Safety Pharmacology, Global Safety Assessment, AstraZeneca, Alderley Park SK10 4TG, UK.
J Pharmacol Toxicol Methods. 2013 Jul-Aug;68(1):112-22. doi: 10.1016/j.vascn.2013.04.007. Epub 2013 May 5.
Unwanted drug interactions with ionic currents in the heart can lead to an increased pro-arrhythmic risk to patients in the clinic. It is therefore a priority for safety pharmacology teams to detect block of cardiac ion channels, and new technologies have enabled the development of automated and high-throughput screening assays using cell lines. As a result of screening multiple ion-channels there is a need to integrate information, particularly for compounds affecting more than one current, and mathematical electrophysiology in-silico action potential models are beginning to be used for this.
We quantified the variability associated with concentration-effect curves fitted to recordings from high-throughput Molecular Devices IonWorks® Quattro™ screens when detecting block of I(Kr) (hERG), I(Na) (NaV1.5), I(CaL) (CaV1.2), I(Ks) (KCNQ1/minK) and I(to) (Kv4.3/KChIP2.2), and the Molecular Devices FLIPR® Tetra fluorescence screen for I(CaL) (CaV1.2), for control compounds used at AstraZeneca and GlaxoSmithKline. We examined how screening variability propagates through in-silico action potential models for whole cell electrical behaviour, and how confidence intervals on model predictions can be estimated with repeated simulations.
There are significant levels of variability associated with high-throughput ion channel electrophysiology screens. This variability is of a similar magnitude for different cardiac ion currents and different compounds. Uncertainty in the Hill coefficients of reported concentration-effect curves is particularly high. Depending on a compound's ion channel blocking profile, the uncertainty introduced into whole-cell predictions can become significant.
Our technique allows confidence intervals to be placed on computational model predictions that are based on high-throughput ion channel screens. This allows us to suggest when repeated screens should be performed to reduce uncertainty in a compound's action to acceptable levels, to allow a meaningful interpretation of the data.
药物与心脏离子电流发生不良相互作用会增加临床患者的心律失常风险。因此,检测心脏离子通道阻滞是安全药理学团队的首要任务,新技术推动了利用细胞系进行自动化高通量筛选检测方法的发展。由于要对多个离子通道进行筛选,所以需要整合信息,尤其是对于影响多种电流的化合物,数学电生理计算机动作电位模型正开始用于此目的。
我们对在检测I(Kr)(hERG)、I(Na)(NaV1.5)、I(CaL)(CaV1.2)、I(Ks)(KCNQ1/minK)和I(to)(Kv4.3/KChIP2.2)时,与高通量分子设备IonWorks® Quattro™ 筛选记录拟合的浓度-效应曲线相关的变异性进行了量化,以及对阿斯利康和葛兰素史克使用的对照化合物,利用分子设备FLIPR® Tetra荧光筛选检测I(CaL)(CaV1.2)的变异性进行了量化。我们研究了筛选变异性如何通过用于全细胞电行为的计算机动作电位模型传播,以及如何通过重复模拟估计模型预测的置信区间。
高通量离子通道电生理筛选存在显著水平的变异性。这种变异性对于不同的心脏离子电流和不同的化合物而言幅度相似。报道的浓度-效应曲线的希尔系数的不确定性特别高。根据化合物的离子通道阻滞情况,引入全细胞预测的不确定性可能会变得很大。
我们的技术能够为基于高通量离子通道筛选的计算模型预测设定置信区间。这使我们能够建议何时应进行重复筛选,以将化合物作用的不确定性降低到可接受水平,从而对数据进行有意义的解读。