Lee William, Windley Monique J, Vandenberg Jamie I, Hill Adam P
Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia.
St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia.
Front Physiol. 2017 Nov 16;8:934. doi: 10.3389/fphys.2017.00934. eCollection 2017.
Acquired long QT syndrome, mostly as a result of drug block of the Kv11. 1 potassium channel in the heart, is characterized by delayed cardiac myocyte repolarization, prolongation of the T interval on the ECG, syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). In recent years, efforts are underway through the Comprehensive proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on simulations of pharmacological disruption of repolarization. However, drug binding to Kv11.1 is more complex than a simple binary molecular reaction, meaning simple steady state measures of potency are poor surrogates for risk. As a result, there is a plethora of mechanistic detail describing the drug/Kv11.1 interaction-such as drug binding kinetics, state preference, temperature dependence and trapping-that needs to be considered when developing models for risk prediction. In addition to this, other factors, such as multichannel pharmacological profile and the nature of the ventricular cell models used in simulations also need to be considered in the search for the optimum approach. Here we consider how much of mechanistic detail needs to be included for models to accurately predict risk and further, how much of this detail can be retrieved from protocols that are practical to implement in high throughout screens as part of next generation of preclinical drug screening approaches?
获得性长QT综合征主要是由于药物阻断心脏中的Kv11.1钾通道所致,其特征为心肌细胞复极化延迟、心电图上T间期延长、多形性室性心律失常尖端扭转型室速(TdP)导致的晕厥和心源性猝死。近年来,正在通过综合促心律失常分析(CiPA)计划努力开发针对这种药物诱导的心律失常的更好检测方法,部分基于复极药理学破坏的模拟。然而,药物与Kv11.1的结合比简单的二元分子反应更为复杂,这意味着简单的效力稳态测量对于风险而言是较差的替代指标。因此,在开发风险预测模型时,有大量描述药物/Kv11.1相互作用的机制细节需要考虑,如药物结合动力学、状态偏好、温度依赖性和捕获。除此之外,在寻找最佳方法时,还需要考虑其他因素,如多通道药理学特征以及模拟中使用的心室细胞模型的性质。在此,我们考虑模型要准确预测风险需要纳入多少机制细节,以及进一步而言,作为下一代临床前药物筛选方法的一部分,在高通量筛选中实际可行的方案能获取多少此类细节?