In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details

Abstract

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 in vitro proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on in silico 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 in silico 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 in silico approach. Here we consider how much of mechanistic detail needs to be included for in silico 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 in silico drug screening approaches?

Protocol-dependent differences in IC50 values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding

“…A recent publication from the Comprehensive in vitro Proarrhythmia assay (CiPA) initiative acknowledged this issue and noted that it is not possible to predict protocol dependence in advance (Fermini et al, 2016). Therefore, if a 'true' IC50 value cannot be reliably determined, then a safety margin is not an effective way of identifying and eliminating risk (Lee et al, 2017).…”

“…One of CiPA's key objectives is to use detailed in vitro electrophysiological characterization of drug interaction with Kv11.1 to build in silico models to predict arrhythmia risk. One factor that is likely important for this approach, is understanding the state dependence of binding (Lee et al, 2017). It has been shown that for some drugs, the affinity can be as much as 30-fold greater for the inactivated state (Ki) relative to the open state (Ko) (Suessbrich et al, 1997;Ficker et al, 1998;Perrin, Kuchel, et al, 2008;Wu et al, 2015) whereas other drugs have no apparent state preference (Hill et al, 2014).…”

“…It has been shown that for some drugs, the affinity can be as much as 30-fold greater for the inactivated state (Ki) relative to the open state (Ko) (Suessbrich et al, 1997;Ficker et al, 1998;Perrin, Kuchel, et al, 2008;Wu et al, 2015) whereas other drugs have no apparent state preference (Hill et al, 2014). Furthermore, previous studies have shown that two drugs with opposite state preference (10 4 fold difference in open vs inactivated state affinity) but the same IC50 can cause up to 56ms (~15%) difference in the extent of prolongation of the action potential duration (APD) at an IC50 dose (Lee et al, 2017). Thus, state preference is an important consideration when determining whether a Kv11.1-drug interaction is likely to be proarrhythmic.…”

Keeping it short and (not so) simple: characterizing hERG kinetics with sinusoidal waves

“…; Lee et al . ). Thus, an important next line of inquiry should be to determine whether this kind of approach can help to assess the kinetics (i.e.…”

“…Indeed, Lee et al (2017) recently discussed some of the challenges and limitations involved in modelling of the hERG channel and emphasized the need for improved voltage-clamp protocols to characterize drug-channel interaction in in vitro experiments. Furthermore, several recent studies have highlighted the importance of accurately describing statedependence and kinetics of drug binding, instead of relying on steady-state concentration-response curves (Di Veroli et al 2013;Grandi et al 2017;Lee et al 2017). Thus, an important next line of inquiry should be to determine whether this kind of approach can help to assess the kinetics (i.e.…”