In the past 20 years, a range of structurally unrelated drugs, including antihistamines, antibiotics and antipsychotics, have been withdrawn from the market due to adverse effects on cardiac repolarization - so called acquired long QT syndrome (aLQTS). aLQTS is characterized by prolongation and sometimes morphological deformation of QT segments on the 12-lead electrocardiogram (ECG), syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). Theoretically, aLQTS can occur due to unwanted drug induced modulation of any of the ionic channels that contribute to cardiac repolarization either through direct modulation of channel conductance (Cavero et al., 2000; Perrin et al., 2008a) or up/down regulation of channel trafficking and expression on the cell membrane (Dennis et al., 2007; Ballou et al., 2015). In practice however, the overwhelming majority of these drugs cause aLQTS through blockade of the Kv11.1 potassium channel that carries the rapid component of the delayed rectifier current in the heart (I_Kr) (Perrin et al., 2008b).

As a result of the prevalence of these proarrhythmic side effects, regulatory guidelines have been put in place as part of preclinical drug development to ensure such dangerous compounds do not get to market. In their current form, these guidelines use simple steady-state measures of Kv11.1 inhibitory concentration and action potential prolongation to estimate arrhythmic risk (E14, 2005; S7B, 2005). However, while these steady-state measures of Kv11.1 block are very sensitive (no new proarrhythmic drugs have knowingly come to market since the inception of these guidelines), they are not specific. The link between Kv11.1 block, repolarization delay, and TdP is poorly understood meaning these measures are poor surrogates for actual risk of TdP. Given that not all drugs that block Kv11.1 are going to be proarrhythmic, this has likely resulted in an unnecessarily high attrition rate of drugs in development (Redfern et al., 2003; Sager et al., 2014).

To address this issue, the Comprehensive in vitro Proarrhythmia assay (CiPA) has been proposed as a new safety paradigm in understanding TdP and assessing proarrhythmia risk (Sager et al., 2014). CiPA has two primary objectives: (1) Detailed in vitro electrophysiological characterization of drug interaction with Kv11.1 (and other cardiac ion channels) and integration of this data into in silico models to predict proarrhythmia in simulations of the cardiac action potential and (2) Validation of in silico models using human induced pluripotent stem cell derived cardiac cardiomyocytes (Fermini et al., 2016). Central to the first of these objectives is our understanding of the mechanistic subtleties of how drugs interact with Kv11.1. There are several key factors that contribute to a drug's pharmacological profile and hence contribute to proarrhythmic risk, including the kinetics of drug binding and unbinding, gating-state preference and temperature dependence. These factors are not easily quantified by simple steady-state measures, yet can have significant effects on the measured potency of a drug as well as profound impact on the degree of repolarization delay and the emergence of proarrhythmic markers seen in in silico simulations. For example, Figure 1 demonstrates an in silico simulation of 6,561 theoretical drugs that block Kv11.1 all at calculated IC₅₀ doses and yet the simulated action potential prolongation is significantly varied. Furthermore, whilst Kv11.1 is the major repolarizing current in the cardiac action potential, there are multiple other currents that contribute to repolarization, a concept known as repolarization reserve (Roden, 1998). In this context, the evolution of drug induced TdP may involve block of multiple ion channel currents and a drug's affinity for a variety of targets may modify the proarrhythmic risk associated with its block of Kv11.1. Determination of the proarrhythmic risk profile of Kv11.1 blocking drugs is therefore a multifaceted problem that goes beyond simple measures of potency. As a result, using in silico means to predict the risk associated with individual drugs is a complex process for which the optimal implementation remains to be decided upon. In this article we will consider what level of mechanistic detail describing the interaction between drug and ion channel target 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 preclinical development?

In many cases, a drug's interactions with its ion channel pharmacological target can be described as a simple bimolecular reaction according to the equation:

The complexity of Kv11.1-drug interactions therefore has clear implications for how the field should approach both measuring of these phenomena in vitro, as well as how we describe them using in silico models that can be used for risk prediction. Current guidelines stipulate the I_Kr current should be assayed but do not specify voltage protocols, or details, such as temperature or cellular expression systems. As a result there is a lack of standardization in how block of I_Kr current is measured (Fermini et al., 2016). Redfern et al. (2003) proposed a 30-fold safety margin between the measured IC₅₀ of a drug and its maximum unbound plasma concentration (C_max) to distinguish between safe and unsafe drugs. However, many studies have shown variance in drug potency dependent as a function of temperature and voltage protocol (Kirsch et al., 2004; Yao et al., 2005; Milnes et al., 2010) and this safety margin becomes unreliable if a true IC₅₀ value cannot be agreed upon. For example, the reported IC₅₀ values for cisapride, span a 60-fold range (Potet et al., 2001; Rezazadeh et al., 2004). One approach therefore is to use in silico modeling to “fine-tune” in vitro experimental protocols to more closely mimic in vivo conditions (Ellinwood et al., 2017). However, even if a standardized protocol could be agreed upon, such as using a physiological cardiac action potential to reproduce the state transitions of Kv11.1 that are seen during the cardiac cycle, this would not take into account the impact of variations in heart rate or action potential prolongation which are paramount to the highly dynamic binding kinetics of the drug/Kv11.1 interaction. For example the data in Figures 3C–F shows two drugs with equal and opposite gating state preference. At 1,000 ms pacing cycle length the APD₉₀ differs by 1 ms. However, at 500 ms pacing cycle length the open state preference drug prolongs the APD₉₀ by 35 ms more than the inactivated state preference drug; while at 2,000 ms pacing cycle length the open state preference drug prolongs the APD₉₀ by 53 ms less than the inactivated state preference drug. (Figures 3C–F) Moreover, these standardized conditions also lack the ability to predict variations in physiological conditions, such as hyper/hypo- kalaemia (Wang et al., 1997) or low pH (and the consequent changes in protonation of drug compounds) (Moreno et al., 2011; Wang et al., 2016), all of which are known to affect the state-dependence of drug binding. An alternative therefore, is to use non-physiological voltage protocols to accurately constrain in silico models of drug binding (Hill et al., 2014; Beattie et al., 2017) that can then be used in silico to evaluate a limitless range of physiological conditions.

This approach however brings with it a new set of challenges. There exists a wide variety of models of Kv11.1/drug interaction in the literature, each with different structures and each constrained by different in vitro datasets. Furthermore, they differ substantially in their ability to describe the key features of Kv11.1/drug binding dynamics discussed above, such as kinetics and state dependence (Figures 4A,B; Di Veroli et al., 2013; Hill et al., 2014), drug trapping (Figure 4C; Li et al., 2017) and temperature dependence (Figure 4D; Windley et al., 2016). While each of these models represents a good description of drug binding under certain conditions, they differ significantly in their predicted state occupancies over any given voltage protocol (Figures 4Aii,Cii), so will result in a difference in state-dependent drug binding. As yet, no Markov model provides a universal solution that we can be sure would be useful for prediction of proarrhythmic risk. As a result, further complexity may need to be added, or the existing models constrained with new in vitro data, to improve the model's predictive accuracy (Fermini et al., 2016). The issue of what is the optimum approach to measuring and modeling drug binding to Kv11.1 is therefore an open question and the optimum balance between how much and what type of data is required to constrain in silico models and what is practical to do in the context of high throughput data acquisition is yet to be determined.

The final piece of the puzzle that needs to be considered in developing ventricular cell simulations for in silico risk prediction is the role of multichannel pharmacology, and how this contributes to characteristics of the cellular action potential. Whilst Kv11.1 blockade is certainly critical to understanding aLQTS and drug induced TdP, it is not the sole determinant of arrhythmogenesis since drugs that block Kv11.1 can often also block other cardiac ion channels (Bril et al., 1996; Aiba et al., 2005; Wu et al., 2008; Vicente et al., 2015) to suppress or promote arrhythmogenesis (Fermini et al., 2016). An evaluation of the potency of a panel of 30 drugs against the seven major currents that contribute to repolarization demonstrated that the primary pharmacological targets that determine proarrhythmia were I_Kr (Kv11.1), I_CaL (Cav 1.2), and I_NaL (Nav1.5-late). Furthermore, drugs with high TdP risk tended toward unopposed Kv11.1 block, while drugs with low TdP risk had similar or higher potency for the inward currents (I_CaL and I_NaL) in conjunction with Kv11.1 block (Crumb et al., 2016). These multichannel pharmacological profiles are reflected in the morphology of the AP waveform (and hence the surface ECG). The AP waveform is formed through summed contribution of all the individual ionic currents in the cardiac myocytes. As a result, varied drug block of different ionic currents will result in a spectrum of AP morphologies and durations, which is idiosyncratic to individual drugs that manifests in vivo as differences in QT duration as well as T wave morphology (Figures 5Ci–Cii; Vicente et al., 2015). Critically for in silico risk prediction, this “AP morphology signature” is in turn linked to the drug's pro-arrhythmic potential and potentially can be used to predict TdP.

In this regard, drug induced morphological changes in the cardiac AP have been shown to correlate with risk of TdP (Hondeghem et al., 2001a). Specifically, this study suggested the presence of AP “triangulation” (slow repolarization, without a distinct plateau or rapid repolarization phase) was a marker of risk of drug induced TdP. Several single drug studies exemplify this point and support the link with multichannel pharmacology. Drugs that block Kv11.1 without significant inward current block, such as Sotalol (Milberg et al., 2004) and dofetilide (Osadchii, 2012; Figure 5Aii), produce AP triangulation in addition to prolongation, (Figure 5Bii) and are considered high TdP risk drugs. Conversely, Verapamil, a potent blocker of Cav1.2 as well as Kv11.1 (Figure 5Ai), does not manifest in triangulation or prolongation of the AP (Figure 5Bi) and is considered a low TdP-risk drug (Aiba et al., 2005). Similarly, other drugs with multichannel pharmacological profiles, such as ranolazine (Jia et al., 2011) and tolterodine (Martin et al., 2006) demonstrate dose dependent AP prolongation without AP triangulation and are also considered low risk. It is clear therefore, that multichannel pharmacology, and its manifestation in morphology of the AP, is an important detail that must be considered for risk prediction.

In silico modeling again provides an ideal solution to integrating pharmacological data from multiple cardiac ion channels. Indeed, recent studies by Li et al. (2017) and Dutta et al. (2017) have shown the value of this approach and demonstrated that incorporating Cav1.2 and Nav1.5-late block into action potential simulations improves arrhythmia risk prediction (Yang et al., 2016; Li et al., 2017). However, in a similar vein to that discussed above for models of the Kv11.1/drug interaction, there are several models of the ventricular cardiac action potential that have been proposed in the literature including the ten Tusscher 2006 (TT06) (Ten Tusscher and Panfilov, 2006), Grandi-Bers 2010 (GB10) (Grandi et al., 2010), and O'hara Rudy 2011 (ORD11) models (O'Hara et al., 2011). Population based studies using these cell models have allowed interrogation of how the variation in repolarization reserve that occurs as a result of differential expression of ion channels between individuals can influence predicted drug effects as well as develop our understanding of multichannel pharmacology (Sobie, 2009; Lancaster and Sobie, 2016). However, each of the models is considerably different in relation to the conductance levels of individual cardiac ion channels. As a result, predictions around APD prolongation and emergence of proarrhythmic markers that each of the models make in response to drug block are significantly different (Mirams et al., 2014) and do not reproduce in vivo data (Britton et al., 2017). For example, Mann et al showed that 50% inhibition of Kv11.1 caused 113, 22, and 34 ms prolongation of APD₉₀ for ORD11, TT06 and GB10 respectively (Mann et al., 2016). This issue is being considered by the field and recent efforts have focused on refining cell models by rescaling their ionic conductances using either patient data from subjects with various subtypes of the long QT syndrome (Mann et al., 2016) or published drug data (Britton et al., 2013; Dutta et al., 2016). Even so, significant disparity still exists between the “optimized” versions of the cell models, meaning the differences in predicted risk that result from using different models are likely to outweigh, or at least match, the differences associated with descriptions of the drug/channel interaction. It may also prove to be true that similar mechanistic descriptors that are becoming routine for drug binding to Kv11.1, such as kinetics and state dependence, also need to be incorporated for other cardiac ion channels for optimum risk prediction. However, the benefit of this relative to the cost of acquiring the data may preclude such an approach. What is clear, is that each of these facets of in silico risk prediction—the Markov descriptions of drug/channel interaction as well as the model of the ventricular cell in which they are incorporated, should each be considered as a priority for the field.

Understanding the intricacies of the Kv11.1/drug interaction and optimizing our approaches to measuring and modeling these characteristics is critical to developing better preclinical in silico risk prediction. In doing this it is important to remember that all models are simplifications. Therefore, the challenge is to determine how much information needs to be included to make them useful rather than how much information is needed to make them accurate for every drug scenario, which would potentially necessitate the collection of very large amounts of data that may be redundant for many drugs. Given the potential significance of factors, such as drug binding kinetics, temperature dependence, state dependence and multichannel pharmacology discussed above, it seems clear that these factors need to be included at some level in models of drug binding. The major challenge faced by the field in the short term is determining what level of detail is necessary, and balancing this against the practicalities of data acquisition in high throughout screens.

WL, MW, JV, and AH all contributed to planning, writing and editing of the manuscript and figures contained herein.