Investigating dynamic protocol-dependence of hERG potassium channel inhibition at 37°C: Cisapride versus dofetilide

Optimizing experimental designs for model selection of ion channel drug-binding mechanisms

The rapid delayed rectifier current carried by the human Ether-à-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction, which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug compound binds to hERG can help reduce uncertainty when quantifying pro-arrhythmic risk. In this study, we introduce a methodology for optimizing experimental voltage protocols to produce data that enable different proposed models for the drug-binding mechanism to be distinguished. We demonstrate the performance of this methodology via a synthetic data study. If the underlying model of hERG current is known exactly, then the optimized protocols generated show noticeable improvements in our ability to select the true model when compared with a simple protocol used in previous studies. However, if the model is not known exactly, and we assume a discrepancy between the data-generating hERG model and the hERG model used in fitting the models, then the optimized protocols become less effective in determining the 'true' binding dynamics. While the introduced methodology shows promise, we must be careful to ensure that, if applied to a real data study, we have a well-calibrated model of hERG current gating.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

Inhibition of hERG K channels by verapamil at physiological temperature: Implications for the CiPA initiative

The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative reassesses using the inhibition of hERG potassium channels by drugs as the major determinant for the potential to cause drug-induced Torsades de Pointes (TdP) cardiac arrhythmias. Here we report our findings on the next phase of CiPA: Determination of hERG inhibitory properties using the standard CiPA-defined data acquisition protocol, here called the standard protocol, at physiological temperature (37 degrees Celsius). To do this, we measured inhibition of hERG1a potassium channels stably expressed in HEK293 cells by the small molecule verapamil, using manual whole-cell patch-clamp electrophysiology recordings with the standard protocol, which is characterized, in part, by a series of 10 s duration voltage steps to 0 mV, ultimately leading to a cumulative recording time of approximately 30 min. Using the standard protocol, we measured an IC50 for verapamil of 225 nM, a Hill coefficient of 1, and time constant of inhibition at 0 mV of 0.64 s. But, using the standard protocol resulted in a very low (5 %) experimental success rate per cell, which had low practicality for future experiments. To address the 5 % success rate, we generated a revised protocol characterized, in part, by a series of 3 s duration voltage steps to 0 mV, leading to a cumulative recording time of approximately 10 min. Using the revised protocol, we found an IC50 for verapamil of 252 nM, a Hill coefficient of 0.8, and time constant of inhibition at 0 mV of 0.67 s. The values measured with the revised protocol were similar to those measured using the standard protocol and, furthermore, our success rate using the revised protocol rose to 25 %, an increase of 5-fold over the standard protocol, and more in line with the success rate for biophysical studies. In summary, we captured key pharmacological data for subsequent analysis in CiPA using a revised protocol with an increased success rate and an overall enhanced feasibility and practicality. We propose that the revised protocol may be more pragmatic for generation of some hERG channel drug inhibition data for CiPA and other regulatory sciences.

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration

BACKGROUND AND OBJECTIVE

Assessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation.

METHODS

Experiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration.

RESULTS

The experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models.

CONCLUSIONS

These new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug-hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

In vitro to in vivo extrapolation from 3D hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrhythmia risk assessment

Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. In vitro human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing in vitro and in vivo models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established 3D human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted in vitro to in vivo therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for 2 physiologically relevant metrics of proarrhythmic risk (i.e. action potential duration and triangulation assessed by optical mapping) across concentrations spanning 3 orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of 4 compounds with a range of known proarrhythmic risk profiles (i.e. quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human-induced pluripotent stem cell-based 3D cardiac tissue models as new approach methodologies that enable in vitro proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.

Supporting an integrated QTc risk assessment using the hERG margin distributions for three positive control agents derived from multiple laboratories and on multiple occasions

BACKGROUND

Determination of a drug's potency in blocking the hERG channel is an established safety pharmacology study. Best practice guidelines have been published for reliable assessment of hERG potency. In addition, a set of plasma concentration and plasma protein binding fraction data were provided as denominators for margin calculations. The aims of the current analysis were five-fold: provide data allowing creation of consistent denominators for the hERG margin distributions of the key reference agents, explore the variation in hERG margins within and across laboratories, provide a hERG margin to 10 ms QTc prolongation based on several newer studies, provide information to use these analyses for reference purposes, and provide recommended hERG margin 'cut-off' values.

METHODS

The analyses used 12 hERG IC50 'best practice' data sets (for the 3 reference agents). A group of 5 data sets came from a single laboratory. The other 7 data sets were collected by 6 different laboratories.

RESULTS

The denominator exposure distributions were consistent with the ICH E14/S7B Training Materials. The inter-occasion and inter-laboratory variability in hERG IC50 values were comparable. Inter-drug differences were most important in determining the pooled margin variability. The combined data provided a robust hERG margin reference based on best practice guidelines and consistent exposure denominators. The sensitivity of hERG margin thresholds were consistent with the sensitivity described over the course of the last two decades.

CONCLUSION

The current data provide further insight into the sensitivity of the 30-fold hERG margin 'cut-off' used for two decades. Using similar hERG assessments and these analyses, a future researcher can use a hERG margin threshold to support a negative QTc integrated risk assessment.

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk

BACKGROUND AND PURPOSE

Drug-induced reduction of the rapid delayed rectifier potassium current carried by the human Ether-à-go-go-Related Gene (hERG) channel is associated with increased risk of arrhythmias. Recent updates to drug safety regulatory guidelines attempt to capture each drug's hERG binding mechanism by combining in vitro assays with in silico simulations. In this study, we investigate the impact on in silico proarrhythmic risk predictions due to uncertainty in the hERG binding mechanism and physiological hERG current model.

EXPERIMENTAL APPROACH

Possible pharmacological binding models were designed for the hERG channel to account for known and postulated small molecule binding mechanisms. After selecting a subset of plausible binding models for each compound through calibration to available voltage-clamp electrophysiology data, we assessed their effects, and the effects of different physiological models, on proarrhythmic risk predictions.

KEY RESULTS

For some compounds, multiple binding mechanisms can explain the same data produced under the safety testing guidelines, which results in different inferred binding rates. This can result in substantial uncertainty in the predicted torsade risk, which often spans more than one risk category. By comparison, we found that the effect of a different hERG physiological current model on risk classification was subtle.

CONCLUSION AND IMPLICATIONS

The approach developed in this study assesses the impact of uncertainty in hERG binding mechanisms on predictions of drug-induced proarrhythmic risk. For some compounds, these results imply the need for additional binding data to decrease uncertainty in safety-critical applications.

Whole-Cell Configuration of the Patch-Clamp Technique in the hERG Channel Assay

In vitro electrophysiological safety studies have become an integral part of the drug development process because, in many instances, compound-induced QT prolongation has been associated with a direct block of human ether-a-go-go-related gene (hERG) potassium channels or their native current, the rapidly activating delayed rectifier potassium current (IKr ). Therefore, according to the ICH S7B guideline, the in vitro hERG channel patch-clamp assay is commonly used as an early screen to predict the ability of a compound to prolong the QT interval prior to first-in-human testing. The protocols described in this article are designed to assess the effects of acute or long-term exposure to new chemical entities on the amplitude of IKr in HEK293 cells stably transfected with the hERG channel (whole-cell configuration of the patch-clamp technique). Examples of results obtained with moxifloxacin, terfenadine, arsenic, pentamidine, erythromycin, and sotalol are provided for illustrative purposes. © 2024 Wiley Periodicals LLC. Basic Protocol: Measurement of the acute effects of test items in the hERG channel test Alternate Protocol: Measurement of the long-term effects of test items in the hERG channel test.

The New S7B/E14 Q&A Document Provides Additional Opportunities to Replace the Thorough QT Study

Since 2015, concentration-QTc (C-QTc) analysis has been used to exclude the possibility that a drug has a concerning effect on the QTc interval. This has enabled the replacement of the designated thorough QT (TQT) study with serial electrocardiograms (ECGs) in routine clinical pharmacology studies, such as the first-in-human (FIH) study. The E14 revision has led to an increased proportion of FIH studies with the added objective of QT evaluation, with the intention of replacing the TQT study. With the more recent revision of the S7B/E14 Q&A document in February 2022, nonclinical assays/studies can be brought into the process of regulatory decisions at the time of marketing application. If the hERG (human ether-a-go-go-related gene) and the non-rodent in vivo study are conducted according to the described best practices and are negative, the previous requirement that a QTc effect of >10 milliseconds must be excluded in healthy subjects at plasma concentrations 2-fold above what can be seen in patients can be reduced to covering the concentrations seen in patients. For drugs that cannot be safely given in high doses to healthy subjects, ECG evaluation is often performed at the therapeutic dose in patients. If a QTc effect of >10 milliseconds can be excluded, an argument can be made that the drug should be considered as having a low likelihood of proarrhythmic effects due to delayedrepolarization, if supported by negative best practices hERG and in vivo studies. In this article, we describe what clinicians involved in early clinical development need to understand in terms of the hERG and in vivo studies to determine whether these meet best practices and therefore can be used in an integrated clinical/nonclinical QT/QTc risk assessment.

hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.

Inhibition of the hERG Potassium Channel by a Methanesulphonate-Free E-4031 Analogue

hERG (human Ether-à-go-go Related Gene)-encoded potassium channels underlie the cardiac rapid delayed rectifier (IKr) potassium current, which is a major target for antiarrhythmic agents and diverse non-cardiac drugs linked to the drug-induced form of long QT syndrome. E-4031 is a high potency hERG channel inhibitor from the methanesulphonanilide drug family. This study utilized a methanesulphonate-lacking E-4031 analogue, "E-4031-17", to evaluate the role of the methanesulphonamide group in E-4031 inhibition of hERG. Whole-cell patch-clamp measurements of the hERG current (IhERG) were made at physiological temperature from HEK 293 cells expressing wild-type (WT) and mutant hERG constructs. For E-4031, WT IhERG was inhibited by a half-maximal inhibitory concentration (IC50) of 15.8 nM, whilst the comparable value for E-4031-17 was 40.3 nM. Both compounds exhibited voltage- and time-dependent inhibition, but they differed in their response to successive applications of a long (10 s) depolarisation protocol, consistent with greater dissociation of E-4031-17 than the parent compound between applied commands. Voltage-dependent inactivation was left-ward voltage shifted for E-4031 but not for E-4031-17; however, inhibition by both compounds was strongly reduced by attenuated-inactivation mutations. Mutations of S6 and S5 aromatic residues (F656V, Y652A, F557L) greatly attenuated actions of both drugs. The S624A mutation also reduced IhERG inhibition by both molecules. Overall, these results demonstrate that the lack of a methanesulphonate in E-4031-17 is not an impediment to high potency inhibition of IhERG.

Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment

Reduction of the rapid delayed rectifier potassium current (IKr) via drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study

BACKGROUND

Assessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to develop a methodology to automatically generate Markovian models of the drug-hERG channel interactions.

METHODS

The training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, distributed into 104 groups with different affinities and kinetics to the conformational states of the channel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization.

RESULTS

We propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynamics and the onset of I_Kr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accuracy being 97.53%. Moreover, generation of the dynamic model of an I_Kr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer.

CONCLUSION

Our methodology could be very useful to model and simulate dynamic drug-hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit I_Kr and the efficacy of antiarrhythmic I_Kr blockers.

Safety pharmacology in 2022: Taking one small step for cardiovascular safety assay development but one giant leap for regulatory drug safety assessment

The 2021 Annual Safety Pharmacology (SP) Society (SPS) meeting was held virtually October 4-8, 2021 due to the continuing COVID-19 global pandemic. This themed issue of J Pharmacol Toxicol Methods comprises articles arising from the meeting. As in previous years the manuscripts reflect various areas of innovation in SP including a perspective on aging and its impact on drug attrition during safety assessments, an integrated assessment of respiratory, cardiovascular and animal activity of in vivo nonclinical studies, development of a dynamic QT-rate correction method in primates, evaluation of the "comprehensive in vitro proarrhythmia assay" (CiPA) ion channel protocol to the automated patch clamp, and best practices regarding the conduct of hERG electrophysiology studies and an analysis of secondary pharmacology assays by the FDA. The meeting also generated 85 abstracts (reproduced in the current volume of J Pharmacol Toxicol Methods). It appears that the validation of methods remains a challenge in SP. Nevertheless, the continued efforts to mine approaches to detection of proarrhythmia liability remains a baffling obsession given the ability of Industry to completely prevent drugs entering into clinical study only to be found to have proarrhythmic properties, with no reports of such for at least ten years. Perhaps it is time to move on from CiPA and find genuine problems to solve?

Translating the measurement of hERG kinetics and drug block for CiPA to a high throughput platform

The Comprehensive in vitro Proarrhythmic Assay (CiPA) has promoted use of in silico models of drug effects on cardiac repolarization to improve proarrhythmic risk prediction. These models contain a pharmacodynamic component describing drug binding to hERG channels that required in vitro data for kinetics of block, in addition to potency, to constrain them. To date, development and validation has been undertaken using data from manual patch-clamp. The application of this approach at scale requires the development of a high-throughput, automated patch-clamp (APC) implementation. Here, we present a comprehensive analysis of the implementation of the Milnes, or CiPA dynamic protocol, on an APC platform, including quality control and data analysis. Kinetics and potency of block were assessed for bepridil, cisapride, terfenadine and verapamil with data retention/QC pass rate of 21.8% overall, or as high as 50.4% when only appropriate sweep lengths were considered for drugs with faster kinetics. The variability in IC₅₀ and kinetics between manual and APC was comparable to that seen between sites/platforms in previous APC studies of potency. Whilst the experimental success is less than observed in screens of potency alone, it is still significantly greater than manual patch. With the modifications to protocol design, including sweep length, number of repetitions, and leak correction recommended in this study, this protocol can be applied on APC to acquire data comparable to manual patch clamp.

Assessment of proarrhythmogenic risk for chloroquine and hydroxychloroquine using the CiPA concept

Chloroquine and hydroxychloroquine have been proposed recently as therapy for SARS-CoV-2-infected patients, but during 3 months of extensive use concerns were raised related to their clinical effectiveness and arrhythmogenic risk. Therefore, we estimated for these compounds several proarrhythmogenic risk predictors according to the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm. Experiments were performed with either CytoPatch™2 automated or manual patch-clamp setups on HEK293T cells stably or transiently transfected with hERG1, hNav1.5, hKir2.1, hKv7.1+hMinK, and on Pluricyte® cardiomyocytes (Ncardia), using physiological solutions. Dose-response plots of hERG1 inhibition fitted with Hill functions yielded IC50 values in the low micromolar range for both compounds. We found hyperpolarizing shifts of tens of mV, larger for chloroquine, in the voltage-dependent activation but not inactivation, as well as a voltage-dependent block of hERG current, larger at positive potentials. We also found inhibitory effects on peak and late INa and on IK1, with IC50 of tens of μM and larger for chloroquine. The two compounds, tested on Pluricyte® cardiomyocytes using the β-escin-perforated method, inhibited IKr, ICaL, INa peak, but had no effect on If. In current-clamp they caused action potential prolongation. Our data and those from literature for Ito were used to compute proarrhythmogenic risk predictors Bnet (Mistry HB, 2018) and Qnet (Dutta S et al., 2017), with hERG1 blocking/unblocking rates estimated from time constants of fractional block. Although the two antimalarials are successfully used in autoimmune diseases, and chloroquine may be effective in atrial fibrillation, assays place these drugs in the intermediate proarrhythmogenic risk group.

In silico Exploration of Interactions Between Potential COVID-19 Antiviral Treatments and the Pore of the hERG Potassium Channel-A Drug Antitarget

Background: In the absence of SARS-CoV-2 specific antiviral treatments, various repurposed pharmaceutical approaches are under investigation for the treatment of COVID-19. Antiviral drugs considered for this condition include atazanavir, remdesivir, lopinavir-ritonavir, and favipiravir. Whilst the combination of lopinavir and ritonavir has been previously linked to prolongation of the QTc interval on the ECG and risk of torsades de pointes arrhythmia, less is known in this regard about atazanavir, remdesivir, and favipiravir. Unwanted abnormalities of drug-induced QTc prolongation by diverse drugs are commonly mediated by a single cardiac anti-target, the hERG potassium channel. This computational modeling study was undertaken in order to explore the ability of these five drugs to interact with known determinants of drug binding to the hERG channel pore. Methods: Atazanavir, remdesivir, ritonavir, lopinavir and favipiravir were docked to in silico models of the pore domain of hERG, derived from cryo-EM structures of hERG and the closely related EAG channel. Results: Atazanavir was readily accommodated in the open hERG channel pore in proximity to the S6 Y652 and F656 residues, consistent with published experimental data implicating these aromatic residues in atazanavir binding to the channel. Lopinavir, ritonavir, and remdesivir were also accommodated in the open channel, making contacts in a model-dependent fashion with S6 aromatic residues and with residues at the base of the selectivity filter/pore helix. The ability of remdesivir (at 30 μM) to inhibit the channel was confirmed using patch-clamp recording. None of these four drugs could be accommodated in the closed channel structure. Favipiravir, a much smaller molecule, was able to fit within the closed channel and could adopt multiple binding poses in the open channel, but with few simultaneous interactions with key binding residues. Only favipiravir and remdesivir showed the potential to interact with lateral pockets below the selectivity filter of the channel. Conclusions: All the antiviral drugs studied here can, in principle, interact with components of the hERG potassium channel canonical binding site, but are likely to differ in their ability to access lateral binding pockets. Favipiravir's small size and relatively paucity of simultaneous interactions may confer reduced hERG liability compared to the other drugs. Experimental structure-function studies are now warranted to validate these observations.

Rutaecarpine targets hERG channels and participates in regulating electrophysiological properties leading to ventricular arrhythmia

Drug-mediated or medical condition-mediated disruption of hERG function accounts for the main cause of acquired long-QT syndrome (acLQTs), which predisposes affected individuals to ventricular arrhythmias (VA) and sudden death. Many Chinese herbal medicines, especially alkaloids, have risks of arrhythmia in clinical application. The characterized mechanisms behind this adverse effect are frequently associated with inhibition of cardiac hERG channels. The present study aimed to assess the potent effect of Rutaecarpine (Rut) on hERG channels. hERG-HEK293 cell was applied for evaluating the effect of Rut on hERG channels and the underlying mechanism. hERG current (I_hERG ) was measured by patch-clamp technique. Protein levels were analysed by Western blot, and the phosphorylation of Sp1 was determined by immunoprecipitation. Optical mapping and programmed electrical stimulation were used to evaluate cardiac electrophysiological activities, such as APD, QT/QTc, occurrence of arrhythmia, phase singularities (PSs), and dominant frequency (DF). Our results demonstrated that Rut reduced the I_hERG by binding to F656 and Y652 amino acid residues of hERG channel instantaneously, subsequently accelerating the channel inactivation, and being trapped in the channel. The level of hERG channels was reduced by incubating with Rut for 24 hours, and Sp1 in nucleus was inhibited simultaneously. Mechanismly, Rut reduced threonine (Thr)/ tyrosine (Tyr) phosphorylation of Sp1 through PI3K/Akt pathway to regulate hERG channels expression. Cell-based model unables to fully reveal the pathological process of arrhythmia. In vivo study, we found that Rut prolonged QT/QTc intervals and increased induction rate of ventricular fibrillation (VF) in guinea pig heart after being dosed Rut for 2 weeks. The critical reasons led to increased incidence of arrhythmias eventually were prolonged APD₉₀ and APD₅₀ and the increase of DF, numbers of PSs, incidence of early after-depolarizations (EADs). Collectively, the results of this study suggest that Rut could reduce the I_hERG by binding to hERG channels through F656 and Y652 instantaneously. While, the PI3K/Akt/Sp1 axis may play an essential role in the regulation of hERG channels, from the perspective of the long-term effects of Rut (incubating for 24 hours). Importantly, the changes of electrophysiological properties by Rut were the main cause of VA.

Molecular dynamics of hERG channel: insights into understanding the binding of small molecules for detuning cardiotoxicity

Evaluation of cardiotoxicity potential of new chemical entities (NCEs) has lately become one of the stringent filters in the drug discovery and development process. Cardiotoxicity is caused mainly by the inhibition of human ether-a-go-go related gene (hERG) channel protein. Inhibition of the hERG channel leads to a life-threatening condition known as cardiac arrhythmia. Knowledge of the structural behaviour of the hERG would aid greatly in the design of new drug molecules that do not interact with the protein and add to the safety index. In this study, a computational model for the active-state of hERG was developed. This model was equilibrated by performing the molecular dynamics simulations for 100 ns followed by clustering and selection of a representative structure based on the largest populated cluster. To study the changes in the protein structure on inhibition, three inhibitory ligands, namely, dofetilide, cisapride and terfenadine were docked, followed by molecular dynamics simulations of 200 ns for the apo and each ligand-bound structure. It was observed that docking and simulation studies of the hERG model exhibited noticeable conformational changes in the protein upon ligand-binding. A significant change in the kink of the S6-transmembrane helix was observed. Inter-chain distances between the crucial residues Y652 and F656 (present below the ion-selectivity filter), their side-chain orientation and hydrogen bonding indicated a probable collapse of the pore. These changes may infer the initiation in transition of hERG from an open to an inactive state. Hence, these findings would help in designing compounds devoid of hERG inhibition with reduced cardiotoxicity.Communicated by Ramaswamy H. Sarma.

Functional evaluation of gene mutations in Long QT Syndrome: strength of evidence from in vitro assays for deciphering variants of uncertain significance

Background

Genetic screening is now commonplace for patients suspected of having inherited cardiac conditions. Variants of uncertain significance (VUS) in disease-associated genes pose problems for the diagnostician and reliable methods for evaluating VUS function are required. Although function is difficult to interrogate for some genes, heritable channelopathies have established mechanisms that should be amenable to well-validated evaluation techniques.

The cellular electrophysiology techniques of ‘voltage-’ and ‘patch-’ clamp have a long history of successful use and have been central to identifying both the roles of genes involved in different forms of congenital Long QT Syndrome (LQTS) and the mechanisms by which mutations lead to aberrant ion channel function underlying clinical phenotypes. This is particularly evident for KCNQ1, KCNH2 and SCN5A, mutations in which underlie > 90% of genotyped LQTS cases (the LQT1-LQT3 subtypes). Recent studies utilizing high throughput (HT) planar patch-clamp recording have shown it to discriminate effectively between rare benign and pathological variants, studied through heterologous expression of recombinant channels. In combination with biochemical methods for evaluating channel trafficking and supported by biophysical modelling, patch clamp also provides detailed mechanistic insight into the functional consequences of identified mutations. Whilst potentially powerful, patient-specific stem-cell derived cardiomyocytes and genetically modified animal models are currently not well-suited to high throughput VUS study.

Conclusion

The widely adopted 2015 American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP) guidelines for the interpretation of sequence variants include the PS3 criterion for consideration of evidence from well-established in vitro or in vivo assays. The wealth of information on underlying mechanisms of LQT1-LQT3 and recent HT patch clamp data support consideration of patch clamp data together (for LQT1 and LQT2) with information from biochemical trafficking assays as meeting the PS3 criterion of well established assays, able to provide ‘strong’ evidence for functional pathogenicity of identified VUS.

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC₅₀ and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T_x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T_qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T_triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T_EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T_x, T_qNet, T_triang, and T_EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC₅₀ test (current biomarker) was 78.9%. When combining T_x, T_qNet and T_triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I_Kr, I_CaL, I_NaL and I_Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.

Time for a Fully Integrated Nonclinical-Clinical Risk Assessment to Streamline QT Prolongation Liability Determinations: A Pharma Industry Perspective

Defining an appropriate and efficient assessment of drug‐induced corrected QT interval (QTc) prolongation (a surrogate marker of torsades de pointes arrhythmia) remains a concern of drug developers and regulators worldwide. In use for over 15 years, the nonclinical International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B and clinical ICH E14 guidances describe three core assays (S7B: in vitro hERG current & in vivo QTc studies; E14: thorough QT study) that are used to assess the potential of drugs to cause delayed ventricular repolarization. Incorporating these assays during nonclinical or human testing of novel compounds has led to a low prevalence of QTc‐prolonging drugs in clinical trials and no new drugs having been removed from the marketplace due to unexpected QTc prolongation. Despite this success, nonclinical evaluations of delayed repolarization still minimally influence ICH E14‐based strategies for assessing clinical QTc prolongation and defining proarrhythmic risk. In particular, the value of ICH S7B‐based “double‐negative” nonclinical findings (low risk for hERG block and in vivo QTc prolongation at relevant clinical exposures) is underappreciated. These nonclinical data have additional value in assessing the risk of clinical QTc prolongation when clinical evaluations are limited by heart rate changes, low drug exposures, or high‐dose safety considerations. The time has come to meaningfully merge nonclinical and clinical data to enable a more comprehensive, but flexible, clinical risk assessment strategy for QTc monitoring discussed in updated ICH E14 Questions and Answers. Implementing a fully integrated nonclinical/clinical risk assessment for compounds with double‐negative nonclinical findings in the context of a low prevalence of clinical QTc prolongation would relieve the burden of unnecessary clinical QTc studies and streamline drug development.

Structure-Activity Relationship Exploration of NNIBP Tolerant Region I Leads to Potent HIV-1 NNRTIs

Previous efforts in our lab have led to the development of human immunodeficiency virus type 1 (HIV-1) non-nucleoside reverse transcriptase inhibitor (NNRTI) thiophene[3,2-d]pyrimidine compound 1 (K-5a2) with promising activity against wild-type and mutant HIV-1 strains. In this work, a series of novel diarylpyrimidines derivatives carrying a structurally diverse motif at the right wing of the lead K-5a2 was designed and synthesized as potential anti-HIV-1 agents. The results demonstrated that 8a yielded exceptionally potent activity against HIV-1 wild-type (50% effective concentration (EC₅₀) = 3.30 nM) and mutant strain RES056 (EC₅₀ = 22.6 nM) in MT-4 cells; in the reverse transcriptase inhibitory assay, 8a (half maximal inhibitory concentration (IC₅₀) = 0.028 μM) was remarkably superior to that of K-5a2 (IC₅₀ = 0.300 μM) and comparable to that of etravirine (ETR; IC₅₀ = 0.011 μM). Notably, 8a exhibited better druggability than that of K-5a2, including significantly reduced CYP enzymatic inhibitory activity (IC₅₀ > 50 μM), lower human ether-à-go-go related gene (hERG) inhibition (IC₅₀ > 30 μM), and improved metabolic stability (short half-life, T_1/2 = 77.5 min) in vitro.

A general procedure to select calibration drugs for lab-specific validation and calibration of proarrhythmia risk prediction models: An illustrative example using the CiPA model

INTRODUCTION

In response to the ongoing shift of the regulatory cardiac safety paradigm, a recent White Paper proposed general principles for developing and implementing proarrhythmia risk prediction models. These principles included development strategies to validate models, and implementation strategies to ensure a model developed by one lab can be used by other labs in a consistent manner in the presence of lab-to-lab experimental variability. While the development strategies were illustrated through the validation of the model under the Comprehensive In vitro Proarrhythmia Assay (CiPA), the implementation strategies have not been adopted yet.

METHODS

The proposed implementation strategies were applied to the CiPA model by performing a sensitivity analysis to identify a subset of calibration drugs that were most critical in determining the classification thresholds for proarrhythmia risk prediction.

RESULTS

The selected calibration drugs were able to recapitulate classification thresholds close to those calculated from the full list of CiPA drugs. Using an illustrative dataset it was shown that a new lab could use these calibration drugs to establish its own classification thresholds (lab-specific calibration), and verify that the model prediction accuracy in the new lab is comparable to that in the original lab where the model was developed (lab-specific validation).

DISCUSSION

This work used the CiPA model as an example to illustrate how to adopt the proposed model implementation strategies to select calibration drugs and perform lab-specific calibration and lab-specific validation. Generic in nature, these strategies could be generally applied to different proarrhythmia risk prediction models using various experimental systems under the new paradigm.

Mechanobiology Assays with Applications in Cardiomyocyte Biology and Cardiotoxicity

Cardiomyocytes are the motor units that drive the contraction and relaxation of the heart. Traditionally, testing of drugs for cardiotoxic effects has relied on primary cardiomyocytes from animal models and focused on short-term, electrophysiological, and arrhythmogenic effects. However, primary cardiomyocytes present challenges arising from their limited viability in culture, and tissue from animal models suffers from a mismatch in their physiology to that of human heart muscle. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) can address these challenges. They also offer the potential to study not only electrophysiological effects but also changes in cardiomyocyte contractile and mechanical function in response to cardiotoxic drugs. With growing recognition of the long-term cardiotoxic effects of some drugs on subcellular structure and function, there is increasing interest in using hiPSC-CMs for in vitro cardiotoxicity studies. This review provides a brief overview of techniques that can be used to quantify changes in the active force that cardiomyocytes generate and variations in their inherent stiffness in response to cardiotoxic drugs. It concludes by discussing the application of these tools in understanding how cardiotoxic drugs directly impact the mechanobiology of cardiomyocytes and how cardiomyocytes sense and respond to mechanical load at the cellular level.

Cross-site and cross-platform variability of automated patch clamp assessments of drug effects on human cardiac currents in recombinant cells

Automated patch clamp (APC) instruments enable efficient evaluation of electrophysiologic effects of drugs on human cardiac currents in heterologous expression systems. Differences in experimental protocols, instruments, and dissimilar site procedures affect the variability of IC50 values characterizing drug block potency. This impacts the utility of APC platforms for assessing a drug's cardiac safety margin. We determined variability of APC data from multiple sites that measured blocking potency of 12 blinded drugs (with different levels of proarrhythmic risk) against four human cardiac currents (hERG [IKr], hCav1.2 [L-Type ICa], peak hNav1.5, [Peak INa], late hNav1.5 [Late INa]) with recommended protocols (to minimize variance) using five APC platforms across 17 sites. IC50 variability (25/75 percentiles) differed for drugs and currents (e.g., 10.4-fold for dofetilide block of hERG current and 4-fold for mexiletine block of hNav1.5 current). Within-platform variance predominated for 4 of 12 hERG blocking drugs and 4 of 6 hNav1.5 blocking drugs. hERG and hNav1.5 block. Bland-Altman plots depicted varying agreement across APC platforms. A follow-up survey suggested multiple sources of experimental variability that could be further minimized by stricter adherence to standard protocols. Adoption of best practices would ensure less variable APC datasets and improved safety margins and proarrhythmic risk assessments.

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Introduction

hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed.

Methods

A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates.

Results

A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used.

Discussion

This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

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hERG potency/safety margin is a widely used nonclinical cardiac safety strategy.

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A new regulatory paradigm promotes the integration of nonclinical and clinical data.

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Lack of uncertainty quantification hindered using hERG potency in the new paradigm.

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A systematic method was established to address this limitation.

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Analysis supports using different safety margin thresholds in different context.

The macrolide drug erythromycin does not protect the hERG channel from inhibition by thioridazine and terfenadine

The macrolide antibiotic erythromycin has been associated with QT interval prolongation and inhibition of the hERG-encoded channels responsible for the rapid delayed rectifier K⁺ current I(_Kr ). It has been suggested that low concentrations of erythromycin may have a protective effect against hERG block and associated drug-induced arrhythmia by reducing the affinity of the pore-binding site for high potency hERG inhibitors. This study aimed to explore further the notion of a potentially protective effect of erythromycin. Whole-cell patch-clamp experiments were performed in which hERG-expressing mammalian (Human Embryonic Kidney; HEK) cells were preincubated with low to moderate concentrations of erythromycin (3 or 30 µM) prior to whole-cell patch clamp recordings of hERG current (I_hERG ) at 37°C. In contrast to a previous report, exposure to low concentrations of erythromycin did not reduce pharmacological sensitivity of hERG to the antipsychotic thioridazine and antihistamine terfenadine. The IC₅₀ value for I_hERG tail inhibition by terfenadine was decreased by ~32-fold in the presence of 3 µM erythromycin (p < .05 vs. no preincubation). Sensitivity to thioridazine remained unchanged (p > .05 vs. no preincubation). The effects of low concentrations of erythromycin were investigated for a series of pore blocking drugs, and the results obtained were consistent with additive and/or synergistic effects. Experiments with the externally acting blocker BeKm-1 on WT hERG and a pore mutant (F656V) were used to explore the location of the binding site for erythromycin. Our data are inconsistent with the use of erythromycin for the management of drug-induced QT prolongation.

When Does the IC50 Accurately Assess the Blocking Potency of a Drug?

Preclinical assessment of drug-induced proarrhythmicity is typically evaluated by the potency of the drug to block the potassium human ether-à-go-go-related gene (hERG) channels, which is currently quantified by the IC₅₀. However, channel block depends on the experimental conditions. Our aim is to improve the evaluation of the blocking potency of drugs by designing experimental stimulation protocols to measure the IC₅₀ that will help to decide whether the IC₅₀ is representative enough. We used the state-of-the-art mathematical models of the cardiac electrophysiological activity to design three stimulation protocols that enhance the differences in the probabilities to occupy a certain conformational state of the channel and, therefore, the potential differences in the blocking effects of a compound. We simulated an extensive set of 144 in silico I_Kr blockers with different kinetics and affinities to conformational states of the channel and we also experimentally validated our key predictions. Our results show that the IC₅₀ protocol dependency relied on the tested compounds. Some of them showed no differences or small differences on the IC₅₀ value, which suggests that the IC₅₀ could be a good indicator of the blocking potency in these cases. However, others provided highly protocol dependent IC₅₀ values, which could differ by even 2 orders of magnitude. Moreover, the protocols yielding the maximum IC₅₀ and minimum IC₅₀ depended on the drug, which complicates the definition of a "standard" protocol to minimize the influence of the stimulation protocol on the IC₅₀ measurement in safety pharmacology. As a conclusion, we propose the adoption of our three-protocol IC₅₀ assay to estimate the potency to block hERG in vitro. If the IC₅₀ values obtained for a compound are similar, then the IC₅₀ could be used as an indicator of its blocking potency, otherwise kinetics and state-dependent binding properties should be accounted.

Blinded In Silico Drug Trial Reveals the Minimum Set of Ion Channels for Torsades de Pointes Risk Assessment

Torsades de Pointes (TdP) is a type of ventricular arrhythmia which could be observed as an unwanted drug-induced cardiac side effect, and it is associated with repolarization abnormalities in single cells. The pharmacological evaluations of TdP risk in previous years mainly focused on the hERG channel due to its vital role in the repolarization of cardiomyocytes. However, only considering drug effects on hERG led to false positive predictions since the drug action on other ion channels can also have crucial regulatory effects on repolarization. To address the limitation of only evaluating hERG, the Comprehensive in Vitro Proarrhythmia Assay initiative has proposed to systematically integrate drug effects on multiple ion channels into in silico drug trial to improve TdP risk assessment. It is not clear how many ion channels are sufficient for reliable TdP risk predictions, and whether differences in IC₅₀ and Hill coefficient values from independent sources can lead to divergent in silico prediction outcomes. The rationale of this work is to investigate the above two questions using a computationally efficient population of human ventricular cells optimized to favor repolarization abnormality. Our blinded results based on two independent data sources confirm that simulations with the optimized population of human ventricular cell models enable efficient in silico drug screening, and also provide direct observation and mechanistic analysis of repolarization abnormality. Our results show that 1) the minimum set of ion channels required for reliable TdP risk predictions are Nav1.5 (peak), Cav1.2, and hERG; 2) for drugs with multiple ion channel blockage effects, moderate IC₅₀ variations combined with variable Hill coefficients can affect the accuracy of in silico predictions.

Human Heart Cardiomyocytes in Drug Discovery and Research: New Opportunities in Translational Sciences

In preclinical drug development, accurate prediction of drug effects on the human heart is critically important, whether in the context of cardiovascular safety or for the purpose of modulating cardiac function to treat heart disease. Current strategies have significant limitations, whereby, cardiotoxic drugs can escape detection or potential life-saving therapies are abandoned due to false positive toxicity signals. Thus, new and more reliable translational approaches are urgently needed to help accelerate the rate of new therapy development. Renewed efforts in the recovery of human donor hearts for research and in cardiomyocyte isolation methods, are providing new opportunities for preclinical studies in adult primary cardiomyocytes. These cells exhibit the native physiological and pharmacological properties, overcoming the limitations presented by artificial cellular models, animal models and have great potential for providing an excellent tool for preclinical drug testing. Adult human primary cardiomyocytes have already shown utility in assessing drug-induced cardiotoxicity risk and helping in the identification of new treatments for cardiac diseases, such as heart failure and atrial fibrillation. Finally, strategies with actionable decision-making trees that rely on data derived from adult human primary cardiomyocytes will provide the holistic insights necessary to accurately predict human heart effects of drugs.

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment

Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents (qNet) have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification. In particular, qNet has been recently proposed as a surrogate metric for early afterdepolarizations (EADs), which are known to be cellular triggers of TdP. Analysis of critical model components and of the ion channels that have major impact on model-derived metrics can lead to improvements in the confidence of the prediction. In this paper, we analyze large populations of virtual drugs to systematically examine the influence of different ion channels on model-derived metrics that have been proposed for proarrhythmic risk assessment. We demonstrate via global sensitivity analysis (GSA) that model-derived metrics are most sensitive to different sets of input parameters. Similarly, important differences in sensitivity to specific channel blocks are highlighted when classifying drugs into different risk categories by either qNet or a metric directly based on simulated EADs. In particular, the higher sensitivity of qNet to the block of the late sodium channel might explain why its classification accuracy is better than that of the EAD-based metric, as shown for a small set of known drugs. Our results highlight the need for a better mechanistic interpretation of promising metrics like qNet based on a formal analysis of models. GSA should, therefore, constitute an essential component of the in silico workflow for proarrhythmic risk assessment, as an improved understanding of the structure of model-derived metrics could increase confidence in model-predicted risk.

Potent hERG channel inhibition by sarizotan, an investigative treatment for Rett Syndrome

Rett Syndrome (RTT) is an X-linked neurodevelopmental disorder associated with respiratory abnormalities and, in up to ~40% of patients, with prolongation of the cardiac QT_c interval. QT_c prolongation calls for cautious use of drugs with a propensity to inhibit hERG channels. The STARS trial has been undertaken to investigate the efficacy of sarizotan, a 5-HT_1A receptor agonist, at correcting RTT respiratory abnormalities. The present study investigated whether sarizotan inhibits hERG potassium channels and prolongs ventricular repolarization. Whole-cell patch-clamp measurements were made at 37 °C from hERG-expressing HEK293 cells. Docking analysis was conducted using a recent cryo-EM structure of hERG. Sarizotan was a potent inhibitor of hERG current (I_hERG; IC₅₀ of 183 nM) and of native ventricular I_Kr from guinea-pig ventricular myocytes. 100 nM and 1 μM sarizotan prolonged ventricular action potential (AP) duration (APD₉₀) by 14.1 ± 3.3% (n = 6) and 29.8 ± 3.1% (n = 5) respectively and promoted AP triangulation. High affinity I_hERG inhibition by sarizotan was contingent upon channel gating and intact inactivation. Mutagenesis experiments and docking analysis implicated F557, S624 and Y652 residues in sarizotan binding, with weaker contribution from F656. In conclusion, sarizotan inhibits I_Kr/I_hERG, accessing key binding residues on channel gating. This action and consequent ventricular AP prolongation occur at concentrations relevant to those proposed to treat breathing dysrhythmia in RTT. Sarizotan should only be used in RTT patients with careful evaluation of risk factors for QT_c prolongation.

Protocol-Dependent Differences in IC50 Values Measured in Human Ether-Á-Go-Go-Related Gene Assays Occur in a Predictable Way and Can Be Used to Quantify State Preference of Drug Binding

Current guidelines around preclinical screening for drug-induced arrhythmias require the measurement of the potency of block of voltage-gated potassium channel subtype 11.1 (Kv11.1) as a surrogate for risk. A shortcoming of this approach is that the measured IC50 of Kv11.1 block varies widely depending on the voltage protocol used in electrophysiological assays. In this study, we aimed to investigate the factors that contribute to these differences and to identify whether it is possible to make predictions about protocol-dependent block that might facilitate the comparison of potencies measured using different assays. Our data demonstrate that state preferential binding, together with drug-binding kinetics and trapping, is an important determinant of the protocol dependence of Kv11.1 block. We show for the first time that differences in IC50 measured between protocols occurs in a predictable way, such that machine-learning algorithms trained using a selection of simple voltage protocols can indeed predict protocol-dependent potency. Furthermore, we also show that the preference of a drug for binding to the open versus the inactivated state of Kv11.1 can also be inferred from differences in IC50 values measured between protocols. Our work therefore identifies how state preferential drug binding is a major determinant of the protocol dependence of IC50 values measured in preclinical Kv11.1 assays. It also provides a novel method for quantifying the state dependence of Kv11.1 drug binding that will facilitate the development of more complete models of drug binding to Kv11.1 and improve our understanding of proarrhythmic risk associated with compounds that block Kv11.1.

Functional and pharmacological characterization of an S5 domain hERG mutation associated with short QT syndrome

Congenital short QT syndrome (SQTS) is a repolarization disorder characterized by abbreviated QT intervals, atrial and ventricular arrhythmias and a risk of sudden death. This study characterized a missense mutation (I560T) in the S5 domain of the hERG K⁺ channel that has been associated with variant 1 of the SQTS. Whole cell patch clamp recordings of wild-type (WT) and I560T hERG current (I_hERG) were made at 37 °C from hERG expressing HEK 293 cells, and the structural context of the mutation was investigated using a recently reported cryo-EM structure of hERG. Under conventional voltage clamp, the I560T mutation increased I_hERG amplitude without altering the voltage-dependence of activation, although it accelerated activation time-course and also slowed deactivation time-course at some voltages. The voltage dependence of I_hERG inactivation was positively shifted (by ∼24 mV) and the time-course of inactivation was slowed by the I560T mutation. There was also a modest decrease in K⁺ over Na⁺ ion selectivity with the I560T mutation. Under action potential (AP) voltage clamp, the net charge carried by hERG was significantly increased during ventricular, Purkinje fibre and atrial APs, with maximal I_hERG also occurring earlier during the plateau phase of ventricular and Purkinje fibre APs. The I560T mutation exerted only a modest effect on quinidine sensitivity of I_hERG: the IC₅₀ for mutant I_hERG was 2.3 fold that for WT I_hERG under conventional voltage clamp. Under AP voltage clamp the inhibitory effect of 1 μM quinidine was largely retained for I560T hERG and the timing of peak I560T I_hERG was altered towards that of the WT channel. In both the open channel structure and a closed hERG channel model based on the closely-related EAG structure, I560T side-chains were oriented towards membrane lipid and away from adjacent domains of the channel, contrasting with previous predictions based on homology modelling. In summary, the I560T mutation produces multiple effects on hERG channel operation that result in a gain-of-function that is expected to abbreviate ventricular, atrial and Purkinje fibre repolarization. Quinidine is likely to be of value in offsetting the increase in I_hERG and altered I_hERG timing during ventricular APs in SQTS with this mutation.

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro-arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi-ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre-specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.

Action potential clamp characterization of the S631A hERG mutation associated with short QT syndrome

The hERG potassium channel is critical to normal repolarization of cardiac action potentials (APs) and loss- and gain-of-function hERG mutations are associated, respectively, with long and short QT syndromes, pathological conditions that can lead to arrhythmias and sudden death. hERG current (IhERG ) exhibits uniquely fast inactivation involving conformational changes to the channel pore. The S631A hERG pore mutation was originally engineered to interrogate hERG channel inactivation, but has very recently been found in a family with short QT syndrome (SQTS). Accordingly, this study characterized the effects of the S631A mutation on IhERG profile during ventricular, atrial, and Purkinje fiber (PF) AP waveforms, using patch clamp recording from hERG expressing HEK 293 cells at 37°C. Under conventional voltage clamp, the current-voltage (I-V) relation for IhERG exhibited a marked right-ward shift in the region of negative slope at positive membrane potentials. Under ventricular AP clamp, the S631A mutation resulted in augmented IhERG , which also peaked much earlier during the AP plateau than did wild-type (WT) IhERG . Instantaneous I-V relations showed a marked positive shift in peak repolarizing current during the ventricular AP in the S631A setting, while the instantaneous conductance-voltage relation showed an earlier and more sustained rise in S631A compared to WT IhERG conductance during ventricular repolarization. Experiments with atrial and PF APs in each case also showed augmented and positively shifted IhERG in the S631A setting, indicating that the S631A mutation is likely to accelerate repolarization in all three cardiac regions. Ventricular AP clamp experiments showed retained effectiveness of the class Ia antiarrhythmic drug quinidine (1 μmol/L) against S631A IhERG . Quinidine is thus likely to be effective in reducing excessively fast repolarization in SQTS resulting from the S631A hERG mutation.

Uncertainty Quantification Reveals the Importance of Data Variability and Experimental Design Considerations for in Silico Proarrhythmia Risk Assessment

The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (C_max). However, when drug effects were extrapolated above 10× C_max, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was <60%, preventing reliable estimation of IC₅₀-values. The results of this study demonstrate that the accuracy of TdP risk prediction depends both on the intrinsic variability in ion channel pharmacology data as well as on experimental design considerations that preclude an accurate determination of drug IC₅₀-values in vitro. Thus, we demonstrate that UQ provides valuable information about in silico modeling predictions that can inform future proarrhythmic risk evaluation of drugs under the CiPA paradigm.

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

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?

Improving the In Silico Assessment of Proarrhythmia Risk by Combining hERG (Human Ether-à-go-go-Related Gene) Channel-Drug Binding Kinetics and Multichannel Pharmacology

BACKGROUND

The current proarrhythmia safety testing paradigm, although highly efficient in preventing new torsadogenic drugs from entering the market, has important limitations that can restrict the development and use of valuable new therapeutics. The CiPA (Comprehensive in vitro Proarrhythmia Assay) proposes to overcome these limitations by evaluating drug effects on multiple cardiac ion channels in vitro and using these data in a predictive in silico model of the adult human ventricular myocyte. A set of drugs with known clinical torsade de pointes risk was selected to develop and calibrate the in silico model.

METHODS AND RESULTS

Manual patch-clamp data assessing drug effects on expressed cardiac ion channels were integrated into the O'Hara-Rudy myocyte model modified to include dynamic drug-hERG channel (human Ether-à-go-go-Related Gene) interactions. Together with multichannel pharmacology data, this model predicts that compounds with high torsadogenic risk are more likely to be trapped within the hERG channel and show stronger reverse use dependency of action potential prolongation. Furthermore, drug-induced changes in the amount of electronic charge carried by the late sodium and L-type calcium currents was evaluated as a potential metric for assigning torsadogenic risk.

CONCLUSIONS

Modeling dynamic drug-hERG channel interactions and multi-ion channel pharmacology improves the prediction of torsadogenic risk. With further development, these methods have the potential to improve the regulatory assessment of drug safety models under the CiPA paradigm.

Safety pharmacology methods and models in an evolving regulatory environment

This editorial prefaces the annual themed issue on safety pharmacology (SP) methods published in the Journal of Pharmacological and Toxicological Methods (JPTM). We highlight here the content derived from the recent 2016 Safety Pharmacology Society (SPS), Canadian Society of Pharmacology and Therapeutics (CSPT), and Japanese Safety Pharmacology Society (JSPS) joint meeting held in Vancouver, B.C., Canada. This issue of JPTM continues the tradition of providing a publication summary of articles primarily presented at the joint meeting with direct bearing on the discipline of SP. As the regulatory landscape is expected to evolve with revision announced for the existing guidance document on non-clinical proarrhythmia risk assessment (ICHS7B) there is also imminent inception of the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. Thus, the field of SP is dynamically progressing with characterization and implementation of numerous alternative non-clinical safety models. Novel method development and refinement in all areas of the discipline are reflected in the content.

Measuring kinetics and potency of hERG block for CiPA

INTRODUCTION

The Comprehensive in vitro Proarrhythmic Assay (CiPA) aims to update current cardiac safety testing to better evaluate arrhythmic risk. A central theme of CiPA is the use of in silico approaches to risk prediction incorporating models of drug binding to hERG. To parameterize these models, accurate in vitro measurement of potency and kinetics of block is required. The Ion Channel Working Group was tasked with: i) selecting a protocol that could measure kinetics of block and was easily implementable on automated platforms for future rollout in industry and ii) acquiring a reference dataset using the standardized protocol.

METHODS

Data were acquired using a 'step depolarisation' protocol using manual patch-clamp at ambient temperature.

RESULTS

Potency, kinetics and trapping characteristics of hERG block for the CiPA training panel of twelve drugs were measured. Timecourse of block and trapping characteristics could be reliably measured if the time constant for onset of block was between ~500ms and ~15s. Seven drugs, however had time courses of block faster than this cut-off.

DISCUSSION

Here we describe the implementation of the standardized protocol for measurement of kinetics and potency of hERG block for CiPA. The results highlight the challenges in identifying a single protocol to measure hERG block over a range of kinetics. The dataset from this study is being used by the In Silico Working Group to develop models of drug binding for risk prediction and is freely available as a 'gold standard' ambient temperature dataset to evaluate variability across high throughput platforms.

Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation

Drug‐induced QTc interval prolongation (ΔQTc) is a main surrogate for proarrhythmic risk assessment. A higher in vivo than in vitro potency for hERG‐mediated QTc prolongation has been suggested. Also, in vivo between‐species and patient populations’ sensitivity to drug‐induced QTc prolongation seems to differ. Here, a systems pharmacology model integrating preclinical in vitro (hERG binding) and in vivo (conscious dog ΔQTc) data of three hERG blockers (dofetilide, sotalol, moxifloxacin) was applied (1) to compare the operational efficacy of the three drugs in vivo and (2) to quantify dog–human differences in sensitivity to drug‐induced QTc prolongation (for dofetilide only). Scaling parameters for translational in vivo extrapolation of drug effects were derived based on the assumption of system‐specific myocardial ion channel densities and transduction of ion channel block: the operational efficacy (transduction of hERG block) in dogs was drug specific (1–19% hERG block corresponded to ≥10 msec ΔQTc). System‐specific maximal achievable ΔQTc was estimated to 28% from baseline in both dog and human, while %hERG block leading to half‐maximal effects was 58% lower in human, suggesting a higher contribution of hERG‐mediated potassium current to cardiac repolarization. These results suggest that differences in sensitivity to drug‐induced QTc prolongation may be well explained by drug‐ and system‐specific differences in operational efficacy (transduction of hERG block), consistent with experimental reports. The proposed scaling approach may thus assist the translational risk assessment of QTc prolongation in different species and patient populations, if mediated by the hERG channel.

Temperature Effects on Kinetics of KV11.1 Drug Block Have Important Consequences for In Silico Proarrhythmic Risk Prediction

Drug block of voltage-gated potassium channel subtype 11.1 human ether-a-go-go related gene (Kv11.1) (hERG) channels, encoded by the KCNH2 gene, is associated with reduced repolarization of the cardiac action potential and is the predominant cause of acquired long QT syndrome that can lead to fatal cardiac arrhythmias. Current safety guidelines require that potency of KV11.1 block is assessed in the preclinical phase of drug development. However, not all drugs that block KV11.1 are proarrhythmic, meaning that screening on the basis of equilibrium measures of block can result in high attrition of potentially low-risk drugs. The basis of the next generation of drug-screening approaches is set to be in silico risk prediction, informed by in vitro mechanistic descriptions of drug binding, including measures of the kinetics of block. A critical issue in this regard is characterizing the temperature dependence of drug binding. Specifically, it is important to address whether kinetics relevant to physiologic temperatures can be inferred or extrapolated from in vitro data gathered at room temperature in high-throughout systems. Here we present the first complete study of the temperature-dependent kinetics of block and unblock of a proarrhythmic drug, cisapride, to KV11.1. Our data highlight a complexity to binding that manifests at higher temperatures and can be explained by accumulation of an intermediate, non-blocking encounter-complex. These results suggest that for cisapride, physiologically relevant kinetic parameters cannot be simply extrapolated from those measured at lower temperatures; rather, data gathered at physiologic temperatures should be used to constrain in silico models that may be used for proarrhythmic risk prediction.

Computational cardiology and risk stratification for sudden cardiac death: one of the grand challenges for cardiology in the 21st century

Risk stratification in the context of sudden cardiac death has been acknowledged as one of the major challenges facing cardiology for the past four decades. In recent years, the advent of high performance computing has facilitated organ-level simulation of the heart, meaning we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico. As a result, computational cardiology, largely driven by the Physiome project, now stands at the threshold of clinical utility in regards to risk stratification and treatment of patients at risk of sudden cardiac death. In this white paper, we outline a roadmap of what needs to be done to make this translational step, using the relatively well-developed case of acquired or drug-induced long QT syndrome as an exemplar case.

The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress

The implementation of the ICH S7B and E14 guidelines has been successful in preventing the introduction of potentially torsadogenic drugs to the market, but it has also unduly constrained drug development by focusing on hERG block and QT prolongation as essential determinants of proarrhythmia risk. The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative was established to develop a new paradigm for assessing proarrhythmic risk, building on the emergence of new technologies and an expanded understanding of torsadogenic mechanisms beyond hERG block. An international multi-disciplinary team of regulatory, industry and academic scientists are working together to develop and validate a set of predominantly nonclinical assays and methods that eliminate the need for the thorough-QT study and enable a more precise prediction of clinical proarrhythmia risk. The CiPA effort is led by a Steering Team that provides guidance, expertise and oversight to the various working groups and includes partners from US FDA, HESI, CSRC, SPS, EMA, Health Canada, Japan NIHS, and PMDA. The working groups address the three pillars of CiPA that evaluate drug effects on: 1) human ventricular ionic channel currents in heterologous expression systems, 2) in silico integration of cellular electrophysiologic effects based on ionic current effects, the ion channel effects, and 3) fully integrated biological systems (stem-cell-derived cardiac myocytes and the human ECG). This article provides an update on the progress of the initiative towards its target date of December 2017 for completing validation.