Identification of Ikr kinetics and drug binding in native myocytes

Model-driven optimal experimental design for calibrating cardiac electrophysiology models

BACKGROUND AND OBJECTIVE

Models of the cardiomyocyte action potential have contributed immensely to the understanding of heart function, pathophysiology, and the origin of heart rhythm disturbances. However, action potential models are highly nonlinear, making them difficult to parameterise and limiting to describing 'average cell' dynamics, when cell-specific models would be ideal to uncover inter-cell variability but are too experimentally challenging to be achieved. Here, we focus on automatically designing experimental protocols that allow us to better identify cell-specific maximum conductance values for each major current type.

METHODS AND RESULTS

We developed an approach that applies optimal experimental designs to patch-clamp experiments, including both voltage-clamp and current-clamp experiments. We assessed the models calibrated to these new optimal designs by comparing them to the models calibrated to some of the commonly used designs in the literature. We showed that optimal designs are not only overall shorter in duration but also able to perform better than many of the existing experiment designs in terms of identifying model parameters and hence model predictive power.

CONCLUSIONS

For cardiac cellular electrophysiology, this approach will allow researchers to define their hypothesis of the dynamics of the system and automatically design experimental protocols that will result in theoretically optimal designs.

Molecular determinants of pro-arrhythmia proclivity of d- and l-sotalol via a multi-scale modeling pipeline

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel – drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking.

Calibration of ionic and cellular cardiac electrophysiology models

Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

Complexity reduction in human atrial modeling using extended Kalman filter

Human atrial tissue electrophysiology is modeled upon biophysical details obtained from cellular level measurements. Data collected for this purpose typically represent a unique state of the tissue. As reproducing dynamic cases such as subject-varied and/or disorder-varied electrophysiological properties is in question, such complex models are typically hard to use. Hence, there is a need for simpler yet biophysically accurate and mathematically tractable models to be used for case-specific reproductions and simulations. In this study, a scheme for parameter estimation of a phenomenological cardiac model to match a targeted behavior generated from a complex model is used. Specifically, an algorithm incorporating extended Kalman filter (EKF) into the scheme is proposed. Its performance is then compared to that of particle swarm optimization (PSO) and sequential quadratic programming (SQP), algorithms that have been widely used for parameter optimization. Both robustness and adaptability performance of the algorithms are tested through various designs. For this, reproducing action potential (AP) waveforms of varying remodeling states of atrial fibrillation (AF) at different stimulus protocols was targeted. Also, randomly generated initial parameter sets are included in the tests. In addition, AP duration (APD) restitution curve (RC) is used for a multiscale evaluation of fitting performance. Finally, wavefront propagation on 2D of a selected AF remodeling state using parameter solutions from each of the algorithms is simulated for a qualitative evaluation. In general, PSO yielded superior performances than EKF and SQP with respect to fitting AP waveforms. Considering both AP and APD RC, however, EKF yielded the best accuracies. Also, more accurate spiral wave reentry is obtained with EKF. Overall, EKF algorithm yielded the best performance in robustness and adaptability. Graphical Abstract.

A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research

In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.

Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms

Mathematical models of cardiac electrophysiology are instrumental in determining mechanisms of cardiac arrhythmias. However, the foundation of a realistic multiscale heart model is only as strong as the underlying cell model. While there have been myriad advances in the improvement of cellular-level models, the identification of model parameters, such as ion channel conductances and rate constants, remains a challenging problem. The primary limitations to this process include: (1) such parameters are usually estimated from data recorded using standard electrophysiology voltage-clamp protocols that have not been developed with model building in mind, and (2) model parameters are typically tuned manually to subjectively match a desired output. Over the last decade, methods aimed at overcoming these disadvantages have emerged. These approaches include the use of optimization or fitting tools for parameter estimation and incorporating more extensive data for output matching. Here, we review recent advances in parameter estimation for cardiomyocyte models, focusing on the use of more complex electrophysiology protocols and global search heuristics. We also discuss future applications of such parameter identification, including development of cell-specific and patient-specific mathematical models to investigate arrhythmia mechanisms and predict therapy strategies.

Cell-specific cardiac electrophysiology models

The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.

Mathematical models of cardiac cell electrophysiology are widely used as predictive and illuminatory tools, but have been developed for decades using a suboptimal process. The models are typically constructed by manual adjustment of parameters to fit simple data and therefore often underperform when used to predict complex behavior such as arrhythmias. We present a novel method of model parameterization using automated optimization and dynamically rich fitting data and then demonstrate that this approach is better at finding the “real” model of a cell. Application of the method to cardiac myocytes leads to cell-specific models, which may enable well-controlled studies of both cellular- and subject-level population heterogeneity in disease propensity and response to therapies.

hERG inhibitors with similar potency but different binding kinetics do not pose the same proarrhythmic risk: implications for drug safety assessment

INTRODUCTION

Since the discovery of the link that exists between drug-induced hERG inhibition and Torsade de Pointes (TdP), extreme attention has been given to avoid new drugs inhibiting this channel. hERG inhibition is routinely screened for in new drugs and, typically, IC50 values are compared to projected plasma concentrations to define a safety margin.

METHODS AND RESULTS

We aimed to show that drugs with similar hERG potency are not uniformly pro-arrhythmic-this depends on the drug binding kinetics and mode of action (trapped or not) rather than the IC50 value only. We used a mathematical model of hERG and its related encoded current IKr to simulate drug binding in different configurations. Expression systems mimicking the screening process were first investigated. hERG model was then incorporated into a canine action potential (AP) and tissue model to study the impact of drug binding configurations on AP and pseudo-ECG (QT interval prolongation). Our data show that: (1) trapped and not trapped configurations and different binding kinetics could be identified during hERG screening; (2) slow binding, not trapped drugs, induced less AP prolongation and minimal QT interval prolongation (4.7%) at a concentration equal to the IC50 whereas maximal pro-arrhythmic risk was observed for trapped drugs at the same concentration (QT interval prolongation, 23.1%).

CONCLUSION

Our study demonstrates the need for screening for hERG binding configurations rather than potency alone. It also demonstrates the potential link between hERG, drug mode of action and TdP, and the need to question the current regulatory guidance.

Developmental changes in expression and biophysics of ion channels in the canine ventricle

BACKGROUND

Developmental changes in the electrical characteristics of the ventricular myocardium are not well defined. This study examines the contribution of inwardly rectifying K(+) current (IK1), transient outward K(+) current (Ito), delayed rectifier K(+) currents (IKr and IKs) and sodium channel current (INa) to repolarization in the canine neonate myocardium.

METHODS

Single myocytes isolated from the left ventricle of 2-3week old canine neonate hearts were studied using patch-clamp techniques.

RESULTS

Neonate cells were ~6-fold smaller than those of adults (28.8±8.8 vs. 176±6.7pF). IK1 was larger in neonate myocytes and displayed a substantial inward component and an outward component with negative slope conductance, peaking at -60mV (4.13 pA/pF). IKr tail currents (at -40mV), were small (<20pA). IKs could not be detected, even after exposure to isoproterenol (100nM). Ito was also absent in the neonate, consistent with the absence of a phase 1 in the action potential. Peak INa, late INa and ICa were smaller in the neonate compared with adults. KCND3, KCNIP2 and KCNQ1 mRNA expression was half, while KCNH2 was equal and KCNJ2 was greater in the neonate when compared with adults.

CONCLUSIONS

Two major repolarizing K(+) currents (IKs and Ito) present in adult ventricular cells are absent in the 2week old neonate. Peak and late INa are significantly smaller in the neonate. Our results suggest that the absence of these two currents in the neonate heart may increase the susceptibility to arrhythmias under certain long QT conditions.

Allocryptopine and benzyltetrahydropalmatine block hERG potassium channels expressed in HEK293 cells

AIM

Allocryptopine (ALL) is an alkaloid extracted from Corydalis decumbens (Thunb) Pers. Papaveraceae, whereas benzyltetrahydropalmatine (BTHP) is a derivative of tetrahydropalmatine extracted from Corydalis ambigua (Pall) Cham et Schlecht. The aim of this study was to investigate the effects of ALL and BTHP on the human ether-a-go-go related gene (hERG) current expressed in HEK293 cells.

METHODS

Cultured HEK293 cells were transiently transfected with hERG channel cDNA plasmid pcDNA3.1 using Lipofectamine. The whole-cell current IHERG was evoked and recorded using Axon MultiClamp 700B amplifier. The drugs were applied via supserfusion.

RESULTS

Both ALL and BTHP reversibly suppressed the amplitude and density of IHERG in concentration- and voltage-dependent manners (the respective IC50 value was 49.65 and 22.38 μmol/L). BTHP (30 μmol/L) caused a significant negative shift of the steady-state inactivation curve of IHERG, while ALL (30 μmol/L) did not affect the steady-state inactivation of IHERG. Furthermore, BTHP, but not ALL, shortened the time constants of fast inactivation and slow time constants of deactivation of IHERG. But both the drugs markedly lengthened the time constants for recovery of IHERG from inactivation. Using action potential waveform pulses, it was found that both the drugs at 30 μmol/L significantly suppressed the current densities in the late phase of action potential, but did not significantly affect the current densities in the early phase of action potential.

CONCLUSION

Both ALL and BTHP derived from Chinese herbs potently block hERG current.

High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment

The use of computational models to predict drug-induced changes in the action potential (AP) is a promising approach to reduce drug safety attrition but requires a better representation of more complex drug-target interactions to improve the quantitative prediction. The blockade of the human ether-a-go-go-related gene (HERG) channel is a major concern for QT prolongation and Torsade de Pointes risk. We aim to develop quantitative in-silico AP predictions based on a new electrophysiological protocol (suitable for high-throughput HERG screening) and mathematical modeling of ionic currents. Electrophysiological recordings using the IonWorks device were made from HERG channels stably expressed in Chinese hamster ovary cells. A new protocol that delineates inhibition over time was applied to assess dofetilide, cisapride, and almokalant effects. Dynamic effects displayed distinct profiles for these drugs compared with concentration-effects curves. Binding kinetics to specific states were identified using a new HERG Markov model. The model was then modified to represent the canine rapid delayed rectifier K+ current at 37°C and carry out AP predictions. Predictions were compared with a simpler model based on conductance reduction and were found to be much closer to experimental data. Improved sensitivity to concentration and pacing frequency variables was obtained when including binding kinetics. Our new electrophysiological protocol is suitable for high-throughput screening and is able to distinguish drug-binding kinetics. The association of this protocol with our modeling approach indicates that quantitative predictions of AP modulation can be obtained, which is a significant improvement compared with traditional conductance reduction methods.

Models of HERG gating

HERG (Kv11.1, KCNH2) is a voltage-gated potassium channel with unique gating characteristics. HERG has fast voltage-dependent inactivation, relatively slow deactivation, and fast recovery from inactivation. This combination of gating kinetics makes study of HERG difficult without using mathematical models. Several HERG models have been developed, with fundamentally different organization. HERG is the molecular basis of I(Kr), which plays a critical role in repolarization. We programmed and compared five distinct HERG models. HERG gating cannot be adequately replicated using Hodgkin-Huxley type formulation. Using Markov models, a five-state model is required with three closed, one open, and one inactivated state, and a voltage-independent step between some of the closed states. A fundamental difference between models is the presence/absence of a transition directly from the proximal closed state to the inactivated state. The only models that effectively reproduce HERG data have no direct closed-inactivated transition, or have a closed-inactivated transition that is effectively zero compared to the closed-open transition, rendering the closed-inactivation transition superfluous. Our single-channel model demonstrates that channels can inactivate without conducting with a flickering or bursting open-state. The various models have qualitative and quantitative differences that are critical to accurate predictions of HERG behavior during repolarization, tachycardia, and premature depolarizations.

Extracellular proton depression of peak and late Na⁺ current in the canine left ventricle

Cardiac ischemia reduces excitability in ventricular tissue. Acidosis (one component of ischemia) affects a number of ion currents. We examined the effects of extracellular acidosis (pH 6.6) on peak and late Na(+) current (I(Na)) in canine ventricular cells. Epicardial and endocardial myocytes were isolated, and patch-clamp techniques were used to record I(Na). Action potential recordings from left ventricular wedges exposed to acidic Tyrode solution showed a widening of the QRS complex, indicating slowing of transmural conduction. In myocytes, exposure to acidic conditions resulted in a 17.3 ± 0.9% reduction in upstroke velocity. Analysis of fast I(Na) showed that current density was similar in epicardial and endocardial cells at normal pH (68.1 ± 7.0 vs. 63.2 ± 7.1 pA/pF, respectively). Extracellular acidosis reduced the fast I(Na) magnitude by 22.7% in epicardial cells and 23.1% in endocardial cells. In addition, a significant slowing of the decay (time constant) of fast I(Na) was observed at pH 6.6. Acidosis did not affect steady-state inactivation of I(Na) or recovery from inactivation. Analysis of late I(Na) during a 500-ms pulse showed that the acidosis significantly reduced late I(Na) at 250 and 500 ms into the pulse. Using action potential clamp techniques, application of an epicardial waveform resulted in a larger late I(Na) compared with when an endocardial waveform was applied to the same cell. Acidosis caused a greater decrease in late I(Na) when an epicardial waveform was applied. These results suggest acidosis reduces both peak and late I(Na) in both cell types and contributes to the depression in cardiac excitability observed under ischemic conditions.

Regulation of the voltage-insensitive step of HERG activation by extracellular pH

Human ether-à-go-go-related gene (HERG, Kv11.1, KCNH2) voltage-gated K(+) channels dominate cardiac action potential repolarization. In addition, HERG channels play a role in neuronal and smooth cell excitability as well as cancer pathology. Extracellular pH (pH(o)) is modified during myocardial ischemia, inflammation, and respiratory alkalosis, so understanding the response of HERG channels to changes in pH is of clinical significance. The relationship between pH(o) and HERG channel gating appears complex. Acidification has previously been reported to speed, slow, or have no effect on activation. We therefore undertook comprehensive analysis of the effect of pH(o) on HERG channel activation. HERG channels have unique and complex activation gating characteristics with both voltage-sensitive and voltage-insensitive steps in the activation pathway. Acidosis decreased the activation rate, suppressed peak current, and altered the sigmoidicity of gating near threshold potentials. At positive voltages, where the voltage-insensitive transition is rate limiting, pH(o) modified the voltage-insensitive step with a pK(a) similar to that of histidine. Hill coefficient analysis was incompatible with a coefficient of 1 but was well described by a Hill coefficient of 4. We derived a pH(o)-sensitive term for a five-state Markov model of HERG channel gating. This model demonstrates the mechanism of pH(o) sensitivity in HERG channel activation. Our experimental data and mathematical model demonstrate that the pH(o) sensitivity of HERG channel activation is dominated by the pH(o) sensitivity of the voltage-insensitive step, in a fashion that is compatible with the presence of at least one proton-binding site on each subunit of the channel tetramer.