Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations

Prediction of arrhythmia susceptibility through mathematical modeling and machine learning

At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual's susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (n = 10,586), and we tested each variant's ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (I_Kr Block), 2) augmentation of the L-type calcium current (I_CaL Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to I_Kr Block or I_CaL Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.

Computer Modeling of the Heart for ECG Interpretation—A Review

Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.

In-silico study of the cardiac arrhythmogenic potential of biomaterial injection therapy

Biomaterial injection is a novel therapy to treat ischemic heart failure (HF) that has shown to reduce remodeling and restore cardiac function in recent preclinical studies. While the effect of biomaterial injection in reducing mechanical wall stress has been recently demonstrated, the influence of biomaterials on the electrical behavior of treated hearts has not been elucidated. In this work, we developed computational models of swine hearts to study the electrophysiological vulnerability associated with biomaterial injection therapy. The propagation of action potentials on realistic biventricular geometries was simulated by numerically solving the monodomain electrophysiology equations on anatomically-detailed models of normal, HF untreated, and HF treated hearts. Heart geometries were constructed from high-resolution magnetic resonance images (MRI) where the healthy, peri-infarcted, infarcted and gel regions were identified, and the orientation of cardiac fibers was informed from diffusion-tensor MRI. Regional restitution properties in each case were evaluated by constructing a probability density function of the action potential duration (APD) at different cycle lengths. A comparative analysis of the ventricular fibrillation (VF) dynamics for every heart was carried out by measuring the number of filaments formed after wave braking. Our results suggest that biomaterial injection therapy does not affect the regional dispersion of repolarization when comparing untreated and treated failing hearts. Further, we found that the treated failing heart is more prone to sustain VF than the normal heart, and is at least as susceptible to sustained VF as the untreated failing heart. Moreover, we show that the main features of VF dynamics in a treated failing heart are not affected by the level of electrical conductivity of the biogel injectates. This work represents a novel proof-of-concept study demonstrating the feasibility of computer simulations of the heart in understanding the arrhythmic behavior in novel therapies for HF.

Inference-based assessment of parameter identifiability in nonlinear biological models

As systems approaches to the development of biological models become more mature, attention is increasingly focusing on the problem of inferring parameter values within those models from experimental data. However, particularly for nonlinear models, it is not obvious, either from inspection of the model or from the experimental data, that the inverse problem of parameter fitting will have a unique solution, or even a non-unique solution that constrains the parameters to lie within a plausible physiological range. Where parameters cannot be constrained they are termed 'unidentifiable'. We focus on gaining insight into the causes of unidentifiability using inference-based methods, and compare a recently developed measure-theoretic approach to inverse sensitivity analysis to the popular Markov chain Monte Carlo and approximate Bayesian computation techniques for Bayesian inference. All three approaches map the uncertainty in quantities of interest in the output space to the probability of sets of parameters in the input space. The geometry of these sets demonstrates how unidentifiability can be caused by parameter compensation and provides an intuitive approach to inference-based experimental design.

Cardiac Rhythm and Molecular Docking Studies of Ion Channel Ligands with Cardiotoxicity in Zebrafish

Safety is one of the most important and critical issues in drug development. Many drugs were abandoned in clinical trials and retracted from the market because of unknown side effects. Cardiotoxicity is one of the most common reasons for drug retraction due to its potential side effects, i.e., inducing either tachycardia, bradycardia or arrhythmia. The zebrafish model could be used to screen drug libraries with potential cardiotoxicity in a high-throughput manner. In addition, the fundamental principles of replacement, reduction, and refinement of laboratory animal usage, 3R, could be achieved by using zebrafish as an alternative to animal models. In this study, we used a simple ImageJ-based method to evaluate and screen 70 ion channel ligands and successfully identify six compounds with strong cardiotoxicity in vivo. Next, we conducted an in silico-based molecular docking simulation to elucidate five identified compounds that might interact with domain III or domain IV of the Danio rerio L-type calcium channel (LTCC), a known pharmaceutically important target for arrhythmia. In conclusion, in this study, we provide a web lab and dry lab combinatorial approach to perform in vivo cardiotoxicity drug screening and in silico mechanistic studies.

In silico assessment of the effects of quinidine, disopyramide and E-4031 on short QT syndrome variant 1 in the human ventricles

Aims

Short QT syndrome (SQTS) is an inherited disorder associated with abnormally abbreviated QT intervals and an increased incidence of atrial and ventricular arrhythmias. SQT1 variant (linked to the rapid delayed rectifier potassium channel current, I_Kr) of SQTS, results from an inactivation-attenuated, gain-of-function mutation (N588K) in the KCNH2-encoded potassium channels. Pro-arrhythmogenic effects of SQT1 have been well characterized, but less is known about the possible pharmacological antiarrhythmic treatment of SQT1. Therefore, this study aimed to assess the potential effects of E-4031, disopyramide and quinidine on SQT1 using a mathematical model of human ventricular electrophysiology.

Methods

The ten Tusscher et al. biophysically detailed model of the human ventricular action potential (AP) was modified to incorporate I_Kr Markov chain (MC) formulations based on experimental data of the kinetics of the N588K mutation of the KCNH2-encoded subunit of the I_Kr channels. The modified ventricular cell model was then integrated into one-dimensional (1D) strand, 2D regular and realistic tissues with transmural heterogeneities. The channel-blocking effect of the drugs on ion currents in healthy and SQT1 cells was modeled using half-maximal inhibitory concentration (IC₅₀) and Hill coefficient (nH) values from literatures. Effects of drugs on cell AP duration (APD), effective refractory period (ERP) and pseudo-ECG traces were calculated. Effects of drugs on the ventricular temporal and spatial vulnerability to re-entrant excitation waves were measured. Re-entry was simulated in both 2D regular and realistic ventricular tissue.

Results

At the single cell level, the drugs E-4031 and disopyramide had hardly noticeable effects on the ventricular cell APD at 90% repolarization (APD₉₀), whereas quinidine caused a significant prolongation of APD₉₀. Quinidine prolonged and decreased the maximal transmural AP heterogeneity (δV); this led to the decreased transmural heterogeneity of APD across the 1D strand. Quinidine caused QT prolongation and a decrease in the T-wave amplitude, and increased ERP and decreased temporal susceptibility of the tissue to the initiation of re-entry and increased the minimum substrate size necessary to prevent re-entry in the 2D regular model, and further terminated re-entrant waves in the 2D realistic model. Quinidine exhibited significantly better therapeutic effects on SQT1 than E-4031 and disopyramide.

Conclusions

The simulated pharmacological actions of quinidine exhibited antiarrhythmic effects on SQT1. This study substantiates a causal link between quinidine and QT interval prolongation in SQT1 and suggests that quinidine may be a potential pharmacological agent for treating SQT1 patients.

Inter-individual variability and modeling of electrical activity: a possible new approach to explore cardiac safety?

Safety pharmacology aims to predict rare side effects of new drugs. We explored whether rare pro-arrhythmic effects could be linked to the variability of the effects of these drugs on ion currents and whether taking into consideration this variability in computational models could help to better detect and predict cardiac side effects. For this purpose, we evaluated how intra- and inter-individual variability influences the effect of hERG inhibition on both the action potential duration and the occurrence of arrhythmias. Using two computer simulation models of human action potentials (endocardial and Purkinje cells), we analyzed the contribution of two biological parameters on the pro-arrhythmic effects of several hERG channel blockers: (i) spermine concentration, which varies with metabolic status, and (ii) L-type calcium conductance, which varies due to single nucleotide polymorphisms or mutations. By varying these parameters, we were able to induce arrhythmias in 1 out of 16 simulations although conventional modeling methods to detect pro-arrhythmic molecules failed. On the basis of our results, taking into consideration only 2 parameters subjected to intra- and inter-individual variability, we propose that in silico computer modeling may help to better define the risks of new drug candidates at early stages of pre-clinical development.

Early drug development: assessment of proarrhythmic risk and cardiovascular safety

INTRODUCTION

hERG assays and thorough ECG trials have been mandated since 2005 to evaluate the QT interval and potential proarrhythmic risk of new chemical entities. The high cost of these studies and the shortcomings inherent in these binary and limited approaches to drug evaluation have prompted regulators to search for more cost effective and mechanistic paradigms to assess drug liability as exemplified by the CiPA initiative and the exposure response ICH E14(R3) guidance document. Areas covered: This review profiles the changing regulatory landscape as it pertains to early drug development and outlines the analyses that can be performed to characterize preclinical and early clinical cardiovascular risk. Expert commentary: It is further acknowledged that the narrow focus on the QT interval needs to be expanded to include a more comprehensive evaluation of cardiovascular risk since unanticipated off target effects have led to the withdrawal of multiple drugs after they had been approved and marketed.

Automated Detection of Benzodiazepine Dosage in ICU Patients through a Computational Analysis of Electrocardiographic Data

To enable automated maintenance of patient sedation in an intensive care unit (ICU) setting, more robust, quantitative metrics of sedation depth must be developed. In this study, we demonstrated the feasibility of a fully computational system that leverages low-quality electrocardiography (ECG) from a single lead to detect the presence of benzodiazepine sedatives in a subject's system. Starting with features commonly examined manually by cardiologists searching for evidence of poisonings, we generalized the extraction of these features to a fully automated process. We tested the predictive power of these features using nine subjects from an intensive care clinical database. Features were found to be significantly indicative of a binary relationship between dose and ECG morphology, but we were unable to find evidence of a predictable continuous relationship. Fitting this binary relationship to a classifier, we achieved a sensitivity of 89% and a specificity of 95%.

ECG-based estimation of dispersion of APD restitution as a tool to stratify sotalol-induced arrhythmic risk

Background

Increased spatial dispersion of restitution properties has been associated to arrhythmic risk. An ECG-based index quantifying restitution dispersion, DRest, is evaluated in patients who experienced Torsades de Pointes (TdP) under sotalol challenge and compared with the response in healthy subjects.

Methods and Results

ECG recordings were analyzed for quantification of DRest and QTc, among others biomarkers. DRest provides improved discrimination following sotalol administration between TdP and healthy subjects ([min–max]: [0.18–0.22] vs [0.02–0.12]), compared to other biomarkers including QTc ([436–548 ms] vs [376–467 ms]). Results in healthy subjects are in agreement with simulations of sotalol effects on a human tissue electrophysiological model.

Conclusions

This case study supports the potential of DRest for improved arrhythmia risk stratification even with QTc values below 450 ms.

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Several rate-dependent ECG-based biomarkers were evaluated in three patients who develop Torsades des Pointes after sotalol intake and in twenty five healthy volunteers who do not.

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Dispersion of restitution quantified from the ECG, DRest, identified better drug-induced cardiotoxicity than the conventionally used QTc.

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Neither DRest nor QTc shows sotalol-induced changes in subjects who do not develop TdP.

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Computer simulations corroborate the results of the ECG analysis and suggest that DRest is able to quantify the arrhythmogenic substrate in patients at risk of developing drug-induced arrhythmias.

The virtual heart as a platform for screening drug cardiotoxicity

To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug–ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug–ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi‐physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro‐arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro‐arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed.

Linked Articles

This article is part of a themed section on Chinese Innovation in Cardiovascular Drug Discovery. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-23

Antihistamines modulate the integrin signaling pathway in h9c2 rat cardiomyocytes: Possible association with cardiotoxicity

The identification of biomarkers for toxicity prediction is crucial for drug development and safety evaluation. The selective and specific biomarkers for antihistamines-induced cardiotoxicity is not well identified yet. In order to evaluate the mechanism of the life-threatening effects caused by antihistamines, we used DNA microarrays to analyze genomic profiles in H9C2 rat cardiomyocytes that were treated with antihistamines. The gene expression profiles from drug-treated cells revealed changes in the integrin signaling pathway, suggesting that cardiac arrhythmias induced by antihistamine treatment may be mediated by changes in integrin-mediated signaling. It has been reported that integrin plays a role in QT prolongation that may induce cardiac arrhythmia. These results indicate that the integrin-mediated signaling pathway induced by antihistamines is involved in various biological mechanisms that lead to cardiac QT prolongation. Therefore, we suggest that genomic profiling of antihistamine-treated cardiomyocytes has the potential to reveal the mechanism of adverse drug reactions, and this signal pathway is applicable to prediction of in vitro cardiotoxicity induced by antihistamines as a biomarker candidate.

Effects of L-type calcium channel and human ether-a-go-go related gene blockers on the electrical activity of the human heart: a simulation study

Aims

Class III and IV drugs affect cardiac human ether-a-go-go related gene (I_Kr) and L-type calcium (I_CaL) channels, resulting in complex alterations in repolarization with both anti- and pro-arrhythmic consequences. Interpretation of their effects on cellular and electrocardiogram (ECG)-based biomarkers for risk stratification is challenging. As pharmaceutical compounds often exhibit multiple ion channel effects, our goal is to investigate the simultaneous effect of I_CaL and I_Kr block on human ventricular electrophysiology from ionic to ECG level.

Methods and results

Simulations are conducted using a human body torso bidomain model, which includes realistic representation of human membrane kinetics, anatomy, and fibre orientation. A simple block pore model is incorporated to simulate drug-induced I_CaL and I_Kr blocks, for drug dose = 0, IC₅₀, 2× IC₅₀, 10× IC₅₀, and 30× IC₅₀. Drug effects on human ventricular activity are quantified for different degrees and combinations of I_CaL and I_Kr blocks from the ionic to the body surface ECG level. Electrocardiogram simulations show that I_CaL block results in shortening of the QT interval, ST elevation, and reduced T-wave amplitude, caused by reduction in action potential duration and action potential amplitude during the plateau phase, and in repolarization times. In contrast, I_Kr block results in QT prolongation and reduced T-wave amplitude. When I_CaL and I_Kr blocks are combined, the degree of I_CaL block strongly determines QT interval whereas the effect of I_Kr block is more pronounced on the T-wave amplitude.

Conclusion

Our simulation study provides new insights into the combined effect of I_CaL and I_Kr blocks on human ventricular activity using a multiscale computational human torso model.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials

Background and Purpose

Understanding drug effects on the heart is key to safety pharmacology assessment and anti-arrhythmic therapy development. Here our goal is to demonstrate the ability of computational models to simulate the effect of drug action on the electrical activity of the heart, at the level of the ion-channel, cell, heart and ECG body surface potential.

Experimental Approach

We use the state-of-the-art mathematical models governing the electrical activity of the heart. A drug model is introduced using an ion channel conductance block for the hERG and fast sodium channels, depending on the IC₅₀ value and the drug dose. We simulate the ECG measurements at the body surface and compare biomarkers under different drug actions.

Key Results

Introducing a 50% hERG-channel current block results in 8% prolongation of the APD₉₀ and 6% QT interval prolongation, hERG block does not affect the QRS interval. Introducing 50% fast sodium current block prolongs the QRS and the QT intervals by 12% and 5% respectively, and delays activation times, whereas APD₉₀ is not affected.

Conclusions and Implications

Both potassium and sodium blocks prolong the QT interval, but the underlying mechanism is different: for potassium it is due to APD prolongation; while for sodium it is due to a reduction of electrical wave velocity. This study shows the applicability of in silico models for the investigation of drug effects on the heart, from the ion channel to the ECG-based biomarkers.

Combining wet and dry research: experience with model development for cardiac mechano-electric structure-function studies

Since the development of the first mathematical cardiac cell model 50 years ago, computational modelling has become an increasingly powerful tool for the analysis of data and for the integration of information related to complex cardiac behaviour. Current models build on decades of iteration between experiment and theory, representing a collective understanding of cardiac function. All models, whether computational, experimental, or conceptual, are simplified representations of reality and, like tools in a toolbox, suitable for specific applications. Their range of applicability can be explored (and expanded) by iterative combination of 'wet' and 'dry' investigation, where experimental or clinical data are used to first build and then validate computational models (allowing integration of previous findings, quantitative assessment of conceptual models, and projection across relevant spatial and temporal scales), while computational simulations are utilized for plausibility assessment, hypotheses-generation, and prediction (thereby defining further experimental research targets). When implemented effectively, this combined wet/dry research approach can support the development of a more complete and cohesive understanding of integrated biological function. This review illustrates the utility of such an approach, based on recent examples of multi-scale studies of cardiac structure and mechano-electric function.

Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing

Concerns over cardiac side effects are the largest single cause of compound attrition during pharmaceutical drug development. For a number of years, biophysically detailed mathematical models of cardiac electrical activity have been used to explore how a compound, interfering with specific ion-channel function, may explain effects at the cell-, tissue- and organ-scales. With the advent of high-throughput screening of multiple ion channels in the wet-lab, and improvements in computational modelling of their effects on cardiac cell activity, more reliable prediction of pro-arrhythmic risk is becoming possible at the earliest stages of drug development. In this paper, we review the current use of biophysically detailed mathematical models of cardiac myocyte electrical activity in drug safety testing, and suggest future directions to employ the full potential of this approach.

The use of integrated and intelligent testing strategies in the prediction of toxic hazard and in risk assessment

There is increasing concern that insurmountable differences between humans and laboratory animals limit the relevance and reliability for hazard identification and risk assessment purposes of animal data produced by traditional toxicity test procedures. A way forward is offered by the emerging new technologies, which can be directly applied to human material or even to human beings themselves. This promises to revolutionise the evaluation of the safety of chemicals and chemical products of various kinds and, in particular, pharmaceuticals. The available and developing technologies are summarised and it is emphasised that they will need to be used selectively, in integrated and intelligent testing strategies, which, in addition to being scientifically sound, must be manageable and affordable. Examples are given of proposed testing strategies for general chemicals, cosmetic ingredients, candidate pharmaceuticals, inhaled substances, nanoparticles and neurotoxicity.

Quickly finding a needle in a haystack: a new automated cardiac arrhythmia detection software for preclinical studies

INTRODUCTION

The occurrence of drug-induced arrhythmias in safety pharmacology or toxicology studies is a primary safety concern. The risk assessment requires an accurate knowledge of background arrhythmia incidence and frequency in the test system/species, as well as a rigorous evaluation of the effects of the potential new medical entities on the electrocardiogram (ECG). However, the direct assessment of arrhythmia in ECG recordings is a time-consuming effort and is rarely achieved due to lack of suitable automated tools. A new software application named ARR30a was developed for fast automated detection in preclinical studies, for the five major arrhythmia types, namely sinus pauses, atrial beats, junctional beats, ventricular beats and type 2 atrio-ventricular blocks (AV-blocks II). The purpose of this study was to characterize the performance of ARR30a in large and small animal species.

METHODS

Detection sensitivity and predictivity were evaluated on a database of 84 ECG recordings representative of each animal species and experimental protocols typically used in efficacy, safety pharmacology and toxicology studies. Automated arrhythmia detection was compared with manual analysis.

RESULTS

In large animals such as dogs, non-human primates and pigs, ARR30a sensitivity reached 90.6%, 82.2% and 78.0% for ventricular beats, AV-blocks II and junctional beats with predictivity of 83.4%, 94.4% and 93.5%, respectively. Significantly lower sensitivity was observed in rats for junctional beats due to challenging problems of detection for low amplitude P-waves. Robustness to noise was assessed by adding increasing noise levels to ECG signals and showed no significant impact on arrhythmia detection at moderate noise levels. Processing time for a 24 hour recording was approximately 4 min for dogs and 6 min for rats on a 3 GHz processor.

DISCUSSION

This newly validated ECG arrhythmia detector ARR30a allows evaluating all major ECG signal abnormalities and enhances the quantification of arrhythmia incidence in all major laboratory animal species. The mark editor RME10a enables manual validation of the automated analysis and refinement of the arrhythmia classification.

Is it time for in silico simulation of drug cardiac side effects?

Cardiac simulation is used to integrate information on drug action to predict side effects on the whole heart. Could simulation begin to replace animal models?

A multiscale investigation of repolarization variability and its role in cardiac arrhythmogenesis

Enhanced temporal and spatial variability in cardiac repolarization has been related to increased arrhythmic risk both clinically and experimentally. Causes and modulators of variability in repolarization and their implications in arrhythmogenesis are however not well understood. At the ionic level, the slow component of the delayed rectifier potassium current (I(Ks)) is an important determinant of ventricular repolarization. In this study, a combination of experimental and computational multiscale studies is used to investigate the role of intrinsic and extrinsic noise in I(Ks) in modulating temporal and spatial variability in ventricular repolarization in human and guinea pig. Results show that under physiological conditions: i), stochastic fluctuations in I(Ks) gating properties (i.e., intrinsic noise) cause significant beat-to-beat variability in action potential duration (APD) in isolated cells, whereas cell-to-cell differences in channel numbers (i.e., extrinsic noise) also contribute to cell-to-cell APD differences; ii), in tissue, electrotonic interactions mask the effect of I(Ks) noise, resulting in a significant decrease in APD temporal and spatial variability compared to isolated cells. Pathological conditions resulting in gap junctional uncoupling or a decrease in repolarization reserve uncover the manifestation of I(Ks) noise at cellular and tissue level, resulting in enhanced ventricular variability and abnormalities in repolarization such as afterdepolarizations and alternans.

Systematic characterization of the ionic basis of rabbit cellular electrophysiology using two ventricular models

Several mathematical models of rabbit ventricular action potential (AP) have been proposed to investigate mechanisms of arrhythmias and excitation-contraction coupling. Our study aims at systematically characterizing how ionic current properties modulate the main cellular biomarkers of arrhythmic risk using two widely-used rabbit ventricular models, and comparing simulation results using the two models with experimental data available for rabbit. A sensitivity analysis of AP properties, Ca²⁺ and Na⁺ dynamics, and their rate dependence to variations (±15% and ±30%) in the main transmembrane current conductances and kinetics was performed using the Shannon et al. (2004) and the Mahajan et al. (2008a,b) AP rabbit models. The effects of severe transmembrane current blocks (up to 100%) on steady-state AP and calcium transients, and AP duration (APD) restitution curves were also simulated using both models. Our simulations show that, in both virtual rabbit cardiomyocytes, APD is significantly modified by most repolarization currents, AP triangulation is regulated mostly by the inward rectifier K⁺ current (I(K1)) whereas APD rate adaptation as well as [Na⁺](i) rate dependence is influenced by the Na⁺/K⁺ pump current (I(NaK)). In addition, steady-state [Ca²⁺](i) levels, APD restitution properties and [Ca²⁺](i) rate dependence are strongly dependent on I(NaK), the L-Type Ca²⁺ current (I(CaL)) and the Na⁺/Ca²⁺ exchanger current (I(NaCa)), although the relative role of these currents is markedly model dependent. Furthermore, our results show that simulations using both models agree with many experimentally-reported electrophysiological characteristics. However, our study shows that the Shannon et al. model mimics rabbit electrophysiology more accurately at normal pacing rates, whereas Mahajan et al. model behaves more appropriately at faster rates. Our results reinforce the usefulness of sensitivity analysis for further understanding of cellular electrophysiology and validation of cardiac AP models.

Successes and failures in modeling heart cell electrophysiology

Mathematical models of the electrical activity of the heart using equations for protein ion channels and other transporters began with the Noble 1962 model. These models then developed over a period of about 50 years. Cell types in all regions have been modeled and now are available for download from the CellML website (www.cellml.org). Simulation is a necessary tool of analysis in attempting to understand biological complexity. We often learn as much from the failures as from the successes of mathematical models. It is the iterative interaction between experiment and simulation that is important.

Rabbit-specific ventricular model of cardiac electrophysiological function including specialized conduction system

The function of the ventricular specialized conduction system in the heart is to ensure the coordinated electrical activation of the ventricles. It is therefore critical to the overall function of the heart, and has also been implicated as an important player in various diseases, including lethal ventricular arrhythmias such as ventricular fibrillation and drug-induced torsades de pointes. However, current ventricular models of electrophysiology usually ignore, or include highly simplified representations of the specialized conduction system. Here, we describe the development of an image-based, species-consistent, anatomically-detailed model of rabbit ventricular electrophysiology that incorporates a detailed description of the free-running part of the specialized conduction system. Techniques used for the construction of the geometrical model of the specialized conduction system from a magnetic resonance dataset and integration of the system model into a ventricular anatomical model, developed from the same dataset, are described. Computer simulations of rabbit ventricular electrophysiology are conducted using the novel anatomical model and rabbit-specific membrane kinetics to investigate the importance of the components and properties of the conduction system in determining ventricular function under physiological conditions. Simulation results are compared to panoramic optical mapping experiments for model validation and results interpretation. Full access is provided to the anatomical models developed in this study.

New drugs vs. old concepts: a fresh look at antiarrhythmics

Common arrhythmias, particularly atrial fibrillation (AF) and ventricular tachycardia/fibrillation (VT/VF) are a major public health concern. Classic antiarrhythmic (AA) drugs for AF are of limited effectiveness, and pose the risk of life-threatening VT/VF. For VT/VF, implantable cardiac defibrillators appear to be the unique, yet unsatisfactory, solution. Very few AA drugs have been successful in the last few decades, due to safety concerns or limited benefits in comparison to existing therapy. The Vaughan-Williams classification (one drug for one molecular target) appears too restrictive in light of current knowledge of molecular and cellular mechanisms. New AA drugs such as atrial-specific and/or multichannel blockers, upstream therapy and anti-remodeling drugs, are emerging. We focus on the cellular mechanisms related to abnormal Na⁺ and Ca²⁺ handling in AF, heart failure, and inherited arrhythmias, and on novel strategies aimed at normalizing ionic homeostasis. Drugs that prevent excessive Na⁺ entry (ranolazine) and aberrant diastolic Ca²⁺ release via the ryanodine receptor RyR2 (rycals, dantrolene, and flecainide) exhibit very interesting antiarrhythmic properties. These drugs act by normalizing, rather than blocking, channel activity. Ranolazine preferentially blocks abnormal persistent (vs. normal peak) Na⁺ currents, with minimal effects on normal channel function (cell excitability, and conduction). A similar "normalization" concept also applies to RyR2 stabilizers, which only prevent aberrant opening and diastolic Ca²⁺ leakage in diseased tissues, with no effect on normal function during systole. The different mechanisms of action of AA drugs may increase the therapeutic options available for the safe treatment of arrhythmias in a wide variety of pathophysiological situations.

The systems biology approach to drug development: application to toxicity assessment of cardiac drugs

Side effects account for most of the instances of failure of candidate drugs at late stages of development. These development failures contribute to the exorbitant cost of bringing new compounds to market: a single withdrawal can represent a loss of more than $1 billion. Many unwanted actions of drugs affect the heart, resulting in potentially proarrhythmic alteration of ion channel function. Because these can be fatal, potential electrophysiological cardiotoxicity is among the most stringent exclusion criteria in the licensing process.