Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

The role of ß-adrenergic system remodeling in human heart failure: A mechanistic investigation.

ß-adrenergic receptor antagonists (ß-blockers) are extensively used to improve cardiac performance in heart failure (HF), but the electrical improvements with these clinical treatments are not fully understood. The aim of this study was to analyze the electrophysiological effects of ß-adrenergic system remodeling in heart failure with reduced ejection fraction and the underlying mechanisms. We used a combined mathematical model that integrated ß-adrenergic signaling with electrophysiology and calcium cycling in human ventricular myocytes. HF remodeling, both in the electrophysiological and signaling systems, was introduced to quantitatively analyze changes in electrophysiological properties due to the stimulation of ß-adrenergic receptors in failing myocytes. We found that the inotropic effect of ß-adrenergic stimulation was reduced in HF due to the altered Ca2+ dynamics resulting from the combination of structural, electrophysiological and signaling remodeling. Isolated cells showed proarrhythmic risk after sympathetic stimulation because early afterdepolarizations appeared, and the vulnerability was greater in failing myocytes. When analyzing coupled cells, ß-adrenergic stimulation reduced transmural repolarization gradients between endocardium and epicardium in normal tissue, but was less effective at reducing these gradients after HF remodeling. The comparison of the selective activation of ß-adrenergic isoforms revealed that the response to ß2-adrenergic receptors stimulation was blunted in HF while ß1-adrenergic receptors downstream effectors regulated most of the changes observed after sympathetic stimulation. In conclusion, this study was able to reproduce an altered ß-adrenergic activity on failing myocytes and to explain the mechanisms involved. The derived predictions could help in the treatment of HF and guide in the design of future experiments.

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: Tx (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), TqNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), Ttriang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and TEAD (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 Tx, TqNet, Ttriang, and TEAD 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 Tx, TqNet and Ttriang 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 IKr, ICaL, INaL and IKs. 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.

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 IKr 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.

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk.

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( IKs and IKr, respectively) and the L-type calcium current ( ICaL) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC50 based test.

Cardiac action potential repolarization revisited: early repolarization shows all-or-none behaviour.

In healthy mammalian hearts the action potential (AP) waveform initiates and modulates each contraction, or heartbeat. As a result, AP height and duration are key physiological variables. In addition, rate-dependent changes in ventricular AP duration (APD), and variations in APD at a fixed heart rate are both reliable biomarkers of electrophysiological stability. Present guidelines for the likelihood that candidate drugs will increase arrhythmias rely on small changes in APD and Q-T intervals as criteria for safety pharmacology decisions. However, both of these measurements correspond to the final repolarization of the AP. Emerging clinical evidence draws attention to the early repolarization phase of the action potential (and the J-wave of the ECG) as an additional important biomarker for arrhythmogenesis. Here we provide a mechanistic background to this early repolarization syndrome by summarizing the evidence that both the initial depolarization and repolarization phases of the cardiac action potential can exhibit distinct time- and voltage-dependent thresholds, and also demonstrating that both can show regenerative all-or-none behaviour. An important consequence of this is that not all of the dynamics of action potential repolarization in human ventricle can be captured by data from single myocytes when these results are expressed as 'repolarization reserve'. For example, the complex pattern of cell-to-cell current flow that is responsible for AP conduction (propagation) within the mammalian myocardium can change APD and the Q-T interval of the electrocardiogram alter APD stability, and modulate responsiveness to pharmacological agents (such as Class III anti-arrhythmic drugs).

Reduced response to IKr blockade and altered hERG1a/1b stoichiometry in human heart failure.

Heart failure (HF) claims 250,000 lives per year in the US, and nearly half of these deaths are sudden and presumably due to ventricular tachyarrhythmias. QT interval and action potential (AP) prolongation are hallmark proarrhythmic changes in the failing myocardium, which potentially result from alterations in repolarizing potassium currents. Thus, we aimed to examine whether decreased expression of the rapid delayed rectifier potassium current, IKr, contributes to repolarization abnormalities in human HF. To map functional IKr expression across the left ventricle (LV), we optically imaged coronary-perfused LV free wall from donor and end-stage failing human hearts. The LV wedge preparation was used to examine transmural AP durations at 80% repolarization (APD80), and treatment with the IKr-blocking drug, E-4031, was utilized to interrogate functional expression. We assessed the percent change in APD80 post-IKr blockade relative to baseline APD80 (∆APD80) and found that ∆APD80s are reduced in failing versus donor hearts in each transmural region, with 0.35-, 0.43-, and 0.41-fold reductions in endo-, mid-, and epicardium, respectively (p=0.008, 0.037, and 0.022). We then assessed hERG1 isoform gene and protein expression levels using qPCR and Western blot. While we did not observe differences in hERG1a or hERG1b gene expression between donor and failing hearts, we found a shift in the hERG1a:hERG1b isoform stoichiometry at the protein level. Computer simulations were then conducted to assess IKr block under E-4031 influence in failing and nonfailing conditions. Our results confirmed the experimental observations and E-4031-induced relative APD80 prolongation was greater in normal conditions than in failing conditions, provided that the cellular model of HF included a significant downregulation of IKr. In human HF, the response to IKr blockade is reduced, suggesting decreased functional IKr expression. This attenuated functional response is associated with altered hERG1a:hERG1b protein stoichiometry in the failing human LV, and failing cardiomyoctye simulations support the experimental findings. Thus, of IKr protein and functional expression may be important determinants of repolarization remodeling in the failing human LV.

Lessons learned from multi-scale modeling of the failing heart.

Heart failure constitutes a major public health problem worldwide. Affected patients experience a number of changes in the electrical function of the heart that predispose to potentially lethal cardiac arrhythmias. Due to the multitude of electrophysiological changes that may occur during heart failure, the scientific literature is complex and sometimes ambiguous, perhaps because these findings are highly dependent on the etiology, the stage of heart failure, and the experimental model used to study these changes. Nevertheless, a number of common features of failing hearts have been documented. Prolongation of the action potential (AP) involving ion channel remodeling and alterations in calcium handling have been established as the hallmark characteristics of myocytes isolated from failing hearts. Intercellular uncoupling and fibrosis are identified as major arrhythmogenic factors. Multi-scale computational simulations are a powerful tool that complements experimental and clinical research. The development of biophysically detailed computer models of single myocytes and cardiac tissues has contributed greatly to our understanding of processes underlying excitation and repolarization in the heart. The electrical, structural, and metabolic remodeling that arises in cardiac tissues during heart failure has been addressed from different computational perspectives to further understand the arrhythmogenic substrate. This review summarizes the contributions from computational modeling and simulation to predict the underlying mechanisms of heart failure phenotypes and their implications for arrhythmogenesis, ranging from the cellular level to whole-heart simulations. The main aspects of heart failure are presented in several related sections. An overview of the main electrophysiological and structural changes that have been observed experimentally in failing hearts is followed by the description and discussion of the simulation work in this field at the cellular level, and then in 2D and 3D cardiac structures. The implications for arrhythmogenesis in heart failure are also discussed including therapeutic measures, such as drug effects and cardiac resynchronization therapy. Finally, the future challenges in heart failure modeling and simulation will be discussed.

In silico screening of the impact of hERG channel kinetic abnormalities on channel block and susceptibility to acquired long QT syndrome.

Accurate diagnosis of predisposition to long QT syndrome is crucial for reducing the risk of cardiac arrhythmias. In recent years, drug-induced provocative tests have proved useful to unmask some latent mutations linked to cardiac arrhythmias. In this study we expanded this concept by developing a prototype for a computational provocative screening test to reveal genetic predisposition to acquired long-QT syndrome (aLQTS). We developed a computational approach to reveal the pharmacological properties of I(Kr) blocking drugs that are most likely to cause aLQTS in the setting of subtle alterations in I(Kr) channel gating that would be expected to result from benign genetic variants.Weused themodel to predict themost potentially lethal combinations of kinetic anomalies and drug properties. In doing so, we also implicitly predicted ideal inverse therapeutic properties of K channel openers that would be expected to remedy a specific defect. We systematically performed "in silico mutagenesis" by altering discrete kinetic transition rates of the Fink et al. Markov model of human I(Kr) channels, corresponding to activation, inactivation, deactivation and recovery from inactivation of I(Kr) channels. We then screened and identified the properties of I(Kr) blockers that caused acquired long QT and therefore unmasked mutant phenotypes formild,moderate and severe variants. Mutant I(Kr) channels were incorporated into the O'Hara et al. human ventricular action potential (AP) model and subjected to simulated application of a wide variety of I(Kr)-drug interactions in order to identify the characteristics that selectively exacerbate the AP duration (APD) differences between wild-type and I(Kr) mutated cells. Our results show that drugs with disparate affinities to conformation states of the I(Kr) channel are key to amplify variants underlying susceptibility to acquired long QT syndrome, an effect that is especially pronounced at slow frequencies. Finally, we developed a mathematical formulation of the M54T MiRP1 latent mutation and simulated a provocative test. In this setting, application of dofetilide dramatically amplified the predicted QT interval duration in the M54T hMiRP1 mutation compared to wild-type.

In silico screening of the impact of hERG channel kinetic abnormalities on channel block and susceptibility to acquired long QT syndrome.

Accurate diagnosis of predisposition to long QT syndrome is crucial for reducing the risk of cardiac arrhythmias. In recent years, drug-induced provocative tests have proved useful to unmask some latent mutations linked to cardiac arrhythmias. In this study we expanded this concept by developing a prototype for a computational provocative screening test to reveal genetic predisposition to acquired long-QT syndrome (aLQTS). We developed a computational approach to reveal the pharmacological properties of IKr blocking drugs that are most likely to cause aLQTS in the setting of subtle alterations in IKr channel gating that would be expected to result from benign genetic variants. We used the model to predict the most potentially lethal combinations of kinetic anomalies and drug properties. In doing so, we also implicitly predicted ideal inverse therapeutic properties of K channel openers that would be expected to remedy a specific defect. We systematically performed "in silico mutagenesis" by altering discrete kinetic transition rates of the Fink et al. Markov model of human IKr channels, corresponding to activation, inactivation, deactivation and recovery from inactivation of IKr channels. We then screened and identified the properties of IKr blockers that caused acquired long QT and therefore unmasked mutant phenotypes for mild, moderate and severe variants. Mutant IKr channels were incorporated into the O'Hara et al. human ventricular action potential (AP) model and subjected to simulated application of a wide variety of IKr-drug interactions in order to identify the characteristics that selectively exacerbate the AP duration (APD) differences between wild-type and IKr mutated cells. Our results show that drugs with disparate affinities to conformation states of the IKr channel are key to amplify variants underlying susceptibility to acquired long QT syndrome, an effect that is especially pronounced at slow frequencies. Finally, we developed a mathematical formulation of the M54T MiRP1 latent mutation and simulated a provocative test. In this setting, application of dofetilide dramatically amplified the predicted QT interval duration in the M54T hMiRP1 mutation compared to wild-type.

Multiscale computational analysis of the bioelectric consequences of myocardial ischaemia and infarction.

Ischaemic heart disease is considered as the single most frequent cause of death, provoking more than 7 000 000 deaths every year worldwide. A high percentage of patients experience sudden cardiac death, caused in most cases by tachyarrhythmic mechanisms associated to myocardial ischaemia and infarction. These diseases are difficult to study using solely experimental means due to their complex dynamics and unstable nature. In the past decades, integrative computational simulation techniques have become a powerful tool to complement experimental and clinical research when trying to elucidate the intimate mechanisms of ischaemic electrophysiological processes and to aid the clinician in the improvement and optimization of therapeutic procedures. The purpose of this paper is to briefly review some of the multiscale computational models of myocardial ischaemia and infarction developed in the past 20 years, ranging from the cellular level to whole-heart simulations.

In silico ischaemia-induced reentry at the Purkinje-ventricle interface.

AIMS: This computational modelling work illustrates the influence of hyperkalaemia and electrical uncoupling induced by defined ischaemia on action potential (AP) propagation and the incidence of reentry at the Purkinje-ventricle interface in mammalian hearts. METHODS AND RESULTS: Unidimensional and bidimensional models of the Purkinje-ventricle subsystem, including ischaemic conditions (defined as phase 1B) in the ventricle and an ischaemic border zone, were developed by altering several important electrophysiological parameters of the Luo-Rudy AP model of the ventricular myocyte. Purkinje electrical activity was modelled using the equations of DiFrancesco and Noble. Our study suggests that an extracellular potassium concentration [K(+)]o >14 mM and a slight decrease in intercellular coupling induced by ischaemia in ventricle can cause conduction block from Purkinje to ventricle. Under these conditions, propagation from ventricle to Purkinje is possible. Thus, unidirectional block (UDB) and reentry can result. When conditions of UDB are met, retrograde propagation with a long delay (320 ms) may re-excite Purkinje cells, and give rise to a reentrant pathway. This induced reentry may be the origin of arrhythmias observed in phase 1B ischaemia. CONCLUSION: In a defined setting of ischaemia (phase 1B), a small amount of uncoupling between ventricular cells, as well as between Purkinje and ventricular tissue, may induce UDBs and reentry. Hyperkalaemia is also confirmed to be an important factor in the genesis of reentrant rhythms, since it regulates the range of coupling in which UDBs may be induced.

Insights from an electro-mechanical heart failure cell model: Role of SERCA enhancement on arrhythmogenesis and myocyte contraction.

BACKGROUND AND OBJECTIVE: Structural and electrical remodeling in heart failure predisposes the heart to ventricular arrhythmias. Computer modeling approaches, used to complement experimental results, can provide a more mechanistic knowledge of the biophysical phenomena underlying cardiac pathologies. Indeed, previous in-silico studies have improved the understanding of the electrical correlates of heart failure involved in arrhythmogenesis; however, information on the crosstalk between electrical activity, intracellular Ca2+ and contraction is still incomplete. This study aims to investigate the electro-mechanical behavior of virtual failing human ventricular myocytes to help in the development of therapies, which should ideally target pump failure and arrhythmias at the same time. METHODS: We implemented characteristic remodeling of heart failure with reduced ejection fraction by including reported changes in ionic conductances, sarcomere function and cell structure (e.g. T-tubules disarray). Model parametrization was based on published experimental data and the outcome of simulations was validated against experimentally observed patterns. We focused on two aspects of myocardial dysfunction central in heart failure: altered force-frequency relationship and susceptibility to arrhythmogenic early afterdepolarizations. Because biological variability is a major problem in the generalization of in-silico findings based on a unique set of model parameters, we generated and evaluated a population of models. RESULTS: The population-based approach is crucial in robust identification of parameters at the core of abnormalities and in generalizing the outcome of their correction. As compared to non-failing ones, failing myocytes had prolonged repolarization, a higher incidence of early afterdepolarizations, reduced contraction and a shallower force-frequency relationship, all features peculiar of heart failure. Component analysis applied to the model population identified reduced SERCA function as a relevant contributor to most of these derangements, which were largely reverted or diminished by restoration of SERCA function alone. CONCLUSIONS: These simulated results encourage the development of strategies comprising SERCA stimulation and highlight the need to evaluate both electrical and mechanical outcomes.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia.

BACKGROUND AND OBJECTIVE: In silico prediction of drug-induced ventricular arrhythmia often requires computationally intensive simulations, making its application tedious and non-interactive. This inconvenience can be mitigated using matrices of precomputed simulation results, allowing instantaneous computation of biomarkers such as action potential duration at 90% of the repolarisation (APD90). However, preparing such matrices can be computationally intensive for the method developers, limiting the range of simulated conditions. In this work, we aim to optimise the generation of these matrices so that they can be obtained with less effort and for a broader range of input values. METHODS: Machine learning methods were applied, building models trained with only a small fraction of the originally simulated results. The predictive performances of the models were assessed by comparing their predicted values with the actual simulation results, using percentual mean absolute error and mean relative error, as well as the percentage of data with a relative error below 5%. RESULTS: Our method obtained highly accurate estimations of the original values, leading to a nearly one hundred-fold decrease in computation time. This method also allows precomputing more complex matrices, describing the effect of more ion channels on the APD90. The best results were obtained by applying Support Vector Machine models, which yielded errors below 1% in most cases. This approach was further validated by predicting the APD90 of a set of 12 CiPA compounds and exporting the optimal settings for predicting APD90 using a different set of ion channels, always with satisfactory results. CONCLUSIONS: The proposed method effectively reduces the computational effort required to generate matrices of precomputed electrophysiological simulation values. The same approach can be applied in other fields where computationally costly simulations are applied repeatedly using slightly different input values.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity.

BACKGROUND AND OBJECTIVE: In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods. METHODS: Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels. RESULTS: Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values. CONCLUSIONS: This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk.

BACKGROUND AND OBJECTIVE: In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. METHODS: The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. RESULTS: Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. CONCLUSIONS: The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.

Analysis of vulnerability to reentry in acute myocardial ischemia using a realistic human heart model.

Electrophysiological alterations of the myocardium caused by acute ischemia constitute a pro-arrhythmic substrate for the generation of potentially lethal arrhythmias. Experimental evidence has shown that the main components of acute ischemia that induce these electrophysiological alterations are hyperkalemia, hypoxia (or anoxia in complete artery occlusion), and acidosis. However, the influence of each ischemic component on the likelihood of reentry is not completely established. Moreover, the role of the His-Purkinje system (HPS) in the initiation and maintenance of arrhythmias is not completely understood. In the present work, we investigate how the three components of ischemia affect the vulnerable window (VW) for reentry using computational simulations. In addition, we analyze the role of the HPS on arrhythmogenesis. A 3D biventricular/torso human model that includes a realistic geometry of the central and border ischemic zones with one of the most electrophysiologically detailed model of ischemia to date, as well as a realistic cardiac conduction system, were used to assess the VW for reentry. Four scenarios of ischemic severity corresponding to different minutes after coronary artery occlusion were simulated. Our results suggest that ischemic severity plays an important role in the generation of reentries. Indeed, this is the first 3D simulation study to show that ventricular arrhythmias could be generated under moderate ischemic conditions, but not in mild and severe ischemia. Moreover, our results show that anoxia is the ischemic component with the most significant effect on the width of the VW. Thus, a change in the level of anoxia from moderate to severe leads to a greater increment in the VW (40 ms), in comparison with the increment of 20 ms and 35 ms produced by the individual change in the level of hyperkalemia and acidosis, respectively. Finally, the HPS was a necessary element for the generation of approximately 17% of reentries obtained. The retrograde conduction from the myocardium to HPS in the ischemic region, conduction blocks in discrete sections of the HPS, and the degree of ischemia affecting Purkinje cells, are suggested as mechanisms that favor the generation of ventricular arrhythmias.

Fibrotic Remodeling during Persistent Atrial Fibrillation: In Silico Investigation of the Role of Calcium for Human Atrial Myofibroblast Electrophysiology.

During atrial fibrillation, cardiac tissue undergoes different remodeling processes at different scales from the molecular level to the tissue level. One central player that contributes to both electrical and structural remodeling is the myofibroblast. Based on recent experimental evidence on myofibroblasts' ability to contract, we extended a biophysical myofibroblast model with Ca2+ handling components and studied the effect on cellular and tissue electrophysiology. Using genetic algorithms, we fitted the myofibroblast model parameters to the existing in vitro data. In silico experiments showed that Ca2+ currents can explain the experimentally observed variability regarding the myofibroblast resting membrane potential. The presence of an L-type Ca2+ current can trigger automaticity in the myofibroblast with a cycle length of 799.9 ms. Myocyte action potentials were prolonged when coupled to myofibroblasts with Ca2+ handling machinery. Different spatial myofibroblast distribution patterns increased the vulnerable window to induce arrhythmia from 12 ms in non-fibrotic tissue to 22 ± 2.5 ms and altered the reentry dynamics. Our findings suggest that Ca2+ handling can considerably affect myofibroblast electrophysiology and alter the electrical propagation in atrial tissue composed of myocytes coupled with myofibroblasts. These findings can inform experimental validation experiments to further elucidate the role of myofibroblast Ca2+ handling in atrial arrhythmogenesis.

Analysis of the response of human iPSC-derived cardiomyocyte tissue to ICaL block. A combined in vitro and in silico approach.

The high incidence of cardiac arrythmias underlines the need for the assessment of pharmacological therapies. In this field of drug efficacy, as in the field of drug safety highlighted by the Comprehensive in Vitro Proarrhythmia Assay initiative, new pillars for research have become crucial: firstly, the integration of in-silico experiments, and secondly the evaluation of fully integrated biological systems, such as human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). In this study, we therefore aimed to combine in-vitro experiments and in-silico simulations to evaluate the antiarrhythmic effect of L-type calcium current (ICaL) block in hiPSC-CMs. For this, hiPSC-CM preparations were cultured and an equivalent virtual tissue was modeled. Re-entry patterns of electrical activation were induced and several biomarkers were obtained before and after ICaL block. The virtual hiPSC-CM simulations were also reproduced using a tissue composed of adult ventricular cardiomyocytes (hAdultV-CMs). The analysis of phases, currents and safety factor for propagation showed an increased size of the re-entry core when ICaL was blocked as a result of depressed cellular excitability. The bigger wavefront curvature yielded reductions of 12.2%, 6.9%, and 4.2% in the frequency of the re-entry for hiPSC-CM cultures, virtual hiPSC-CM, and hAdultV-CM tissues, respectively. Furthermore, ICaL block led to a 47.8% shortening of the vulnerable window for re-entry in the virtual hiPSC-CM tissue and to re-entry vanishment in hAdultV-CM tissue. The consistent behavior between in-vitro and in-silico hiPSC-CMs and between in-silico hiPSC-CMs and hAdultV-CMs evidences that virtual hiPSC-CM tissues are suitable for assessing cardiac efficacy, as done in the present study through the analysis of ICaL block.

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset.

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.