Pei Chi Yang

A Computational Model to Predict the Effects of Class I Anti-Arrhythmic Drugs on Ventricular Rhythms

Two- and three-dimensional models of cardiac excitability based on sodium channel kinetics can predict the adverse effects of class I anti-arrhythmic drugs. Crowdsourcing the Heart for Drug Screening The old way: Consult a specialist to answer your question. The new way: Consult a crowd of generalists who in the aggregate can come up with a better answer. The old way—testing drugs on single cardiac cells in vitro—has not worked well for screening out potential anti-arrhythmia agents that can occasionally block conduction in the heart or exacerbate arrhythmia, serious problems that cause sudden death in treated patients. Instead, Moreno et al. have called on the crowd by building a model of heart tissue that includes many cardiac cells and their interactions. When anti-arrhythmia drugs are “applied” to the model’s beating heart tissue—but not when they are applied to the single cardiac cells that make up the model—the drugs that cause side effects, and the concentrations at which they do so, are revealed, results that the authors were able to validate experimentally. The model starts with the detailed kinetics of the heart’s sodium channels, first in the context of a single cell, then in two- and three-dimensional cardiac tissue. The authors compared the action of lidocaine, a class 1B anti-arrhythmic drug not known to cause conduction block, and flecainide, a prototypical class 1C drug that carries a warning from the Food and Drug Administration. In the modeled analyses of single cardiac cells, both drugs slowed excitability at concentrations that matched those used in patients, but the cells retained the ability to generate action potentials. But when the model incorporated coupled groups of cells, the behavior of the drugs diverged. Lidocaine lowered excitability without causing block, but at the higher concentrations (used clinically), flecainide caused serious conduction block when heart rates reached 160 beats per minute. Experiments in rabbit heart confirmed the results of the model. In scaled-up, 500 by 500 groups of cells, the authors’ model could also successfully predict the tendency of flecainide, but not lidocaine, to make the heart extra sensitive to heartbeats occurring too early or too late, an effect that causes even more severe arrhythmias in patients when they take anti-arrhythmia drugs. Again, experiments in rabbit hearts replicated the model’s predictions, as did simulations of anatomically accurate human hearts derived from magnetic resonance imaging images. The ability of this sophisticated model of living cardiac tissue to replicate the clinical adverse effects of lidocaine and flecainide is promising, but it will be necessary to validate its performance with other drugs to understand how to deploy it most effectively. Ideally, such models will be useful for screening out potential arrhythmic drugs that promote conduction block or exacerbate arrhythmias. Such a view of how drugs affect the collective activity of cardiac cells should help in these situations in which the cure proves more deadly than the disease. A long-sought, and thus far elusive, goal has been to develop drugs to manage diseases of excitability. One such disease that affects millions each year is cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered, sometimes causing sudden death. Pharmacological management of cardiac arrhythmia has failed because it is not possible to predict how drugs that target cardiac ion channels, and have intrinsically complex dynamic interactions with ion channels, will alter the emergent electrical behavior generated in the heart. Here, we applied a computational model, which was informed and validated by experimental data, that defined key measurable parameters necessary to simulate the interaction kinetics of the anti-arrhythmic drugs flecainide and lidocaine with cardiac sodium channels. We then used the model to predict the effects of these drugs on normal human ventricular cellular and tissue electrical activity in the setting of a common arrhythmia trigger, spontaneous ventricular ectopy. The model forecasts the clinically relevant concentrations at which flecainide and lidocaine exacerbate, rather than ameliorate, arrhythmia. Experiments in rabbit hearts and simulations in human ventricles based on magnetic resonance images validated the model predictions. This computational framework initiates the first steps toward development of a virtual drug-screening system that models drug-channel interactions and predicts the effects of drugs on emergent electrical activity in the heart.

Computational approaches to understand cardiac electrophysiology and arrhythmias

Cardiac rhythms arise from electrical activity generated by precisely timed opening and closing of ion channels in individual cardiac myocytes. These impulses spread throughout the cardiac muscle to manifest as electrical waves in the whole heart. Regularity of electrical waves is critically important since they signal the heart muscle to contract, driving the primary function of the heart to act as a pump and deliver blood to the brain and vital organs. When electrical activity goes awry during a cardiac arrhythmia, the pump does not function, the brain does not receive oxygenated blood, and death ensues. For more than 50 years, mathematically based models of cardiac electrical activity have been used to improve understanding of basic mechanisms of normal and abnormal cardiac electrical function. Computer-based modeling approaches to understand cardiac activity are uniquely helpful because they allow for distillation of complex emergent behaviors into the key contributing components underlying them. Here we review the latest advances and novel concepts in the field as they relate to understanding the complex interplay between electrical, mechanical, structural, and genetic mechanisms during arrhythmia development at the level of ion channels, cells, and tissues. We also discuss the latest computational approaches to guiding arrhythmia therapy.

Ranolazine for Congenital and Acquired Late INa-Linked Arrhythmias In Silico Pharmacological Screening

Rationale: The antianginal ranolazine blocks the human ether-a-go-go–related gene–based current IKr at therapeutic concentrations and causes QT interval prolongation. Thus, ranolazine is contraindicated for patients with preexisting long-QT and those with repolarization abnormalities. However, with its preferential targeting of late INa (INaL), patients with disease resulting from increased INaL from inherited defects (eg, long-QT syndrome type 3 or disease-induced electric remodeling (eg, ischemic heart failure) might be exactly the ones to benefit most from the presumed antiarrhythmic properties of ranolazine. Objective: We developed a computational model to predict if therapeutic effects of pharmacological targeting of INaL by ranolazine prevailed over the off-target block of IKr in the setting of inherited long-QT syndrome type 3 and heart failure. Methods and Results: We developed computational models describing the kinetics and the interaction of ranolazine with cardiac Na+ channels in the setting of normal physiology, long-QT syndrome type 3–linked &Dgr;KPQ mutation, and heart failure. We then simulated clinically relevant concentrations of ranolazine and predicted the combined effects of Na+ channel and IKr blockade by both the parent compound ranolazine and its active metabolites, which have shown potent blocking effects in the therapeutically relevant range. Our simulations suggest that ranolazine is effective at normalizing arrhythmia triggers in bradycardia-dependent arrhythmias in long-QT syndrome type 3 as well tachyarrhythmogenic triggers arising from heart failure–induced remodeling. Conclusions: Our model predictions suggest that acute targeting of INaL with ranolazine may be an effective therapeutic strategy in diverse arrhythmia-provoking situations that arise from a common pathway of increased pathological INaL.

Acute Effects of Sex Steroid Hormones on Susceptibility to Cardiac Arrhythmias: A Simulation Study

Acute effects of sex steroid hormones likely contribute to the observation that post-pubescent males have shorter QT intervals than females. However, the specific role for hormones in modulating cardiac electrophysiological parameters and arrhythmia vulnerability is unclear. Here we use a computational modeling approach to incorporate experimentally measured effects of physiological concentrations of testosterone, estrogen and progesterone on cardiac ion channel targets. We then study the hormone effects on ventricular cell and tissue dynamics comprised of Faber-Rudy computational models. The “female” model predicts changes in action potential duration (APD) at different stages of the menstrual cycle that are consistent with clinically observed QT interval fluctuations. The “male” model predicts shortening of APD and QT interval at physiological testosterone concentrations. The model suggests increased susceptibility to drug-induced arrhythmia when estradiol levels are high, while testosterone and progesterone are apparently protective. Simulations predict the effects of sex steroid hormones on clinically observed QT intervals and reveal mechanisms of estrogen-mediated susceptibility to prolongation of QT interval. The simulations also indicate that acute effects of estrogen are not alone sufficient to cause arrhythmia triggers and explain the increased risk of females to Torsades de Pointes. Our results suggest that acute effects of sex steroid hormones on cardiac ion channels are sufficient to account for some aspects of gender specific susceptibility to long-QT linked arrhythmias.

Role of membrane microdomains in compartmentation of cAMP signaling.

Spatially restricting cAMP production to discrete subcellular locations permits selective regulation of specific functional responses. But exactly where and how cAMP signaling is confined is not fully understood. Different receptors and adenylyl cyclase isoforms responsible for cAMP production are not uniformly distributed between lipid raft and non-lipid raft domains of the plasma membrane. We sought to determine the role that these membrane domains play in organizing cAMP responses in HEK293 cells. The freely diffusible FRET-based biosensor Epac2-camps was used to measure global cAMP responses, while versions of the probe targeted to lipid raft (Epac2-MyrPalm) and non-raft (Epac2-CAAX) domains were used to monitor local cAMP production near the plasma membrane. Disruption of lipid rafts by cholesterol depletion selectively altered cAMP responses produced by raft-associated receptors. The results indicate that receptors associated with lipid raft as well as non-lipid raft domains can contribute to global cAMP responses. In addition, basal cAMP activity was found to be significantly higher in non-raft domains. This was supported by the fact that pharmacologic inhibition of adenylyl cyclase activity reduced basal cAMP activity detected by Epac2-CAAX but not Epac2-MyrPalm or Epac2-camps. Responses detected by Epac2-CAAX were also more sensitive to direct stimulation of adenylyl cyclase activity, but less sensitive to inhibition of phosphodiesterase activity. Quantitative modeling was used to demonstrate that differences in adenylyl cyclase and phosphodiesterase activities are necessary but not sufficient to explain compartmentation of cAMP associated with different microdomains of the plasma membrane.

Effects of Sex Hormones on Cardiac Repolarization

This article reviews the current findings on the topic of gender differences derived from effects of sex steroid hormones on cardiac repolarization and drug-induced torsade de pointes. Published studies have shown that sex-related differences exist in clinical cardiac electrophysiologic measurements, and that gender is an independent risk factor in vulnerability to certain types of arrhythmia. For example, women are especially susceptible to increased arrhythmia risk in response to QT-prolonging drugs. However, a number of questions remain-especially regarding the specific influence of sex steroid hormones in gender differences. Further experimental or clinical investigations are necessary to elucidate gender differences in cardiac repolarization, QT interval prolongation, and susceptibility to cardiac arrhythmias.

In silico Prediction of Sex-Based Differences in Human Susceptibility to Cardiac Ventricular Tachyarrhythmias

Sex-based differences in human susceptibility to cardiac ventricular tachyarrhythmias likely result from the emergent effects of multiple intersecting processes that fundamentally differ in male and female hearts. Included are measured differences in the genes encoding key cardiac ion channels and effects of sex steroid hormones to acutely modify electrical activity. At the genome-scale, human females have recently been shown to have lower expression of genes encoding key cardiac repolarizing potassium currents and connexin43, the primary ventricular gap-junction subunit. Human males and females also have distinct sex steroid hormones. Here, we developed mathematical models for male and female ventricular human heart cells by incorporating experimentally determined genomic differences and effects of sex steroid hormones into the O’Hara–Rudy model. These “male” and “female” model cells and tissues then were used to predict how various sex-based differences underlie arrhythmia risk. Genomic-based differences in ion channel expression were alone sufficient to determine longer female cardiac action potential durations (APD) in both epicardial and endocardial cells compared to males. Subsequent addition of sex steroid hormones exacerbated these differences, as testosterone further shortened APDs, while estrogen and progesterone application resulted in disparate effects on APDs. Our results indicate that incorporation of experimentally determined genomic differences from human hearts in conjunction with sex steroid hormones are consistent with clinically observed differences in QT interval, T-wave shape and morphology, and critically, in the higher vulnerability of adult human females to Torsades de Pointes type arrhythmias. The model suggests that female susceptibility to alternans stems from longer female action potentials, while reentrant arrhythmia derives largely from sex-based differences in conduction play an important role in arrhythmia vulnerability.

In silico prediction of drug therapy in catecholaminergic polymorphic ventricular tachycardia

The mechanism of therapeutic efficacy of flecainide for catecholaminergic polymorphic ventricular tachycardia (CPVT) is unclear. Model predictions suggest that Na+ channel effects are insufficient to explain flecainide efficacy in CPVT. This study represents a first step toward predicting therapeutic mechanisms of drug efficacy in the setting of CPVT and then using these mechanisms to guide modelling and simulation to predict alternative drug therapies.

A computational modelling approach combined with cellular electrophysiology data provides insights into the therapeutic benefit of targeting the late Na+ current

The ventricular action potential plateau is a phase of high resistance, which makes ventricular myocytes vulnerable to small electrical perturbations. We developed a computationally based model of GS‐458967 interaction with the cardiac Na+ channel, informed by experimental data recorded from guinea pig isolated single ventricular myocytes. The model predicts that the therapeutic potential of GS‐458967 derives largely from the designed property of significant potent selectivity for INaL.

A Computational Modeling and Simulation Approach to Investigate Mechanisms of Subcellular cAMP Compartmentation

Subcellular compartmentation of the ubiquitous second messenger cAMP has been widely proposed as a mechanism to explain unique receptor-dependent functional responses. How exactly compartmentation is achieved, however, has remained a mystery for more than 40 years. In this study, we developed computational and mathematical models to represent a subcellular sarcomeric space in a cardiac myocyte with varying detail. We then used these models to predict the contributions of various mechanisms that establish subcellular cAMP microdomains. We used the models to test the hypothesis that phosphodiesterases act as functional barriers to diffusion, creating discrete cAMP signaling domains. We also used the models to predict the effect of a range of experimentally measured diffusion rates on cAMP compartmentation. Finally, we modeled the anatomical structures in a cardiac myocyte diad, to predict the effects of anatomical diffusion barriers on cAMP compartmentation. When we incorporated experimentally informed model parameters to reconstruct an in silico subcellular sarcomeric space with spatially distinct cAMP production sites linked to caveloar domains, the models predict that under realistic conditions phosphodiesterases alone were insufficient to generate significant cAMP gradients. This prediction persisted even when combined with slow cAMP diffusion. When we additionally considered the effects of anatomic barriers to diffusion that are expected in the cardiac myocyte dyadic space, cAMP compartmentation did occur, but only when diffusion was slow. Our model simulations suggest that additional mechanisms likely contribute to cAMP gradients occurring in submicroscopic domains. The difference between the physiological and pathological effects resulting from the production of cAMP may be a function of appropriate compartmentation of cAMP signaling. Therefore, understanding the contribution of factors that are responsible for coordinating the spatial and temporal distribution of cAMP at the subcellular level could be important for developing new strategies for the prevention or treatment of unfavorable responses associated with different disease states.