Re-evaluating the efficacy of beta-adrenergic agonists and antagonists in long QT-3 syndrome through computational modelling

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

The Efficacy of Beta-Blockers in Patients With Long QT Syndrome 1-3 According to Individuals' Gender, Age, and QTc Intervals: A Network Meta-analysis

Long QT syndrome (LQTS) is an arrhythmic heart disease caused by congenital genetic mutations, and results in increased occurrence rates of polymorphic ventricular tachyarrhythmias and sudden cardiac death (SCD). Clinical evidence from numerous previous studies suggested that beta blockers (BBs), including atenolol, propranolol, metoprolol, and nadolol, exhibit different efficacies for reducing the risk of cardiac events (CEs), such as syncope, arrest cardiac arrest (ACA), and SCD, in patients with LQTS. In this study, we identified relevant studies in MEDLINE, PubMed, embase, and Cochrane databases and performed a meta-analysis to assess the relationship between the rate of CEs and LQTS individuals with confounding variables, including different gender, age, and QTc intervals. Moreover, a network meta-analysis was not only established to evaluate the effectiveness of different BBs, but also to provide the ranked efficacies of BBs treatment for preventing the recurrence of CEs in LQT1 and LQT2 patients. In conclusion, nadolol was recommended as a relatively effective strategy for LQT2 in order to improve the prognosis of patients during a long follow-up period.

Targeting the β-adrenergic receptor in the clinical management of congenital long QT syndrome

The long QT syndrome (LQTS) is largely treated pharmacologically with β-blockers, despite the role of sympathetic activity in LQTS being poorly understood. Using the trigger-substrate model of cardiac arrhythmias in this review, we amalgamate current experimental and clinical data from both animal and human studies to explain the mechanism of adrenergic stimulation and blockade on LQT arrhythmic risk and hence assess the efficacy of β-adrenoceptor blockade in the management of LQTS. In LQTS1 and LQTS2, sympathetic stimulation increases arrhythmic risk by enhancing early afterdepolarizations and transmural dispersion of repolarization. β-Blockers successfully reduce cardiac events by reducing these triggers and substrates; however, these effects are less marked in LQTS2 compared with LQTS1. In LQTS3, clinical and experimental investigations of the effects of sympathetic stimulation and β-blocker use have produced contradictory findings, resulting in significant clinical uncertainty. We offer explanations for these contradicting results relating to study sample size, the dose of the β-blocker administered associated with its off-target Na⁺ channel effects, as well as the type of β-blocker used. We conclude that the antiarrhythmic efficacy of β-blockers is a genotype-specific phenomenon, and hence the use of β-blockers in clinical practice should be genotype dependent.

R-From-T as a Common Mechanism of Arrhythmia Initiation in Long QT Syndromes

BACKGROUND

Long QT syndromes (LQTS) arise from many genetic and nongenetic causes with certain characteristic ECG features preceding polymorphic ventricular tachyarrhythmias (PVTs). However, how the many molecular causes result in these characteristic ECG patterns and how these patterns are mechanistically linked to the spontaneous initiation of PVT remain poorly understood.

METHODS

Anatomic human ventricle and simplified tissue models were used to investigate the mechanisms of spontaneous initiation of PVT in LQTS.

RESULTS

Spontaneous initiation of PVT was elicited by gradually ramping up ICa,L to simulate the initial phase of a sympathetic surge or by changing the heart rate, reproducing the different genotype-dependent clinical ECG features. In LQTS type 2 (LQT2) and LQTS type 3 (LQT3), T-wave alternans was observed followed by premature ventricular complexes (PVCs). Compensatory pauses occurred resulting in short-long-short sequences. As ICa,L increased further, PVT episodes occurred, always preceded by a short-long-short sequence. However, in LQTS type 1 (LQT1), once a PVC occurred, it always immediately led to an episode of PVT. Arrhythmias in LQT2 and LQT3 were bradycardia dependent, whereas those in LQT1 were not. In all 3 genotypes, PVCs always originated spontaneously from the steep repolarization gradient region and manifested on ECG as R-on-T. We call this mechanism R-from-T, to distinguish it from the classic explanation of R-on-T arrhythmogenesis in which an exogenous PVC coincidentally encounters a repolarizing region. In R-from-T, the PVC and the T wave are causally related, where steep repolarization gradients combined with enhanced ICa,L lead to PVCs emerging from the T wave. Since enhanced ICa,L was required for R-from-T to occur, suppressing window ICa,L effectively prevented arrhythmias in all 3 genotypes.

CONCLUSIONS

Despite the complex molecular causes, these results suggest that R-from-T is likely a common mechanism for PVT initiation in LQTS. Targeting ICa,L properties, such as suppressing window ICa,L or preventing excessive ICa,L increase, could be an effective unified therapy for arrhythmia prevention in LQTS.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Ageing, the autonomic nervous system and arrhythmia: From brain to heart

An ageing myocardium possesses significant electrophysiological alterations that predisposes the elderly patient to arrhythmic risk. Whilst these alterations are intrinsic to the cardiac myocytes, they are modulated by the cardiac autonomic nervous system (ANS) and consequently, ageing of the cardiac ANS is fundamental to the development of arrhythmias. A systems-based approach that incorporates the influence of the cardiac ANS could lead to better mechanistic understanding of how arrhythmogenic triggers and substrates interact spatially and temporally to produce sustained arrhythmia and why its incidence increases with age. Despite the existence of physiological oscillations of ANS activity on the heart, pathological oscillations can lead to defective activation and recovery properties of the myocardium. Such changes can be attributable to the decrease in functionality and structural alterations to ANS specific receptors in the myocardium with age. These altered ANS adaptive responses can occur either as a normal ageing process or accelerated in the presence of specific cardiac pathologies, such as genetic mutations or neurodegenerative conditions. Targeted intervention that seek to manipulate the ageing ANS influence on the myocardium may prove to be an efficacious approach for the management of arrhythmia in the ageing population.

A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research

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

The Beta Adrenergic Receptor Blocker Propranolol Counteracts Retinal Dysfunction in a Mouse Model of Oxygen Induced Retinopathy: Restoring the Balance between Apoptosis and Autophagy

In a mouse model of oxygen induced retinopathy (OIR), beta adrenergic receptor (BAR) blockade has been shown to recover hypoxia-associated retinal damages. Although the adrenergic signaling is an important regulator of apoptotic and autophagic processes, the role of BARs in retinal cell death remains to be elucidated. The present study was aimed at investigating whether ameliorative effects of BAR blockers may occur through their coordinated action on apoptosis and autophagy. To this aim, retinas from control and OIR mice untreated or treated with propranolol, a non-selective BAR1/2 blocker, were characterized in terms of expression and localization of apoptosis and autophagy markers. The effects of propranolol on autophagy signaling were also evaluated and specific autophagy modulators were used to get functional information on the autophagic effects of BAR antagonism. Finally, propranolol effects on neurodegenerative processes were associated to an electrophysiological investigation of retinal function by recording electroretinogram (ERG). We found that retinas of OIR mice are characterized by increased apoptosis and decreased autophagy, while propranolol reduces apoptosis and stimulates autophagy. In particular, propranolol triggers autophagosome formation in bipolar, amacrine and ganglion cells that are committed to die by apoptosis in response to hypoxia. Also our data argue that propranolol, through the inhibition of the Akt-mammalian target of rapamycin pathway, activates autophagy which decreases retinal cell death. At the functional level, propranolol recovers dysfunctional ERG by recovering the amplitude of a- and b-waves, and oscillatory potentials, thus indicating an efficient restoring of retinal transduction. Overall, our results demonstrate that BAR1/2 are key regulators of retinal apoptosis/autophagy, and that BAR1/2 blockade leads to autophagy-mediated neuroprotection. Reinstating the balance between apoptotic and autophagic machines may therefore be viewed as a future goal in the treatment of retinopathies.

Revealing the Concealed Nature of Long-QT Type 3 Syndrome

BACKGROUND

Gain-of-function mutations in the voltage-gated sodium channel (Nav1.5) are associated with the long-QT-3 (LQT3) syndrome. Nav1.5 is densely expressed at the intercalated disk, and narrow intercellular separation can modulate cell-to-cell coupling via extracellular electric fields and depletion of local sodium ion nanodomains. Models predict that significantly decreasing intercellular cleft widths slows conduction because of reduced sodium current driving force, termed "self-attenuation." We tested the novel hypothesis that self-attenuation can "mask" the LQT3 phenotype by reducing the driving force and late sodium current that produces early afterdepolarizations (EADs).

METHODS AND RESULTS

Acute interstitial edema was used to increase intercellular cleft width in isolated guinea pig heart experiments. In a drug-induced LQT3 model, acute interstitial edema exacerbated action potential duration prolongation and produced EADs, in particular, at slow pacing rates. In a computational cardiac tissue model incorporating extracellular electric field coupling, intercellular cleft sodium nanodomains, and LQT3-associated mutant channels, myocytes produced EADs for wide intercellular clefts, whereas for narrow clefts, EADs were suppressed. For both wide and narrow clefts, mutant channels were incompletely inactivated. However, for narrow clefts, late sodium current was reduced via self-attenuation, a protective negative feedback mechanism, masking EADs.

CONCLUSIONS

We demonstrated a novel mechanism leading to the concealing and revealing of EADs in LQT3 models. Simulations predict that this mechanism may operate independent of the specific mutation, suggesting that future therapies could target intercellular cleft separation as a compliment or alternative to sodium channels.

Molecular Pathophysiology of Congenital Long QT Syndrome

Ion channels represent the molecular entities that give rise to the cardiac action potential, the fundamental cellular electrical event in the heart. The concerted function of these channels leads to normal cyclical excitation and resultant contraction of cardiac muscle. Research into cardiac ion channel regulation and mutations that underlie disease pathogenesis has greatly enhanced our knowledge of the causes and clinical management of cardiac arrhythmia. Here we review the molecular determinants, pathogenesis, and pharmacology of congenital Long QT Syndrome. We examine mechanisms of dysfunction associated with three critical cardiac currents that comprise the majority of congenital Long QT Syndrome cases: 1) IKs, the slow delayed rectifier current; 2) IKr, the rapid delayed rectifier current; and 3) INa, the voltage-dependent sodium current. Less common subtypes of congenital Long QT Syndrome affect other cardiac ionic currents that contribute to the dynamic nature of cardiac electrophysiology. Through the study of mutations that cause congenital Long QT Syndrome, the scientific community has advanced understanding of ion channel structure-function relationships, physiology, and pharmacological response to clinically employed and experimental pharmacological agents. Our understanding of congenital Long QT Syndrome continues to evolve rapidly and with great benefits: genotype-driven clinical management of the disease has improved patient care as precision medicine becomes even more a reality.

Deranged sodium to sudden death

In February 2014, a group of scientists convened as part of the University of California Davis Cardiovascular Symposium to bring together experimental and mathematical modelling perspectives and discuss points of consensus and controversy on the topic of sodium in the heart. This paper summarizes the topics of presentation and discussion from the symposium, with a focus on the role of aberrant sodium channels and abnormal sodium homeostasis in cardiac arrhythmias and pharmacotherapy from the subcellular scale to the whole heart. Two following papers focus on Na(+) channel structure, function and regulation, and Na(+)/Ca(2+) exchange and Na(+)/K(+) ATPase. The UC Davis Cardiovascular Symposium is a biannual event that aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The focus on Na(+) in the 2014 symposium stemmed from the multitude of recent studies that point to the importance of maintaining Na(+) homeostasis in the heart, as disruption of homeostatic processes are increasingly identified in cardiac disease states. Understanding how disruption in cardiac Na(+)-based processes leads to derangement in multiple cardiac components at the level of the cell and to then connect these perturbations to emergent behaviour in the heart to cause disease is a critical area of research. The ubiquity of disruption of Na(+) channels and Na(+) homeostasis in cardiac disorders of excitability and mechanics emphasizes the importance of a fundamental understanding of the associated mechanisms and disease processes to ultimately reveal new targets for human therapy.

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

Computational Models for Predictive Cardiac Ion Channel Pharmacology

A wealth of experimental data exists describing the elementary building blocks of complex physiological systems. However, it is increasingly apparent in the biomedical sciences that mechanisms of biological function cannot be observed or readily predicted via study of constituent elements alone. This is especially clear in the longstanding failures in prediction of effects of drug treatment for heart rhythm disturbances. These failures stem in part from classical assumptions that have been made in cardiac antiarrhythmic drug development - that a drug operates by one mechanism via one target receptor that arises from one gene.

Interpreting genetic effects through models of cardiac electromechanics

Multiscale models of cardiac electromechanics are being increasingly focused on understanding how genetic variation and environment underpin multiple disease states. In this paper we review the current state of the art in both the development of specific models and the physiological insights they have produced. This growing research body includes the development of models for capturing the effects of changes in function in both single and multiple proteins in both specific expression systems and in vivo contexts. Finally, the potential for using this approach for ultimately predicting phenotypes from genetic sequence information is discussed.

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.

Cardiac myocytes and local signaling in nano-domains

It is well known that calcium-induced calcium-release in cardiac myocytes takes place in spatially restricted regions known as dyads, where discrete patches of junctional sarcoplasmic reticulum tightly associate with the t-tubule membrane. The dimensions of a dyad are so small that it contains only a few Ca²⁺ ions at any given time. Ca²⁺ signaling in the dyad is therefore noisy, and dominated by the Brownian motion of Ca²⁺ ions in a potential field. Remarkably, from this complexity emerges the integrated behavior of the myocyte in which, under normal conditions, precise control of Ca²⁺ release and muscle contraction is maintained over the life of the cell. This is but one example of how signal processing within the cardiac myocyte and other cells often occurs in small "nano-domains" where proteins and protein complexes interact at spatial dimensions on the order of ∼1-10 nm and at time-scales on the order of nanoseconds to perform the functions of the cell. In this article, we will review several examples of local signaling in nano-domains, how it contributes to the integrative behavior of the cardiac myocyte, and present computational methods for modeling signal processing within these domains across differing spatio-temporal scales.

Integrative systems models of cardiac excitation-contraction coupling

Excitation-contraction coupling in the cardiac myocyte is mediated by a number of highly integrated mechanisms of intracellular Ca²(+) transport. The complexity and integrative nature of heart cell electrophysiology and Ca²(+) cycling has led to an evolution of computational models that have played a crucial role in shaping our understanding of heart function. An important emerging theme in systems biology is that the detailed nature of local signaling events, such as those that occur in the cardiac dyad, have important consequences at higher biological scales. Multiscale modeling techniques have revealed many mechanistic links between microscale events, such as Ca²(+) binding to a channel protein, and macroscale phenomena, such as excitation-contraction coupling gain. Here, we review experimentally based multiscale computational models of excitation-contraction coupling and the insights that have been gained through their application.

Cardiac models in drug discovery and development: a review

Cardiovascular diseases are among the leading causes of death in the developed world. Developing novel therapies for diseases like heart failure is crucial, but this is hampered by the high attrition rate in drug development. The withdrawal of drugs at the final hurdle of approval is mostly because of their unpredictable effects on normal cardiac rhythm. The advent of cardiac computational modeling in the last 5 decades has aided the understanding of heart function significantly. Recently, these models increasingly have been applied toward designing and understanding therapies for cardiac disease. This article will discuss how cellular models of electrophysiology, cell signaling, and metabolism have been used to investigate pharmacologic therapies for cardiac diseases including arrhythmia, ischemia, and heart failure.

Propranolol blocks cardiac and neuronal voltage-gated sodium channels

Propranolol is a widely used, non-selective β-adrenergic receptor antagonist with proven efficacy in treating cardiovascular disorders and in the prevention of migraine headaches. At plasma concentrations exceeding those required for β-adrenergic receptor inhibition, propranolol also exhibits anti-arrhythmic ("membrane stabilizing") effects that are not fully explained by β-blockade. Previous in vitro studies suggested that propranolol may have local anesthetic effects. We directly tested the effects of propranolol on heterologously expressed recombinant human cardiac (NaV1.5) and brain (NaV1.1, NaV1.2, NaV1.3) sodium channels using whole-cell patch-clamp recording. We found that block was not stereospecific as we observed approximately equal IC50 values for tonic and use-dependent block by R-(+) and S-(-) propranolol (tonic block: R: 21.4 μM vs S: 23.6 μM; use-dependent block: R: 2.7 μM vs S: 2.6 μM). Metoprolol and nadolol did not block NaV1.5 indicating that sodium channel block is not a class effect of β-blockers. The biophysical effects of R-(+)-propranolol on NaV1.5 and NaV1.1 resembled that of the prototypical local anesthetic lidocaine including the requirement for a critical phenylalanine residue (F1760 in NaV1.5) in the domain 4 S6 segment. Finally, we observed that brain sodium channels exhibited less sensitivity to R-(+)-propranolol than NaV1.5 channels. Our findings establish sodium channels as targets for propranolol and may help explain some beneficial effects of the drug in treating cardiac arrhythmias, and may explain certain adverse central nervous system effects.

Integrative modeling of the cardiac ventricular myocyte

Cardiac electrophysiology is a discipline with a rich 50-year history of experimental research coupled with integrative modeling which has enabled us to achieve a quantitative understanding of the relationships between molecular function and the integrated behavior of the cardiac myocyte in health and disease. In this paper, we review the development of integrative computational models of the cardiac myocyte. We begin with a historical overview of key cardiac cell models that helped shape the field. We then narrow our focus to models of the cardiac ventricular myocyte and describe these models in the context of their subcellular functional systems including dynamic models of voltage-gated ion channels, mitochondrial energy production, ATP-dependent and electrogenic membrane transporters, intracellular Ca dynamics, mechanical contraction, and regulatory signal transduction pathways. We describe key advances and limitations of the models as well as point to new directions for future modeling research. WIREs Syst Biol Med 2011 3 392-413 DOI: 10.1002/wsbm.122

Using computational modeling to predict arrhythmogenesis and antiarrhythmic therapy

The use of computational modeling to predict arrhythmia and arrhythmogensis is a relatively new field, but has nonetheless dramatically enhanced our understanding of the physiological and pathophysiological mechanisms that lead to arrhythmia. This review summarizes recent advances in the field of computational modeling approaches with a brief review of the evolution of cellular action potential models, and the incorporation of genetic mutations to understand fundamental arrhythmia mechanisms, including how simulations have revealed situation specific mechanisms leading to multiple phenotypes for the same genotype. The review then focuses on modeling drug blockade to understand how the less-than-intuitive effects some drugs have to either ameliorate or paradoxically exacerbate arrhythmia. Quantification of specific arrhythmia indicies are discussed at each spatial scale, from channel to tissue. The utility of hERG modeling to assess altered repolarization in response to drug blockade is also briefly discussed. Finally, insights gained from Ca(2+) dynamical modeling and EC coupling, neurohumoral regulation of cardiac dynamics, and cell signaling pathways are also reviewed.

Molecular determinants of local anesthetic action of beta-blocking drugs: Implications for therapeutic management of long QT syndrome variant 3

The congenital long QT syndrome (LQTS) is a heritable arrhythmia in which mutations in genes coding for ion channels or ion channel associated proteins delay ventricular repolarization and place mutation carriers at risk for serious or fatal arrhythmias. Triggers and therapeutic management of LQTS arrhythmias have been shown to differ in a manner that depends strikingly on the gene that is mutated. Additionally, beta-blockers, effective in the management of LQT-1, have been thought to be potentially proarrhythmic in the treatment of LQT-3 because of concomitant slowing of heart rate that accompanies decreased adrenergic activity. Here we report that the beta-blocker propranolol interacts with wild type (WT) and LQT-3 mutant Na(+) channels in a manner that resembles the actions of local anesthetic drugs. We demonstrate that propranolol blocks Na(+) channels in a use-dependent manner; that propranolol efficacy is dependent on the inactivated state of the channel; that propranolol blocks late non-inactivating current more effectively than peak sodium current; and that mutation of the local anesthetic binding site greatly reduces the efficacy of propranolol block of peak and late Na(+) channel current. Furthermore our results indicate that this activity, like that of local anesthetic drugs, differs both with drug structure and the biophysical changes in Na(+) channel function caused by specific LQT-3 mutations.