An Introduction to Computational Modeling of Cardiac Electrophysiology and Arrhythmogenicity

Enhanced Drug Classification Using Machine Learning with Multiplexed Cardiac Contractility Assays

Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90% of lead compounds fail to meet safety and efficacy benchmarks during human clinical trials. A preclinical model more representative of the human cardiac response is needed; heart tissue engineered from human pluripotent stem cell derived cardiomyocytes offers such a platform. In this study, three functionally distinct and independently validated engineered cardiac tissue assays are exposed to increasing concentrations of known compounds representing 5 classes of mechanistic action, creating a robust electrophysiology and contractility dataset. Combining results from six individual models, the resulting ensemble algorithm can classify the mechanistic action of unknown compounds with 86.2% predictive accuracy. This outperforms single-assay models and offers a strategy to enhance future clinical trial success align with the recent FDA Modernization Act 2.0.

An inner-outer subcycling algorithm for parallel cardiac electrophysiology simulations

This paper explores cardiac electrophysiological simulations of the monodomain equations and introduces a novel subcycling time integration algorithm to exploit the structure of the ionic model. The aim of this work is to improve upon the efficiency of parallel cardiac monodomain simulations by using our subcycling algorithm in the computation of the ionic model to handle the local sharp changes of the solution. This will reduce the turnaround time for the simulation of basic cardiac electrical function on both idealized and patient-specific geometry. Numerical experiments show that the proposed approach is accurate and also has close to linear parallel scalability on a computer with more than 1000 processor cores. Ultimately, the reduction in simulation time can be beneficial in clinical applications, where multiple simulations are often required to tune a model to match clinical measurements.

Reentry in cardiac ventricular epicardial tissue due to SCN5A L812Q gene mutation: a computational study

Cardiovascular diseases are the major cause of sudden death. Brugada syndrome is an inherited rare disease, that leads to death due to ventricular fibrillation (VF). Brugada Syndrome is related to mutations in the genes that encode SCN5A, a subunit of sodium ion channel (NaV). This computational study investigates the mechanism of loss of function gene mutation (SCN5A L812Q) in sodium ion channel that leads to spiral wave and further develops into VF in an epicardial tissue with homozygous condition. Study was made on wild type, L812Q heterozygous mutated and homozygous mutated ventricular tissues. Ten Tusscher human ventricular cell model (TP06) was used for the simulation study. VF is developed when a spiral wave that causes ventricular arrhythmia breaks. This leads to the formation of multiple spiral waves that are activated on different regions of the ventricles called wave break. This is observed in the epicardial tissue with homozygous condition as the effect of SCN5A L812Q gene mutation. This indicates that VF occurs in the SCN5A L812Q gene mutated homozygous ventricular epicardial tissue that may further lead to Brugada syndrome.

Integrative Computational Modeling of Cardiomyocyte Calcium Handling and Cardiac Arrhythmias: Current Status and Future Challenges

Cardiomyocyte calcium-handling is the key mediator of cardiac excitation-contraction coupling. In the healthy heart, calcium controls both electrical impulse propagation and myofilament cross-bridge cycling, providing synchronous and adequate contraction of cardiac muscles. However, calcium-handling abnormalities are increasingly implicated as a cause of cardiac arrhythmias. Due to the complex, dynamic and localized interactions between calcium and other molecules within a cardiomyocyte, it remains experimentally challenging to study the exact contributions of calcium-handling abnormalities to arrhythmogenesis. Therefore, multiscale computational modeling is increasingly being used together with laboratory experiments to unravel the exact mechanisms of calcium-mediated arrhythmogenesis. This article describes various examples of how integrative computational modeling makes it possible to unravel the arrhythmogenic consequences of alterations to cardiac calcium handling at subcellular, cellular and tissue levels, and discusses the future challenges on the integration and interpretation of such computational data.

Modeling Human Cardiac Arrhythmias: Insights from Zebrafish

Cardiac arrhythmia, or irregular heart rhythm, is associated with morbidity and mortality and is described as one of the most important future public health challenges. Therefore, developing new models of cardiac arrhythmia is critical for understanding disease mechanisms, determining genetic underpinnings, and developing new therapeutic strategies. In the last few decades, the zebrafish has emerged as an attractive model to reproduce in vivo human cardiac pathologies, including arrhythmias. Here, we highlight the contribution of zebrafish to the field and discuss the available cardiac arrhythmia models. Further, we outline techniques to assess potential heart rhythm defects in larval and adult zebrafish. As genetic tools in zebrafish continue to bloom, this model will be crucial for functional genomics studies and to develop personalized anti-arrhythmic therapies.

In silico Cell Therapy Model Restores Failing Human Myocyte Electrophysiology and Calcium Cycling in Fibrotic Myocardium

Myocardial delivery of human c-kit+ cardiac interstitial cells (hCICs) and human mesenchymal stem cells (hMSCs), an emerging approach for treating the failing heart, has been limited by an incomplete understanding of the effects on host myocardium. This computational study aims to model hCIC and hMSC effects on electrophysiology and calcium cycling of healthy and diseased human cardiomyocytes (hCM), and reveals a possible cardiotherapeutic benefit independent of putative regeneration processes. First, we developed an original hCIC mathematical model with an electrical profile comprised of distinct experimentally identified ion currents. Next, we verified the model by confirming it is representative of published experiments on hCIC whole-cell electrophysiology and on hCIC co-cultures with rodent cardiomyocytes. We then used our model to compare electrophysiological effects of hCICs to other non-excitable cells, as well as clinically relevant hCIC-hMSC combination therapies and fused hCIC-hMSC CardioChimeras. Simulation of direct coupling of hCICs to healthy or failing hCMs through gap junctions led to greater increases in calcium cycling with lesser reductions in action potential duration (APD) compared with hMSCs. Combined coupling of hCICs and hMSCs to healthy or diseased hCMs led to intermediate effects on electrophysiology and calcium cycling compared to individually coupled hCICs or hMSCs. Fused hCIC-hMSC CardioChimeras decreased healthy and diseased hCM APD and calcium transient amplitude compared to individual or combined cell treatments. Finally, to provide a theoretical basis for optimizing cell-based therapies, we randomized populations of 2,500 models incorporating variable hMSC and hCIC interventions and simulated their effects on restoring diseased cardiomyocyte electrophysiology and calcium handling. The permutation simulation predicted the ability to correct abnormal properties of heart failure hCMs in fibrotic, but not non-fibrotic, myocardium. This permutation experiment also predicted paracrine signaling to be a necessary and sufficient mechanism for this correction, counteracting the fibrotic effects while also restoring arrhythmia-related metrics such as upstroke velocity and resting membrane potential. Altogether, our in silico findings suggest anti-fibrotic effects of paracrine signaling are critical to abrogating pathological cardiomyocyte electrophysiology and calcium cycling in fibrotic heart failure, and support further investigation of delivering an optimized cellular secretome as a potential strategy for improving heart failure therapy.

A Simulation Study on Electrical Activity of Ventricular Endocardial Tissue due to SCN5A L812Q Mutation

Brugada Syndrome is a rare arrhythmia, hereditary in nature. It is caused due to mutation in genes that encodes sodium ion channels and it results sudden cardiac death in young adults. This paper aims to model a two dimensional SCN5A L812Q mutated endocardial tissue by modifying the model equations for sodium ion channel in the Ten Tusscher model for human ventricular tissue. Results show that the propagation of electrical activity in the mutated cells is slower when compared to the normal cells of the endocardial tissue. From this it is concluded that there is a large reduction of sodium current in the mutated region of the endocardial tissue. This leads to reduction in the total ionic current as well and further reduces the membrane potential. It also leads to the slower propagation of action potential in the mutated region when compared to the normal endocardial tissue.Clinical Relevance- This establishes the propagation of electrical activity in endocardial tissue for SCN5A L812Q gene mutation that results in arrhythmia called Brugada Syndrome.

Does the Heart Want What It Wants? A Case for Self-Adapting, Mechano-Sensitive Therapies After Infarction

There is a critical need for interventions to control the development and remodeling of scar tissue after myocardial infarction. A significant hurdle to fibrosis-related therapy is presented by the complex spatial needs of the infarcted ventricle, namely that collagenous buildup is beneficial in the ischemic zone but detrimental in the border and remote zones. As a new, alternative approach, we present a case to develop self-adapting, mechano-sensitive drug targets in order to leverage local, microenvironmental mechanics to modulate a therapy's pharmacologic effect. Such approaches could provide self-tuning control to either promote fibrosis or reduce fibrosis only when and where it is beneficial to do so.

Model Systems for Addressing Mechanism of Arrhythmogenesis in Cardiac Repair

PURPOSE OF REVIEW

Cardiac cell-based therapy represents a promising approach for cardiac repair. However, one of the main challenges is cardiac arrhythmias associated with stem cell transplantation. The current review summarizes the recent progress in model systems for addressing mechanisms of arrhythmogenesis in cardiac repair.

RECENT FINDINGS

Animal models have been extensively developed for mechanistic studies of cardiac arrhythmogenesis. Advances in human induced pluripotent stem cells (hiPSCs), patient-specific disease models, tissue engineering, and gene editing have greatly enhanced our ability to probe the mechanistic bases of cardiac arrhythmias. Additionally, recent development in multiscale computational studies and machine learning provides yet another powerful tool to quantitatively decipher the mechanisms of cardiac arrhythmias. Advancing efforts towards the integrations of experimental and computational studies are critical to gain insights into novel mitigation strategies for cardiac arrhythmias in cell-based therapy.

In Vitro Methods to Model Cardiac Mechanobiology in Health and Disease

In vitro cardiac modeling has taken great strides in the past decade. While most cell and engineered tissue models have focused on cell and tissue contractile function as readouts, mechanobiological cues from the cell environment that affect this function, such as matrix stiffness or organization, are less well explored. In this study, we review two-dimensional (2D) and three-dimensional (3D) models of cardiac function that allow for systematic manipulation or precise control of mechanobiological cues under simulated (patho)physiological conditions while acquiring multiple readouts of cell and tissue function. We summarize the cell types used in these models and highlight the importance of linking 2D and 3D models to address the multiscale organization and mechanical behavior. Finally, we provide directions on how to advance in vitro modeling for cardiac mechanobiology using next generation hydrogels that mimic mechanical and structural environmental features at different length scales and diseased cell types, along with the development of new tissue fabrication and readout techniques. Impact statement Understanding the impact of mechanobiology in cardiac (patho)physiology is essential for developing effective tissue regeneration and drug discovery strategies and requires detailed cause-effect studies. The development of three-dimensional in vitro models allows for such studies with high experimental control, while integrating knowledge from complementary cell culture models and in vivo studies for this purpose. Complemented by the use of human-induced pluripotent stem cells, with or without predisposed genetic diseases, these in vitro models will offer promising outlooks to delineate the impact of mechanobiological cues on human cardiac (patho)physiology in a dish.

In vitro and In silico Models to Study SARS-CoV-2 Infection: Integrating Experimental and Computational Tools to Mimic "COVID-19 Cardiomyocyte"

The rapid dissemination of SARS-CoV-2 has made COVID-19 a tremendous social, economic, and health burden. Despite the efforts to understand the virus and treat the disease, many questions remain unanswered about COVID-19 mechanisms of infection and progression. Severe Acute Respiratory Syndrome (SARS) infection can affect several organs in the body including the heart, which can result in thromboembolism, myocardial injury, acute coronary syndromes, and arrhythmias. Numerous cardiac adverse events, from cardiomyocyte death to secondary effects caused by exaggerated immunological response against the virus, have been clinically reported. In addition to the disease itself, repurposing of treatments by using "off label" drugs can also contribute to cardiotoxicity. Over the past several decades, animal models and more recently, stem cell-derived cardiomyocytes have been proposed for studying diseases and testing treatments in vitro. In addition, mechanistic in silico models have been widely used for disease and drug studies. In these models, several characteristics such as gender, electrolyte imbalance, and comorbidities can be implemented to study pathophysiology of cardiac diseases and to predict cardiotoxicity of drug treatments. In this Mini Review, we (1) present the state of the art of in vitro and in silico cardiomyocyte modeling currently in use to study COVID-19, (2) review in vitro and in silico models that can be adopted to mimic the effects of SARS-CoV-2 infection on cardiac function, and (3) provide a perspective on how to combine some of these models to mimic "COVID-19 cardiomyocytes environment.".

Limitations of Animal Studies for Predicting Toxicity in Clinical Trials: Part 2: Potential Alternatives to the Use of Animals in Preclinical Trials

Dramatically rising costs in drug development are in large part because of the high failure rates in clinical phase trials. The poor correlation of animal studies to human toxicity and efficacy have led many developers to question the value of requiring animal studies in determining which drugs should enter in-human trials. Part 1 of this 2-part series examined some of the data regarding the lack of concordance between animal toxicity studies and human trials, as well as some of the potential reasons behind it. This second part of the series focuses on some alternatives to animal trials (hereafter referred to as animal research) as well as current regulatory discussions and developments regarding such alternatives.

Exploring cardiac form and function: A length-scale computational biology approach

The ability to adequately pump blood throughout the body is the result of tightly regulated feedback mechanisms that exist across many spatial scales in the heart. Diseases which impede the function at any one of the spatial scales can cause detrimental cardiac remodeling and eventual heart failure. An overarching goal of cardiac research is to use engineered heart tissue in vitro to study the physiology of diseased heart tissue, develop cell replacement therapies, and explore drug testing applications. A commonality within the field is to manipulate the flow of mechanical signals across the various spatial scales to direct self-organization and build functional tissue. Doing so requires an understanding of how chemical, electrical, and mechanical cues can be used to alter the cellular microenvironment. We discuss how mathematical models have been used in conjunction with experimental techniques to explore various structure-function relations that exist across numerous spatial scales. We highlight how a systems biology approach can be employed to recapitulate in vivo characteristics in vitro at the tissue, cell, and subcellular scales. Specific focus is placed on the interplay between experimental and theoretical approaches. Various modeling methods are showcased to demonstrate the breadth and power afforded to the systems biology approach. An overview of modeling methodologies exemplifies how the strengths of different scientific disciplines can be used to supplement and/or inspire new avenues of experimental exploration. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.