Using computational modeling to predict arrhythmogenesis and antiarrhythmic therapy

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.

A Molecularly Detailed NaV1.5 Model Reveals a New Class I Antiarrhythmic Target

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Antiarrhythmic therapies remain suboptimal due to our inability to predict how drug interactions with ion channels will affect the ability of the tissue to initiate and sustain an arrhythmia.

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We built a computational framework that allows for in silico design of precision-targeted therapeutic agents that simultaneously assesses antiarrhythmic markers of success and failure at multiple spatial and time scales. Using this framework, a novel in silico mexiletine “booster” was designed that may dramatically improve the efficacy of mexiletine in suppression of arrhythmia triggers.

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These results provide a roadmap for the design of novel molecular-based therapy to treat myriad arrhythmia syndromes, including ventricular tachycardia, heart failure arrhythmias, and inherited arrhythmia syndromes.

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In summary, computational modeling approaches to drug discovery represent a novel tool to design and test precision-targeted therapeutic agents. By exploiting nontraditional ion channel drug targets, an entirely new dimension can be added to the wide parameter space of traditional antiarrhythmic drugs to develop more precision-targeted and potent Class I therapeutic agents.

Antiarrhythmic treatment strategies remain suboptimal due to our inability to predict how drug interactions with ion channels will affect the ability of the tissues to initiate and sustain an arrhythmia. We built a multiscale molecular model of the Na⁺ channel domain III (domain III voltage-sensing domain) to highlight the molecular underpinnings responsible for mexiletine drug efficacy. This model predicts that a hyperpolarizing shift in the domain III voltage-sensing domain is critical for drug efficacy and may be leveraged to design more potent Class I molecules. The model was therefore used to design, in silico, a theoretical mexiletine booster that can dramatically rescue a mutant resistant to the potent antiarrhythmic effects of mexiletine. Our framework provides a strategy for in silico design of precision-targeted therapeutic agents that simultaneously assesses antiarrhythmic markers of success and failure at multiple spatial and time scales. This approach provides a roadmap for the design of novel molecular-based therapy to treat myriad arrhythmia syndromes, including ventricular tachycardia, heart failure arrhythmias, and inherited arrhythmia syndromes.

Inversion and computational maturation of drug response using human stem cell derived cardiomyocytes in microphysiological systems

While cardiomyocytes differentiated from human induced pluripotent stems cells (hiPSCs) hold great promise for drug screening, the electrophysiological properties of these cells can be variable and immature, producing results that are significantly different from their human adult counterparts. Here, we describe a computational framework to address this limitation, and show how in silico methods, applied to measurements on immature cardiomyocytes, can be used to both identify drug action and to predict its effect in mature cells. Our synthetic and experimental results indicate that optically obtained waveforms of voltage and calcium from microphysiological systems can be inverted into information on drug ion channel blockage, and then, through assuming functional invariance of proteins during maturation, this data can be used to predict drug induced changes in mature ventricular cells. Together, this pipeline of measurements and computational analysis could significantly improve the ability of hiPSC derived cardiomycocytes to predict dangerous drug side effects.

Confocal Microscopy-Based Estimation of Parameters for Computational Modeling of Electrical Conduction in the Normal and Infarcted Heart

Computational modeling is an important tool to advance our knowledge on cardiac diseases and their underlying mechanisms. Computational models of conduction in cardiac tissues require identification of parameters. Our knowledge on these parameters is limited, especially for diseased tissues. Here, we assessed and quantified parameters for computational modeling of conduction in cardiac tissues. We used a rabbit model of myocardial infarction (MI) and an imaging-based approach to derive the parameters. Left ventricular tissue samples were obtained from fixed control hearts (animals: 5) and infarcted hearts (animals: 6) within 200 μm (region 1), 250-750 μm (region 2) and 1,000-1,250 μm (region 3) of the MI border. We assessed extracellular space, fibroblasts, smooth muscle cells, nuclei and gap junctions by a multi-label staining protocol. With confocal microscopy we acquired three-dimensional (3D) image stacks with a voxel size of 200 × 200 × 200 nm. Image segmentation yielded 3D reconstructions of tissue microstructure, which were used to numerically derive extracellular conductivity tensors. Volume fractions of myocyte, extracellular, interlaminar cleft, vessel and fibroblast domains in control were (in %) 65.03 ± 3.60, 24.68 ± 3.05, 3.95 ± 4.84, 7.71 ± 2.15, and 2.48 ± 1.11, respectively. Volume fractions in regions 1 and 2 were different for myocyte, myofibroblast, vessel, and extracellular domains. Fibrosis, defined as increase in fibrotic tissue constituents, was (in %) 21.21 ± 1.73, 16.90 ± 9.86, and 3.58 ± 8.64 in MI regions 1, 2, and 3, respectively. For control tissues, image-based computation of longitudinal, transverse and normal extracellular conductivity yielded (in S/m) 0.36 ± 0.11, 0.17 ± 0.07, and 0.1 ± 0.06, respectively. Conductivities were markedly increased in regions 1 (+75, +171, and +100%), 2 (+53, +165, and +80%), and 3 (+42, +141, and +60%). Volume fractions of the extracellular space including interlaminar clefts strongly correlated with conductivities in control and MI hearts. Our study provides novel quantitative data for computational modeling of conduction in normal and MI hearts. Notably, our study introduces comprehensive statistical information on tissue composition and extracellular conductivities on a microscopic scale in the MI border zone. We suggest that the presented data fill a significant gap in modeling parameters and extend our foundation for computational modeling of cardiac conduction.

Computer-based prediction of the drug proarrhythmic effect: problems, issues, known and suspected challenges

It is likely that computer modelling and simulations will become an element of comprehensive cardiac safety testing. Their role would be primarily the integration and the interpretation of previously gathered data. There are still unanswered questions and issues which we list and describe below. They include sources of data used for the development of the models as well as data utilized as input information, which can come from the in vitro studies and the quantitative structure-activity relationship models. The pharmacokinetics of the drugs in question play a crucial role as their active concentration should be considered, yet the question remains where is the right place to assess it. The pharmacodynamic angle includes complications coming from multiple drugs (i.e. active metabolites) acting in parallel as well as the type of interaction with (potentially) multiple affected channels. Once established, the model and the methodology of its use should be further validated, optimistically against individual data reported at the clinical level as the physiological, anatomical, and genetic parameters play a crucial role in the drug-triggered arrhythmia induction. All the abovementioned issues should be at least considered and-hopefully-resolved, to properly utilize the mathematical models for a cardiac safety assessment.

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

Aims

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

Methods and results

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

Conclusion

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

Instantaneous current-voltage relationships during the course of the human cardiac ventricular action potential: new computational insights into repolarization dynamics

AIMS

To adopt a novel three-dimensional (3D) representation of cardiac action potential (AP) to compactly visualize dynamical properties of human cellular ventricular repolarization.

METHODS AND RESULTS

We have recently established a novel 3D representation of cardiac AP, which is based on the iterative measurement of instantaneous ion current-voltage profiles during the course of an AP. Such an approach has been originally developed on real patch-clamped ventricular cells, and subsequently improved in silico on several cardiac ventricular AP models of different mammals, and on models of different AP types of the human heart. We apply it here on two different models of human ventricular AP, and show that it compactly provides further insights into repolarization dynamics. The 3D representation of the AP includes equilibrium points during repolarization, and can be screened in terms of what we have shown to be a region, during late repolarization, when membrane conductance becomes negative and repolarization therefore auto-regenerative. We have called this time window auto-regenerative-repolarization-phase (ARRP).

CONCLUSION

In addition to previous findings obtained through the same procedure, we show here that 3D current-voltage-time representations of human ventricular AP allow compact visualization of dynamical properties, which are relevant for the physiology and pathology of ventricular repolarization. In particular, we suggest that the volume under the current surface corresponding to the ARRP might be used as a predictor of safety of repolarization, in single cells and during AP conduction in cell pairs.

Inter-individual variability in the pre-clinical drug cardiotoxic safety assessment--analysis of the age-cardiomyocytes electric capacitance dependence

Electrical phenomena located within the plasma membrane of the mammalian cardiac cells are connected with the cells' main physiological functions--signals processing and contractility. They were extensively studied and described mathematically in so-called Hodgkin-Huxley paradigm. One of the physiological parameters, namely cell electric capacitance, has not been analyzed in-depth. The aim of the study was to validate the mechanistic model describing the capacitive properties of cells, based on a collected experimental dataset which describes the electric capacitance of human ventricular myocytes. The gathered data was further utilized for developing an empirical correlation between a healthy individual's age and cardiomyocyte electric capacitance.

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.