MS06 - CARD-01

Digital Twins in Cardiac Electrophysiology

Thursday, July 17 at 10:20am

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Ning Wei (Purdue University)

Description:

In recent years, the advent of digital twin technology has emerged as a transformative force in healthcare, particularly in the field of cardiac electrophysiology. This minisymposium aims to explore the current advancements, applications, and challenges associated with the implementation of digital twins in understanding cardiac electrophysiology and treating cardiac arrhythmias. We will bring together leading experts in the field who will present cutting-edge research on creating patient-specific digital models that simulate cardiac function and behavior. Topics will include the integration of real-time patient data, the calibration of models, and the implications for precision medicine in arrhythmia management. Furthermore, discussions will address the technical challenges faced in developing functional digital twins and the strategies for overcoming these hurdles. Participants will have the opportunity to engage in interactive discussions, fostering collaboration between researchers, clinicians, and technologists. By illuminating the potential benefits of digital twins for personalized treatment plans, this symposium will serve as a crucial platform for generating innovative ideas and approaches to enhance patient care. We envisage that this gathering will not only advance scientific knowledge but also inspire collaborative research efforts that can lead to improved clinical outcomes in cardiac electrophysiology.

Igor Vorobyov

University of California, Davis

"Digital twins for cardiac safety pharmacology and neuromodulation: from the atom to the rhythm"

It is increasingly clear that individual variability may be a key factor in determining the emergence of rare disease phenotypes in the setting of inherited and acquired disease. To begin to address personalized susceptibility to disease and drug responses, we have been working to develop a transformative experimentally informed and validated digital twin technology for patient-specific prediction of physiological processes and pharmacological interventions. Here we will describe such digital twins approach for prediction of the cardiotoxicity of drugs and efficacy of neuromodulation therapy in individuals. We established atomic-protein-structure digital twins of the cardiac ion channels including hERG, a major drug anti-target, which plays a critical role in the cardiac action potential. We used multiple machine learning based molecular modeling approaches including AlphaFold for predictions of physiologically and pharmacologically important conformational states of the hERG channel and its state-specific drug interactions. We used enhanced sampling molecular dynamics (MD) simulations to estimate hERG - drug binding affinities and rates, which were used to parameterize new digital twin representations at the cardiac protein, cell and tissue function scales to predict emergent drug-induced arrhythmia risks. Recently we expanded this multiscale digital twins pipeline to include multi-protein drug effects and acute effects of sex hormones on cardiac ion channel – drug interactions for more accurate predictions of arrhythmogenesis. We used a similar multiscale digital twins approach for the prediction of the autonomic nervous system stimulation effects to combat arrhythmia in the diseased heart tissue as an alternative to anti-arrhythmic medications. At the molecular level we focused on beta-adrenergic receptor – neurotransmitter interactions, a key event in the sympathetic nervous system stimulation. As a result of our studies, we aim to develop robust and efficient experimentally validated multiscale digital twins pipeline for an accurate prediction of arrhythmia risks starting from drug chemical structures and patients’ genetic information.

Karli Gillette

University of Utah

"Generation of cardiac digital twins of whole-heart electrophysiology under normal sinus rhythm"

Introduction: Personalized medicine using cardiac digital twins of cardiac electrophysiology has shown great promise for enhancing diagnostics and therapy planning for cardiac arrhythmias. Whole-heart cardiac digital twins, however, are challenging to personalize in terms of both anatomy and function. We present a novel computational pipeline for generating single snapshots of cardiac digital twins of whole-heart electrophysiology based on non-invasive clinical imaging and 12 lead electrocardiogram (ECG) data. Methods: Our computational pipeline produces anatomically highly detailed heart-torso models of patient hearts from clinical cardiac magnetic resonance images and calibrates their electrophysiological model properties to replicate the measured 12 lead ECGs. Efficient modeling pipelines in the atria and ventricles are deployed with modifications for atrioventricular entities. We utilize a novel optimization approach termed Geodesic-BP to infer ventricular activation during normal sinus rhythm based on the QRS complex. T-wave morphology is based on ventricular repolarization gradients related to activation, and the P-wave depends on fitted atrial electrophysiology through electrophysiological parameters. The method is demonstrated for two healthy subjects under normal sinus rhythm. Results: The novel computational pipeline can generate cardiac digital twins of whole-heart electrophysiology at scale within clinical time frames under 10 hours. Segmentation and optimization of the ventricular activation constituted the highest temporal costs. Simulated 12 lead ECGs are high fidelity with a mechanistic basis, especially in the QRS complex. Discussion: Our robust and non-invasive computational pipeline facilitates the generation of cardiac digital twins based on non-invasive clinical data. The method is scalable for additional subjects. In future work, we aim to generate time-integrated cardiac digital twins and apply the cardiac digital twins across various cardiac arrhythmias. Depending on the application, a detailed His-Purkinje system must be incorporated, and further optimization of atrial parameters may be needed.

Trine Krogh-Madsen

Weill Cornell Medical College

"Population modeling to explain heterogeneity of single stem cell-derived cardiomyocytes"

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) are a promising tool to study arrhythmia-related factors, but the variability of action potential (AP) recordings from these cells limits their use as an in vitro model. We have recently developed an efficient voltage clamp protocol to quantify the relative size of key ionic currents within a single cardiomyocyte. Applying this protocol to tens of cells, correlating features of the recorded current to AP recordings from the same cells, and using computational models, we can generate mechanistic insights into the ionic currents contributing to AP heterogeneity.

Ning Wei

Purdue University

"The impact of ephaptic coupling and ionic electrodiffusion on arrhythmogenesis in the heart"

Cardiac myocytes synchronize through electrical signaling to contract heart muscles, facilitated by gap junctions (GJs) in the intercalated disc (ID). GJs provide low-resistance pathways for electrical impulse propagation between myocytes, serving as the primary mechanism for electrical communication in the heart. However, research indicates that conduction can persist without GJs. For instance, GJ knockout mice still exhibit slow, discontinuous electrical propagation, suggesting alternative communication mechanisms. Ephaptic coupling (EpC) serves as an alternative way for cell communication, relying on electrical fields within narrow clefts between neighboring myocytes. Studies show that EpC can enhance conduction velocity (CV) and reduce conduction block (CB), especially when GJs are compromised. Reduced GJs and significant electrochemical gradients are prevalent in various heart diseases. However, existing models often fail to capture their combined influence on cardiac conduction, which limits our understanding of both the physiological and pathological aspects of the heart. Our study aims to address this gap by developing a two-dimensional (2D) multidomain electrodiffusion model that incorporates EpC. This is the first model to capture the dynamics of all ions across multiple domains, enabling us to reveal the impact of EpC in the heart. In particular, we investigated the interplay between ionic electrodiffusion and EpC on action potential propagation, morphology, electrochemical properties and arrhythmogenesis in both healthy and ischemic hearts. Our findings indicate that ionic electrodiffusion enhances CV and reduces CB under strong EpC. Specifically, the electrodiffusion of Ca$^{2+}$ and K$^+$ intensifies the effects of EpC on action potential morphology, whereas Na$^+$ diffusion mitigates these effects. Ionic electrodiffusion also facilitates action potential propagation into ischemic regions when EpC is substantial. Moreover, strong EpC can effectively terminate reentry, prevent its initiation, and lower the maximum dominant frequency (max DF), irrespective of GJ functionality. However, weak EpC may help counteract proarrhythmic effects when GJ coupling is slightly to moderately reduced, contributing to the stabilization of conduction patterns. Additionally, strong EpC notably alters ionic concentrations in the cleft, significantly increasing [K$^+$] and nearly depleting [Ca$^{2+}$], while causing moderate changes in [Na$^+$]. This multidomain electrodiffusion model sheds light on the mechanisms of EpC in the heart.

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Annual Meeting for the Society for Mathematical Biology, 2025.