In Silico Assessment of Class I Antiarrhythmic Drug Effects on Pitx2-Induced Atrial Fibrillation: Insights from Populations of Electrophysiological Models of Human Atrial Cells and Tissues

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.

Mechanisms of pulmonary arterial hypertension-induced atrial fibrillation: insights from multi-scale models of the human atria

This study aimed to use multi-scale atrial models to investigate pulmonary arterial hypertension (PAH)-induced atrial fibrillation mechanisms. The results of our computer simulations revealed that, at the single-cell level, PAH-induced remodelling led to a prolonged action potential (AP) (ΔAPD: 49.6 ms in the right atria (RA) versus 41.6 ms in the left atria (LA)) and an increased calcium transient (CaT) (ΔCaT: 7.5 × 10-2 µM in the RA versus 0.9 × 10-3 µM in the LA). Moreover, heterogeneous remodelling increased susceptibility to afterdepolarizations, particularly in the RA. At the tissue level, we observed a significant reduction in conduction velocity (CV) (ΔCV: -0.5 m s-1 in the RA versus -0.05 m s-1 in the LA), leading to a shortened wavelength in the RA, but not in the LA. Additionally, afterdepolarizations in the RA contributed to enhanced repolarization dispersion and facilitated unidirectional conduction block. Furthermore, the increased fibrosis in the RA amplified the likelihood of excitation wave breakdown and the occurrence of sustained re-entries. Our results indicated that the RA is characterized by increased susceptibility to afterdepolarizations, slow conduction, reduced wavelength and upregulated fibrosis. These findings shed light on the underlying factors that may promote atrial fibrillation in patients with PAH.

Ablation and antiarrhythmic drug effects on PITX2+/− deficient atrial fibrillation: A computational modeling study

Introduction

Atrial fibrillation (AF) is a heritable disease, and the paired-like homeodomain transcription factor 2 (PITX2) gene is highly associated with AF. We explored the differences in the circumferential pulmonary vein isolation (CPVI), which is the cornerstone procedure for AF catheter ablation, additional high dominant frequency (DF) site ablation, and antiarrhythmic drug (AAD) effects according to the patient genotype (wild-type and PITX2+/− deficient) using computational modeling.

Methods

We included 25 patients with AF (68% men, 59.8 ± 9.8 years of age, 32% paroxysmal AF) who underwent AF catheter ablation to develop a realistic computational AF model. The ion currents for baseline AF and the amiodarone, dronedarone, and flecainide AADs according to the patient genotype (wild type and PITX2+/− deficient) were defined by relevant publications. We tested the virtual CPVI (V-CPVI) with and without DF ablation (±DFA) and three virtual AADs (V-AADs, amiodarone, dronedarone, and flecainide) and evaluated the AF defragmentation rates (AF termination or changes to regular atrial tachycardia (AT), DF, and maximal slope of the action potential duration restitution curves (Smax), which indicates the vulnerability of wave-breaks.

Results

At the baseline AF, mean DF (p = 0.003), and Smax (p < 0.001) were significantly lower in PITX2+/− deficient patients than wild-type patients. In the overall AF episodes, V-CPVI (±DFA) resulted in a higher AF defragmentation relative to V-AADs (65 vs. 42%, p < 0.001) without changing the DF or Smax. Although a PITX2+/− deficiency did not affect the AF defragmentation rate after the V-CPVI (±DFA), V-AADs had a higher AF defragmentation rate (p = 0.014), lower DF (p < 0.001), and lower Smax (p = 0.001) in PITX2+/− deficient AF than in wild-type patients. In the clinical setting, the PITX2+/− genetic risk score did not affect the AF ablation rhythm outcome (Log-rank p = 0.273).

Conclusion

Consistent with previous clinical studies, the V-CPVI had effective anti-AF effects regardless of the PITX2 genotype, whereas V-AADs exhibited more significant defragmentation or wave-dynamic change in the PITX2+/− deficient patients.

The Atrium in Atrial Fibrillation - A Clinical Review on How to Manage Atrial Fibrotic Substrates

Atrial fibrillation (AF) is the most common sustained arrhythmia in the population and is associated with a significant clinical and economic burden. Rigorous assessment of the presence and degree of an atrial arrhythmic substrate is essential for determining treatment options, predicting long-term success after catheter ablation, and as a substrate critical in the pathophysiology of atrial thrombogenesis. Catheter ablation of AF has developed into an essential rhythm-control strategy. Nowadays is one of the most common cardiac ablation procedures performed worldwide, with its success inversely related to the extent of atrial structural disease. Although atrial substrate evaluation remains complex, several diagnostic resources allow for a more comprehensive assessment and quantification of the extent of left atrial structural remodeling and the presence of atrial fibrosis. In this review, we summarize the current knowledge on the pathophysiology, etiology, and electrophysiological aspects of atrial substrates promoting the development of AF. We also describe the risk factors for its development and how to diagnose its presence using imaging, electrocardiograms, and electroanatomic voltage mapping. Finally, we discuss recent data regarding fibrosis biomarkers that could help diagnose atrial fibrotic substrates.

Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach

Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence. Objective: We performed a proof-of-concept study using artificial intelligence (AI) that enabled us to identify proarrhythmic profiles based on pattern identification from in silico simulations. Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (G _Na , I _NaK , G _K1, G _CaL , G _Kur , I _KCa , [Na] _ext , and [K] _ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm²) and dilated (22.5 cm²) atrium. The simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide, and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross-validation (k = 5). Results: We found two electrophysiological patterns derived from the AI algorithm that was associated with proarrhythmic behavior in most of the profiles, where G _K1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles). Conclusion: Artificial intelligence algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.

Spatial Changes in the Atrial Fibrillation Wave-Dynamics After Using Antiarrhythmic Drugs: A Computational Modeling Study

Background: We previously reported that a computational modeling-guided antiarrhythmic drug (AAD) test was feasible for evaluating multiple AADs in patients with atrial fibrillation (AF). We explored the anti-AF mechanisms of AADs and spatial change in the AF wave-dynamics by a realistic computational model. Methods: We used realistic computational modeling of 25 AF patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal AF) reflecting the anatomy, histology, and electrophysiology of the left atrium (LA) to characterize the effects of five AADs (amiodarone, sotalol, dronedarone, flecainide, and propafenone). We evaluated the spatial change in the AF wave-dynamics by measuring the mean dominant frequency (DF) and its coefficient of variation [dominant frequency-coefficient of variation (DF-COV)] in 10 segments of the LA. The mean DF and DF-COV were compared according to the pulmonary vein (PV) vs. extra-PV, maximal slope of the restitution curves (Smax), and defragmentation of AF. Results: The mean DF decreased after the administration of AADs in the dose dependent manner (p < 0.001). Under AADs, the DF was significantly lower (p < 0.001) and COV-DF higher (p = 0.003) in the PV than extra-PV region. The mean DF was significantly lower at a high Smax (≥1.4) than a lower Smax condition under AADs. During the episodes of AF defragmentation, the mean DF was lower (p < 0.001), but the COV-DF was higher (p < 0.001) than that in those without defragmentation. Conclusions: The DF reduction with AADs is predominant in the PVs and during a high Smax condition and causes AF termination or defragmentation during a lower DF and spatially unstable (higher DF-COV) condition.

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

Background: The efficacy of antiarrhythmic drugs (AAD) can vary in patients with atrial fibrillation (AF), and the PITX2 gene affects the responsiveness of AADs. We explored the virtual AAD (V-AAD) responses between wild-type and PITX2 ^+/--deficient AF conditions by realistic in silico AF modeling. Methods: We tested the V-AADs in AF modeling integrated with patients' 3D-computed tomography and 3D-electroanatomical mapping, acquired in 25 patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal type). The ion currents for the PITX2 ^+/- deficiency and each AAD (amiodarone, sotalol, dronedarone, flecainide, and propafenone) were defined based on previous publications. Results: We compared the wild-type and PITX2 ^+/- deficiency in terms of the action potential duration (APD₉₀), conduction velocity (CV), maximal slope of restitution (Smax), and wave-dynamic parameters, such as the dominant frequency (DF), phase singularities (PS), and AF termination rates according to the V-AADs. The PITX2 ^+/--deficient model exhibited a shorter APD₉₀ (p < 0.001), a lower Smax (p < 0.001), mean DF (p = 0.012), PS number (p < 0.001), and a longer AF cycle length (AFCL, p = 0.011). Five V-AADs changed the electrophysiology in a dose-dependent manner. AAD-induced AFCL lengthening (p < 0.001) and reductions in the CV (p = 0.033), peak DF (p < 0.001), and PS number (p < 0.001) were more significant in PITX2 ^+/--deficient than wild-type AF. PITX2 ^+/--deficient AF was easier to terminate with class IC AADs than the wild-type AF (p = 0.018). Conclusions: The computational modeling-guided AAD test was feasible for evaluating the efficacy of multiple AADs in patients with AF. AF wave-dynamic and electrophysiological characteristics are different among the PITX2-deficient and the wild-type genotype models.