In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria

Abstract

Atrial fibrillation (AF) inducibility, sustainability and response to pharmacological treatment of individual patients are expected to be determined by their ionic current properties, especially in structurally-healthy atria. Mechanisms underlying AF and optimal cardioversion are however still unclear. In this study, in-silico drug trials were conducted using a population of human structurally-healthy atria models to 1) identify key ionic current properties determining AF inducibility, maintenance and pharmacological cardioversion, and 2) compare the prognostic value for predicting individual AF cardioversion of ionic current properties and electrocardiogram (ECG) metrics. In the population of structurally-healthy atria, 477 AF episodes were induced in ionic current profiles with both steep action potential duration (APD) restitution (eliciting APD alternans), and high excitability (enabling propagation at fast rates that transformed alternans into discordant). High excitability also favored 211 sustained AF episodes, so its decrease, through prolonged refractoriness, explained pharmacological cardioversion. In-silico trials over 200 AF episodes, 100 ionic profiles and 10 antiarrhythmic compounds were consistent with previous clinical trials, and identified optimal treatments for individual electrophysiological properties of the atria. Algorithms trained on 211 simulated AF episodes exhibited >70% accuracy in predictions of cardioversion for individual treatments using either ionic current profiles or ECG metrics. In structurally-healthy atria, AF inducibility and sustainability are enabled by discordant alternans, under high excitability and steep restitution conditions. Successful pharmacological cardioversion is predicted with 70% accuracy from either ionic or ECG properties, and it is optimal for treatments maximizing refractoriness (thus reducing excitability) for the given ionic current profile of the atria.

Effects of hydroxychloroquine on atrial electrophysiology in in silico wild-type and PITX2+/- atrial cardiomyocytes

“…Thus, interruption of autophagy flux by HCQ may prevent the electrical and structural remodeling of AF. Cardiac computational modeling approaches can be used to quantitatively understand complex subcellular or spatiotemporal dynamics of AF [23,25,33] and to perform personalized AF ablation as shown in the OPTIMA [22] and PersonAL [23] studies. Recently, these computational models have also been utilized for in-silico antiarrhythmic drug trials and drug toxicity screening [18,19,24,25].…”

“…The copyright holder for this preprint this version posted December 11, 2022. ; https://doi.org/10.1101/2022.12.07.519115 doi: bioRxiv preprint would provide more statistically robust results by considering the biological uncertainty/variability in ion channel parameters [17,25,31,32]. Since atrial cell models have pacing protocol-dependent memory effects [56], various pacing protocol-related sensitivity analyses should be considered to obtain more robust and reliable results [57].…”

“…Although we used a state-of-the-art computational model of the human atrial cardiomyocyte [20], the results need to be validated using human induced pluripotent stem cells, animal models, or clinical electrophysiology data. We assumed fixed baseline parameter conditions; however, population-based approaches would provide more statistically robust results by considering the biological uncertainty/variability in ion channel parameters [17, 25, 31, 32]. Since atrial cell models have pacing protocol-dependent memory effects [56], various pacing protocol-related sensitivity analyses should be considered to obtain more robust and reliable results [57].…”

“…The copyright holder for this preprint this version posted December 11, 2022. ; https://doi.org/10.1101/2022.12.07.519115 doi: bioRxiv preprint models have been developed based on quantitative patch-clamp data of human cardiomyocytes [20,21] and recently utilized to perform computational modeling-guided ablation of atrial fibrillation (AF) [22,23] or in-silico antiarrhythmic drug trials [24,25]. However, most recent preclinical studies have focused on the HCQ-associated TdP risk stratification in ventricular cells.…”

“…To examine the potential proarrhythmogenic risk of non-cardiovascular drugs such as HCQ, computational modeling-based drug toxicity screening frameworks (e.g., CiPA [18, 19]) have been proposed and tested [15, 16]. These computational electrophysiology models have been developed based on quantitative patch-clamp data of human cardiomyocytes [20, 21] and recently utilized to perform computational modeling-guided ablation of atrial fibrillation (AF) [22, 23] or in-silico antiarrhythmic drug trials [24, 25]. However, most recent preclinical studies have focused on the HCQ-associated TdP risk stratification in ventricular cells.…”