Atrial fibrillation (AF) is maintained by reentrant excitation forming stable or meandering rotors, leading circle reentry, or multiple circulating wavelets (Allessie et al., 2001; Nattel et al., 2017). While pulmonary vein triggers are critical initiators of AF (Haissaguerre et al., 1998; Pison et al., 2016), an appropriate substrate that is generated through progressive electrical and structural remodeling is required for its long-term perpetuation. Electrical remodeling comprises effective refractory period shortening, conduction slowing, wavelength reduction, and calcium-dependent triggers (Nattel, 2003; Heijman et al., 2014; Nattel et al., 2014). On the other hand, structural remodeling is hallmarked by atrial stretch and enlargement as well as interstitial fibrosis, which disrupts cell-to-cell coupling, hinders action potential propagation, and promotes reentrant excitation. Although electrical and structural remodeling is considered to be separate entities, they are highly interactive processes that influence one another during the progression of the disease. For one, fibroblast proliferation and differentiation into myofibroblasts modulates myocyte electrical function through direct coupling and paracrine signaling. Hence, a major challenge to understanding AF mechanisms is the identification of the specific contributions of electrical versus structural remodeling to fibrillatory dynamics in the disease states that promote persistent AF. Since experimentally one cannot readily separate these two processes, a rigorous computational approach is needed to isolate their individual contributions to the formation of the substrate that facilitates the maintenance of reentrant AF circuits. Multi-scale computational modeling has increasingly gained prominence in its ability to fill critical gaps that are not addressable experimentally (Cherry and Evans, 2008; Aslanidi et al., 2011; Dossel et al., 2012; Krummen et al., 2012; Colman et al., 2013; Labarthe et al., 2014; McDowell et al., 2015; Bayer et al., 2016; Boyle et al., 2016; Grandi and Maleckar, 2016; Lombardo et al., 2016; Zahid et al., 2016; Richter et al., 2017; Roney et al., 2017).
In this issue of Front Bioeng Biotechnol, Sanchez et al. (2017) leveraged a previously validated (Seemann et al., 2006) virtual human whole-atria model to determine how variations in the action potential morphology and repolarization gradients affect AF dynamics. Specifically, the authors constructed six electrophysiologically distinct human whole-atria models with uniform anatomical structure and fiber orientation. Electrophysiological variability in early and late repolarization was studied by incorporating cells with different action potential durations (APD) at 20, 50, and 90% of repolarization. Using an in silico approach that is based on experimentally calibrated human atrial action potential models, the authors confirmed key properties of AF circuits including their higher dominant frequencies in the left compared to right atria. They further provided quantitative insights to explain the predominant organization of fibrillatory activity in the regions of the pulmonary veins and right atrial appendage (Pandit and Jalife, 2013).
As expected, prolonged APD in their model impacted the organization of fibrillation patterns. Surprisingly, however, the authors identified a role for early (not just terminal) repolarization in antiarrhythmic drug therapy. AF circuits were found to be less stable and more likely to self-terminate when APD20 and APD50 were prolonged. To probe the relationship between repolarization gradients and AF dynamics, authors examined the effects of partial IK1, INaK, and INa block on reentry organization in their virtual human atria.
This line of inquiry has clear implications for ion channel pharmacotherapy, an area of major challenge considering the suboptimal efficacy of many ion channel drugs against AF as well as their risk of inducing ventricular pro-arrhythmia. The focus on the aforementioned targets in an atria-only model is interesting from a theoretical perspective but less so from a pragmatic one. For example, INa blockade using Class I drugs has been extensively tested in clinical, experimental, and in silico studies. While these drugs are effective in treating paroxysmal AF, their efficacy may be related to suppression of triggered activity via non-canonical effects on RYR2 rather than INa (Salvage et al., 2017). More importantly, the use of flecainide in the context of persistent AF is problematic since these arrhythmias typically arise in the context of heart failure in which flecainide increases mortality (Echt et al., 1991). On the other hand, INaK blockade impacts nodal cell firing via regulation of the so-called calcium clock (Sirenko et al., 2016). As such, this approach, which mimics digitalis treatment has merit as a rate (not rhythm) control strategy. Therefore, an atria-only model is less appealing for testing the impact of INaK blockade than a whole-heart virtual model that incorporates neural feedback and the conduction system, simulates the ventricular response rate to AF, and tests the potential risk of proarrhythmia by digitalis toxicity. Finally, the importance of IK1 in fibrillatory dynamics is well-established. Noujaim and colleagues (Noujaim et al., 2011) demonstrated potent effects of the antimalarial drug Chloroquine in AF suppression via its inhibitory effects on IK1. However, this strategy must be approached with caution since IK1 density is greater in ventricular compared to atrial myocardium. A notable concern is the potential for unmasking ventricular ectopy (Miake et al., 2002) or eliciting a drug-induced form of the Andersen–Tawil syndrome (Radwanski and Poelzing, 2011).
Nonetheless, the present work by Sanchez et al. (2017) establishes a robust computational platform that should be leveraged in future studies to reveal the efficacy of promising atrial-selective channel ligands by quantifying their ability to destabilize AF circuits. For the most promising candidates, simulations should be extended to more computationally intensive, anatomically correct whole heart models that incorporate patient-specific atrial anatomy, fibrosis, and ventricular remodeling that mimic the conditions that give rise to sustained AF (Trayanova, 2011). This secondary, lower throughput strategy would allow the identification of potential pro-arrhythmic activity, which is not possible in an atria-only model. The challenge is to ensure that in silico studies are always constrained by strong experimental and clinical measurements to guarantee their relevance for human AF.
Statements
Author contributions
FA and CC drafted, revised, and approved the manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
AllessieM. A.BoydenP. A.CammA. J.KleberA. G.LabM. J.LegatoM. J.et al (2001). Pathophysiology and prevention of atrial fibrillation. Circulation103, 769–777.10.1161/01.CIR.103.5.769
2
AslanidiO. V.ColmanM. A.StottJ.DobrzynskiH.BoyettM. R.HoldenA. V.et al (2011). 3D virtual human atria: a computational platform for studying clinical atrial fibrillation. Prog. Biophys. Mol. Biol.107, 156–168.10.1016/j.pbiomolbio.2011.06.011
3
BayerJ. D.RoneyC. H.PashaeiA.JaisP.VigmondE. J. (2016). Novel radiofrequency ablation strategies for terminating atrial fibrillation in the left atrium: a simulation study. Front. Physiol.7:108.10.3389/fphys.2016.00108
4
BoyleP. M.ZahidS.TrayanovaN. A. (2016). Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. Europace18, iv136–iv145.10.1093/europace/euw358
5
CherryE. M.EvansS. J. (2008). Properties of two human atrial cell models in tissue: restitution, memory, propagation, and reentry. J. Theor. Biol.254, 674–690.10.1016/j.jtbi.2008.06.030
6
ColmanM. A.AslanidiO. V.KharcheS.BoyettM. R.GarrattC.HancoxJ. C.et al (2013). Pro-arrhythmogenic effects of atrial fibrillation-induced electrical remodelling: insights from the three-dimensional virtual human atria. J. Physiol.591, 4249–4272.10.1113/jphysiol.2013.254987
7
DosselO.KruegerM. W.WeberF. M.WilhelmsM.SeemannG. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Med. Biol. Eng. Comput.50, 773–799.10.1007/s11517-012-0924-6
8
EchtD. S.LiebsonP. R.MitchellL. B.PetersR. W.Obias-MannoD.BarkerA. H.et al (1991). Mortality and morbidity in patients receiving encainide, flecainide, or placebo. The cardiac arrhythmia suppression trial. N. Engl. J. Med.324, 781–788.10.1056/NEJM199103213241201
9
GrandiE.MaleckarM. M. (2016). Anti-arrhythmic strategies for atrial fibrillation: the role of computational modeling in discovery, development, and optimization. Pharmacol. Ther.168, 126–142.10.1016/j.pharmthera.2016.09.012
10
HaissaguerreM.JaisP.ShahD. C.TakahashiA.HociniM.QuiniouG.et al (1998). Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N. Engl. J. Med.339, 659–666.10.1056/NEJM199809033391003
11
HeijmanJ.VoigtN.NattelS.DobrevD. (2014). Cellular and molecular electrophysiology of atrial fibrillation initiation, maintenance, and progression. Circ. Res.114, 1483–1499.10.1161/CIRCRESAHA.114.302226
12
KrummenD. E.BayerJ. D.HoJ.HoG.SmetakM. R.CloptonP.et al (2012). Mechanisms of human atrial fibrillation initiation: clinical and computational studies of repolarization restitution and activation latency. Circ. Arrhythm. Electrophysiol.5, 1149–1159.10.1161/CIRCEP.111.969022
13
LabartheS.BayerJ.CoudiereY.HenryJ.CochetH.JaisP.et al (2014). A bilayer model of human atria: mathematical background, construction, and assessment. Europace16(Suppl. 4), iv21–iv29.10.1093/europace/euu256
14
LombardoD. M.FentonF. H.NarayanS. M.RappelW. J. (2016). Comparison of detailed and simplified models of human atrial myocytes to recapitulate patient specific properties. PLoS Comput. Biol.12:e1005060.10.1371/journal.pcbi.1005060
15
McDowellK. S.ZahidS.VadakkumpadanF.BlauerJ.MacLeodR. S.TrayanovaN. A. (2015). Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling. PLoS ONE10:e0117110.10.1371/journal.pone.0117110
16
MiakeJ.MarbanE.NussH. B. (2002). Biological pacemaker created by gene transfer. Nature419, 132–133.10.1038/419132b
17
NattelS. (2003). Atrial electrophysiology and mechanisms of atrial fibrillation. J. Cardiovasc. Pharmacol. Ther.8(Suppl. 1), S5–S11.10.1177/107424840300800102
18
NattelS.GuaschE.SavelievaI.CosioF. G.ValverdeI.HalperinJ. L.et al (2014). Early management of atrial fibrillation to prevent cardiovascular complications. Eur. Heart J.35, 1448–1456.10.1093/eurheartj/ehu028
19
NattelS.XiongF.AguilarM. (2017). Demystifying rotors and their place in clinical translation of atrial fibrillation mechanisms. Nat. Rev. Cardiol.14, 509–520.10.1038/nrcardio.2017.37
20
NoujaimS. F.StuckeyJ. A.Ponce-BalbuenaD.Ferrer-VilladaT.Lopez-IzquierdoA.PanditS. V.et al (2011). Structural bases for the different anti-fibrillatory effects of chloroquine and quinidine. Cardiovasc. Res.89, 862–869.10.1093/cvr/cvr008
21
PanditS. V.JalifeJ. (2013). Rotors and the dynamics of cardiac fibrillation. Circ. Res.112, 849–862.10.1161/CIRCRESAHA.111.300158
22
PisonL.TilzR.JalifeJ.HaissaguerreM. (2016). Pulmonary vein triggers, focal sources, rotors and atrial cardiomyopathy: implications for the choice of the most effective ablation therapy. J. Intern. Med.279, 449–456.10.1111/joim.12490
23
RadwanskiP. B.PoelzingS. (2011). NCX is an important determinant for premature ventricular activity in a drug-induced model of Andersen-Tawil syndrome. Cardiovasc. Res.92, 57–66.10.1093/cvr/cvr180
24
RichterY.LindP. G.SeemannG.MaassP. (2017). Anatomical and spiral wave reentry in a simplified model for atrial electrophysiology. J. Theor. Biol.419, 100–107.10.1016/j.jtbi.2017.02.008
25
RoneyC. H.CantwellC. D.BayerJ. D.QureshiN. A.LimP. B.TweedyJ. H.et al (2017). Spatial resolution requirements for accurate identification of drivers of atrial fibrillation. Circ. Arrhythm. Electrophysiol.10, e004899.10.1161/CIRCEP.116.004899
26
SalvageS. C.ChandrasekharanK. H.JeevaratnamK.DulhuntyA. F.ThompsonA. J.JacksonA. P.et al (2017). Multiple targets for flecainide action: implications for cardiac arrhythmogenesis. Br. J. Pharmacol.10.1111/bph.13807
27
SanchezC.Bueno-OrovioA.PueyoE.RodriguezB. (2017). Atrial fibrillation dynamics and ionic block effects in six heterogeneous human 3D virtual atria with distinct repolarization dynamics. Front. Bioeng. Biotechnol.5:29.10.3389/fbioe.2017.00029
28
SeemannG.HoperC.SachseF. B.DosselO.HoldenA. V.ZhangH. (2006). Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Philos. Trans. A Math. Phys. Eng. Sci.364, 1465–1481.10.1098/rsta.2006.1781
29
SirenkoS. G.MaltsevV. A.YanivY.BychkovR.YaegerD.VinogradovaT.et al (2016). Electrochemical Na+ and Ca2+ gradients drive coupled-clock regulation of automaticity of isolated rabbit sinoatrial nodal pacemaker cells. Am. J. Physiol. Heart Circ. Physiol.311, H251–H267.10.1152/ajpheart.00667.2015
30
TrayanovaN. A. (2011). Whole-heart modeling: applications to cardiac electrophysiology and electromechanics. Circ. Res.108, 113–128.10.1161/CIRCRESAHA.110.223610
31
ZahidS.CochetH.BoyleP. M.SchwarzE. L.WhyteK. N.VigmondE. J.et al (2016). Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. Cardiovasc. Res.110, 443–454.10.1093/cvr/cvw073
Summary
Keywords
atrial fibrillation, pharmacology, rhythm control, potassium channels, repolarization
Citation
Campana C and Akar FG (2017) Commentary: Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Front. Bioeng. Biotechnol. 5:59. doi: 10.3389/fbioe.2017.00059
Received
08 June 2017
Accepted
20 September 2017
Published
06 October 2017
Volume
5 - 2017
Edited by
Joseph L. Greenstein, Johns Hopkins University, United States
Reviewed by
Alexander Panfilov, Ghent University, Belgium; Jason D. Bayer, Université de Bordeaux, France; Yael Yaniv, Technion – Israel Institute of Technology, Israel
Updates
Copyright
© 2017 Campana and Akar.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Fadi G. Akar, fadi.akar@mssm.edu
Specialty section: This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Bioengineering and Biotechnology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.