A major avenue of scientific inquiry in computational cardiology relates the binding/unbinding of drugs to molecular target(s) to the instigation, termination or prevention of cardiac arrhythmias. In this section we focus on examples where emergent behavior, resulting from integrations across the scales of biological hierarchy, has shed new light on existing or novel drug actions for treatment of arrhythmia.
At the level of the ion channel, Markov models with state specific drug binding/unbinding have been used to test hypotheses regarding the mechanisms of drug effects on macroscopic currents. Since the arrhythmogenic long QT syndrome (LQTS) type 2 is characterized by loss of repolarizing rapid delayed rectifier K+
], a straightforward approach for prevention of LQT-2 arrhythmia, therefore, would be pharmacological enhancement of IKr
. Perry et al. [84
] explored the novel compound RPR260243, shown to enhance IKr
, represented by the KCNH2 isoform 1a current expressed in xenopus oocytes
. Rate constants in a proposed modal gating scheme were determined to best fit the experimental data. The model revealed that IKr
enhancement could be explained by dose-dependent loss of deactivation; point mutation analysis provided the structural mechanism behind this model prediction. Going a step further, Sale et al. [85
] simulated the effects of the drug E4031, known to block IKr
in its open state in a use dependent manner [86
] on the human ventricular action potential. Drug E4031 is known to block IKr
in its open state, in a use-dependent manner [86
]. Previously it was unclear why isoform 1a current, which is smaller than 1a/1b combination, was more sensitive to E4031. The Markov model for 1a alone versus 1a/1b mechanistically explained the discrepancy, and related action potential prolongation with E4031 as seen experimentally.
Type 3 LQTS is brought on by enhancement of the noninactivating, or late Na+
]. Using Markov models, Clancy et al. [87
] compared the effect of two INa
blocking drugs, lidocaine and mexiletine, revealing that mexiletine preferentially binds to the population of channels undergoing late burst opening; the latter events are dangerously common in LQT3, leading to arrhythmogenic early afterdepolarizations (EADs). By contrast, lidocaine preferentially binds to channels during the rapid activation/inactivation phase of INa
. The model showed that there are mexiletine doses, which selectively remove late current and EADs without detrimental effect to excitability.
Nesterenko et al. [88
] also drew connections between drug binding kinetics and emergent effects on the AP in the investigation of the novel antiarrhythmic drug ranolazine. Among its many targets [89
], ranolazine reduces late INa
in atrial-selective fashion [90
]; Nesterenko et al. explained the mechanism behind this selectivity by introducing the concept of a “pre-open” state to the INa
Markov model. The effect of late INa
block by ranolazine on tissue electrophysiology was examined by Morita et al. [91
], who demonstrated that late INa
block suppresses EADs that lead to focal reentry after hydrogen peroxide application, as observed in experiments. Importantly, ranolazine is a mild IKr
blocker at clinical doses, an effect of major significance for drug safety. Since current FDA regulations require that new candidate drugs not block IKr
, ranolazine would not have been approved if discovered today. Rodriguez et al. [92
] proposed that computer modeling of multichannel affecting drugs, such as ranolazine, could be a testbed for determining the utility of new or previously rejected compounds or drug combination approaches, with modeling as a force against rigid standards, and toward rational, more holistic drug candidate selection. Fundamentally, this is the modeling approach of Sarkar and Sobie [93
] whose recent article explores basic mechanisms by which interrelated model parameters contribute to the consequence of IKr
block, a phenomenon known as the “repolarization reserve” [94
]. The article makes the important discovery that subtle changes in ion channel substrate can have profound and indirect effects on the response to drugs. Simulating the subtle differences between species [95
] and the effects of sex hormones [96
] also demonstrated changes in drug block effects. Personalized medicine requires clear delineation of the subtle interspecies and interindividual differences, which determine outcomes; this delineation is made possible in part by mechanistic simulation.
Relating effects of drugs on ion channels beyond the AP require virtual tissue or whole heart organ simulation, to examine arrhythmia onset, termination, and prevention. Recently, Benson et al. [98
] related the effects of d-sotalol, an IKr
blocker, and amiodarone, a complex multichannel effector, to arrhythmia formation in the heterogenous canine ventricular wedge. An emergent finding was the understanding of how the vulnerability window is enhanced by d-sotalol but reduced by amiodarone due to different effects of the drugs on different cell types. Whereas the drug models used by Benson et al. were implemented by simple conduction scaling, a new study by Moreno et al. has incorporated both state-dependent Markov modeling of drug effects and full integration to the human AP, human tissue, and finally realistic MRI image-based human heart [66
]. This is the first instance of such massive integration across the space and time scales at play. Moreno et al. showed that the effects of flecainide and lidocaine on INa
block are globally similar in response to dynamic protocols. However, clinical trials have shown previously that flecainide tended to be proarrhythmic at therapeutic doses, while lidocaine was not. Moreno et al. results make clear that neither simple reduction in sodium conductance nor single cell simulation can resolve this paradox. At the macroscopic scale, the vulnerable window was greater for flecainide than for lidocaine (especially in heart failure simulations due to shortened diastole) and reentrant arrhythmia in the ventricle persisted (). At the microscopic scale, Markov models explained that this was due to the relatively slow accumulation of and recovery from use dependent block with flecainide.
Figure 6 Simulation of drug-related arrhythmias. (a) Schematic for drug binding to sodium channels. Maps of the phase variable in (b), sustained figure-of-eight reentry with 2μM flecainide, and (c) nonsustained reentry with 20μ (more ...)