Using computational modeling to predict arrhythmogenesis and antiarrhythmic therapy

Abstract

The use of computational modeling to predict arrhythmia and arrhythmogensis is a relatively new field, but has nonetheless dramatically enhanced our understanding of the physiological and pathophysiological mechanisms that lead to arrhythmia. This review summarizes recent advances in the field of computational modeling approaches with a brief review of the evolution of cellular action potential models, and the incorporation of genetic mutations to understand fundamental arrhythmia mechanisms, including how simulations have revealed situation specific mechanisms leading to multiple phenotypes for the same genotype. The review then focuses on modeling drug blockade to understand how the less-than-intuitive effects some drugs have to either ameliorate or paradoxically exacerbate arrhythmia. Quantification of specific arrhythmia indicies are discussed at each spatial scale, from channel to tissue. The utility of hERG modeling to assess altered repolarization in response to drug blockade is also briefly discussed. Finally, insights gained from Ca(2+) dynamical modeling and EC coupling, neurohumoral regulation of cardiac dynamics, and cell signaling pathways are also reviewed.

Figure 1. The Vulnerable Window

A. Schematic of a 1D fiber of 500 cells, stimulated at cell 0 (S1) until steady-state is reached for a given pacing cycle length and drug concentration. S2, an ectopic stimuli at cell 125 is repeated at increasing inter-stimulus S1-S2 intervals until the MPB and LPB boundaries are reached. B. Schematic of the vulnerable window as a function of different pacing cycle lengths (PCLs). Studies by Starmer et al.[61] have suggested that as PCL increases, increased use-dependent Na⁺ channel block develops which increases the refractory period of the tissue (an antiarrhythmic effect), and also increases the vulnerable window (a proarrhythmic effect). C. A space-time plot of the vulnerable window for PCL = 625ms (~90 beats per minute). Each of the 4 subplots (different simulations) depicts the S2 at different S1-S2 coupling intervals after the 500th paced beat from cell 0. (the fully propagating beat in each subpanel). Two transitions are seen: between 307ms and 307.5ms, the tissue transitions from bidirectional block to unidirectional conduction (MPB); and between 310.5ms and 311ms the tissue transitions from unidirectional conduction to bidirectional conduction (LPB). The vulnerable window = 3.0 ms (310.5 – 307.5).

Figure 2. Spectrum of Mutations in SCN5A

Topological map of the Na⁺ channel α subunit, with identified amino acid mutations. LQT – Long QT Syndrome; BrS – Brugada Syndrome; CCD/SSD – Cardiac Conduction Disease / Sick Sinus Disease; DCM – Dilated Cardiomyopathy; MIX – Mixed phenotype. Reproduced from[68].

Figure 3. Schematic from the Saucerman β-Adrenergic cell signaling network

A. A rat ventricular myocyte model with the inclusion of the β-adrenergic network, Ca2+ handling, and electrophysiology. B. Network topology and reaction mechanisms in the β-adrenergic signaling model. Single-headed filled arrows denote kinetics; single-headed empty arrows denote enzyme catalysis, and double-headed arrows denote quasi-equilibrium reactions. NE, norepinephrine; Iso, isoproterenol; □1-AR, 1-adrenergic receptor; ARK, β-adrenergic receptor kinase; AC, adenylyl cyclase; Fsk, forskolin; PDE, phosphodiesterase; PKA, protein kinase A; RIC/RIIC, type I/II PKA holoenzyme (b only); PKI, heat-stable protein kinase inhibitor; PP1, protein phosphatase-1; PP2A, protein phosphatase-2A; I1, inhibitor-1; PLB, phospholamban; LCC, L-type calcium channel; SERCA, sarcoplasmic reticulum Ca2□-ATPase; RyR, ryanodine receptor. Figure and legend adapted from[111].