A comparison of in silico cardiac action potential simulations with electrophysiological effects in the isolated rabbit wedge preparation for compounds with different ion channel blocking profiles

ABSTRACTComputational models of the cardiac action potential are increasingly being used to investigate the effects of genetic mutations, predict pro-arrhythmic risk in drug development, and to guide clinical interventions. These safety-critical applications, and indeed our understanding of the cardiac action potential, depend on accurate characterisation of the underlying ionic currents. Four different methods can be found in the literature to fit ionic current models to single-cell measurements: (Method 1) fitting model equations directly to time constant, steady-state, and I-V summary curves; (Method 2) fitting by comparing simulated versions of these summary curves to their experimental counterparts; (Method 3) fitting to the current traces themselves from a range of protocols; and (Method 4) fitting to a single current trace from an information-rich voltage clamp protocol. We compare these methods using a set of experiments in which hERG1a current from single Chinese Hamster Ovary (CHO) cells was characterised using multiple fitting protocols and an independent validation protocol. We show that Methods 3 and 4 provide the best predictions on the independent validation set, and that the short information-rich protocols of Method 4 can replace much longer conventional protocols without loss of predictive ability. While data for Method 2 is most readily available from the literature, we find it performs poorly compared to Methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.Statement of SignificanceMathematical models have been constructed to capture and share our understanding of the kinetics of ion channel currents for almost 70 years, and hundreds of models have been developed, using a variety of techniques. We compare how well four of the main methods fit data, how reliable and efficient the process of fitting is, and how predictive the resulting models are for physiological situations. The most widely-used traditional approaches based on current-voltage and time constant-voltage curves do not produce the most predictive models. Short, optimised experimental voltage clamp protocols can be used to create models that are as predictive as ones derived from traditional protocols, opening up possibilities for measuring ion channel kinetics faster, more accurately and in single cells. As these models often form part of larger multi-scale action potential and tissue electrophysiology models, improved ion channel kinetics models could influence the findings of thousands of simulation studies.

Get full-text (via PubEx)

Computational biology in the study of cardiac ion channels and cell electrophysiology

Quarterly Reviews of Biophysics ◽

10.1017/s0033583506004227 ◽

2006 ◽

Vol 39 (1) ◽

pp. 57-116 ◽

Cited By ~ 178

Author(s):

Yoram Rudy ◽

Jonathan R. Silva

Keyword(s):

Ion Channels ◽

Action Potential ◽

Ion Channel ◽

Markov Models ◽

Cardiac Action Potential ◽

Genetic Mutations ◽

Cardiac Ion Channels ◽

Cardiac Action ◽

Ion Channel Kinetics

1. Prologue 582. The Hodgkin–Huxley formalism for computing the action potential 592.1 The axon action potential model 592.2 Cardiac action potential models 623. Ion-channel based formulation of the action potential 653.1 Ion-channel structure 653.2 Markov models of ion-channel kinetics 663.3 Role of selected ion channels in rate dependence of the cardiac action potential 713.4 Physiological implications of IKs subunit interaction 773.5 Mechanism of cardiac action potential rate-adaptation is species dependent 784. Simulating ion-channel mutations and their electrophysiological consequences 814.1 Mutations in SCN5A, the gene that encodes the cardiac sodium channel 824.1.1 The ΔKPQ mutation and LQT3 824.1.2 SCN5A mutation that underlies a dual phenotype 874.2 Mutations in HERG, the gene that encodes IKr: re-examination of the ‘gain of function/loss of function’ concept 944.3 Role of IKs as ‘repolarization reserve’ 1005. Modeling cell signaling in electrophysiology 1025.1 CaMKII regulation of the Ca2+ transient 1025.2 The β-adrenergic signaling cascade 1056. Epilogue 1077. Acknowledgments 1088. References 109The cardiac cell is a complex biological system where various processes interact to generate electrical excitation (the action potential, AP) and contraction. During AP generation, membrane ion channels interact nonlinearly with dynamically changing ionic concentrations and varying transmembrane voltage, and are subject to regulatory processes. In recent years, a large body of knowledge has accumulated on the molecular structure of cardiac ion channels, their function, and their modification by genetic mutations that are associated with cardiac arrhythmias and sudden death. However, ion channels are typically studied in isolation (in expression systems or isolated membrane patches), away from the physiological environment of the cell where they interact to generate the AP. A major challenge remains the integration of ion-channel properties into the functioning, complex and highly interactive cell system, with the objective to relate molecular-level processes and their modification by disease to whole-cell function and clinical phenotype. In this article we describe how computational biology can be used to achieve such integration. We explain how mathematical (Markov) models of ion-channel kinetics are incorporated into integrated models of cardiac cells to compute the AP. We provide examples of mathematical (computer) simulations of physiological and pathological phenomena, including AP adaptation to changes in heart rate, genetic mutations in SCN5A and HERG genes that are associated with fatal cardiac arrhythmias, and effects of the CaMKII regulatory pathway and β-adrenergic cascade on the cell electrophysiological function.

Get full-text (via PubEx)