Cardiac Dynamics In-Silico: Pharmacological Targeting of Long QT3 Syndrome

Abstract

The treatment of cardiac arrhythmia is largely empirical due to incomplete understanding of antiarrhythmic drug-receptor dynamics. However, our laboratory has recently determined that drug receptor interactions of commonly prescribed local anesthetics (LAs) can be described using theoretical models based on a limited number of experimentally determined parameters. Here, we use experimental function data from cardiac Na+ channels harboring disease-linked mutations to build models that simulate measured properties of drug interactions with mutant channels. We developed computational models of 3 closely linked LQT3 mutants: deltaKPQ, D1790G, and Y1795C that account for 1) mutant-specific biophysical channel gating characteristics including mean open time and persistent Na+ current; 2) drug partitioning that underlies situation-dependent blockade; and 3) drug sensitivity to LAs that matches experimental data for steady-state inactivation (SSI), tonic block (TB), concentration and frequency dependence of use-dependent block (UDB), and recovery from UDB. Experimental 30υM flecainide block of D1790G induces a 5.5 ± 1.4mV hyperpolarizing shift in SSI, similar to our simulations (6.25mV). Experimental fits to TB reveal an IC50 of 48υM (simulations: 53.5υM), and for UDB, an IC50 of 1.7υM (simulations: 2υM). Frequency dependence of UDB is in excellent agreement with experimental data over a broad range of pacing frequencies (1 - 10Hz). We have built similarly predictive models of LA interactions for deltaKPQ and Y1795C. The in-silico platform that we have developed to predict drug-receptor interactions can be used to predict how subtle alterations to gating resulting from the mutations, as well as differential sensitivities to LAs, predispose patients to under- or overly-effective therapy with commonly prescribed antiarrhythmic drugs.