Computational Approaches to Atrial Fibrillation-selective Antiarrhythmic Therapy
Author | : Nicholas Ellinwood |
Publisher | : |
Total Pages | : |
Release | : 2018 |
ISBN-10 | : 0438290607 |
ISBN-13 | : 9780438290600 |
Rating | : 4/5 (600 Downloads) |
Download or read book Computational Approaches to Atrial Fibrillation-selective Antiarrhythmic Therapy written by Nicholas Ellinwood and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Atrial fibrillation (AF) is the most common cardiac arrhythmia (affecting ~1-2% of the general population), which results in markedly reduced quality of life, and increased mortality, due to a combination of altered hemodynamics, progressive atrial and ventricular dysfunction, and embolic stroke. Current pharmacotherapy is limited by low efficacy and adverse side effects, which often actually increase the propensity for life-threatening ventricular arrhythmias. This dissertation project implemented a computational pharmacology framework to facilitate the ongoing search for atrial-selective antiarrhythmic drugs. To maximize efficacy and minimize proarrhythmic risk, an AF-selective drug should exert potent effects on fibrillating atria without significantly impacting ventricular tissue function during normal sinus rhythm (nSR). A potential strategy to achieve this goal is to target ion channels that are predominantly expressed in atria vs. ventricles, such as K[subscript V]1.5, which carries the ultra-rapid delayed-rectifier K+ current (I[subscript Kur]). However, while numerous K[subscript V]1.5-selective compounds have been screened in vitro and in animal models of AF, evidence of antiarrhythmic efficacy in human clinical trials is lacking. The lack of clinically effective I[subscript Kur] inhibitors might be partly explained by the fact that preclinical assessment of candidate drugs overly relies on steady-state drug concentration-response curves rather than accounting for channel conformational state specificity and kinetics of drug binding. In this doctoral research, we simulated a novel Markov-type model of I[subscript Kur] gating and drug-channel interaction within a comprehensive computational atrial myocyte model to reveal the ideal binding properties of I[subscript Kur] inhibitors that maximize AF-selectivity in nSR and chronic AF (cAF). We identified drugs exhibiting anti-AF properties at fast-pacing rates (prolongation of effective refractory period, ERP), while having little effect during normal heart rhythm (limited prolongation of action potential duration, APD). We also found that despite being downregulated in our simulations (by 50%), I[subscript Kur] contributes more prominently to APD and ERP in cAF than in nSR, and I[subscript Kur] inhibition in cAF has less cardiotoxic effects and increased efficacy. We propose that our in silico strategy can be combined with in vitro and in vivo assays, as championed by the Comprehensive in Vitro Proarrhythmia initiative, to identify the complex net impact of I[subscript Kur] inhibitors at the different stages of AF-induced remodeling. In addition, the methodological framework developed in this dissertation project can be used to study the efficacy and safety of other pharmacological interventions that target other ion channels or receptors.