Elucidating the Molecular Determinants of Pro-arrhythmic Proclivities of Beta-blocking Drugs

Sympathetic stimulation of cardiac β1 and β2 adrenergic receptors (β1AR and β2AR) is essential for controlling heart rhythm and vascular tone. While endogenous neurotransmitters like norepinephrine activate these receptors in normal physiological conditions, a variety of their antagonists, called beta-blockers, are utilized to downregulate their activity and thus reduce heart rate, lower blood pressure, and prevent arrhythmias. However, these drugs are capable of binding to cardiac ion channels, eliciting deleterious side-effects that remain poorly understood. For instance, some beta-blockers also modulate the cardiac voltage-gated potassium channel KV11.1 encoded by the human Ether-à-go-go-Related Gene(hERG), which is responsible for the repolarizing potassium current IKr and is a notorious drug anti-target. Drug-induced IKr blockade is clinically manifested as a prolongation of QT interval on the ECG, associated with increased arrhythmogenic risk, and central to the Thorough-QT studies mandated by the FDA for novel drugs. We seek to understand how the combination of hERG and beta-block alters cardiovascular function and thus pro-arrhythmia risks by examining the underlying molecular mechanisms of these interactions via molecular simulations. Therefore, we developed atomistic structural models of β1AR, β2AR, and hERG in different conformational states using Rosetta structural modeling software and assessed their stabilities using all-atom molecular dynamics (MD) simulations. We also developed empirical force field models of beta-blockers propranolol and sotalol, which have different pro-arrhythmia risks, as well as βAR agonist norepinephrine and validated them via water - lipid membrane partitioning MD simulations. Finally, we determined protein-ligand binding affinities and association/dissociation rates using enhanced sampling MD simulations. These computed thermodynamic and kinetic parameters will be used in functional models of cardiac cells and tissues to predict emergent pro-arrhythmia risks for different combinations of existing hERG and beta-blocking drugs in order to help design cardiac-safe formulations.