Personalizing Class I anti-Arrhythmic Drug Therapy

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Summary We want to improve arrhythmia therapy outcomes by predicting an optimal anti-arrhythmic drug regimen for ventricular arrhythmia patients using genomic data and heart rhythm reports from wearable and implanted devices. Today, amiodarone is commonly prescribed to prevent painful shocks from defibrillators that are implanted in patients who suffer from ventricular arrhythmia. Nevertheless, amiodarone is toxic for many patients when it is used over a period of years. In contrast, the class 1b molecule, mexiletine has many fewer side effects, but is effective in fewer patients. We have shown that cardiac Na+ channel variants in patients with Long QT Type 3 Syndrome can significantly affect key biophysical gating parameters that determine whether patients respond to mexiletine therapy. We propose to apply a similar approach to determine how common genetic variants and β-adrenergic tone regulate mexiletine response in patients with ventricular arrhythmias. The project aims have been developed to 1) gain novel insight into the molecular mechanisms of β-adrenergic and variant regulation of the drug response, 2) create a predictive model by mapping parameters associated with these mechanisms to patient response in a clinical study, and 3) improve the model by gaining insight from an optimized induced pluripotent stem cell based model of the drug response. If the aims of the proposal are successful, we will set the stage for a prospective clinical trial that uses a predictive model to identify ventricular arrhythmia patients who will respond to mexiletine based on data that is becoming readily available to physicians.