Personalizing Class I anti-Arrhythmic Drug Therapy

总结 我们希望通过预测最佳抗心律失常药物来改善心律失常治疗的结果 使用基因组数据和心律报告的室性心律失常患者的方案 可穿戴和植入设备。今天，胺碘酮通常用于预防疼痛 植入室性心律失常患者体内的起搏器所产生的电击。 然而，胺碘酮在使用多年后对许多患者是有毒的。在 相比之下，1b类分子，美沙芬的副作用要少得多，但在更少的 患者我们已经证明，长QT 3型患者的心脏Na+通道变异 综合征可以显著影响决定是否 患者对美西律治疗有反应。我们建议采用类似的方法来确定 常见遗传变异和β-肾上腺素能紧张度如何调节患者的Mexilavine反应 室性心律失常该项目的目标是：1）获得新的见解， 药物反应的β-肾上腺素能和变体调节的分子机制，2） 通过将与这些机制相关的参数映射到患者， 临床研究中的反应，以及3）通过从优化的 基于诱导多能干细胞的药物反应模型。如果提案的目的是 如果成功，我们将为使用预测模型的前瞻性临床试验奠定基础， 根据以下数据确定对美西律有反应的室性心律失常患者： 变得容易被医生利用。

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.