Personalizing Class I anti-Arrhythmic Drug Therapy

个性化 I 类抗心律失常药物治疗

基本信息

批准号：
10606511
负责人：
JONATHAN R SILVA
金额：
$ 57.22万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10606511
关键词：
Adrenergic Agents Affect Amiodarone Anti-Arrhythmia Agents Arrhythmia Biophysics Blinded Cardiac Cardiac Myocytes Cardiomyopathies Clinical Clinical Research Clinical Trials Computer Models Data Diagnosis Discipline Disease Dose Drug Interactions Drug Modelings Drug Prescriptions Drug Regulations Genetic Genetic Variation Genotype Guidelines Implantable Defibrillators Macromolecular Complexes Maps Mass Spectrum Analysis Measures Membrane Methods Mexiletine Modeling Molecular Molecular Conformation Movement Mutation Outcome Outcome Measure Pain Patients Peripheral Blood Mononuclear Cell Pharmaceutical Preparations Pharmacology Pharmacotherapy Phosphorylation Phosphorylation Site Physicians Precision therapeutics Process Protocols documentation Regimen Registries Regulation Reporting Shock Sodium Channel Structure Syndrome Testing Toxic effect Variant Ventricular Ventricular Arrhythmia Ventricular Fibrillation Ventricular Tachycardia biophysical properties cohort cost drug efficacy experimental study genetic information genetic variant genomic data heart rate variability heart rhythm implantable device improved improved outcome induced pluripotent stem cell insight novel outcome prediction patient registry patient response patient subsets personalized medicine predicting response predictive modeling prevent prospective rational design response side effect simulation stem cell differentiation stem cell model therapy outcome translational approach treatment strategy voltage wearable device

项目摘要

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.

概括我们希望通过预测最佳抗心律失常药物来改善心律失常的治疗结果使用基因组数据和心律报告的心室心律失常患者方案可穿戴和植入设备。如今，通常开处方以防止痛苦植入心室心律不齐的患者的除颤器的冲击。然而，胺在几年内使用时对许多患者来说是有毒的。对比，1b级分子，墨西甲氨酸的副作用较少，但有效较少患者。我们已经表明，长QT 3型患者的心脏NA+通道变体综合征可以显着影响关键的生物物理门控参数，以确定是否是否患者对梅克西汀治疗有反应。我们建议采用类似的方法来确定常见的遗传变异和β-肾上腺素能如何调节患者的墨西利素反应与心室心律不齐。该项目的目的已开发为1）获得新颖的洞察力 β-肾上腺素能和药物反应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来创建预测模型在临床研究中的反应和3）通过从优化的诱导多能干细胞的药物反应模型。如果该提议的目的是成功，我们将为一项使用预测模型的前瞻性临床试验奠定了基础识别基于数据的室心律失常患者很容易被医生使用。