Functional Biomarkers for ALS

项目摘要 功能性非侵入性生物标记物对治疗靶点介入的结果至关重要 进行性罕见疾病。肌萎缩侧索硬化症(ALS)的临床试验开发迫切需要 用于评估治疗反应和靶向参与的功能性生物标志物。我们将满足这一迫切需求。 我们有在ALS临床试验中使用基于血液的功能生物标记物的历史。Rasagaline(A) 神经保护化合物)在一项开放标记试验中显示线粒体生物标记物靶标结合。一个 随后的多中心安慰剂对照试验未能复制目标参与。不同国家之间的差距 临床试验结果显示，样本处理存在问题，需要一个概括性的估计 方程(GEE)统计模型。如果不能准确地检测目标参与治疗 机会将会丧失。 ALS的病理特征包括线粒体功能障碍和蛋白平衡丧失(蛋白质 聚合)。线粒体功能障碍和蛋白平衡是错综复杂的生物方式，它们是 即将进行的肌萎缩侧索硬化症临床试验的关键目标。这些临床试验将受益于我们的功能生物标记物。 我们将在临床上验证基于血液的线粒体和蛋白稳定生物标记物方案作为指标 未来临床试验的目标参与度。我们将比较线粒体和蛋白质平衡生物标记物 肌萎缩侧索硬化症和对照组受试者超过8个月。我们将开发一个GEE统计模型，为 在我们未来计划的临床试验中进行功率计算。我们将确定生物标记物的关系，并开发一种 我们的新功能生物标记物的预测统计方法。

Project Summary Functional non-invasive biomarkers are critical for therapeutic target engagement outcomes in progressive rare diseases. Amyotrophic lateral sclerosis (ALS) clinical trial development has an urgent need for functional biomarkers to assess therapeutic response and target engagement. We will fulfill that urgent need. We have a history using functional blood-based biomarkers for clinical trials in ALS. Rasagaline (a neuroprotective compound) showed mitochondrial biomarker target -engagement in an open-labeled trial. A subsequent multi-center placebo-controlled trial failed to replicate target engagement. Disparities among clinical trial outcomes revealed issues with sample handling and the need for a generalized estimating equation (GEE) statistical model. Without the ability to accurately detect target engagement therapeutic opportunities will be lost. ALS pathological features include mitochondrial dysfunction and loss of proteostasis (protein aggregation). Mitochondrial dysfunction and proteostasis are intricately linked biological modalities which are key targets for upcoming clinical trials in ALS. These clinical trials will benefit from our functional biomarkers. We will clinically validate blood based mitochondrial and proteostasis biomarker protocols for use as indices of target engagement in future clinical trials. We will compare mitochondrial and proteostasis biomarkers within ALS and control subjects over eight months. We will develop a GEE statistical model to provide guidance for power calculations in our future planned clinical trials. We will determine biomarker relationships and develop a predictive statistical approach for our novel functional biomarkers.