Functional Biomarkers for ALS

项目摘要 功能性非侵入性生物标志物对于治疗靶点参与结果至关重要， 进行性罕见疾病。肌萎缩侧索硬化症（ALS）临床试验开发迫切需要 功能性生物标志物，以评估治疗反应和靶标接合。我们将满足这一紧迫需求。 我们有使用功能性血液生物标志物进行ALS临床试验的历史。Rasagaline（a 神经保护性化合物）在开放标记试验中显示线粒体生物标志物靶标接合。一 随后多中心安慰剂对照试验未能复制目标参与。之间的差距 临床试验结果显示了样本处理的问题，需要进行广义估计 方程（GEE）统计模型。如果没有准确检测目标接合治疗的能力， 机会就会失去。 ALS病理学特征包括线粒体功能障碍和蛋白质稳态（蛋白质稳态）的丧失。 聚合）。线粒体功能障碍和蛋白质稳态是错综复杂地联系在一起的生物学模式， 即将到来的ALS临床试验的关键目标。这些临床试验将受益于我们的功能性生物标志物。 我们将在临床上验证基于血液的线粒体和蛋白质稳态生物标志物方案，用作以下指标： 未来临床试验的目标参与。我们将比较线粒体和蛋白质稳态生物标志物， ALS和对照组超过8个月。我们将开发GEE统计模型，为以下方面提供指导： 在我们未来计划的临床试验中进行功效计算。我们将确定生物标志物的关系，并制定一个 我们的新型功能性生物标志物的预测统计方法。

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.