Clinical Trial Readiness In X-Linked Dystonia Parkinsonism: Assessment of Sensor-Based And Blood Biomarkers for Early Detection and Monitoring Disease Progression

X 连锁肌张力障碍帕金森症的临床试验准备：评估基于传感器和血液生物标记物以进行早期检测和监测疾病进展

• 批准号: 10475732
• 负责人: Christopher D Stephen
• 金额: $ 19.76万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10475732
• 关键词: Address; Affect; American; Award; Biochemical; Biochemical Markers; Bioinformatics; Biological Markers; Biology; Blood; Clinical; Clinical Trials; Clinical assessments; Complement; Complex; Data; Deltastab; Detection; Development; Development Plans; Disease; Disease Progression; Dystonia; Enrollment; Evaluation; Female; Filipino; Foundations; Functional disorder; Genetic; Goals; Hereditary Dystonia; Inherited; Introns; Knowledge; Learning Skill; Light; Literature; Machine Learning; Measures; Mentorship; Methods; Minisatellite Repeats; Modeling; Monitor; Morbidity - disease rate; Motion; Motor; Movement Disorders; National Institute of Neurological Disorders and Stroke; Natural History; Nerve Degeneration; Neurologic; Neurologic Examination; Parkinson Disease; Parkinsonian Disorders; Patients; Performance; Phenotype; Philippines; Population; Positioning Attribute; Posture; Prevalence; Psychological Transfer; RNA Splicing; Rare Diseases; Research; Research Personnel; Risk; Science; Severities; Severity of illness; Short Interspersed Nucleotide Elements; Source; Standardization; Symptoms; TAF1 gene; Techniques; Technology; Training; Transcript; X Inactivation; X-linked dystonia parkinsonism; base; biochemical model; career development; clinical trial readiness; clinically relevant; cohort; differential expression; disability; disease phenotype; early detection biomarkers; follow-up; genetic risk factor; innovative technologies; insight; machine learning algorithm; molecular marker; motion sensor; motor behavior; movement analysis; nervous system disorder; neurofilament; neurogenetics; predictive modeling; sensor; skills; tool

PROJECT SUMMARY Parkinsonism, the second most common movement disorder, affects over 900,000 Americans and its prevalence is rising, while dystonia, the third most common, afflicts 250,000 Americans. X-linked dystonia parkinsonism (XDP) is a rare neurogenetic movement disorder with a wide phenotypic spectrum ranging from a parkinsonism indistinguishable from Parkinson's disease (PD), generalized dystonia, similar to DYT1 (a hereditary dystonia), or combined dystonia and parkinsonism. Female XDP carriers are at risk for parkinsonism, given X-inactivation and represents a possible genetic risk factor. As such, XDP serves as an excellent model of both dystonia and parkinsonism, and insights can inform both phenotypes and mixed movement disorders, which are notoriously challenging to assess. To provide adequate clinical trial endpoints, rater-independent, quantitative assessments of motor function are urgently needed to identify early abnormalities which may predate overt clinical symptoms and track disease progression, given the unreliability of rater-dependent clinical rating scales. This project will provide Dr. Stephen with a skill set that will allow him to assess potential quantitative measures of disease severity and progression in dystonia and parkinsonism using motion sensors and compare these measures with proposed biochemical biomarkers, using machine learning to define optimal parameters. This project aims to address three key knowledge gaps in dystonia and parkinsonism, using pure phenotypes (DYT1 and PD) and in combination (XDP): Aim 1) to utilize technology-based evaluations as more sensitive and accurate measures of dystonia in isolation (DYT1) vs. in combination with parkinsonism (XDP) compared to clinical scales; Aim 2) to examine the accuracy of sensor-based monitoring of disease progression over 1 year in XDP, PD and DYT1 patients; and Aim 3) to analyze 2 proposed blood biomarkers (one specific to XDP, and neurofilament light chain, a general marker of neurodegeneration) in XDP and combine these motor and blood markers, using machine learning to predict disease phenotype/biotype and clinical course. This research complements the NINDS objective of clinical trial readiness in rare neurological disorders, with a wider goal of better understanding these common phenotypes in the context of a mixed movement disorder, using innovative technology and analysis methods. The goal of this award is to prepare the candidate to become a fully independent investigator in the quantitative assessment of dystonia, parkinsonism and other movement disorders, in the setting of expert mentorship. The career development plan includes training goals: 1) learning transferrable motion analysis skills, 2) learning machine learning techniques to develop predictive models of motor behaviors and combining these with blood biomarker data; and 3) an introduction to biomarker science and bioinformatics. Successful completion of this project will put Dr. Stephen in a unique position to inform clinical trial readiness efforts in XDP, relevant to other dystonia and to prepare for submission of a competitive R01 application focusing on quantitative movement analysis and biofluid markers in genetic dystonia, parkinsonism and mixed movement disorders.

项目摘要 帕金森症是第二常见的运动障碍，影响着90多万美国人， 而肌张力障碍，第三常见的疾病，折磨着25万美国人。X连锁肌张力障碍性帕金森综合征 (XDP)是一种罕见的神经源性运动障碍，具有广泛的表型谱， 与帕金森病（PD），全身性肌张力障碍，类似于DYT 1（遗传性肌张力障碍）， 或者是肌张力障碍和帕金森综合症女性XDP携带者有患帕金森病的风险，因为X失活 并且代表了一种可能的遗传风险因素。因此，XDP作为肌张力障碍和 帕金森症，洞察力可以告知表型和混合运动障碍，这是众所周知的 挑战性评估。提供充分的临床试验终点、独立于评估者的定量评估 运动功能的早期异常可能早于明显的临床症状， 并跟踪疾病进展，鉴于评分依赖性临床评分量表的不可靠性。该项目将 为斯蒂芬博士提供一套技能，使他能够评估潜在的疾病定量指标 使用运动传感器测量肌张力障碍和帕金森症的严重程度和进展，并将这些测量结果与 提出了生物化学生物标志物，使用机器学习来定义最佳参数。该项目旨在 解决肌张力障碍和帕金森综合征的三个关键知识缺口，使用纯表型（DYT 1和PD）和 XDP：目标1）利用基于技术的评价作为更敏感和准确的衡量标准， 与临床量表相比，单独肌张力障碍（DYT 1）与合并帕金森综合征（XDP）;目的2） 检查XDP、PD和DYT患者1年内基于传感器监测疾病进展的准确性1 患者;和目的3）分析2种提出的血液生物标志物（一种特异于XDP，和神经丝轻链， 神经变性的一般标志物），并将这些运动和血液标志物联合收割机，使用机器 学习预测疾病表型/生物型和临床过程。这项研究补充了NINDS 罕见神经系统疾病临床试验准备的目的，更广泛的目标是更好地了解这些 混合运动障碍背景下的常见表型，使用创新技术和分析 方法.该奖项的目标是准备候选人成为一个完全独立的调查员， 肌张力障碍、帕金森综合征和其他运动障碍的定量评估， 导师制职业发展计划包括培训目标：1）学习可迁移的动作分析技能， 2)学习机器学习技术来开发运动行为的预测模型，并将这些技术结合起来， 血液生物标志物数据;和3）生物标志物科学和生物信息学的介绍。成功 该项目的完成将使Stephen博士处于一个独特的位置，为XDP的临床试验准备工作提供信息， 与其他肌张力障碍相关，并准备提交竞争性R 01申请，重点是定量 遗传性肌张力障碍、帕金森综合征和混合性运动障碍的运动分析和生物流体标记物。