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病的临床试验准备情况:评估基于传感器的生物标志物和血液生物标志物,以早期检测和监测疾病进展
基本信息
- 批准号:10662355
- 负责人:
- 金额:$ 23.44万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAffectAmericanAwardBiochemicalBiochemical MarkersBioinformaticsBiological MarkersBiologyBloodClinicalClinical TrialsClinical assessmentsComplementComplexDataDetectionDevelopmentDevelopment PlansDiseaseDisease ProgressionDystoniaEarly DiagnosisEarly identificationEvaluationFemaleFilipinoFoundationsFunctional disorderGeneticGoalsHereditary DystoniaHeterozygoteInheritedIntronsKnowledgeLearningLightLiteratureMachine LearningMeasuresMentorshipMethodsMinisatellite RepeatsModelingMonitorMorbidity - disease rateMotionMotorMovement DisordersNational Institute of Neurological Disorders and StrokeNatural HistoryNerve DegenerationNeurologic ExaminationParkinson DiseaseParkinsonian DisordersPatientsPerformancePhenotypePhilippinesPopulationPositioning AttributePosturePrevalenceRNA SplicingRare DiseasesResearchResearch PersonnelRiskScienceSeveritiesSeverity of illnessSourceStandardizationSymptomsTAF1 geneTechniquesTechnologyTrainingTranscriptX ChromosomeX InactivationX-linked dystonia parkinsonismbiochemical modelbiotypescareer developmentclinical trial readinessclinically relevantcohortdifferential expressiondisabilitydisease phenotypeearly detection biomarkersfollow-upgenetic risk factorinnovative technologiesinsightmachine learning algorithmmolecular markermotion sensormotor behaviormovement analysisnervous system disorderneurofilamentneurogeneticsparticipant enrollmentpredictive modelingsensorskill acquisitionskillstool
项目摘要
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.
项目概要
帕金森症是第二常见的运动障碍,影响着超过 900,000 名美国人及其患病率
肌张力障碍是第三大常见疾病,困扰着 25 万美国人。 X连锁肌张力障碍帕金森症
(XDP) 是一种罕见的神经遗传性运动障碍,具有从帕金森症到广泛的表型谱
与帕金森病 (PD)、全身性肌张力障碍难以区分,类似于 DYT1(遗传性肌张力障碍),
或合并肌张力障碍和帕金森症。由于 X 失活,女性 XDP 携带者面临帕金森病的风险
并代表一个可能的遗传风险因素。因此,XDP 是肌张力障碍和肌张力障碍的优秀模型。
帕金森病和洞察力可以告知表型和混合运动障碍,这是众所周知的
评估具有挑战性。提供足够的临床试验终点、独立于评估者的定量评估
迫切需要对运动功能进行检查,以识别可能早于明显临床症状的早期异常
考虑到依赖于评估者的临床评级量表的不可靠性,跟踪疾病进展。该项目将
为斯蒂芬博士提供一套技能,使他能够评估疾病的潜在定量指标
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,并将这些测量值与
提出了生化生物标志物,使用机器学习来定义最佳参数。该项目旨在
使用纯表型(DYT1 和 PD)解决肌张力障碍和帕金森病的三个关键知识差距
组合(XDP):目标 1)利用基于技术的评估作为更敏感和更准确的衡量标准
与临床量表相比,孤立性肌张力障碍(DYT1)与帕金森病合并肌张力障碍(XDP);目标 2)
检查 XDP、PD 和 DYT1 中基于传感器的一年多疾病进展监测的准确性
患者;目标 3)分析 2 种提议的血液生物标志物(一种针对 XDP 和神经丝轻链,
XDP 中的神经退行性疾病的一般标记),并使用机器结合这些运动标记和血液标记
学习预测疾病表型/生物型和临床病程。这项研究补充了 NINDS
罕见神经系统疾病临床试验准备的目标,以及更好地了解这些疾病的更广泛目标
使用创新技术和分析,了解混合运动障碍背景下的常见表型
方法。该奖项的目标是让候选人做好准备,成为一名完全独立的调查员
在专家的指导下对肌张力障碍、帕金森症和其他运动障碍进行定量评估
指导。职业发展计划包括培训目标:1)学习可转移的运动分析技能,
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模型结合起来
具有血液生物标志物数据; 3) 生物标记科学和生物信息学简介。成功的
该项目的完成将使 Stephen 博士处于独特的地位,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,并准备提交侧重于定量的竞争性 R01 申请
遗传性肌张力障碍、帕金森症和混合运动障碍的运动分析和生物流体标记。
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Dystonias: Clinical Recognition and the Role of Additional Diagnostic Testing.
- DOI:10.1055/s-0043-1764292
- 发表时间:2023-02
- 期刊:
- 影响因子:2.7
- 作者:Stephen, Christopher D. D.;Dy-Hollins, Marisela;De Gusmao, Claudio Melo;Qahtani, Xena Al;Sharma, Nutan
- 通讯作者:Sharma, Nutan
The Dystonias.
- DOI:10.1212/con.0000000000000747
- 发表时间:2019-08-01
- 期刊:
- 影响因子:0
- 作者:Jinnah, H A
- 通讯作者:Jinnah, H A
Clinical and imaging predictors of late-onset GM2 gangliosidosis: A scoping review.
- DOI:10.1002/acn3.51947
- 发表时间:2024-01
- 期刊:
- 影响因子:5.3
- 作者:Godbole NP;Haxton E;Rowe OE;Locascio JJ;Schmahmann JD;Eichler FS;Ratai EM;Stephen CD
- 通讯作者:Stephen CD
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Christopher D Stephen其他文献
Christopher D Stephen的其他文献
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{{ truncateString('Christopher D Stephen', 18)}}的其他基金
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 - 财政年份:2021
- 资助金额:
$ 23.44万 - 项目类别:
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病的临床试验准备情况:评估基于传感器的生物标志物和血液生物标志物,以早期检测和监测疾病进展
- 批准号:
10302039 - 财政年份:2021
- 资助金额:
$ 23.44万 - 项目类别:
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