Optimizing Clinical Trial Endpoints in Frontotemporal Dementia

优化额颞叶痴呆的临床试验终点

基本信息

批准号：
10660926
负责人：
Adam Mark Staffaroni
金额：
$ 17.43万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10660926
关键词：
Address Affect Age Age of Onset Aging Algorithms Atrophic Biological Markers Biometry California Characteristics Clinical Clinical Research Clinical Trials Clinical Trials Design Clinical dementia rating scale Cognition Cohort Studies Complex Conduct Clinical Trials Cost Effectiveness Analysis Data Dedications Dementia Development Diffusion Disease Disease model Drug Targeting Education Enrollment Environment Etiology Face Family Frontotemporal Dementia Frontotemporal Lobar Degenerations Functional disorder Future Genetic Genotype Goals Health Policy Heterogeneity Image Inherited K-Series Research Career Programs Knowledge Light Liquid substance Longitudinal Studies Measures Memory Mentors Mentorship Methodology Methods Modality Molecular Multicenter Studies Mutation Nerve Degeneration Neurodegenerative Disorders Neurologist Neurology Neuropsychology Outcome Outcome Measure Patient Selection Patients Pattern Pharmaceutical Preparations Phenotype Pre-Clinical Model Prevention trial Primary Progressive Aphasia Proteins Public Health Research Research Personnel Resources Risk Sample Size San Francisco Semantics Specific qualifier value Symptoms Syndrome Techniques Time Training Translating Universities Variant Work autosome behavioral variant frontotemporal dementia biological heterogeneity clinical center clinical heterogeneity clinical outcome measures clinical phenotype clinical predictors cost effective drug development early onset gray matter improved longitudinal analysis multimodal data multimodal neuroimaging multimodality neurofilament neuroimaging neuroimaging marker novel novel strategies participant enrollment perfusion imaging personalized approach predictive modeling prevent professor prognostication programs protein TDP-43 targeted agent tau Proteins treatment optimization treatment trial

项目摘要

PROJECT SUMMARY In this K23 career development award, Dr. Adam Staffaroni will obtain training in clinical trial design, advanced biostatistics, and multimodal neuroimaging to improve clinical trial endpoints for frontotemporal dementia (FTD). Dr. Staffaroni is an Assistant Professor of Neurology and neuropsychologist at the University of California, San Francisco’s (UCSF) Memory and Aging Center (MAC). His long-term goal is to become a leading clinical researcher in neurodegenerative disease, establishing a lab that develops new approaches to clinical trials, through deep phenotyping and integrating individualized biomarkers. Through the support of this K23 and the vibrant, interdisciplinary training environment and enriched resources at the MAC, Dr. Staffaroni aims to accomplish the following training goals: 1) obtain training in clinical trials methodology, 2) deepen his knowledge of advanced biostatistics and neuropsychological assessment, 3) gain expertise in multimodal neuroimaging biomarkers of neurodegeneration, and 4) translate the K23 training and findings into an R01 that validates efficient approaches to clinical trial design. To achieve these goals, Dr. Staffaroni has assembled an exemplary mentorship team, including his primary mentor, Dr. Howard Rosen, a neurologist and expert in neuroimaging biomarkers of neurodegeneration; co-mentor Dr. Adam Boxer, a professor of neurology and director of the UCSF MAC’s Clinical Trials Program; co-mentor Dr. Joel Kramer, a neuropsychologist with decades of research dedicated to quantifying cognition in aging and dementia; collaborator Dr. John Kornak, a biostatistician who is renowned for his work on longitudinal and data-driven analyses; collaborator, Dr. Jennifer Yokoyama, a geneticist who focuses on the genetic contributions to neurodegeneration; and collaborator Dr. James G. Kahn, a professor of Health Policy and expert in cost-effectiveness analysis. The central premise of this project is that FTD is a model disease to develop treatments for neurodegeneration, but clinical trials face the challenge of accommodating the significant phenotypic heterogeneity associated with FTD. The overarching goal of this study is to optimize treatment trials by improving enrollment strategies and developing methods for selecting precise outcome measures. This project will improve enrollment strategies by creating baseline risk scores that incorporate several modalities of biomarkers, such as neuroimaging, genetic, and fluid biomarkers. Individualized, cost-effective risk scores would allow clinical trials to stratify or enroll patients who would maximize the likelihood of detecting a drug effect. We will also predict symptom onset in presymptomatic carriers of autosomal dominant FTD mutations; prediction of conversion would allow treatment and prevention trials to target the earliest stages of disease. Finally, we will develop an algorithm that leverages baseline patient characteristics to choose individualized trial endpoints. This is imperative for addressing the significant clinical heterogeneity associated with FTD, which renders traditional “one-size-fits-all” endpoints unable to sensitively detect clinically meaningful changes.

项目概要在本次 K23 职业发展奖中，Adam Staffaroni 博士将获得临床试验设计、高级培训等方面的培训生物统计学和多模式神经影像学可改善额颞叶痴呆的临床试验终点 (FTD) 斯塔法罗尼博士是纽约大学神经病学助理教授和神经心理学家。加州旧金山 (UCSF) 记忆与衰老中心 (MAC) 他的长期目标是成为一名记忆与衰老中心。领先的神经退行性疾病临床研究人员，建立了一个开发新方法的实验室临床试验，通过深入的表型分析和整合个体化的生物标志物通过这一支持。 K23 以及 MAC 充满活力的跨学科培训环境和丰富的资源，斯塔法罗尼博士旨在实现以下培训目标：1）获得临床试验方法方面的培训，2）加深他的先进的生物统计学和神经心理学评估知识，3）获得多模式专业知识神经退行性变的神经影像生物标志物，以及 4) 将 K23 训练和结果转化为 R01 验证有效的临床试验设计方法为了实现这些目标，Staffaroni 博士制定了一套方法。模范导师团队，包括他的主要导师 Howard Rosen 博士，一位神经科医生和专家神经退行性疾病的神经影像生物标志物；共同导师 Adam Boxer 博士，神经病学教授加州大学旧金山分校 MAC 临床试验项目主任 Joel Kramer 博士，神经心理学家数十年致力于量化衰老和痴呆症认知的研究；合作者 John Kornak 博士，生物统计学家，因其在纵向和数据驱动分析方面的工作而闻名；合作者 Jennifer 博士；横山（Yokoyama）是一位遗传学家，主要研究遗传对神经退行性疾病的影响；詹姆斯·G·卡恩 (James G. Kahn)，卫生政策教授和成本效益分析专家。该项目的核心前提是 FTD 是一种模型疾病，旨在开发治疗方法神经退行性疾病，但临床试验面临着适应显着表型的挑战与 FTD 相关的异质性本研究的总体目标是通过以下方式优化治疗试验。改进招生策略并制定选择精确结果衡量标准的方法。将通过创建包含多种模式的基线风险评分来改进注册策略生物标志物，例如神经影像、遗传和体液生物标志物个性化、具有成本效益的风险评分。将允许临床试验对患者进行分层或招募，从而最大限度地提高检测药物的可能性我们还将预测常染色体显性 FTD 突变的症状前携带者的症状发作；预测转化将使治疗和预防试验能够针对疾病的最早阶段。最后，我们将开发一种算法，利用基线患者特征来选择个体化治疗这对于解决与 FTD 相关的显着临床异质性至关重要。这使得传统的“一刀切”终点无法敏感地检测有临床意义的变化。