Optimizing Clinical Trial Endpoints in Frontotemporal Dementia

项目总结 在K23职业发展奖中，Adam Staffaroni博士将接受临床试验设计方面的培训，高级 生物统计学和多模式神经成像改善额颞部痴呆的临床试验终点 (FTD)。斯塔法罗尼博士是加州大学神经学和神经心理学家助理教授 加州大学旧金山分校(UCSF)记忆和衰老中心(MAC)。他的长期目标是成为一名 神经退行性疾病的领先临床研究人员，建立一个实验室，开发新的治疗方法 临床试验，通过深入的表型和整合个性化的生物标志物。通过这个项目的支持 K23和充满活力的跨学科培训环境以及MAC丰富的资源，斯塔法罗尼博士 旨在实现以下培训目标：1)获得临床试验方法学方面的培训；2)深化HIS 高级生物统计学和神经心理学评估知识，3)获得多式联运的专业知识 神经退行性变的神经成像生物标志物，以及4)将K23训练和发现转化为R01 验证临床试验设计的有效方法。为了实现这些目标，斯塔法罗尼博士组装了一个 模范导师团队，包括他的主要导师，神经学家和 神经变性的神经成像生物标记物；共同导师、神经学教授亚当·博克瑟博士和 加州大学旧金山分校MAC临床试验项目主任；共同导师Joel Kramer博士， 几十年来致力于量化衰老和痴呆症认知的研究；合作者约翰·科尔纳克博士是 生物统计学家，以其在纵向和数据驱动的分析方面的工作而闻名；合作者，詹妮弗博士 横山博士是一位遗传学家，他专注于基因对神经退化的贡献； 卫生政策教授、成本效益分析专家詹姆斯·G·卡恩说。 这个项目的中心前提是FTD是一种开发治疗方法的模型疾病 神经变性，但临床试验面临着适应显著表型的挑战 与FTD相关的异质性。这项研究的首要目标是通过以下方式优化治疗试验 改进招生策略和制定方法，以选择准确的结果衡量标准。这个项目 将通过创建基准风险分数来改进注册策略，该分数包含以下几种模式 生物标记物，如神经成像、遗传和流体生物标记物。个性化、经济高效的风险分值 将允许临床试验对那些将最大限度地检测到药物的患者进行分层或招募 效果。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开始； 对转化率的预测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，利用基线患者的特征来选择个性化 试验终点。这对于解决与FTD相关的显著临床异质性是必要的， 这使得传统的“一刀切”的端点无法敏感地检测到具有临床意义的变化。

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.