Optimizing Clinical Trial Endpoints in Frontotemporal Dementia

项目摘要 在K23职业发展奖中，Adam Staffaroni博士将获得临床试验设计培训， 生物统计学和多模式神经成像，以改善额颞叶痴呆的临床试验终点 （FTD）。Staffaroni博士是芝加哥大学神经病学和神经心理学家助理教授。 加州，旧金山弗朗西斯科（UCSF）记忆和衰老中心（MAC）。他的长期目标是成为 神经退行性疾病的领先临床研究员，建立了一个实验室，开发新的方法， 临床试验，通过深入的表型和整合个性化的生物标志物。通过这种支持， K23和MAC充满活力的跨学科培训环境和丰富的资源，Staffaroni博士 旨在完成以下培训目标：1）获得临床试验方法学培训，2）加深他的 先进的生物统计学和神经心理学评估的知识，3）获得多模式的专业知识 神经变性的神经成像生物标志物，以及4）将K23训练和发现转化为R 01， 验证了临床试验设计的有效方法。为了实现这些目标，Staffaroni博士召集了一个 模范导师团队，包括他的主要导师，霍华德罗森博士，神经学家和专家，在 神经退行性疾病的神经影像学生物标志物;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;共同导师乔尔克雷默博士，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，以纵向和数据驱动分析工作而闻名;合作者Jennifer博士 遗传学家Yokoyama博士和他的合作者Dr. James G.卡恩是卫生政策教授和成本效益分析专家。 该项目的中心前提是FTD是开发治疗方法的模型疾病 神经变性，但临床试验面临的挑战，容纳显着的表型 FTD相关异质性。本研究的总体目标是通过以下方式优化治疗试验： 改善入组策略，开发选择精确结果测量的方法。这个项目 将通过创建基线风险评分来改善招募策略，该评分包含几种模式， 生物标志物，例如神经成像、遗传和流体生物标志物。个性化、成本效益高的风险评分 将允许临床试验对患者进行分层或招募，以最大限度地提高检测药物的可能性 效果我们还将预测常染色体显性FTD突变的前驱症状携带者的症状发作; 对转化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，该算法利用基线患者特征来选择个性化的 试验终点。这对于解决FTD相关的显著临床异质性至关重要， 这使得传统的“一刀切”终点不能灵敏地检测临床上有意义的变化。

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.