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.

Public Health Relevance

Dementia is a major public health concern, and many of the clinical trials for dementia drugs are targeting frontotemporal dementia (FTD). It is challenging, however, to conduct clinical trials in FTD because patients present with very diverse clinical phenotypes, making it difficult to detect a drug effect using current methodology. The proposed project will improve the likelihood that upcoming trials succeed by leveraging multiple biomarker modalities and applying advanced statistical techniques to improve enrichment strategies and develop more precise outcome measures.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Mentored Patient-Oriented Research Career Development Award (K23)
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Neuroscience of Aging Review Committee (NIA)
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Hsiao, John
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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