# Optimizing Clinical Trial Endpoints in Frontotemporal Dementia

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $199,098

## Abstract

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 individual...

## Key facts

- **NIH application ID:** 10146263
- **Project number:** 5K23AG061253-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Adam Mark Staffaroni
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $199,098
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10146263

## Citation

> US National Institutes of Health, RePORTER application 10146263, Optimizing Clinical Trial Endpoints in Frontotemporal Dementia (5K23AG061253-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10146263. Licensed CC0.

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