额颞叶变性临床研究的新方向：方法和结果测量。
New directions in clinical trials for frontotemporal lobar degeneration: Methods and outcome measures.
机构信息
Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Development Neurosciences, AbbVie, Chicago, IL, USA.
出版信息
Alzheimers Dement. 2020 Jan;16(1):131-143. doi: 10.1016/j.jalz.2019.06.4956. Epub 2020 Jan 6.
INTRODUCTION
Frontotemporal lobar degeneration (FTLD) is the most common form of dementia for those under 60 years of age. Increasing numbers of therapeutics targeting FTLD syndromes are being developed.
METHODS
In March 2018, the Association for Frontotemporal Degeneration convened the Frontotemporal Degeneration Study Group meeting in Washington, DC, to discuss advances in the clinical science of FTLD.
RESULTS
Challenges exist for conducting clinical trials in FTLD. Two of the greatest challenges are (1) the heterogeneity of FTLD syndromes leading to difficulties in efficiently measuring treatment effects and (2) the rarity of FTLD disorders leading to recruitment challenges.
DISCUSSION
New personalized endpoints that are clinically meaningful to individuals and their families should be developed. Personalized approaches to analyzing MRI data, development of new fluid biomarkers and wearable technologies will help to improve the power to detect treatment effects in FTLD clinical trials and enable new, clinical trial designs, possibly leveraged from the experience of oncology trials. A computational visualization and analysis platform that can support novel analyses of combined clinical, genetic, imaging, biomarker data with other novel modalities will be critical to the success of these endeavors.
简介
额颞叶痴呆（FTLD）是 60 岁以下人群最常见的痴呆类型。越来越多的针对 FTLD 综合征的治疗方法正在被开发。
方法
2018 年 3 月，额颞叶痴呆协会在华盛顿特区召开额颞叶痴呆研究组会议，讨论 FTLD 临床科学的进展。
结果
在 FTLD 中进行临床试验存在挑战。两个最大的挑战是（1）FTLD 综合征的异质性导致难以有效地衡量治疗效果，以及（2）FTLD 疾病的罕见性导致招募困难。
讨论
应该开发对个人及其家人具有临床意义的新的个性化终点。对 MRI 数据进行个性化分析、开发新的液体生物标志物和可穿戴技术的方法将有助于提高在 FTLD 临床试验中检测治疗效果的能力，并能够采用新的临床试验设计，可能借鉴肿瘤学试验的经验。一个能够支持结合临床、遗传、影像、生物标志物数据以及其他新模态的新型分析的计算可视化和分析平台，对于这些努力的成功至关重要。
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