Validating Digital Cognitive Biomarkers to Advance Alzheimer's Drug Development

验证数字认知生物标志物以推进阿尔茨海默氏症药物开发

• 批准号: 10325519
• 负责人: William Rodman Shankle
• 金额: $ 45.53万
• 依托单位: MEDICAL CARE CORPORATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325519
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease therapy; Alzheimer’s disease biomarker; Amyloid; Area Under Curve; Auditory; Biological Markers; Caring; Cerebrospinal Fluid; Classification; Cognition; Cognitive; Competence; Data; Data Set; Databases; Development; Disease; Economics; Enrollment; Epidemic; Failure; Family; Future; Goals; Gold; Health Care Costs; Human; Industry Standard; Intervention; Lead; Logistics; Measurement; Measures; Medical; Memory; Methods; Modeling; Modification; Patients; Performance; Play; Positron-Emission Tomography; Process; Quality of life; Receiver Operating Characteristics; Research; Research Personnel; Role; Spinal Puncture; Testing; Time; Verbal Learning; clinical trial enrollment; combat; cost; cost effective; cost efficient; digital; drug development; imaging biomarker; improved; neuroimaging; neuroinformatics; novel; performance tests; pre-clinical; pre-clinical therapy; prevent; response; screening; success; tau Proteins

PROJECT SUMMARY / ABSTRACT The goal of this study is to evaluate the utility of non-invasive and cost-effective digital cognitive biomarkers for concurrent prediction of amyloid positivity in pre-clinical stages of Alzheimer’s disease (AD). As AD research has shifted its focus to earlier stages of the disease course, overcoming the economic and logistical barriers of identifying cognitively normal subjects with accumulating AD biomarkers (e.g., amyloid and tau) is of paramount importance. The current gold standard method of identifying cognitively normal subjects with accumulating AD pathology includes invasive and costly biomarker imaging or lumbar punctures, which result in high screen failure rates for biomarker positivity and unnecessarily long lead times for clinical trial enrollment. In our preliminary study, we used Hierarchical Bayesian Cognitive Processing (HBCP) models to analyze baseline item response data from wordlist memory (WLM) tests, and we generated digital biomarkers that distinguished between amyloid positive and amyloid negative groups. This study was conducted using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including ADAS-Cog WLM tests and amyloid measurement by PET and cerebrospinal fluid (CSF). In the proposed study, we will replicate previous results and further evaluate the utility of HBCP model- generated digital cognitive biomarkers (DCBs) for concurrent prediction of amyloid positivity in pre-clinical stages of AD. For this study, we will use CSF and PiB-PET biomarker data plus Auditory-Verbal Learning Test (AVLT) WLM item response data from the ADNI database. Replicating the preliminary results and further refining those DCBs will enable a pragmatic and cost-effective approach to identifying cognitively normal but amyloid positive subjects who are in the pre-clinical stages of AD. While physical biomarkers will remain as industry standards for the foreseeable future, DCBs could play an important complementary role in the screening process. This would significantly expedite clinical trial enrollment and bring new AD therapies more quickly to market, while also enabling a scalable approach to identifying patients who might benefit from disease-modifying therapies once approved.

项目摘要/摘要 这项研究的目标是评估非侵入性和成本效益的数字认知的效用 同时预测阿尔茨海默病临床前期淀粉样蛋白阳性的生物标志物 (Ad)。随着AD研究将重点转移到疾病过程的早期阶段，克服 累积性阿尔茨海默病认知正常受试者识别的经济和后勤障碍 生物标志物(如淀粉样蛋白和tau蛋白)至关重要。现行的金本位法 识别具有累积AD病理的认知正常受试者需要侵入性且代价高昂 生物标记物成像或腰椎穿刺会导致生物标记物阳性筛查失败率高 以及临床试验登记的不必要的长时间。 在我们的初步研究中，我们使用了分层贝叶斯认知加工(HBCP)模型来分析 来自单词列表记忆(WLM)测试的基线项目反应数据，我们生成了数字生物标记物 区分淀粉样蛋白阳性组和淀粉样蛋白阴性组。这项研究是在 使用阿尔茨海默病神经成像倡议(ADNI)的数据，包括ADAS-Cog WLM测试 用PET和脑脊液(CSF)测定淀粉样蛋白。 在拟议的研究中，我们将复制以前的结果，并进一步评估HBCP模型的实用性- 生成的数字认知生物标记物(DCB)用于同时预测临床前淀粉样蛋白阳性 AD的各个阶段。在这项研究中，我们将使用脑脊液和PIB-PET生物标记物数据以及听觉-语言学习 来自ADNI数据库的测试(AVLT)WLM项目响应数据。复制初步结果并 进一步完善这些DCB将能够以一种务实和具有成本效益的方法来识别认知 处于AD临床前阶段的正常但淀粉样蛋白阳性的受试者。而物理生物标记物 在可预见的未来，DCB仍将是行业标准，DCB可能会发挥重要作用 在筛选过程中的补充作用。这将大大加快临床试验的登记速度 并将新的AD疗法更快地推向市场，同时还支持可扩展的方法来识别 一旦获得批准，可能会从疾病修改疗法中受益的患者。