Screening for Alzheimer's Disease Based on Raman Spectroscopy of Blood

基于血液拉曼光谱的阿尔茨海默病筛查

• 批准号: 10547295
• 负责人: IGOR K LEDNEV
• 金额: $ 30.7万
• 依托单位: EARLY ALZHEIMER'S DIAGNOSTICS LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10547295
• 关键词: Affect; Algorithmic Analysis; Alzheimer disease screening; Alzheimer' s Disease; Alzheimer' s disease diagnostic; Alzheimer' s disease patient; Alzheimer' s disease therapy; American; Amyloid; Biochemical; Biological Assay; Biological Markers; Blood; Blood Coagulation Disorders; Blood Tests; Brain; Cause of Death; Cerebrospinal Fluid; Clinical; Clinical Trials; Cognitive; Collaborations; Coupled; Data; Data Collection; Data Set; Dementia; Detection; Development; Devices; Diagnostic; Diagnostic tests; Disease; Disease Progression; Early Diagnosis; Emotional; Environment; Evaluation; Family; Health; High Fat Diet; Human Resources; Impaired cognition; Individual; Industry Standard; Intervention; Ionizing radiation; Lasers; Light; Longitudinal Studies; Memory Loss; Methodology; Methods; Modeling; Monitor; Neurodegenerative Disorders; Pathologic; Pathology; Pathology processes; Patients; Persons; Pharmaceutical Preparations; Phase; Positioning Attribute; Positron-Emission Tomography; Preparation; Prevention; Process; Raman Spectrum Analysis; Rattus; Research; Resolution; Risk; Saliva; Sampling; Serum; Small Business Technology Transfer Research; Solid; Symptoms; Technology; Testing; Time; Training; Universities; Validation; abeta deposition; algorithm training; base; blind; brain health; cohort; commercial application; companion diagnostics; data acquisition; diagnostic tool; effective therapy; healthy volunteer; imager; improved; improved outcome; in vivo; industry partner; machine learning algorithm; machine learning model; minimally invasive; novel; population based; pre-clinical; preservation; prevent; preventive intervention; recruit; research and development; response; screening; tool

Abstract - Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects an estimated 6.2 million Americans and the 6th leading cause of death in the U.S. AD is progressive and incurable; dementia symptoms gradually worsen over a number of years. In its early stages, memory loss is mild, but in late-stage AD, individuals lose the ability to carry on a conversation and respond to their environment. AD is a devastating condition that creates vast emotional, financial, and physical challenges for the person and their family. In AD, pathological changes may arise up to 20 years before the onset of dementia, providing a unique window of opportunity for interventions aimed at preserving cognitive health and delaying disease progression. However, there is currently no diagnostic tool that can be widely applied for the detection of preclinical AD. When potentially effective therapies are initiated late in the underlying disease pathology process (i.e., after cognitive decline is apparent), the true impact of prevention is not achieved. In response to the critical need for an accessible diagnostic tool for early and preclinical AD, Early Alzheimer’s Diagnostics proposes to develop a screening technology based on Raman Spectroscopy combined with machine learning (ML) models trained to detect spectral signature changes based on the contribution of multiple biomarkers found in the blood. The proposed technology has the potential to greatly improve outcomes by allowing patients to identify early signs of AD, and therefore start preventive interventions and active monitoring of disease progression, delaying the onset of dementia, and preserving brain health for longer. Such a tool would also have significant utility in clinical trials for critically needed new AD therapies, facilitating recruitment and selection of healthy volunteers and AD patients at various stages of disease progression. Preliminary results show that the approach can differentiate the biochemical composition of blood from patients at different stages of AD from healthy controls. The team has also developed a novel method using automated mapping of solid samples to detect ultra-small amounts of biomarkers by preventing them from leaving a small volume interrogated by the focused laser light during spectral acquisition. This Phase I project will provide de-risk key aspects in the process of adapting the technology into a clinical commercial application and provide proof-of-feasibility via a blind test. The Specific Aims of this STTR project are: 1) Optimize a scalable, rapid methodology for obtaining and analyzing Raman spectral data from blood serum; 2) Develop ML algorithm approaches for analyzing Raman spectral data; and 3) Validate the Raman spectroscopy-based approach in a blind test. Successful completion of proposed research will position Early Alzheimer’s Diagnostics to perform initial clinical trials in Phase II, and advance discussions with potential industry partners to establish partnerships to develop the proposed approach into either a stand- alone diagnostic test or possible companion diagnostic.

抽象的 - 阿尔茨海默病 (AD) 是一种进行性神经退行性疾病，估计影响 620 万人 美国人的第六大死因是进行性且无法治愈的痴呆症状； 在早期阶段，记忆丧失是轻微的，但在 AD 晚期，个体的记忆丧失会在几年内逐渐增加。 失去进行对话和对环境做出反应的能力是一种毁灭性的情况。 在 AD 中，给个人及其家庭带来巨大的情感、经济和身体挑战。 痴呆症发病前 20 年可能会发生变化，这提供了独特的机会之窗 然而，旨在保持认知健康和延缓疾病进展的干预措施是存在的。 目前尚无可广泛应用于临床前 AD 检测的诊断工具。 有效的治疗是在潜在疾病病理过程的后期开始的（即认知能力下降之后） 显然），预防的真正影响尚未实现。 作为早期和临床前 AD 的诊断工具，早期阿尔茨海默病诊断公司建议开发一种筛查方法 基于拉曼光谱的技术与经过训练的机器学习 (ML) 模型相结合 根据血液中发现的多种生物标志物的贡献检测光谱特征变化。 所提出的技术有可能通过让患者及早识别来极大地改善结果 AD 的迹象，因此开始预防性干预并积极监测疾病进展，延缓 痴呆症的发作，以及更长时间地保持大脑健康，这种工具也将具有重要的用途。 急需的新 AD 疗法的临床试验，促进健康志愿者的招募和选择 初步结果表明，该方法可以治疗处于疾病进展各个阶段的 AD 患者。 区分 AD 不同阶段患者和健康对照者的血液生化成分。 该团队还开发了一种新颖的方法，利用固体样品的自动绘图来检测超小 通过防止生物标记物留下被聚焦激光询问的小体积来检测大量生物标记物 该第一阶段项目将在适应过程中提供降低风险的关键方面。 技术进入临床商业应用，并通过盲测提供可行性证明。 该 STTR 项目的目标是： 1) 优化用于获取和分析拉曼的可扩展、快速的方法 血清光谱数据；2) 开发用于分析拉曼光谱数据的机器学习算法方法； 3) 在盲测中验证基于拉曼光谱的方法成功完成拟议的研究。 将定位早期阿尔茨海默病诊断公司进行第二阶段的初步临床试验，并推进讨论 与潜在的行业合作伙伴建立伙伴关系，将拟议的方法发展为标准 单独的诊断测试或可能的伴随诊断。