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

摘要- 阿尔茨海默病(AD)是一种进行性神经退行性疾病，估计有620万人受到影响 美国人和美国AD的第六大死因是进行性的和不可治愈的；痴呆症状 在几年的时间里逐渐恶化。在其早期阶段，记忆丧失是轻微的，但在晚期AD中，个体 失去继续对话和对周围环境做出反应的能力。广告是一种毁灭性的状况， 给患者及其家人带来巨大的情感、经济和身体上的挑战。在AD中，病理性 在痴呆症发病前20年可能会出现变化，这提供了一个独特的机会之窗 旨在保护认知健康和延缓疾病进展的干预措施。然而，还有 目前尚无可广泛应用于临床前AD检测的诊断工具。当潜在地 有效的治疗是在基础疾病病理过程的后期开始的(即，在认知能力下降之后 显然)，没有达到预防的真正效果。为了满足对无障碍环境的迫切需求 早期阿尔茨海默病诊断工具，早期阿尔茨海默病诊断建议开发一种筛查 基于拉曼光谱与机器学习(ML)模型相结合的技术训练 根据血液中发现的多个生物标志物的贡献来检测光谱特征的变化。 这项拟议的技术有可能通过让患者及早识别来极大地改善预后。 阿尔茨海默病的征兆，并因此开始预防性干预和积极监测疾病进展，延迟 防止痴呆症的发作，并更长时间地保持大脑健康。这样的工具在以下方面也将具有重要的实用价值 对急需的新AD疗法进行临床试验，促进招募和选择健康志愿者 以及处于疾病进展不同阶段的AD患者。初步结果表明，该方法能够 区分AD不同阶段患者和健康对照的血液生化成分。 该团队还开发了一种新的方法，使用固体样本的自动映射来检测超微小 通过防止生物标志物离开被聚焦的激光询问的小体积 在光谱采集过程中。该第一阶段项目将在适应 技术转化为临床商业应用，并通过盲法测试提供可行性证明。具体的 本项目的目标是：1)优化可扩展的、快速获取和分析拉曼光谱的方法 2)开发分析拉曼光谱数据的最大似然算法方法；以及 3)对基于拉曼光谱的检测方法进行了盲法验证。成功完成拟议的研究 将定位早期阿尔茨海默氏症诊断公司在第二阶段进行初步临床试验，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一种立场- 单独诊断测试或可能的伴随诊断。

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.