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 par...