Phase I Amount
$1,004,853
Definitive diagnosis of Alzheimer's Disease (AD) is currently conferred upon autopsy. Probable AD diagnosis is based on a combination of clinical/cognitive measures, often corroborated by structural MRI scans. Limitations of current neuropsychological and clinical tools for precise and early indications of cognitive decline in AD provide the impetus for our focus on developing improved cognitive assessments that are easy to use across platforms, age groups, and diverse cultural groups, and provide an earlier and more accurate indication of preclinical disease. Early diagnosis and intervention are critical for therapeutics to be maximally effective despite the dearth of new therapeutic options for AD. Augnition Labs is developing the Augmem digital biomarker platform based on work by Dr. Yassa and colleagues that empirically demonstrated, using a pattern separation task, that the chief function of the hippocampus is pattern separation - the ability to discriminate among similar memories by storing them using unique neural codes. We have developed, validated, and demonstrated the utility of a full suite of pattern separation tasks across the three key dimensions of episodic memory, (1) what happened (object), (2) where it happened (spatial), and (3) when it happened (temporal). Prior work has been neurobiologically validated with high resolution imaging as well as clinically validated against traditional clinical memory measures. In this Direct to Phase II SBIR, we incorporate object, spatial, and temporal pattern separation techniques with feature-rich AI models to produce a more effective digital biomarker for the early prediction of cognitive decline and treatment response. Aim 1. Develop and launch secure and scalable Augmem platform. We will develop and implement test management architecture and study administration modules in support of data collection, quality checks, and data analytics. A commercially ready front-end interface for digital delivery of assessments will be iteratively developed and tested. Goal: Completion of User Acceptance Testing with recruited user personas (study participant, study administrator, data scientist), and initiation of FDA regulatory pathway for Clinical Outcome Assessment qualification. Aim 2. Develop and train AI models for predicting subtle impairments based on cognitive and biomarker profiles. Data collection, data cleaning, feature extraction and selection, model building, and model evaluation and analysis will incorporate object, spatial, and temporal pattern separation measures from data collected through the Precision Aging Network as well as directly by Augnition. Goal: A representative sample of up to 500,000 participants across the age spectrum of 18-85, AI engine training, and achievement of predictive accuracy for age of 0.85 ROC AUC (classification) and RMSE ⤠0.3 (regression). Upon successful completion of the proposed development, we will conduct prospective trials in preclinical/prodromal Alzheimer's disease to fully validate the predictive power of the Augmem platform and initiate the Software as a Medical Device FDA regulatory pathway for AD early detection, stratification, and prediction of treatment response.
Public Health Relevance Statement: Narrative: The establishment of early cognitive and biological indicators of Alzheimer's Disease (AD) can have a transformative impact on clinical care, as early diagnosis and intervention are critical for therapeutics to be maximally effective. Augnition Labs is developing the Augmem cognitive testing platform for rapid, reliable and objective assessment of cognitive decline during the earliest stages of the disease, and prior to clinical symptom onset. Augmem has the potential to transform clinical trial design in preclinical AD, dramatically improve healthcare systems' ability to monitor the cognitive health of patients at risk for AD, and introduce evidence- based consumer cognitive monitoring for personalized health applications, leading to more effective treatment protocols and better health outcomes for patients.
Project Terms: Adult; 21+ years old; Adult Human; adulthood; Age; ages; Elderly; advanced age; elders; geriatric; late life; later life; older adult; older person; senior citizen; Aging; Alzheimer's Disease; AD dementia; Alzheimer; Alzheimer Type Dementia; Alzheimer disease; Alzheimer sclerosis; Alzheimer syndrome; Alzheimer's; Alzheimer's disease dementia; Alzheimers Dementia; Alzheimers disease; Primary Senile Degenerative Dementia; dementia of the Alzheimer type; primary degenerative dementia; senile dementia of the Alzheimer type; Amyloid; Amyloid Substance; Architecture; Engineering / Architecture; Autopsy; necropsy; postmortem; Brain; Brain Nervous System; Encephalon; Classification; Systematics; Clinical Trials; Data Collection; Disease; Disorder; Pharmaceutical Preparations; Drugs; Medication; Pharmaceutic Preparations; drug/agent; Educational Status; Educational Achievement; educational level; training achievement; training level; training status; Goals; Health; Healthcare Systems; Health Care Systems; Medicare; Health Insurance for Aged and Disabled, Title 18; Health Insurance for Disabled Title 18; Title 18; health insurance for disabled; Hippocampus (Brain); Ammon Horn; Cornu Ammonis; Hippocampus; hippocampal; Immunotherapy; Immune mediated therapy; Immunologically Directed Therapy; immune therapeutic approach; immune therapeutic interventions; immune therapeutic regimens; immune therapeutic strategy; immune therapy; immune-based therapies; immune-based treatments; immuno therapy; Intelligence; Marketing; Medicaid; Medical Device; Memory; neurobiological; Neurobiology; Neuropsychologies; neuropsychologic; Neuropsychology; Patients; Psychometrics; Public Health; Research; Social Sciences; Software; Computer software; Survey Instrument; Surveys; Technology; Testing; Time; Treatment Protocols; Treatment Regimen; Treatment Schedule; United States; Work; Measures; Neurofibrillary Tangles; neurofibrillary degeneration; neurofibrillary lesion; neurofibrillary pathology; tangle; tau Proteins; MT-bound tau; microtubule bound tau; microtubule-bound tau; tau; tau factor; Ï Proteins; Caregivers; Care Givers; Administrator; base; improved; Clinical; Phase; Biological; biologic; Evaluation; Training; Early Intervention; Collaborations; Therapeutic; Amyloid Plaques; Neuritic Plaques; amyloid beta plaque; amyloid-b plaque; aß plaques; cored plaque; diffuse plaque; Senile Plaques; tool; Cognitive Disturbance; Cognitive Impairment; Cognitive decline; Cognitive function abnormal; Disturbance in cognition; cognitive dysfunction; cognitive loss; Impaired cognition; Dimensions; Pattern; Techniques; Amentia; Dementia; Episodic memory; age group; American; early detection; Early Diagnosis; 65+ years old; Aged 65 and Over; age 65 and greater; age 65 and older; aged 65 and greater; aged â¥65; old age; human old age (65+); Performance; neural; relating to nervous system; novel; Participant; payment; economic cost; Prevention; Coding System; Code; Modeling; Sampling; Property; Magnetic Resonance Imaging Scan; MRI Scans; Symptoms; Data; predict therapeutic response; predict therapy response; predict treatment response; therapy prediction; treatment prediction; treatment response prediction; Prediction of Response to Therapy; Regulatory Pathway; Stratification; Clinical Trials Design; Cognitive; Collection; Patient-Focused Outcomes; Patient outcome; Patient-Centered Outcomes; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Alzheimer's Disease Pathway; Monitor; Process; Development; developmental; pre-clinical; preclinical; digital; predictive modeling; computer based prediction; prediction model; Population; prospective; innovation; innovate; innovative; Impairment; health application; novel therapeutics; new drug treatments; new drugs; new therapeutics; new therapy; next generation therapeutics; novel drug treatments; novel drugs; novel therapy; Alzheimer disease detection; AD detection; Alzheimer's detection; Alzheimer's disease risk; Alzheimer risk factor; alzheimer risk; clinical care; evidence base; standard of care; treatment response; response to therapy; response to treatment; therapeutic response; therapy response; effective therapy; effective treatment; Biological Markers; bio-markers; biologic marker; biomarker; Secure; cognitive testing; cognitive assessment; model building; Data Analytics; patient stratification; stratified patient; early detection biomarkers; early biomarkers; early detection markers; high resolution imaging; recruit; care costs; baby boomer; Infrastructure; Data Scientist; data cleaning; data cleansing; large scale data; large scale data sets; large scale datasets; feature extraction; feature selection; Aducanumab; BIIB037; aduhelm; prodromal Alzheimer's disease; prodromal AD; prodromal Alzheimer's; Alzheimer's disease diagnosis; Alzheimer's diagnosis; Alzheimer's disease care; Alzheimer's care; Alzheimer's disease patient; Alzheimer's patient; digital delivery; clinical outcome assessment; diagnostic tool
