# Big data and small molecules for Alzheimer's disease > **NIH NIH RF1** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $412,500 ## Abstract ABSTRACT More than half of residents in nursing home communities suffer from cognitive impairment with Alzheimer’s disease (AD) or AD related dementia (ADRD), and one-third of all US COVID-19 deaths are long- term care facility residents. The COVID-19 pandemic places AD patients at a greater risk of respiratory failure and mortality. Hypertension, which is prevalent among elderly populations, results in more severe COVID-19 symptoms. The goal of this supplement NIH application is to examine whether AD patients vulnerable to infection by severe acute respiratory syndrome coronavirus (SARS-CoV-2) can improve clinical outcomes of COVID-19 with angiotensin converting enzyme inhibitors (ACEI). This supplement application fits within the scope of our NIA-funded parent project, “Big data and small molecules for Alzheimer’s disease (RF1- AG063913).” The hypothesis from the parent project was that tauopathy and related neurodegenerative disease pathologies can be suppressed in mice treated with ACEI and statins. The hypothesis for this supplement project is that ACEI reduces the susceptibility, severity, and improves outcomes of SARS- CoV-2 infection in AD patients. This supplement research shares the same goal of the parent project, which aims to meet an urgent need to identify and fast-track new AD therapies (ACEI) with a clear efficacy readout. The scientific premise for our approach is strong. First, angiotensin II is elevated in both COVID-19 and AD patients, making ACE (converting angiotensin I to II) the prime target for inhibition. Second, SARS-CoV-2 binds to its target cells through ACE homolog ACE2. Third, treating human cell organoids with recombinant ACE2 reduces the viral load of SARS-CoV-2, and treating patients with ACEI up-regulates ACE2 in those with hypertension. Using a national database, we have reported significantly longer preclinical (asymptomatic) intervals before AD onset in subjects treated with ACEI and statins compared to those taking neither drug. We have identified ~350,000 subjects on an ACEI, with sufficient power to determine whether there is an association between ACEI and COVID-19 among AD patients. We propose to achieve three Specific Aims. Aim 1. To determine the susceptibility of AD to SARS-CoV-2 infection. This is a unique Aim that supplements the parent grant by using the original data set extended with data on COVID-19 and other variables including geographic regions. Aim 2. To determine the association of individual ACEI with the reduced occurrence of COVID-19 in medicated AD patients. We will divide all ten prescribed ACEIs into blood-brain barrier (BBB) crossing and non-crossing ACEIs and determine which group of/individual ACEIs reduce the occurrence of COVID-19 in AD patients. Aim 3. To determine the association of ACEI therapies with the severity of COVID- 19 symptoms in AD patients. The severity of COVID-19 will be defined by self-quarantine, hospitalization, intensive care unit admission, use of mechanical ... ## Key facts - **NIH application ID:** 10168854 - **Project number:** 3RF1AG063913-01S1 - **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS - **Principal Investigator:** LEE E. GOLDSTEIN - **Activity code:** RF1 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $412,500 - **Award type:** 3 - **Project period:** 2019-08-15 → 2024-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10168854 ## Citation > US National Institutes of Health, RePORTER application 10168854, Big data and small molecules for Alzheimer's disease (3RF1AG063913-01S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10168854. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*