# A knowledge map to find Alzheimer's disease drugs

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $386,555

## Abstract

ABSTRACT. This Supplement extends Aims 1 and 2 of the parent grant on Alzheimer’s Disease (AD) by
developing: prospective benchmarks for algorithms that predict biomarkers of disease risk (Aim 1) and new
algorithms to support drug repositioning (Aim 2). Both extensions strengthen Aims 1 and 2 for AD but also have
immediate applications for research on COVID-19 disease in keeping with NOT-AG-20-022.
AIM 1 of the parent grant develops EA-ML, a Machine Learning (ML) pipeline to compare coding mutations in
individuals with and without AD. The output is a list of genes with which to predict AD risk from their mutations.
While the parent grant has multiple criteria for success, none are prospective given the vast lead-time between
AD onset and symptoms. Supplemental Aim 1 adds prospective testing, using COVID-19. This is possible
because the UK Biobank has begun to annotate its 50,000 public exomes with the COVID-19 status of
individuals, including who had severe morbidity or mild symptoms at worst. The biobank will also add 150,000
more exomes by end 2020. Accordingly, we will apply EA-ML to the current UK biobank data to identify human
genetic biomarkers that distinguish severe from mild cases and then test EA-ML predictions of COVID-19
virulence prospectively, on the exomes that are newly added to the biobank. As a further new benchmark, we
will also compare EA-ML to a novel “EA-Wavelet” algorithm, also tested prospectively on COVID-19. EA-
Wavelet sorts cases from controls by factoring EA over the entire network of human protein-protein interactions.
The results will tell us which of EA-ML, EA-Wavelet, or combination thereof is the best at identifying
critical biomarkers and clinical risk of AD, while also doing the same for COVID-19.
Aim 2 of the parent grant develops drug repositioning for AD by linking target genes and drugs via knowledge
maps of functional interactions. Here, we propose a complementary approach that connect genes to drugs
via structural maps of binding epitopes. For this we will comprehensively map evolutionarily important sites
in the structural proteome of genes that are associated with AD. The approach exploits EA theory to measure
past and present evolutionary forces in fitness landscapes, and it takes into account current sequence variations
to guard against any possible mutational escape from drugs that target these epitopes. The output will be surface
accessible regions of proteins that can then be used for (i) computational docking of small molecules towards
drug repurposing, combination therapy, and lead discovery for drug design3-5; (ii) engineering mimetic peptides
or other molecules that can inhibit normal interactions6; and (iii) CRISPR engineering or peptide synthesis that
create antigens for more effective vaccines7, 8. These automated mapping tools are general, and besides in
SARS-CoV-2, will identify an entire new structural library of functional sites to target for AD therapy with
repurposed drugs.

## Key facts

- **NIH application ID:** 10198233
- **Project number:** 3R01AG061105-03S1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** OLIVIER LICHTARGE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $386,555
- **Award type:** 3
- **Project period:** 2018-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10198233

## Citation

> US National Institutes of Health, RePORTER application 10198233, A knowledge map to find Alzheimer's disease drugs (3R01AG061105-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10198233. Licensed CC0.

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