# A knowledge map to find Alzheimer's disease drugs

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $792,498

## Abstract

To stem the rising incidence of Alzheimer's disease (AD) in our aging population, new methods to repurpose
and combine drugs against Alzheimer's disease (AD) are acutely required. This is a challenge, however,
because the complex polygenic basis of AD remains opaque, and rational methods to repurpose drugs are in
early years, even for well-defined gene targets. To address these problems, we propose new algorithms to
integrate data on a very large scale so as to combine evolutionary information and high-throughput experimental
observations with the knowledge conveyed by text in the literature. First, to detect disease-relevant genome
variations in AD patients, Aim 1 will combine a novel mathematical calculus of mutational landscapes with
machine learning, in so doing suggesting primary candidate genes for drug targeting based on signs of
mutational selection in cases or controls. Next, to repurpose and combine drugs targeting these genes, Aim 2
will map a large fraction of all that is known about genes, phenotypes, and drugs into a single high-dimensional
network that represents their interactions as described in various databases (structured data) and in the literature
(unstructured data). The topology of this network will determine the optimal choice of single drug or combination
therapy in an approach that can be personalized. Finally, to validate efficacy experimentally, Aim 3 will test both
our candidate genes and drugs with state-of-the-art in vitro and in vivo screens. Feasibility rests with prior studies
on evolution, networks, systems, and text-mining that demonstrate accurate predictions of deleterious mutations
and their clinical sequelae and the discovery of drivers of diseases. Broadly, this work will yield proof of principle
for a novel quantitative model that integrates fundamental concepts from mathematics and molecular evolution,
and for a low resolution but large-scale map of biomedical knowledge in which network notions of distance
computed by machine learning identify relevant functional hypothesis that would otherwise be easily overlooked.
The result will be a new experimental ability to unravel the genotype-phenotype relationship in Alzheimer's
Disease so as to guide drug therapy.

## Key facts

- **NIH application ID:** 9975673
- **Project number:** 5R01AG061105-03
- **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:** $792,498
- **Award type:** 5
- **Project period:** 2018-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9975673

## Citation

> US National Institutes of Health, RePORTER application 9975673, A knowledge map to find Alzheimer's disease drugs (5R01AG061105-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9975673. Licensed CC0.

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