# In Silico Screening of Medications for Slowing Alzheimer's Disease Progression.

> **NIH NIH R01** · UNIVERSITY OF ALABAMA AT BIRMINGHAM · 2020 · $645,978

## Abstract

Drug development in Alzheimer's disease (AD) requires a considerable investment of time and re-
sources, often with little reward as the vast majority of medications ultimately prove unsuccessful. Drug repur-
posing, in which medications that already have been approved for treatment are evaluated for therapeutic effects
in other disorders, has the potential to markedly increase the number of agents in the drug development pipeline
but requires methods for effective screening of candidate medications for activity. In silico or computational ap-
proaches to medication screening are rapidly growing, and have been successful in illnesses such as cancer,
but their application to AD remains understudied. There is also intense interest in drug repurposing approaches
that will utilize the vast amounts of clinical data that are being collected from epidemiological studies and clinical
encounters documented through electronic health records (EHRs). In this proposal, we present a novel approach
to drug repurposing that uses large-scale data mining (i.e., pattern recognition) algorithms applied to concurrent
medication taken by participants in AD clinical trials and in Medicare administrative data to determine which of
these medications show potential therapeutic benefits. With over 30 years of AD clinical trial data available to us
through a recently developed meta-database and 10 years of prescription data available through Medicare Part
D, the administration of concurrent medications to patients as part of their routine clinical care constitutes a large-
scale natural experiment. This information can be harnessed for AD treatment discovery if appropriate methods
can be developed to detect effects on disease progression within this high-dimensional data. Data mining al-
gorithms that discover patterns of associations in data, rather than testing predetermined hypotheses, are well
suited to application in large-scale screening for drug repurposing. Using our meta-database and Medicare data,
we will be able to evaluate most of the more than 6,000 currently available prescription medications for efficacy
in AD using well-accepted endpoints for measuring disease progression. The discovery phase will be followed
by a validation phase of promising candidate medications in independent data sets, as well as identification of
plausible gene targets for each medication from the biomedical literature. This study will set the groundwork for a
series of follow-up in vivo studies to conclusively demonstrate effects of selected medications for AD, expanding
the current armamentarium for treating this common and debilitating disorder.

## Key facts

- **NIH application ID:** 9884696
- **Project number:** 5R01AG057684-04
- **Recipient organization:** UNIVERSITY OF ALABAMA AT BIRMINGHAM
- **Principal Investigator:** RICHARD E KENNEDY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $645,978
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9884696

## Citation

> US National Institutes of Health, RePORTER application 9884696, In Silico Screening of Medications for Slowing Alzheimer's Disease Progression. (5R01AG057684-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9884696. Licensed CC0.

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