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Drug Development.

Qing Zeng1,2, Edward Zamrini3, Ali Ahmed4

  • 1George Washington University, Washington, DC, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

This study used a medication-wide association study (MWAS) and AI to find drugs for Alzheimer's disease and related disorders (ADRD) prevention. Atorvastatin showed potential for ADRD risk reduction, especially when combined with escitalopram.

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Area of Science:

  • Pharmacogenomics and Computational Biology
  • Neuroscience and Aging Research
  • Artificial Intelligence in Healthcare

Background:

  • Pilot medication-wide association study (MWAS) to identify drugs for Alzheimer's disease and related disorders (ADRD) prevention.
  • Utilized the Million Veteran Project (MVP) dataset, encompassing genotypes, drug exposure, and ADRD incidence.
  • Hypothesis-free, agnostic exploration of real-world data.

Purpose of the Study:

  • To identify candidate drugs for ADRD repurposing using a large-scale real-world dataset.
  • To explore the impact of medications, genomic data, and social determinants of health (SDOH) on ADRD risk.
  • To leverage explainable AI for novel drug-risk factor interaction discovery.

Main Methods:

  • Trained a Histogram-Based Gradient Boosting (HGB) model on a matched cohort (n=263,256) from the MVP.
  • Incorporated PTSD status, SDOH (ADI scores), APOE ε4 status, and Polygenic Risk Scores (PRS) for ADRD.
  • Applied novel explainable AI methods to calculate impact and interaction scores for medications like atorvastatin.

Main Results:

  • Confirmed existing findings and identified novel associations between medications and ADRD risk.
  • Atorvastatin demonstrated a negative impact score, suggesting reduced ADRD risk.
  • Observed a novel negative interaction between atorvastatin and escitalopram, potentially lowering ADRD risk.

Conclusions:

  • The MWAS approach, integrating genomic and SDOH data, is robust for identifying ADRD prevention strategies.
  • Explainable AI effectively reveals complex drug-drug and drug-risk factor interactions.
  • Highlights the potential of real-world data and AI in discovering novel therapeutic candidates for ADRD prevention.