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CauReL: Dynamic Counterfactual Learning for Precision Drug Repurposing in Alzheimer's Disease.

Yanfei Wang1, Minghao Zhou1, Zijia Tang2

  • 1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.

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|January 9, 2026
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Summary

A new framework, CauReL, uses real-world data to identify repurposed drugs for Alzheimer's disease (AD), predicting patient-specific treatment effects for precision medicine. It found four promising drugs, including metabolic agents, that may slow AD progression.

Keywords:
Alzheimer’s Disease (AD)Causal AICounterfactual representation learningDrug RepurposingElectronic Health Records (EHRs)Individualized Treatment Effects (ITE)

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Area of Science:

  • Computational biology and bioinformatics
  • Pharmacology and drug discovery
  • Neuroscience and neurology

Background:

  • Alzheimer's disease (AD) lacks effective treatments, with existing therapies offering limited benefits and significant toxicity.
  • Current drug repurposing methods often generalize treatment effects, failing to identify patient-specific benefits.
  • There is a critical need for precise methods to identify suitable candidates for drug repurposing in AD.

Purpose of the Study:

  • To introduce CauReL, a novel framework for dynamic counterfactual representation learning.
  • To enable patient-specific estimation of treatment effects from electronic health records for precision drug repurposing in AD.
  • To identify and validate repurposed drugs with potential therapeutic benefits for individuals with mild cognitive impairment (MCI) and AD.

Main Methods:

  • Developed CauReL, a framework using Integral Probability Metric regularization for balanced latent representations.
  • Jointly predicted AD incidence and MCI-to-AD progression time to generate paired counterfactual outcomes.
  • Employed a counterfactual explanation module and uplift trees for patient-level benefit quantification and subgroup identification.

Main Results:

  • CauReL demonstrated improved covariate balance and predictive accuracy for AD incidence (AUC > 0.90) and progression (C-index 0.81-0.84).
  • Screened 28,605 individuals, identifying 20 drugs with protective associations, including liraglutide, empagliflozin, entacapone, and amantadine.
  • Metabolic drugs showed greater benefit in patients with diabetes, obesity, or cardiovascular disease; neuroactive drugs offered consistent protection.

Conclusions:

  • CauReL provides a scalable and interpretable framework for precision drug repurposing in Alzheimer's disease.
  • Identified specific repurposed drugs, including metabolic and neuroactive agents, with potential to reduce AD risk and delay progression.
  • The framework facilitates targeted clinical trial design by identifying patient subgroups most likely to benefit from specific treatments.