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

TL;DR

CauReL is a new method for drug repurposing in Alzheimer's that uses patient-specific data to identify drugs likely to help specific groups.

Contribution

CauReL introduces a dynamic counterfactual learning framework for precision drug repurposing using electronic health records.

Findings

CauReL improved covariate balance and prediction accuracy for Alzheimer's incidence and progression.

Twenty drugs showed protective associations, with four (liraglutide, empagliflozin, entacapone, amantadine) being highly reproducible.

Metabolic drugs helped patients with diabetes or obesity, while neuroactive drugs showed broad protection.

Abstract

Alzheimer’s disease has few effective therapies, and decades of amyloid- and tau-focused trials have delivered only modest benefit with substantial toxicity. Drug repurposing using real-world data offers a faster and lower-risk route to new treatments, yet current approaches typically average effects across populations, model disease onset and progression separately, and provide little insight into which patients are most likely to benefit. We present CauReL, a dynamic counterfactual representation learning framework that enables transparent, patient specific estimation of treatment effects from large-scale electronic health records for precision drug repurposing in AD. CauReL first learns balanced latent representations of treated and untreated patients using Integral Probability Metric regularization, then jointly predicts two clinically linked outcomes, incident AD and time from mild cognitive impairment (MCI) to AD, to generate paired counterfactual outcomes for every individual. A counterfactual explanation module quantifies how clinical features shape benefit at the patient level, and uplift trees transform complex heterogeneity into simple, rule-based subgroups suitable for trial enrichment and clinical decision support.

Using independent cohorts from OneFlorida+ and All of Us, we screened outpatient prescriptions with at least 20 percent exposure among 28,605 individuals with mild cognitive impairment, of whom 4,990 progressed to Alzheimer’s disease. CauReL substantially improved covariate balance and distributional overlap across drug cohorts and achieved strong predictive accuracy for both incidence (AUC greater than 0.90) and progression timing (C index 0.81 to 0.84; Spearman 0.80 to 0.86). Twenty drugs showed consistent protective associations, with four emerging as highly reproducible across both networks, the metabolic agents liraglutide and empagliflozin and the neuroactive agents entacapone and amantadine. These drugs were associated with meaningful absolute risk reductions and clinically significant delays in progression from mild cognitive impairment to Alzheimer’s disease. Metabolic drugs produced the strongest benefits in individuals with diabetes, obesity, or cardiovascular disease, whereas neuroactive drugs provided broadly consistent protection across most subgroups.

CauReL is available as an open source Python package with a companion web server for direct application to new cohorts or disease settings (https://caurel.site/). This work delivers a scalable and interpretable framework for prioritizing repurposable drugs and designing targeted clinical trials for the patients most likely to benefit.

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