A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data

2024-12-29

AI Summary

Existing drug repurposing methods often overlook the heterogeneity of real-world patient responses, leading to the omission of subgroup-specific therapeutic effects. To address this, we propose STEDR—a novel framework that deeply couples subgroup discovery with causal treatment effect estimation for precision stratified repurposing. STEDR integrates deep representation learning, counterfactual causal inference, and clinically interpretable clustering guided by established Alzheimer’s disease (AD) risk factors. Evaluated on 8 million real-world electronic health records, it conducts large-scale (1,000+ drugs) AD clinical trial simulation. Experimental results identify 14 candidate drugs exhibiting significant subgroup-specific efficacy; their associated subgroup characteristics align closely with known AD risk factors—demonstrating both clinical interpretability and methodological advancement. STEDR thus enables more accurate, biologically grounded, and actionable drug repurposing decisions.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing studies using real-world patient data often treat the entire population as homogeneous, ignoring the heterogeneity of treatment responses across patient subgroups. This approach may overlook promising drugs that benefit specific subgroups but lack notable treatment effects across the entire population, potentially limiting the number of repurposable candidates identified. To address this, we introduce STEDR, a novel drug repurposing framework that integrates subgroup analysis with treatment effect estimation. Our approach first identifies repurposing candidates by emulating multiple clinical trials on real-world patient data and then characterizes patient subgroups by learning subgroup-specific treatment effects. We deploy model to Alzheimer's Disease (AD), a condition with few approved drugs and known heterogeneity in treatment responses. We emulate trials for over one thousand medications on a large-scale real-world database covering over 8 million patients, identifying 14 drug candidates with beneficial effects to AD in characterized subgroups. Experiments demonstrate STEDR's superior capability in identifying repurposing candidates compared to existing approaches. Additionally, our method can characterize clinically relevant patient subgroups associated with important AD-related risk factors, paving the way for precision drug repurposing.

Problem

• Drug Repurposing
• Population Variability
• Patient-specific Efficacy

Innovation

• STEDR
• Drug Repurposing
• Precision Medicine