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

TL;DR

This paper presents STEDR, a framework that combines subgroup analysis with treatment effect estimation to improve drug repurposing by identifying patient subgroups that benefit from existing drugs, demonstrated on Alzheimer's Disease data.

Contribution

The paper introduces STEDR, a novel method that integrates clinical trial emulation and subgroup-specific treatment effect learning for precision drug repurposing.

Findings

Identified 14 drug candidates beneficial for Alzheimer's Disease in specific subgroups.

Emulated over 1,000 clinical trials on a large real-world dataset of 8 million patients.

Demonstrated superior performance over existing drug repurposing approaches.

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.