High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data

Chengxi Zang, Hao Zhang, Jie Xu, Hansi Zhang, Sajjad Fouladvand, Shreyas Havaldar, Feixiong Cheng, Kun Chen, Yong Chen, Benjamin S. Glicksberg, Jin Chen, Jiang Bian and Fei Wang ()
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Chengxi Zang: Weill Cornell Medicine
Hao Zhang: Weill Cornell Medicine
Jie Xu: University of Florida
Hansi Zhang: University of Florida
Sajjad Fouladvand: University of Kentucky
Shreyas Havaldar: Icahn School of Medicine at Mount Sinai
Feixiong Cheng: Lerner Research Institute, Cleveland Clinic
Kun Chen: University of Connecticut
Yong Chen: University of Pennsylvania
Benjamin S. Glicksberg: Icahn School of Medicine at Mount Sinai
Jin Chen: University of Kentucky
Jiang Bian: University of Florida
Fei Wang: Weill Cornell Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer’s disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer’s patients.

Date: 2023
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DOI: 10.1038/s41467-023-43929-1

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