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

• Published in: Nature communications Vol. 14; no. 1; pp. 8180 - 16
• Main Authors: Zang, Chengxi, Zhang, Hao, Xu, Jie, Zhang, Hansi, Fouladvand, Sajjad, Havaldar, Shreyas, Cheng, Feixiong, Chen, Kun, Chen, Yong, Glicksberg, Benjamin S., Chen, Jin, Bian, Jiang, Wang, Fei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.12.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/154/1435; 692/700/478/174; 692/700/565; Alzheimer's disease; Artificial intelligence; Atorvastatin; Balancing; Clinical trials; Data warehouses; Deep learning; Drug development; Drugs; Electronic health records; Emulation; Fluticasone; Gabapentin; Health services; Humanities and Social Sciences; Hypotheses; Informatics; Machine learning; Medicine; multidisciplinary; Neural networks; Neurodegenerative diseases; Omeprazole; Patients; Pharmaceuticals; Regression analysis; Science; Science (multidisciplinary); Statistical analysis; Survival analysis
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-43929-1

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. Target trial emulation (TTE) simulates randomized controlled trials using real world data (RWD). Here, authors show the effectiveness of different TTE strategies to identify drug candidates that could be potentially repurposed to Alzheimer’s disease using two large scale RWD warehouses.