Discovering repurposable drugs for Alzheimer’s disease and related dementias: target trial emulation using decentralised real-world data
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Alzheimer's disease and related dementias (ADRD) affect nearly 6.9 million Americans, with the number expected to triple by 2050, while disease-modifying therapies remain unavailable. Drug repurposing, which identifies new indications for already approved medications, offers a more efficient and cost-effective pathway to accelerate development of effective therapies for ADRD. The aim of this study is to identify potential drug repurposing signals by systematically screening routinely prescribed drugs for associations with progression from mild cognitive impairment (MCI) to ADRD. METHODS: We conducted a multi-site target trial emulation using electronic health record (EHR) data from four decentralised databases: INSIGHT Clinical Research Network, OneFlorida + Clinical Research Consortium, the University of Pennsylvania Health System, and Yale New Haven Health System. We performed an independent validation using EHR data from the TriNetX Research Network and a genetic risk-stratified sensitivity analysis in the Penn Medicine BioBank (PMBB) database. Eligible participants were adults aged 50 years or older at the time of MCI diagnosis, with no prior diagnosis of ADRD and no prior use of the trial drugs. Initiation of each of 181 routinely prescribed drugs was compared with two active control groups defined by initiation of supplements or cardiovascular medications. Risk ratios (RRs) and 95% CIs were estimated using a federated target trial emulation framework (LATTE) with stabilised inverse probability of treatment weighting and Poisson regression. FINDINGS: A total of 122,972 eligible patients were identified from the four decentralised databases, 335,506 patients identified from the TriNetX network for validation and 898 from PMBB database. Federated, multi-site target trial emulation identified 20 drug repurposing hypotheses with statistically significant protective effects, including anti-inflammatory and pain-modulating agents (celecoxib: RR 0.43; 95% CI: 0.23-0.81; dexamethasone RR 0.46; 95% CI: 0.29-0.73; gabapentin: RR 0.55; 95% CI: 0.36-0.83; ketorolac: RR 0.50; 95% CI: 0.31-0.80; methylprednisolone: RR 0.43; 95% CI: 0.24-0.76; prednisone: RR 0.48; 95% CI: 0.28-0.83; pregabalin: RR 0.53; 95% CI: 0.35-0.79), antimicrobial and microbiome-associated agents (cefazolin: RR 0.62; 95% CI: 0.45-0.84; clavulanate: RR 0.56; 95% CI: 0.44-0.71; fluconazole: RR 0.36; 95% CI: 0.23-0.58), neuromodulators and adrenergic agents (epinephrine: RR 0.42; 95% CI: 0.31-0.56; propranolol: RR 0.56; 95% CI: 0.37-0.85; salmeterol: RR 0.49; 95% CI: 0.32-0.74; tizanidine: RR 0.29; 95% CI: 0.14-0.57), vascular, metabolic, and hormonal modulators (empagliflozin: RR 0.29; 95% CI: 0.17-0.50; oestradiol: RR 0.47; 95% CI: 0.28-0.81; ezetimibe: RR 0.69; 95% CI: 0.52-0.91; sodium bicarbonate: RR 0.49; 95% CI: 0.29-0.84; spironolactone: RR 0.43; 95% CI: 0.31-0.60), and histamine-related and gastrointestinal agents (famotidine: RR 0.64; 95% CI: 0.55-0.74). Results were consistent in the independent validation using TriNetX network and sensitivity analysis in PMBB database. INTERPRETATION: 20 widely used medications may be associated with reduced progression from MCI to ADRD and represent promising candidates for clinical evaluation as repurposed therapies for dementia. FUNDING: National Institutes of Health.
Source: EBioMedicine
Authors: Qiong Wu, Lu Li, Yuqing Lei, Ting Zhou, Huilin Tang, Bingyu Zhang, Yiwen Lu, Maxwell Salvatore, Dazheng Zhang, Jiayi Tong, Tingyin Wang, Siqi Chen, Haoyang Li, Zhenxing Xu, Yu Huang, Yu Hu, Na Hong, Yujia Zhou, Fongci Lin, Kyra S. O’Brien, Yong Chen, Yong Chen, Ruowang Li, Hua Xu, Fei Wang, Jiang Bian, David A. Wolk, Yong Chen, Yong Chen
Institutions: University of Pittsburgh, University of Pennsylvania, Johns Hopkins University, Cornell University, Weill Cornell Medicine, Indiana University Bloomington, Indiana University, University of Florida, Yale University, Leonard Davis Institute of Health Economics, Thomas Jefferson University, University of Pennsylvania Health System, Cedars-Sinai Medical Center
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,780
Awards: Post-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of Health, Methods for Estimating Disease Burden of Seasonal Influenza, AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methods, Standardizing and Harmonizing Behavioral and Social Science Research Factors in Alzheimer's Disease through Ontology-Based Approaches, Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity, G9000030512, G9000036590, G9000047826, G9000053926, G9000079008, G9000319873, An end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health records, Novel evidence-accumulation-driven methods for characterizing kidney stone progression, PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System Diseases, ReCARDO: Using Real-World Data to Derive Common Data Elements for Alzheimer's Disease and AD-Related Dementias Research Through Ontological Innovation, TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting bias
OpenAlex ID: W7208746402