Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease

Alzheimer’s Research & Therapy

Jeffrey Cummings¹  Chien-Wei Chiang²  Lang Li²  Pengyue Zhang³  James B. Leverenz⁴  Rui Chen⁵  Bingshan Li⁵  Quan Wang⁵  Stephen J. Lewis⁶  Yadi Zhou⁷  Jielin Xu⁷  Yuan Hou⁷  Feixiong Cheng⁷  Jiansong Fang⁷  Bin Zhang⁷  Andrew A. Pieper⁸ 

[1] Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas;Department of Biomedical Informatics, College of Medicine, Ohio State University;Department of Biostatistics and Health Data Science, School of Medicine, Indiana University;Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University;Department of Molecular Physiology and Biophysics, Vanderbilt University;Department of Pediatrics, Case Western Reserve University;Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic;Harrington Discovery Institute, University Hospitals Cleveland Medical Center;

关键词: Alzheimer’s disease;    Drug repurposing;    Genome-wide association studies (GWAS);    Multi-omics;    Network medicine;    Pioglitazone;   

DOI  :  10.1186/s13195-021-00951-z

来源: DOAJ

【 摘 要 】

Abstract Background Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer’s disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein–protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein–protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. Results Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861–0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862–0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. Conclusions In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

【 授权许可】

Unknown