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

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.

Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH)

“…In this study, we utilized a computational method for knowledge discovery in iBKH based on the advanced graph learning approaches (Nicholson and Greene, 2020; Su et al ., 2020). As a proof of concept, we performed in silico hypothesis generation for AD drug repurposing, i.e., predicting drugs that potentially connect to the AD entity (Fang et al, 2022; Fang et al, 2021; Zeng et al, 2020; Zhou et al, 2021). We utilized knowledge graph embedding (KGE) algorithms to calculate machine-readable embedding vectors for entities and/or relations in iBKH, while preserving the graph structure (Mohamed et al ., 2021; Su et al ., 2020; Wang et al, 2017), using Deep Graph Library - Knowledge Embedding (DGL-KE) (Zheng et al ., 2020).…”

Regulation of aging by balancing mitochondrial function and antioxidant levels

“…S1), FBX has a potential therapeutic or preventive agent for the treatment of AD and PD. The therapeutic effects of FBX may be enhanced by coadministration of FBX and vitamin C. A recent study identified FBX as a potential new treatment for AD using artificial intelligence approaches [47].…”

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

“…Recent artificial intelligence-based drug discovery and drug repositioning techniques can dramatically shorten the preclinical research period and cost while increasing the possibility of treating various neurological diseases that were not previously available [ 88 , 89 ]. Several online tools have been developed and are available for free to detect repurposing drugs according to gene expression profiles [ 23 , 90 , 91 ].…”