Harnessing network pharmacology and in silico drug discovery to uncover new targets and therapeutics for Alzheimer's disease

Abstract

Alzheimer's disease (AD) is the leading cause of progressive neurodegenerative dementia, affecting approximately 50 million individuals globally. Recent studies have highlighted the differential expression of circular RNAs (circRNAs) in AD, which may disrupt the circRNA-miRNA-mRNA regulatory networks in neuronal cells. This work aims to integrate network pharmacology with in silico drug design to identify novel druggable targets for AD and propose promising drug candidates. We analyzed two circRNA datasets from the Gene Expression Omnibus, employing enrichment analysis and constructing a circRNA-miRNA-mRNA network. The RNAenrich platform facilitated the identification of hub genes and potential druggable targets. The identified target was subjected to virtual screening against a chemical drug library comprising over 6000 compounds in clinical trials while ensuring compliance with Lipinski's Rule of Five. Our findings reveal that differentially expressed circRNAs are significantly involved in gland development, apoptosis regulation, hypoxic response, and neuronal death. Notably, CDK-6 emerged as the most promising druggable target, exhibiting strong binding affinity with five selected ligands: DB06963, DB06888, DB07020, DB08683, and DB06976. These ligands demonstrated distinct binding modes and stable interactions over 500 ns of molecular dynamics simulations conducted via Desmond. In conclusion, our study identifies CDK-6 as a viable target for therapeutic intervention in Alzheimer's disease. The top five ligands present a compelling case for further investigation as innovative CDK-6 inhibitors and potential drug candidates for AD treatment.