The Potential Mechanism of Eriodictyol in Treating Alzheimer's Disease: A Study on Computer-assisted Investigational Strategies

Background: At present, drug development for treating Alzheimer’s disease (AD) is still highly challenging. Eriodictyol (ERD) has shown great potential in treating AD, but its molecular mechanism is unknown. Objective: We aimed to explore the potential targets and mechanisms of ERD in the treatment of AD through network pharmacology, molecular docking, and molecular dynamics simulations. Methods: ERD-related targets were predicted based on the CTD, SEA, PharmMapper, Swiss TargetPrediction, and ETCM databases, and AD-related targets were predicted through the TTD, OMIM, DrugBank, GeneCards, Disgenet, and PharmGKB databases. Protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomics analyses (KEGG) were used to analyse the potential targets and key pathways of the anti-AD effect of ERD. Subsequently, potential DEGs affected by AD were analysed using the AlzData database, and their relationships with ERD were evaluated through molecular docking and molecular dynamics simulations. Results: A total of 198 ERD-related targets, 3716 AD-related targets, and 122 intersecting targets were identified. GO annotation analysis revealed 1497 biological processes, 78 cellular components, and 132 molecular functions of 15 core targets. KEGG enrichment analysis identified 168 signalling pathways. We ultimately identified 9 DEGs associated with AD through analysis of the AlzData data. Molecular docking results showed good affinity between the selected targets and ERD, with PTGS2, HSP90AA1, and BCL2. The interactions were confirmed by molecular dynamics simulations. Conclusion: ERD exerts anti-AD effects through multiple targets, pathways, and levels, providing a theoretical foundation and valuable reference for the development of ERD as a natural anti-AD drug.