Network pharmacology and molecular dynamic simulation integrated strategy for the screening of active components and mechanisms of phytochemicals from on Alzheimer and cognitive decline.

Mubarak A Alamri, Muhammad Tahir Ul Qamar

Journal: Journal of biomolecular structure & dynamics 2025;43(11):5296-5312

PMID: 38287491

Abstract

Alzheimer's disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI's anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.

Address: Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.; Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan.
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