Network pharmacology with molecular docking-based anti-aging activity of Vayasthapana Mahakashaya: Computational study

Abstract BACKGROUND: Ayurveda has a well-established history for preventing senescence since centuries in India. However, there is currently no systematic approach to investigate Ayurveda drugs for their antiaging activity and underlying molecular mechanism. OBJECTIVE: This study aims to identify the aging targets and mechanisms of herbs from Vayasthapana Mahakashaya ( VM ) through a methodical approach based on network pharmacology and molecular docking. MATERIALS AND METHODS: Using the Indian Medicinal Plants, Phytochemistry and Therapeutics database, bioactives in VM were found, and target proteins identified through SwissTargetPrediction (~database for prediction of targets). Alignment using the GeneCard database revealed proteins relevant to aging. A protein network study of the VM–bioactives–target identified important flavonoids that target aging. Using protein–protein interaction (~PPI) network analysis, the essential proteins linked to aging were identified. Binding activities between core targets and aging-related proteins were verified by molecular docking. RESULTS: A total of 640 bioactives were obtained from VM, 93 bioactives qualified absorption, distribution, metabolism, and excretion, and 31 bioactives showed more than 70% target prediction with 127 targets; after removing the duplicates, 49 targets were obtained. A total of 2901 aging targets were retrieved from the GeneCards database, and 25 common targets were obtained between VM and GeneCards database for aging. The PPI network yielded three subnetworks with 18 key targets along with top five core targets. Gene ontology on key targets identified 33 Molecular function, 153 Biological process, 13 Cellular component, and 3 Kyoto Encyclopedia of Genes and Genomes pathway analyses. Docked top compounds on the corresponding target; the docking score between CYP2D6 and berberine was discovered to be the lowest, at −10.3 kcal/mol –1, and it was highest between 5-hydroxytryptamine receptor 1A and N, N-dimethyl-5-methoxytryptamine, at −6.3 kcal. mol –1 . CONCLUSION: This work demonstrates the probable molecular mechanisms of VM in the aging process and provides new insights into those properties. It advances our knowledge of conventional treatments in antiaging research.