Network pharmacology and molecular docking-based evaluation of the ethnomedical use of Rasnapanchakam in complementary treatment of rheumatoid arthritis

Background Rasnapanchakam decoction is a traditional Indian Ayurvedic formula that is commonly used to treat rheumatoid arthritis. Despite its common use, the specific mechanism by which this decoction affects rheumatoid arthritis remains unclear. Therefore, this research sought to explore the anti-arthritic potential of Rasnapanchakam through the application of network pharmacology, molecular docking, and dynamics simulation analysis. Materials and methods The plants used in the preparation of the decoction and bioactive compounds were identified, and potential targets of Rasnapanchakam and rheumatoid arthritis-related genes were retrieved using public databases, such as IMMPAT, PubMed, PubChem, GeneCards, OMIM, and SwissTargetPrediction. Furthermore, the targets of rheumatoid arthritis were selected from the databases. Various network, gene ontology, and pathway analyses were performed to pinpoint crucial bioactive compounds, potential targets, and key pathways. Finally, molecular docking and simulation analyses verified the interaction between essential bioactive compounds and primary targets of rheumatoid arthritis. Results This approach revealed five plants, 1151 active compounds, 1264 predicted targets, and 7033 targets of rheumatoid arthritis, yielding 705 common targets at the intersection. GAPDH, IL-6, TNF, and TP53 were identified as the core targets, and 6-shagaol, ecdysterone, 15-Hydroxyabieta-7,13-dien-18-oic acid, and 15-Hydroxydehydroabietic acid were the most potent anti-arthritic bioactive compounds identified in Rasnapanchakam to target the core hub genes. The gene ontology and KEGG analysis highlighted the involvement of Rasnapanchakam in the treatment of RA through PI3K-AKT and lipid and atherosclerosis pathways. Molecular docking analysis suggested favorable interaction between selected bioactives and identified hub targets, while molecular dynamics simulations supported persistence of the protein-ligand interactions, along with post-simulation analyses such as MM-PBSA, principal component analysis, and VMD. Conclusion This study provides computational insights into the potential anti-arthritic mechanisms of Rasnapanchakam and suggests that its bioactive components may modulate RA-associated inflammatory and oxidative stress-related pathways through interactions with key targets such as GAPDH, IL6, TNF, and TP53. The predicted effects may involve PI3K-AKT and lipid-atherosclerosis pathways. These findings provide a basis for future experimental studies to validate the therapeutic relevance of Rasnapanchakam in RA.