Network pharmacology–based prediction of the multi-target mechanism of Simhanada Guggulu in rheumatoid arthritis: an in silico study

Background: Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disorder characterised by persistent synovitis, progressive cartilage and subchondral bone destruction, functional disability and systemic complications. Current disease-modifying therapy has substantially improved disease control, but long-term use remains constrained by hepatotoxicity, gastrointestinal injury, sustained immunosuppression and secondary treatment failure. This has renewed interest in multi-component traditional formulations as candidate sources of multi-target anti-inflammatory strategies. Objective: To predict, using an integrated network pharmacology workflow, the molecular targets and inflammatory signalling pathways through which Simhanada Guggulu — a classical Ayurvedic polyherbal-mineral formulation prescribed for Amavata — may act in rheumatoid arthritis. Methods: Four phytochemical markers of Simhanada Guggulu — gallic acid, epigallocatechin, guggulsterone-Z and ricinoleic acid — were selected on the basis of independently validated, ICH Q2(R1)-compliant HPLC quantification confirming their presence in the finished tablet. Their structures were verified against PubChem and filtered for drug-likeness using SwissADME. Putative human protein targets of the four markers were predicted with SwissTargetPrediction. RA-associated genes were assembled from the Open Targets Platform, and the intersection with predicted compound targets was taken as the candidate target set. Protein–protein interaction analysis and KEGG pathway over-representation analysis were planned via STRING and clusterProfiler respectively; the extent to which each was completed is detailed in the main text. Results: The analysis was restricted to four phytochemical markers with independently validated quantitative confirmation in the finished tablet — gallic acid, epigallocatechin, guggulsterone-Z and ricinoleic acid — all of which pass the drug-likeness screen (Table 2). SwissTargetPrediction (probability ≥ 0.1) returned 204 unique predicted targets across the four compounds; intersection with the Open Targets rheumatoid arthritis gene set (MONDO_0008383, score ≥ 0.1; 1301 genes) gave a candidate target set of 55 genes, including TNF, STAT1, MAPK3, MAPK14, AKT1, PIK3CA, MTOR, HIF1A, VEGFA and KDR — genes mapping onto NF-κB, TNF, JAK-STAT, MAPK, PI3K-AKT, HIF-1 and VEGF signalling, all independently established as central to RA pathogenesis. Formal statistical pathway enrichment and protein–protein interaction network topology remain incomplete, for reasons given in the main text; the candidate target set itself is a complete, reproducible result. Conclusion: These computational findings are intended to generate a coherent, testable hypothesis that Simhanada Guggulu acts through simultaneous, low-intensity modulation of several interconnected inflammatory and oxidative pathways rather than through a single dominant target. The predictions are hypothesis-generating only and require experimental verification before any therapeutic inference is drawn.