Integrative multi-target analysis of Urtica dioica for gout arthritis treatment: a network pharmacology and clustering approach

Abstract

Urtica dioica (stinging nettle) has been traditionally used in Chinese medicine for the treatment of joint pain and rheumatoid arthritis. This study aims to elucidate the active compounds and mechanisms by which it acts against gout arthritis (GA). Gout-related genes were identified from the DisGeNet, GeneCards, and OMIM databases. These genes may play a role in inhibiting corresponding proteins targeted by the active compounds identified from the literature, which have an oral bioavailability of ≥ 30% and a drug-likeness score of ≥ 0.18. A human protein-protein interaction network was constructed, resulting in sixteen clusters containing plant-targeted genes, including ABCG2, SLC22A12, MAP2K7, ADCY10, RELA, and TP53. The key bioactive compounds, apigenin-7-O-glucoside and kaempferol, demonstrated significant binding to SLC22A12 and ABCG2, suggesting their potential to reduce uric acid levels and inflammation. Pathway enrichment analysis further identified key metabolic pathways involved, highlighting a dual mechanism of anti-inflammatory and urate-lowering effects. These findings underscore the potential of U. dioica in targeting multiple pathways involved in GA, combining traditional medicine with modern pharmacology. This integrated approach provides a foundation for future research and the development of multi-target therapeutic strategies for managing gout arthritis.

Supplementary information: The online version contains supplementary material available at 10.1007/s40203-024-00254-9.

Keywords: Gout arthritis; Leiden algorithm; Network pharmacology; OMIM; Urtica dioica.