A reverse network pharmacology and bioinformatics-based approach to exploring medication patterns for polycystic...

To predict potential herbal medicines targeting polycystic ovary syndrome (PCOS)-related infertility using an in silico reverse network pharmacology approach and identify core herbal candidates.

This computational study began by collecting disease targets for PCOS and infertility from multiple public databases. Common targets were identified, and active compounds associated with these targets were retrieved from the Uniprot and TCMSP databases. These compounds were subsequently filtered using PubChem and SwissADME based on pharmacokinetic properties and mapped to corresponding herbs via TCMSP. Herbal properties (nature, flavor, meridian tropism) were statistically analyzed. A core network of targets-compounds-herbs was constructed using Cytoscape 3.8.0, and a subnetwork was generated from nodes with a Degree > 20. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the targets of the core herbal combination to elucidate potential mechanisms.

A total of 2,500 common targets for PCOS and infertility, 1,545 active compounds, and 488 corresponding herbs were identified. Analysis of herbal properties revealed a predominance of Warm and Pungent medicines, followed by Cold, Bitter, Neutral, and Sweet medicines. A core herbal combination consisting of Ephedra sinica (Mahuang), Magnolia officinalis (Houpo), Bupleurum chinense (Chaihu), Chrysanthemum morifolium (Juhua), Angelica dahurica (Baizhi), and Morus alba (Sangye) was identified through frequency statistics, association rules, and cluster analysis. GO and KEGG enrichment analyses of the core combination's targets highlighted mechanisms involving oxidative stress, inflammatory responses, and endocrine regulation, including the TNF and PI3K-Akt signaling pathways.

This study successfully employed reverse network pharmacology to predict a core herbal combination for treating PCOS-related infertility. The findings, while requiring experimental validation, offer novel insights for developing therapeutic strategies and provide a foundation for future clinical management.