Metabolomics and bioinformatics study of baicalin in treating pulmonary fibrosis.

Pulmonary fibrosis (PF) is a key pathological feature of various lung diseases, highlighting the urgent need for precise diagnostic biomarkers and effective therapeutic agents. This study aims to elucidate the mechanism of baicalin in treating PF using metabolomics and bioinformatics approaches. A PF rat model was established by bleomycin injection, with lung tissue pathology assessed at weeks 1, 3, and 5. After model establishment, rats were administered baicalin by gavage at doses of 25, 50, and 100 mg·kg⁻1 once daily for 4 weeks, while the control and model groups received equivalent volumes of saline, and a prednisone group served as a positive control. Urine samples were collected at weeks 1, 3, and 5 for metabolomics analysis using UPLC-QTOF/MS. Bioinformatics and network analysis identified baicalin's targets in PF treatment. The study identified five urinary metabolites-5-L-glutamyl-taurine, 3-sulfinoalanine, indoxyl glucuronide, oxoglutaric acid, and uridine-as multiply-filtered candidate biomarkers associated with PF. An additional sterol-related feature at m/z 369.35 was retained only as a putative MSI level 3 signal and was not included in the core biomarker panel. Baicalin showed specific regulatory effects on the core metabolites. Bioinformatics analysis revealed 23 PF-related targets, with TYMS highlighted as a putative baicalin-related target in PF. Baicalin demonstrates promising anti-PF effects, suggesting its potential as a natural treatment for PF.