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Establishing Identifiable Characteristic fingerprints of Mulberry Leaves: Integrating Chemical Composition and Bioactivity through Machine Learning.

Che-Chun Lin,San-Yuan Wang,Kowit-Yu Chong,Vinh-Tuyen T Le,Yi Lin,Lih-Geeng Chen,C. Lee,Ching-Chiung Wang

Published 2025 in Journal of Ethnopharmacology

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE Mulberry leaves (Morus alba L.) are used in traditional Chinese medicine to clear the lungs and dispel wind-heat. Despite their common use, chemical reference substance rely solely on rutin, which may not reflect their full pharmacological potential. AIM OF THE STUDY To develop a multicomponent quality evaluation strategy for mulberry leaves by integrating HPLC fingerprinting, chemometrics, and biological validation. MATERIALS AND METHODS Twenty-seven mulberry leaf samples were analyzed using HPLC. PCA, PLS-DA, and Pearson correlation were applied to identify quality markers. An artificial neural network (ANN) model was constructed based on 17 characteristic peaks. Anti-fibrotic effects were evaluated in bleomycin-induced pulmonary fibrosis mice. RESULTS Based on the distribution of chemical reference substances contents in the 27 samples, the mulberry leaves could be categorized into high- and low-content groups, with 0.1% rutin serving as the classification threshold. An ANN analysis of the HPLC fingerprint was then employed to establish a recognition model based on the full fingerprint, achieving a classification accuracy of 100%. Rutin correlated with MMP-13 inhibition, and cryptochlorogenic acid with both MMP-13 and PAI-1 inhibition. In vivo studies demonstrated that qualified extracts of mulberry leaves reduced the progression of bleomycin-induced pulmonary fibrosis. CONCLUSIONS This study establishes a comprehensive and bioactivity-linked quality evaluation framework for mulberry leaves, aligning traditional knowledge with modern scientific assessment.

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