Establishing identifiable characteristic fingerprints of mulberry leaves: Integrating chemical composition and bioactivity through machine learning

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.