Baicalin

The clinical applications of natural compounds are limited by their inherent physicochemical properties. This study reports a hierarchical supramolecular engineering strategy for constructing a dual-assembly nanosystem for the treatment of skin hyperpigmentation. Using an AI-assisted computational screening model, tranexamic acid was identified as a suitable molecular partner of the hydrophobic and active, baicalin. Subsequent dual assembly processes yielded a stable hybrid nanoplatform (DHBTC) that enhanced the solubility and delivery efficiency of baicalin. Single-cell transcriptomics revealed an unexpected mechanism of "functional inhibition"; despite the depigmenting efficacy of DHBTC, the melanogenesis-related gene network in melanocytes was upregulated. This was identified as compensatory transcriptional feedback triggered by drug-induced autophagy. DHBTC functionally inhibits pigment accumulation by accelerating melanosome degradation, which elicits ineffective transcriptional activation as the cell attempts to restore homeostasis. Furthermore, the platform remodeled the cutaneous immune microenvironment toward an anti-inflammatory state. This study presents a strategy for designing drug delivery systems, from computational prediction to supramolecular assembly, and describes a therapeutic mechanism based on the modulation of post-translational and organellar homeostasis.

Scutellariae Radix (SR), a traditional herbal medicine with significant medicinal value, is widely used worldwide. To address the quality differences caused by its various commercial specifications, processing methods, and provenances, a multi-dimensional quality evaluation system was established based on content, biological potency, and spectroscopy, compensating for the inadequacy of using baicalin as sole indicator for assessing SR quality. UHPLC-Q-TOF-MS/MS technology was employed to systematically identify the chemical profiles of SR and confirm differential components among multi-source samples. The varying distribution of flavone glycosides and their aglycones in the samples was identified as the primary reason for differences in commercial specifications and processing methods. Based on this, ten marker compounds with high content and significant bioactivity were selected for quantitative analysis using UHPLC-QqQ-MS/MS. Six components are proposed as the content markers for each sample category, with corresponding limits established. Additionally, the anti-inflammatory activity of the samples was evaluated through biological potency assays, and the potency significantly increased after SR processing. To achieve real-time and efficient detection, an innovative technology combined near-infrared spectroscopy (NIR) with intelligent algorithm was developed. After preprocessing with SG+ SNV + MSC, and selecting characteristic wavelengths by SPA, the GWO-SVM model achieved 100% accuracy in sample classification. Meanwhile, after sequential optimization, the PLSR model reliably predicting the concentration of active constituents and anti-inflammatory potency. This study has accomplished a shift in quality control of SR, moving from single-component quantitative analysis to comprehensive evaluation combining active ingredient detection and bioactivity assessment, while also advancing from precise measurement to rapid predictive methods.