期刊
FRONTIERS IN PHARMACOLOGY
卷 12, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fphar.2021.810748
关键词
Lonicerae japonicae Flos; Lonicerae Flos; Anti-SARS-CoV2 activity; chemical pattern recognition; quality analysis
资金
- Special Project for Sustainable Development of Shenzhen Science and Technology Innovation Committee [KCXFZ20201221173005015]
In this study, a chemical pattern recognition model was established based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity to analyze the quality of Lonicerae japonicae flos and Lonicerae flos. A total of 11 characteristic components were identified and could be recommended as quality control markers. The established model effectively differentiated between Lonicerae japonicae flos and Lonicerae flos, providing a feasible tool for quality assessment of natural products and reference for quality standard setting.
Lonicerae japonicae flos (L. japonicae flos, Lonicera japonica Thunb.) is one of the most commonly prescribed botanical drugs in the treatment or prevention of corona virus disease 2019. However, L. japonicae flos is often confused or adulterated with Lonicerae flos (L. flos, Lonicera macrantha (D.Don) Spreng., Shanyinhua in Chinese). The anti-SARS-CoV2 activity and related differentiation method of L. japonicae flos and L. flos have not been documented. In this study, we established a chemical pattern recognition model for quality analysis of L. japonicae flos and L. flos based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity. Firstly, chemical data of 59 batches of L. japonicae flos and L. flos were obtained by UHPLC, and partial least squares-discriminant analysis was applied to extract the components that lead to classification. Next, anti-SARS-CoV2 activity was measured and bioactive components were acquired by spectrum-effect relationship analysis. Finally, characteristic components were explored by overlapping feature extracted components and bioactive components. Accordingly, eleven characteristic components were successfully selected, identified, quantified and could be recommended as quality control marker. In addition, chemical pattern recognition model based on these eleven components was established to effectively discriminate L. japonicae flos and L. flos. In sum, the demonstrated strategy provided effective and highly feasible tool for quality assessment of natural products, and offer reference for the quality standard setting.
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