基于关键成分群构建“含量-基原-药效”关联的枸杞子质量评价体系研究

Wolfberry (the fruit of Lycium barbarum L.) has been widely used as a kind of traditional Chinese medicine (TCM) and functional food around the world. To date, the quality control methods for wolfberry still focus on the total amount of polysaccharides and a few non-proprietary small molecules, indicating a poor specificity and weak correlation with wolfberry’s origin and therapeutic efficacy. We previously revealed the chemical profile of small molecules in wolfberry for the first time, and innovatively discovered some anti-Alzheimer (anti-AD) small molecules, which laid the foundation for improving wolfberry’s quality standards. In this study, we aim to establish a new satisfactory wolfberry quality evaluation system based on small molecular group. Firstly, we will discover characteristic components combination to identify wolfberry’s origin, by using pseudo-targeted metabolomics, and by constructing the accuracy-guided discriminant analysis and probabilistic neural network classification model. Secondly, we will explore anti-AD effective compositions combination of wolfberry in neural cell and drosophila AD models by using contribution/accuracy-guided regression analysis, artificial neural network analysis, and further validate their effect in transgenic AD mouse experiment. Finally, combined characteristic components with effective compositions as the key components combination, “content-origin-efficacy” associated quality evaluation system will be put forward to intelligently evaluate wolfberry’s origin and overall represent its anti-AD effect, by using quantitative analysis of multi-component with single marker method and chemometrics. Our program will be conducive to wolfberry quality control improvement, with important theoretical significance and practical application value, and provide a methodological support for the quality control of other TCMs.

中药枸杞子应用广泛，现有质控方法局限于多糖总量和少数非专属小分子，难以鉴别枸杞子的基原和反映其含量药效关系。前期我们系统揭示了枸杞子的小分子化学轮廓，突破性地发现了其抗阿尔兹海默（Alzheimer，AD）的药效成分。基于前期研究，本项目拟：(1)采用拟靶向代谢组学方法研究枸杞子与其近缘种植物果实的差异成分，建立判别分析和概率神经网络分类模型，以分类准确度为导向，快速阐明能鉴别基原的特征成分群；(2)在细胞和果蝇模型中，用回归分析、人工神经网络等算法，以活性预测贡献率和准确度为导向，快速筛选表征抗AD药效的效应成分群，并用小鼠模型验证；(3)将特征和效应成分群整合为关键成分群，通过一标多测定量和化学计量学建模，构建“含量-基源-药效”关联的质控体系，实现对枸杞子基原的智能鉴定和抗AD药效的整体表征。本研究对改进枸杞子的质量控制具有重要的理论意义和实用价值，也为其他中药质量控制提供新的思路。

中药枸杞子应用广泛，现有质控方法局限于多糖总量和少数非专属小分子，难以鉴别枸杞子的基原和反映其含量药效关系。前期我们系统揭示了枸杞子的小分子化学轮廓，突破性地发现了其抗阿尔兹海默（Alzheimer，AD）的药效成分。基于前期研究，本项目建立了枸杞子化学轮廓分析方法，研究了枸杞子与其近缘种植物中华枸杞果实的成分差异，比较了不同来源枸杞子的成分差异，发现了具有基原鉴别能力的特征成分；进一步利用体内外神经保护活性评价模型，系统评价枸杞子中抗AD的效应成分；最后针对发现的特征成分和效应成分，开发含量测定方法，建立了“含量-基源-药效”关联的质量评价方法，实现对枸杞子基原的智能鉴定和抗AD药效的整体表征。本研究对改进枸杞子的质量控制具有重要的理论意义和实用价值，也为其他中药质量控制提供新的思路。