目的 基于厚朴中具有Ca²⁺拮抗作用的关键药效成分以及《中国药典》2020年版规定的质控指标，建立一种快速分析厚朴药材品质与等级的方法。方法 采用HPLC技术结合瞬时受体电位V4（TRPV4）-Ca²⁺双荧光素酶报告基因实验对厚朴中的活性成分进行筛选，确定厚朴中的潜在生物标志物。采用《中国药典》2020年版方法对厚朴药材中厚朴酚、和厚朴酚、灰分、酸不溶性灰分和水分等5项指标进行测定。利用近红外光谱技术（NIRS）构建上述检测指标与光谱之间的拟合模型，确定综合评价指数（F_q）用于厚朴药材检测及分级评定，并采用TRPV4-Ca²⁺拮抗活性检测实验对上述评价系统进行验证。结果 23批次厚朴的HPLC图谱中共检测出16个共有成分，经活性筛选确定其中的关键药效成分为厚朴酚与和厚朴酚。经对60批次厚朴样品的检测，建立了上述5项检测指标的近红外光谱预测模型。经各指标的偏离度计算及权重分配优化，最终确定厚朴药材F_q的计算公式。经验证厚朴样品的F_q值呈高斯分布，依据正态分布概率将其划分为5个等级，经实际测试及活性验证检测，证实本研究所构建的等级评价方法效果良好。结论 该方法基于现行的《中国药典》监管体系，实现了对厚朴药材品质的快速综合评价与等级区分，为中药材的科学监管提供了新的解决方案。

Objective To establish a method for rapid analysis of the quality and grade of Magnolia Officinalis Cortex (MOC) based on the key pharmacodynamic components with Ca²⁺ antagonism and the quality control indexes specified in the 2020 edition of Chinese Pharmacopoeia. Methods HPLC combined with TRPV4-Ca²⁺ dependent double luciferase reporter gene system was used to screen the active components in MOC to determine its potential biomarkers. The Chinese Pharmacopeia (2020 Edition) was used to determine five indexes of magnolol, honokiol, ash, acid insoluble ash and moisture in MOC. Near-infrared spectroscopy (NIRS) was used to construct a fitting model between the above detection indexes and spectra. A comprehensive evaluation formula (F_q) was built to detect and grade herb drug samples. Evaluation test of TRPV4-Ca²⁺ antagonistic activity was used to verify the above evaluation system. Results A total of 16 common components were detected in the HPLC patterns of 23 batches of MOC samples and the key pharmacodynamic ingredients were identified to be magnolol and honokiol. The NIRS prediction model was built based on 60 batches of MOC samples and incorporated the five detection indices. After calculating the deviation of each indicator and optimizing the weight distribution coefficients, the final F_q calculation method for MOC was established. The F_q values detection curve was found to follow a Gaussian distribution. Based on this normal distribution, the samples were classified into five categories according to their probability density. Empirical testing and activity validation confirmed the effectiveness of the grading evaluation method established. Conclusion Based on the current regulatory system of the Chinese Pharmacopeia, the method realizes the rapid comprehensive evaluation and grade differentiation of the quality of MOC. It provides a new solution for the scientific supervision of Chinese herbal medicines.

R282.5

“带土移植”人才引育计划项目（桂科AA23026008）