目的 基于厚樸中具有Ca²⁺拮抗作用的關(guān)鍵藥效成分以及《中國(guó)藥典》2020年版規(guī)定的質(zhì)控指標(biāo)，建立一種快速分析厚樸藥材品質(zhì)與等級(jí)的方法。方法 采用HPLC技術(shù)結(jié)合瞬時(shí)受體電位V4（TRPV4）-Ca²⁺雙熒光素酶報(bào)告基因?qū)嶒?yàn)對(duì)厚樸中的活性成分進(jìn)行篩選，確定厚樸中的潛在生物標(biāo)志物。采用《中國(guó)藥典》2020年版方法對(duì)厚樸藥材中厚樸酚、和厚樸酚、灰分、酸不溶性灰分和水分等5項(xiàng)指標(biāo)進(jìn)行測(cè)定。利用近紅外光譜技術(shù)（NIRS）構(gòu)建上述檢測(cè)指標(biāo)與光譜之間的擬合模型，確定綜合評(píng)價(jià)指數(shù)（F_q）用于厚樸藥材檢測(cè)及分級(jí)評(píng)定，并采用TRPV4-Ca²⁺拮抗活性檢測(cè)實(shí)驗(yàn)對(duì)上述評(píng)價(jià)系統(tǒng)進(jìn)行驗(yàn)證。結(jié)果 23批次厚樸的HPLC圖譜中共檢測(cè)出16個(gè)共有成分，經(jīng)活性篩選確定其中的關(guān)鍵藥效成分為厚樸酚與和厚樸酚。經(jīng)對(duì)60批次厚樸樣品的檢測(cè)，建立了上述5項(xiàng)檢測(cè)指標(biāo)的近紅外光譜預(yù)測(cè)模型。經(jīng)各指標(biāo)的偏離度計(jì)算及權(quán)重分配優(yōu)化，最終確定厚樸藥材F_q的計(jì)算公式。經(jīng)驗(yàn)證厚樸樣品的F_q值呈高斯分布，依據(jù)正態(tài)分布概率將其劃分為5個(gè)等級(jí)，經(jīng)實(shí)際測(cè)試及活性驗(yàn)證檢測(cè)，證實(shí)本研究所構(gòu)建的等級(jí)評(píng)價(jià)方法效果良好。結(jié)論 該方法基于現(xiàn)行的《中國(guó)藥典》監(jiān)管體系，實(shí)現(xiàn)了對(duì)厚樸藥材品質(zhì)的快速綜合評(píng)價(jià)與等級(jí)區(qū)分，為中藥材的科學(xué)監(jiān)管提供了新的解決方案。

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

“帶土移植”人才引育計(jì)劃項(xiàng)目（桂科AA23026008）