摘要
20世纪是化学合成药物的大发展时期,随着时间的推移,从合成的化合物中筛选药物的命准率越来越低,而成本愈来愈高。人们不得不重新回到从天然产物中寻找新药的老路上来。因而,21世纪对中药／天然药物来说充满了机遇与挑战,伴随科学技术与先进仪器设备的不断出现,中药研究将迎来一个全新的大发展时期。我国有丰富的中药资源、有悠久的中药使用历史,在中药的临床应用和研究方面积累了非常丰富的经验。随着人们对中药不断的深入研究,一系列复杂的问题摆在了研究者的面前,诸如：中药发挥作用的主要药效物质是什么?中药药效发挥的作用机理是什么?如何有效的控制包含有几十种甚至几百种化合物的中药质量?这些问题的出现成了制约中药现代化发展的瓶颈问题,也成了研究者面临的重要科研任务。中药是包含了众多化学物质的有机复杂体系,其药效的发挥是该体系中所有物质的共同贡献,因此,中药的研究必须遵循“整体”的思想。脱离了中药的“整体”去研究,就背离了中医药博大的理论体系,形成的研究结论就会带有很大的局限性和片面性。
近年来,研究者提出了中药的“大质量观”、“药效质量观”等一些符合中药理论体系的研究思路和方法,对于中药的进一步研究具有重要的指导意义。复杂问题的解决离不开先进的仪器设备和处理复杂数据的手段,现代色谱、光谱技术的发展和化学计量学方法的介入为中药复杂体系的研究带来了有效的手段。本论文主要针对中药产地、质量控制、药效物质研究以及植物中挥发油提取、鉴别等的研究中存在的问题,结合现代色谱、光谱技术和化学计量学中相关算法对这些问题进行初步探讨,以期为中药的深入研究提供一些有意义的研究思路和值得借鉴的方法。
第一章对本文研究中涉及的支持向量机(support vector machine, SVM)、最小二乘-支持向量机(least square support vector machine, LS-SVM)、多元线性回归(multiple linear regression, MLR)、遗传算法(genetic algorithm, GA)、随机森林(random forests, RF)、k邻近算法(k-nearest neighbor, kNN)的理论原理进行了简单介绍,并对这些方法在中药研究中的应用进行了综述。
第二章采用近红外光谱技术(near infrared spectroscopy, NIR)获取不同产地当归药材粉末的光谱图,原始光谱数据经标准化处理后求一阶导数后分别采用随机森林(RF)和k邻近算法(kNN)构建当归产地分类模型。RF模型训练集和测试集的分类准确率分别为92.21%和94.74%,kNN模型训练集和测试集的分类准确率分别为94.81%和94.74%,3倍交互检验值为94.81%。此外,RF选择出的9个变量(波数)与样品中化合物组成信息高度相关,在一定程度上可以反映模型与当归中化学组成的相关性。此外,所建RF和kNN模型用于30个不同产地、产期当归的产地判别,RF模型的判别准确率为80.00%,而kNN模型的判别准确率为86.67%。以上结果表明RF和kNN结合近红外光谱可以对当归产地进行判别,且kNN模型具有更好的应用能力。
采用HPLC法测定当归样品中阿魏酸的含量、以中国药典法测定当归醇提物的含量,分别采用遗传算法-最小二乘支持向量机(GA-LSSVM)和遗传算法-多元线性回归(GA-MLR)结合当归近红外光谱建立当归中指标成分的定量模型。阿魏酸的定量模型GA-MLR (R2=0.719、Q2LOO=0.684、RMSEP=0.010)、GA-LSSVM (R2=0.649. Q2LOO=0.646、RMSEP=0.014);醇提物定量模型GA-MLR (R2=0.704、Q2LOO=0.664、RMSEP=0.027). LS-SVM (R2=0.990、Q2LOO=0.574、RMSEP=0.045)。结果表明两种定量指标的GA-MLR模型结果均好于GA-LSSVM的结果,说明当归中阿魏酸和醇提物的含量与近红外光谱之间的定量关系更符合线性体系。
建立HPLC-DAD-ELSD色谱联用方法对复方丹参片中11个活性成分进行含量测定,以活性成分含量为变量采用特征递归消除-支持向量机(RFE-SVM)对变量的重要性进行排序,采用最小二乘支持向量机(LS-SVM)建立分类模型,对影响模型的重要核函数参数进行优化。LS-SVM模型的准确率为96.67%,交互检验的准确率为90.00%,选出的两个重要变量为人参皂苷Rb1和Rd,说明这两个化合物在复方丹参片质量方面有重要影响,可以认为它们是复方丹参片潜在的质量控制指标。
第三章采用氧自由基清除法(DPPH)对56个当归样本的醇提物的抗氧化活性进行评价,以抗氧化能力为指标结合醇提物紫外光谱构建GA-MLR定量模型,从推荐的100个模型中最终筛选出1个最佳模型,训练集的模型参数为R2=0.721、Q2LOO=0.623. RMSE=0.085;测试集参数R2test=0.711、RMSEP=0.080,结果表明GA-MLR模型具有一定的可靠性和预测能力,可以实现以当归醇提物的紫外光谱对其抗氧化能力进行预测,从而达到以药效控制当归药材质量的目的。
采用近红外光谱(NIR)获取27个复方丹参片粉末样本的近红外光谱,以各样本对大鼠的活血化瘀(出血时间)药效为指标,采用GA-MLR方法构建定量谱-效关系(quantitative spectrum-activity relationship, QSAR)模型,并将模型初步应用于外部样本的药效预测。最佳GA-MLR模型参数为R2=0.888、Q2Loo=0.832、R2-scrambling=0.102、RMSE=0.085,模型的外部检验参数r2和：RMSEEXT分别为0.825和0.112,该结果表明所建GA-MLR模型具有较好的预测和应用能力,可以通过近红外光谱对复方丹参片的质量进行有效控制。
第四章平胃散为传统中药芳香剂典范,具有调节胃肠动力的作用,因而挥发性成分可能在其药理作用中具有重要贡献。本节对平胃散挥发油的组成及其成分来源进行了分析,并对挥发油及其组方药材挥发油促进大鼠胃排空作用进行研究,采用GA-MLR和GA-SVM建立了平胃散挥发油促进大鼠胃排空作用的定量组-效关系(quantitative composition-activity relationship, QCAR)模型,对平胃散挥发油中促进大鼠胃排空作用的药效物质进行筛选。平胃散挥发油中主要含有β-桉叶醇、茅术醇、D-柠檬烯和沉香螺醇；研究结果表明平胃散、厚朴、陈皮挥发油具有较强的促进大鼠胃排空的作用,但苍术挥发油对大鼠胃排空无明显促进作用。GA-MLR模型的R2(0.826)和RMSE (4.297)值略好于GA-SVM模型(R2=0.804,RMSE=4.666),但GA-SVM模型的Q2LOO(0.783)和RMSECV(4.861)优于GA-MLR模型(Q2LOO=0.697,RMSEcv=5.664)。该结果初步表明,挥发油化学组成与促进胃排空作用之间可能存在非线性关系,GA-SVM模型能够较好的反映挥发油组成与药效之间的QCAR关系,GA所选出的模型变量D-柠檬烯和β-桉叶醇可以认为是挥发油促进大鼠胃排空作用的主要药效物质,同时Cyclohexanemethanol可能具有抑制大鼠胃排空的作用。
第五章针对目前水蒸气蒸馏装置的缺点,自行设计了一套同时水蒸气蒸馏萃取装置用于植物中挥发油的提取；采用该装置对薄荷、陈皮、当归、辛夷四种中药材中挥发油进行提取并采用气质联用技术(gas chromatography-mass spectroscopy, GC-MS)对其化学组成进行分析,结果表明采用新型装置四种植物中挥发油产率分别增加了25%、39%、50%和42%,所鉴定出来的化合物个数也有不同的增加,同时,新型装置的提取时间仅为传统装置的一半,研究结果表明,设计的新型挥发油提取装置可作为一种高效的挥发油提取器用于植物中挥发油的提取。
挥发油GC/GC-MS分析过程中化学成分的鉴别是一个难题,尤其对于具有相似结构的化合物来说。研究中以本章研究中四种植物的挥发油中化合物的结构与GC保留时间建立定量结构-色谱保留时间模型(quantitative structure-retention relationship, QSRR),并对模型进行内部和外部验证,同时将模型应用到挥发油中化合物色谱保留时间的预测及以保留时间为指标的化合物辅助鉴别中。所建最佳GA-MLR模型具有良好的内部预测能力(R2=0.974, Q2LOO=0.910, RMSETrain=0.489)和外部预测能力(Q2EXT=0.984,r2=0.960和RMSEEXT=0.361).对相同色谱保留时间推荐的化合物的色谱保留时间进行预测用于辅助化合物鉴别,结果表明该模型具有较好的应用能力。
The synthetic drugs have been greatly developed during the 20th century. The successful ratio becomes lower by screening potential drugs from synthetic compounds while the cost increases. People had to return to find new drugs from natural products. Thus, there are many opportunities and challenges for the traditional Chinese medicine (TCM) in the 21st century. Developing with science and technology, the research on TCM will be obtained a new period of great development. There are abundant resources of herbs and has a long history about application of TCM in China. A series of complex issues have been emerged when the TCM is studied deeply step by step, such as:What is the main effective substance in TCM? What is the mechanism of TCM in the process of curing diseases? How does efficiently control the TCM containing hundreds of compounds? These issues have become bottleneck problems that restrict the development of TCM in the process of modernization, and also has become an important research task for researchers. TCM is a complex organic system. The efficacy of the TCM should be contribution of all chemical compounds contained in this system. Therefore, the research on TCM must follow the view of "whole". If the "whole" is ignored in the study, which will deviate from the theoretical system of TCM, and the results obtained might have some limitations and one-sidedness in some extent.
In recent years, the views of "Great Quality", "quality with efficacy" have been proposed by some researches, and these views are consistent with the theoretical system of TCM. The views must have important effect on the investigation of TCM. As for settling a complex problem, advanced equipment and the methods of data mining will play an important role. Combining cromatographic, spectroscopic techniques with chemometric methods will provide a feasible strategy for the problems of TCM complex system. In this dissertation, considering the problems existing in the origins, quality control, effective substance, and compounds identifying, some works had been performed combining modern chromatography, spectroscopy and chemometrics.
In chapter 1, the main contents were focused on the introduction of the principle about several chemometric algorithms, including support vector machines (SVM), least squares support vector machine (LS-SVM), multiple linear regression (MLR), genetic algorithm (GA), random forest (RF), k nearest neighbor algorithm (kNN), and their application in the study of TCM were also reviewed.
In chapter 2, the near infrared spectroscopy (NIR) of different Angelica sinensis samples were obtained, and the original spectral data were pretreated by standardization and first derivation. Random forest (RF) and k nearest neighbor algorithm (kNN) were used to build the classification models of Angelica sinensis. The classification accuracy of training set and test set of RF model was 92.21% and 94.74%, respectively. The responding value of kNN model was 94.81% and 94.74%, respectively, and the classification accuracy of 3-fold cross-validation was 94.81% for kNN model. In addition, nine variables (wave number) were chosen by RF, and the compound containing in the samples was highly relevant with the variables selected. Moreover, the proposed models (RF and kNN) were applyied to predict 30 Angelica samples from different origins. The classification accuracy of RF and kNN model was 80.00% and 86.67, respectively. The results obtained from this study indicated that NIR technique combining with RF or kNN method could discriminate the Angelica origin, and the kNN had better application ability than RF.
The contents of ferulic acid and ethanol extract of Angelica sinensis samples were determined by the methods described in the Chinese Pharmacopoeia. Genetic algorithm-multiple linear regressions (GA-MLR) and least squares support vector machine (LSSVM) combined with near infrared spectroscopy were used to establish the quantitative model. As for ferulic acid, the GA-MLR model parameters were R2=0.719, Q2LOO=0.684, RMSEP=0.010, and the GA-LSSVM model parameters were R2=0.649, Q2LOO=0.646, RMSEP=0.014. As for alcohol extract, the GA-MLR model parameters were R2=0.704, Q2LOO=0.664, RMSEP=0.027, the GA-LSSVM model parameters were R2=0.990, Q2LOO=0.574, RMSEP=0.045. The results showed that the predictive ability of GA-MLR models were better than that of GA-LSSVM, which suggested that this complex system was more fit with the linear system.
A high-performance liquid chromatography combined with diode array detector and evaporative light scatter detector (HPLC-DAD-ELSD) was developed to determine the contents of eleven active compounds in compound Danshen Tablet. The contents of each compound were regarded as the variables to establish the classification model. The oder of variables were ranked firstly using recursive elimination feature with support vector machines (RFE-SVM), and least squares support vector machine (LS-SVM) method was performed to build the classification model. The important parameters were also optimized in this study. The accuracy of LS-SVM model was 96.67%, and the cross-validation accuracy was 90.00%. Two important variables were selected as ginsenosides Rbl and Rd. Therefore, Rbl and Rd might have major impact in the quality of compound Danshen Tablet; they could be regarded as potential indicators of quality control.
In chapter 3, the oxygen free radical scavenging method (DPPH) was applied to test the antioxidant activity of ethanol extract of 56 Angelica samples. The ultraviolet (UV) spectra of each sample were also scanned by an UV spectrophotometer. The GA-MLR method was used to construct the quantitative spectrum-activity relationship (QSAR) between UV spectra and antioxidant activity of Angelica samples. The statistic parameters of the best model proposed for training set were R2=0.721, Q2LOO=0.623, RMSE=0.085, and R2test=0.711, RMSEP=0.080 for test set. The results obtained showed that the GA-MLR model proposed was reliabile and predictive, and the model could be used to predict the antioxidant capacity of Angelica ethanol extract through UV spectra, and could be used to evaluate the quality of Angelica sinensis in efficacy level.
In this section, NIR of 27 compound Danshen Tablets were scanned, and the efficacy of samples (bleeding time) in mouse was also investigated. The GA-MLR method was used to construct the QSAR between NIR spectra and active blood efficacy of compound Danshen Tablet samples. The statistic parameters of the best model proposed for training set were R2=0.888, Q2LOO=:0.832, RMSE=0.085, and R2test=0.825, RMSEP=0.112 for test set. The results obtained showed that the GA-MLR model proposed was reliabile and predictive, and the model could be used to predict the active efficacy of compound Danshen Tablets through NIR spectra, and could be applied to evaluate the quality of compound Danshen Tablets in efficacy level.
In chapter 4, Pingwei Powder is a respresentive aromatic TCM, it has the function of regulating gastrointestinal motility. In therotery, the volatile components in this formulation have an important contribution to the pharmacological effects. In this study, the volatile components in Pingwei Powder and their sources had been analyzed by GC-MS. The gastric emptying of essential oils from different formulation containing herbs that consisted of Pingwei Powder in rats was investigated by single photo emission computed tomography (SPECT) technique. GA-MLR and GA-SVM methods were performed to establish the quantitative composition activity relationship (QCAR) between the composition of essential oils and their gastric emptying efficacy. The results demonstrated that the essential oil of Pingwei Powder mainly consisted ofβ-Eudesmol, Hinesol, D-limonene and Agarospirol. The essential oils from Pingwei Powder, Magnolia officinalis and Citrus reticulate had strong efficacy in promoting gastric emptying, while the essential oil from Atractylodes iancea had not effect on gastric emptying in health rat. The statistic parameters of GA-MLR model (R2=0.826 and RMSE=4.297) was slightly better than that of the GA-SVM model (R2=0.804, RMSE=4.666), but the results of LOO cross validation of GA-SVM model (Q2L00=0.783 and RMSECV=4.861) was better than that of GA-MLR model (Q2LOO=0.697, RMSECV= 5.664). The preliminary results showed that the essential oil from Pingwei Powder had the gastric emptying efficacy, and the relationship between composition of essential oil and efficacy was fitting for non-linear relationship.β-Eudesmol, and D-limonene selected by GA as the model variables could be considered the main active substance to promote gastric emptying while cyclohexanemethanol might have a role in inhibiting gastric emptying.
In chapter 5, an integrated steam distillation extraction apparatus had been developed for extracting essential oils from herbs. The essential oils of Flos Magnoliae, Citrus peel, Mint and Chinese Angelica was respectively extracted by the developed apparatus, and then analyzed by GC-MS. The oil yields and composition were compared with those extracted by traditional steam distillation apparatus. The results indicated that the oil yields of Flos Magnoliae, Citrus peel, Mint and Chinese Angelica increased 42%,39%,25%and 50%, respectively. The composition of essential oil extracted by different apparatus were different and the number increased by new apparatus. Therefore, the new apparatus developed could be an effective extracting apparatus of essential oil from herbal materials.
The essential oils extracted from three kinds of herbs were separated by a 5% phenylmethyl silicone (DB-5MS) bonded phase fused silica capillary column and identified by mass spectrometry.74 of compounds identified were selected as origin data, and their chemical structure and gas chromatographic retention times were performed to build a quantitative structure-retention relationship (QSRR) model by genetic algorithm and multiple linear regressions (GA-MLR) analysis. The model predictive ability was verified by internal validation (R2=0.974, Q2LOO=0.970, RMSETrain=0.489). As for external validation, the model was also applied to predict the gas chromatographic retention times of the 14 not used for model development volatile compounds from essential oil of Radix Angelicae Sinensis (Q2EXT=0.984, r2=0.960 and RMSEEXT=0-361). The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicated that the best QSRR model was robust and satisfactory, and could provide a feasible and effective tool for predicting the gas chromatographic retention time of volatile compounds, and could be also applied to help in identifying the compound with the same gas chromatographic retention time.
引文
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