(试用)中药谱效关系研究.pptx

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中药谱-效关系研究进展

社。２００６，８（２）：６０，３０．
指纹图谱研究的高级阶段是指对指纹图谱中的指纹峰进行系统的指认以及指纹特征和药效相关性研究。即“谱一效关系”研究，指纹图谱的生物等效性研究等。这种包括了体现了药效信息的多个有效部位的各种指纹图谱，将更进一步解决如何体现中药制剂的整体性和复杂性的难题【１３１。同时，能够建立一种合理、有效的中药内在品质的研究和评价方法，并填补中药质量控制指标与药效研究相脱节的空白，这对中药的现代化
医药大学，２００９：４３—６１．
综上所述，中药谱一效关系对中药的发展有着巨大的推动作用，但是现阶段的研究仍存在着许多未解决的问题，我们应克服实验技术，统计方法，计算机技术等方面的困难，建立起具有代表性和实用性的谱一效关系，让我国中药的发展早日跨上
［１３］刘荣霞，叶敏，果德安．中药质量控制研究的思路与方法［Ｊ］．中国天然药物，２００６，４（５）：３３２—３３７．
测指标。
物随外界环境变化要发生很大的改变，它的化学指纹图谱也就具有无法精密度量的模糊性。相同品种中药样品间所存在的个体差异在其内在特性上将会有所反映，这些差异必然会导致色谱图的差异，因而指纹图谱强调的是色谱的完整面貌而不是精确的细微差别，反映的质量信息是综合的结果。而怎样在众多潜在不稳定因素存在的情况下测得稳定的指纹图谱又是我们要解决的首要难题。３．２新技术不能普及［８】对于中药这样一个复杂体系，采用单一分离模式或单一色谱技术容易丢失大量信息，不利于中药及中成药质量的真实反映。解决这些同题的一个方法是运用
和国际化有着重要的意义。
［１０］陆卫根，杨士泉，唐锦高．灰色关联度分析及其在死因分析中的应用［Ｊ］．现代预防医学，２００１，２８（３）：３９５—３９６．［１１］黄正南．医用多因素分析［Ｍ］．３版，长沙：湖南科学技术

谱效关系法历史

谱效关系法是一种研究中药药效物质基础的方法，最早由李戎提出。

它基于中药指纹图谱的研究，通过获取中药中丰富的化学信息，将标示物质群特征峰的指纹图谱与药效结果联系起来，为确立中药药效物质基础、制定反映产品内在质量的控制标准而进行的研究。

传统的谱效关系研究方法可以在一定程度上揭示中药的药效物质，但由于中药化学成分极其复杂，化学成分和药效作用之间存在复杂的相关性，既有正相关，也有负相关，化学成分之间还存在协同和拮抗作用，加上传统的化学方法所得的各个部位存在成分交叉的情况，因此，难以有效揭示中药谱效关系，也无法准确揭示中药药效物质基础。

目前，谱效关系研究已经从传统的方法向更为现代化的方法过渡，如色谱-质谱联用技术等。

这些现代技术的应用，使得我们可以更全面地了解中药中的化学成分，进一步揭示其药效物质基础。

以上内容仅供参考，如需更多关于谱效关系法的历史信息，建议查阅相关文献或咨询中医药专家。

谱效关系建立的方法

指纹图谱和药效实验都完成的基础上，就需要将两者合理的联系起来，也就是所谓的数据处理。

数据的处理也是中药谱效关系研究的关键。

近年来，应用到中药谱关系研究中的分析方法主要有：灰色关联度分析、聚类分析、相关分析、回归分析、主成分分析、典型相关分析及图谱对比法。

其中灰色关联度分析和聚类分析使用的居多。

中药谱效关系研究弥补了目前以化学指纹图谱控制中药质量时与药效脱钩的不足，可以中药质量控制提供更为科学有效的数据。

近年来，中药谱效学在研究单味药及复方制剂方面应用较为广泛。

中药谱效学的提出为中药及中药制剂的质量评价提供了一个新的研究思路，它能更好的揭示中药的药效物质基础，现已经广泛应用于中药药效物质基础研究、炮制机制研究、组分配伍研究、药效预测、中药毒性成分筛选、工艺优化等方面菝葜是民间常用抗炎中药，在临床上治疗盆腔炎、附件炎等妇科炎症，疗效显著，但其效应成分尚不十分明确。

本本论文旨在通过谱效关系的研究，把化学指纹图谱与抗炎药效关联起来，以期进一步诠释菝葜抗炎的药效物质基础，也为完善菝葜质量评价体系提供科学依据。

中药指纹图谱相似度计算软件(2004 年A 版)、SPSS 22.0、EXCEL众所周知，中药的化学组成非常复杂。

中药发挥药效常常是通过多种化学成分的相互作用，中药所含的有效物质是中药疗效的保证。

为了控制中药的质量，指纹图谱技术被广泛用于构建中药评价体系。

因为指纹图谱只能反应出中药的化学成分信息，与中药的药理作用没有太多的关联，而谱效的研究恰好弥补了这个缺陷。

本章节用灰色关联度法分析了10 批菝葜药材指纹图谱中各峰对各单一药效指标的贡献，采用了层次分析法确定了各个单一指标对总药效的权重，再用灰色关联度法分析了10 批菝葜药材指纹图谱中各峰对总药效的贡献，从而为探索菝葜抗炎活性的药效物质基础提供科学依据。

灰色关联分析灰色关联分析，是以“关联度”作为发展趋势相似或相异的因素间关联程度的一种方法，关联度是对两个系统中的因素关联性大小的评价。

(精)《中药药理学》第四章中药作用特点和研究方法PPT课件

在尚无理想的方法揭示中药粗制剂时效关系的情况下，近来有学者通过中药血清药理研究，提出多数中药煎剂给动物灌胃后1-2小时内采血，可能得到血药浓度较高的血清。起效较慢的中药灌胃，每日2次，连续给药2日，第3 日给药1次，即连续给药5次，可基本达到稳态血药浓度。
六、中药作用的两重性
1、治疗作用
证的动物模型
大黄脾虚模型氢化可的松肾阳虚模型冷水浸泡加肾上腺素肝郁气滞模型
“证”的动物模型的缺陷
动物模型少研究难度很大认同度差
人和动物在生理生化机能等方面尽管有许多相同之处，但在形体、语言、反应等方面有很大差别。
三、中药分类对比研究
按传统分类的解表药、清热药、泻下药、利水药、活血化瘀药以及补益药等的药理作用已基本清楚，但对每一类药中的分类药的对比研究不够。如辛凉解表药和辛温解表药、清热解毒药和清热泻火药、凉血止血药与温经止血药、平肝息风药与平肝潜阳药、补气药与补血药等药理作用的异同，尚需研究和归纳。
二、中药作用的双向性
中药具有双向作用，某一中药可使机体从亢进状态向正常状态转化，也可时机体从机能低下状态向正常状态转化，因机体所处病理状态不同而产生截然相反的药理作用，最终使机体达到平衡状态。中药作用的双向性与所用剂量大小和所含不同化学成分有关，可出现小剂量兴奋，大剂量抑制，或大剂量兴奋，小剂量抑制的现象。
有毒无毒、十八反、十九畏、禁忌等，强调了中药的不良反应和毒性。而中药传统口服给药方法显示中药具有毒性低，不良反应少的特点。
近年来，随着中药单体制剂和静脉注射剂的应用，中药的不良反应和毒性问题也越来越突出，常见有胃肠道反应、过敏反应、肝肾毒性等。
某些中药的毒性严重影响了其临床应用如朱砂长期应用引起慢性汞中毒，雷

中药药性理论的现代研究幻灯片PPT

寒凉药、滋阴药
改善
副交感神经-M受体-cGMP系统 ↑
温热药、助阳药
滋阴药
+ T3、HCA注射→大鼠阴虚→脑、肾β-R 最大结合点位数↑；M-R ↓
甲硫氧嘧啶po→小鼠“甲减〞阳虚→副交感神经-M受体-cGMP系
统↑
温热药
寒凉药抑制交感神经- β受体-cAMP系统功能的亢进温热药抑制副交感神经- M受体-cGMP系统功能的亢进
中药升降浮沉理论研究现状
• 药物的性能在人体内呈现的一种走向和趋势 • 现代研究资料有限 • 实验：补中益气汤〔柴胡、升麻〕--- 子宫平滑肌
中药归经理论研究现状
• 药物作用选择性地归属于一定的脏腑经络 • 现代研究 • △药理作用 • △药动学
1. 归经与药理作用的关系抗惊厥 --- 入肝经--- 肝主筋、诸风掉眩皆属于肝
温热药制造热证模型动物〔附子\干
相关姜指〕标：
痛阈和惊厥阈；
寒证动物模型：
中枢神经递质；
〔痛阈、惊厥阈增高〕热证动物模型
〔痛阈、惊厥阈降低〕
动物脑内中枢神经递质含量变化？
药性 NA、 DA 5-HT
热性药增加寒性药减少
减少增加
酪氨酸羟化酶
增加减少
兴奋性递质：去甲肾上腺素〔NA〕；多巴胺〔DA〕
抑制性递质：5-羟色胺〔5-HT〕
多巴胺
酪氨酸酪氨酸羟化酶多巴
NA
2、对植物神经系统功能的影响
热证与寒证患者具有植物神经功能紊乱的病症。植物神经平衡指数：唾液分泌量，心率，体温，呼吸频率，血压〔包括了心血管系统、内脏平滑肌、腺体等〕
热证病人指数寒证病人指数
寒凉药使指数
治疗

中药与方剂课件pptx

紫苏
发汗解表，行气宽中。
生姜
发汗解表，温中止呕，温肺止咳。
清热类中药
石膏
清热泻火，除烦止渴。
知母
清热泻火，滋阴润燥。
栀子
清热泻火，凉血解毒。
夏枯草
清热泻火，明目，散结消肿。
祛湿类中药
茯苓
利水渗湿，健脾，宁心。
苍术
燥湿健脾，祛风散寒。
薏苡仁
利水渗湿，健脾止泻，除痹，排脓，解毒散结。
厚朴
燥湿消痰，下气除满。
五味
指中药的辛、甘、酸、苦、咸五种味道。辛味药物多具有发散、行气的作用；甘味药物多具有补益、和中的作用；酸味药物多具有收敛、固涩的作用；苦味药物多具有清热、燥湿的作用；咸味药物多具有软坚散结的作用。
中药归经与配伍
归经
指中药对于机体某部分的选择性作用，即某药对某些脏腑经络有特殊的亲和作用，因而对这些部位的病变起着主要或特殊的治疗作用。如肺经病变常用桑叶、菊花等药物治疗。
方剂药理作用机制
方剂的药理作用机制复杂多样，包括调节机体免疫功能、改善血液循环、促进组织修复等多个方面。
方剂药理作用与临床应用
通过对方剂药理作用的研究，可以为方剂的临床应用提供科学依据，指导方剂的合理用药和配伍。
中药与方剂在临床应用中的优势
个体化治疗
中药和方剂可以根据患者的具体病情和体质特点进行个体化治疗，提高治疗效果。
中药药理作用特点
中药具有多成分、多靶点、多途径的药理作用特点，能够调节机体多个系统和器官的功能，达到治疗疾病的目的。
中药药理作用与临床应用
通过对中药药理作用的研究，可以为中药的临床应用提供科学依据，指导中药的合理用药和配伍。
方剂药理作用研究

姜黄谱效关系研究---Chem Pharm Bull (Tokyo).2008V56N7

Traditional herbal drugs and their preparations have been practiced to maintain good health and cure diseases in many oriental countries for thousands of years. In the past decade they have attracted the attention of researchers in western countries because of their high pharmacological activities with low adverse effects. However, herbal drugs have not been ofﬁcially recognized world-wide yet because qualitative and quantitative data on their safety and efﬁciency are not sufﬁcient to meet the general criteria of medicines, as deﬁned in “General Guidelines for Methodologies on Research and Evaluation of Traditional Medicines”.1)It is well known that the therapeutic effect of a herbal drug is based on the synergic effect of its mass constituents and this may be the main reason why quality control of herbal drugs is more difﬁcult than that of western medicines.2,3)In traditional standardizations, a few marker or pharmacologi-cally active constituents are generally employed to assess the quality and authenticity of a complex herbal drug. However, those compounds are not sufﬁcient to fully represent the complex pharmacological activities of herbal drugs and preparations.4)Recently, chromatographic ﬁngerprint analysis methods have been introduced and accepted by the WHO and other governmental organizations as a strategy for the assessment of the quality of herbal drugs.5—7)A chromatographic ﬁnger-print is a chromatogram representing all the detectable chem-ical components in an extract of herbal drugs. Chromato-graphic ﬁngerprinting gives some conﬁdence in measuring the chemical identities of herbal drugs. However, this quality control and quality assurance methods can not provide infor-mation on the biological activities of herbal drugs, because the marker compounds may not be the ones responsible for the bioactivities of herbal drugs.4)Therefore, it is still neces-sary to develop new methods for the quality control of herbal drugs from both chemical and pharmacological view points. Multivariate data analysis allows the extraction of addi-tional information from huge data matrices generated by chromatographic or spectrometric analyses. I n recent years, multivariate calibrations such as principal component regres-sion (PCR) and partial least squares regression (PLS-R) have been applied to the determination and quantitation of certain compounds in mixtures.8)Useful pharmacological properties of Curcuma drugs in-cluding Turmeric, Zedoary and Chinese Ezhu, such as anti-inﬂammatory and immunological effects, have been reported. Tohda et al.have reported that oral administration of the methanol extract of C. phaeocaulis to adjuvant arthritic model mice signiﬁcantly inhibited paw swelling and de-creased the serum haptgloblin concentration. Furthermore, it showed signiﬁcant cyclooxygenase (COX, EC; 1. 14. 99. 1) inhibitory activity in comparative studies of the anti-inﬂam-matory activities of 6 Curcuma rhizomes.9)In this study, we verify the potential use of PLS-R models for the prediction of COX-2 inhibitory activities of various extracts and their solvent partitioned fractions of Chinese Ezhu derived from the rhizome of C. phaeocaulis from the LC chromatographic proﬁles of them, aiming at the estab-lishment of a new procedure for standardization of herbal drugs from both chemical and biological view points. In ad-dition, a new approach for investigation of bioactive con-stituents in the herbal drugs using PLS-R models was exam-ined.ExperimentalMaterials and Analytical Sample Preparation Four crude drug sam-ples were purchased from pharmaceutical companies or markets (Table 1). All samples were deposited in the Museum of Materia Medica, Institute of Natural Medicine, University of Toyama (TMPW).A sample was pulverized and the powder screened through 850m m sieves. Fifty grams of the ﬁne powder was accurately weighed and extracted three times with 50ml of methanol under reﬂux conditions for 30min. The organic solvents were combined and evaporated in vacuo to give an extract. Five hundred milligrams of the extract was dissolved in 20ml of methanol and extracted with hexane (25ml) to separate the methanol extract into hexane and methanol soluble fractions. The amounts of the methanol extracts, hexane fractions and methanol fractions obtained from samples936V ol. 56, No. 7 Prediction of Cyclooxygenase Inhibitory Activity of Curcuma Rhizomefrom Chromatograms by Multivariate AnalysisKen T ANAKA,a Y oshiaki K UBA,a Atsutoshi I NA,a Hiroshi W ATANABE,b and Katsuko K OMATSU*,a,ca Division of Pharmacognosy, Department of Medicinal Resources, Institute of Natural Medicine, University of Toyama;c21st Century COE program, U niversity of Toyama; 2630 Sugitani, Toyama 930–0194, Japan: and b InternationalResearch Center for Traditional Medicine, Toyama International Health Complex; 151 Tomosugi, Toyama 939–8224,Japan.Received February 24, 2008; accepted May 1, 2008; published online May 8, 2008The potential use of partial least square regression (PLS-R) models for the prediction of biological activities of a herbal drug based on its liquid chromatography (LC) proﬁle was veriﬁed using various extracts of Curcumaphaeocaulis and their cyclooxygenase-2 (COX-2) inhibitory activities as the model experiment. The correlation ofpractically measured inhibitory activities and predicted values by PLS-R analysis was quite good (correlation co-efﬁcient؍0.9935) and the possibility of transforming chromatographic information into a measure of biologicalactivity was conﬁrmed. In addition, furanodienone and curcumenol were identiﬁed as the major active anti-in-ﬂammatory constituents of C. phaeocaulis, through detailed analysis of the regression vector, followed by isola-tion of these compounds and their COX-2 inhibitory assays. The selectivity indices (SI), IC50of COX-1/IC50ofCOX-2, of both compounds were higher than that of indomethacin and it is considered that furanodienone andcurcumenol are the most promising compounds as lead anti-inﬂammatory agents.Key words partial least square regression; Curcuma phaeocaulis; cyclooxygenase-2 inhibitory activity; furanodienone;curcumenolChem. Pharm. Bull.56(7) 936—940 (2008)© 2008 Pharmaceutical Society of Japan ∗To whom correspondence should be addressed.e-mail: katsukok@inm.u-toyama.ac.jpare the residuals.In order to predict y i for a new chromatogram x i (1,p ), the following equa-tion is used:ϭy ¯ϩx i ·bwhere y ˆi is the predicted inhibition value for the i th new sample, y ¯denotes the mean of the inhibition values for the calibration samples, and b (p the vector of PLS regression coefﬁcients computed as:ϭP ·qAll statistical analyses were carried out by Piroruet software (GL Science Inc., Tokyo).COX Inhibition Assay COX inhibitory activity was measured using a Colorimetric COX (ovine) inhibitor screening assay kit (Cayman Chemical Company, MI, U.S.A.). The assay kit was used to measure the peroxidase ac-tivity of COX to monitor the oxidized N ,N ,N Ј,N Ј-tetramethyl-p -phenylenedi-amine (TMPD). All samples and positive control (indomethacin) were added as methanol solutions to assay solutions. All procedures were performed as indicated in the assay kit instructions. Oxidized TMPD was measured using an Immuno Reader at 590nm (NJ-2100, Inter Med, Tokyo). Absorbance of assay buffer (160m l of 100m M Tris–HCl (pH 8.0), 10m l of Heme and 10of methanol) and assay buffer with COX enzyme were measured as back-ground and 100% initial activity.Results and DiscussionThree chromatograms of the methanol extract, its fraction partitioned with hexane and the remaining methanol fraction C. phaeocaulis (TMPW No. 20237) are shown in Fig. 1.Their corresponding COX-2 inhibitory activities are listed in Table 1. In general, qualitative evaluation of herbal drugs is performed by the quantitative analysis of a few active con-stituents. As shown in Table 1, the COX-2 inhibitory activity of the hexane fraction of C. phaeocaulis (TMPW No. 20237)(COX-2 inhibitory activity: 73.0%) showed approximately 5.5 times higher inhibitory activities than the remainingJuly 20089371.Chromatograms of the Extract of Curcuma phaeocaulis (TMPW No. 20237) and Solvent Partitioned Fractions(A) Hexane fraction of the methanol extract, (B) methanol fraction, (C) methanolmethanol fraction (COX-2 inhibitory activity: 13.3%). How-ever, comparisons of the chromatographic ﬁngerprints shown in Fig. 1 do not provide the quantitative information for eval-uating the quality of herbal drugs. To compensate for this weakness of chromatographic ﬁngerprints, additional quanti-tative information about the bioactivities of the drugs are re-quired. I t is considered that the components in the herbal drugs act in a synergistic manner with respect to their biolog-ical activities. This makes it difﬁcult to create a model for predicting biological activity using concentration informa-tion of the individual components in herbal drugs by ordi-nary multivariate calibration.Partial least squares (PLS) regression technique is a method for constructing predictive models and is especially useful in quite common cases where the number of descrip-tors (independent variables) is comparable to or greater than the number of compounds (data points) and/or there exist other factors leading to correlations between variables. To create the PLS model, methanol extracts of four samples of C. phaeocaulis were fractionated into hexane soluble frac-tions and remaining methanol fractions.Removing of Leverage Objects and Outliers If a sam-ple’s proﬁle differs greatly from the average training set pro-ﬁle, it will have a great inﬂuence on the model, drawing the model closer to its location in factor space. A sample’s inﬂu-ence is quantiﬁed by its “leverage”. In addition, if a sample’s y value is extreme, it has a greater inﬂuence on the model than a sample close to an average y value (y ). The extreme sample “pulls” the model toward it. Therefore, leverage ob-jects and outliers have to be removed prior to constructing the calibration model. After peak alignment of the HPLC chromatographic proﬁles, examination of the presence of high leverage samples and the outlying observations in the data of chromatograms, X , and COX-2 inhibition values, y ,were initially carried out. The outlier of the y data (outside of 95% line of Studentized Residuals) and X variables having high-leverage values were removed from the original data set to recreate the new analytical data set. I n this study, the COX-2 inhibitory activity value of the methanol extract of the sample TMPW No. 20238 (Table 1, COX-2 inhibitory activity ϭ39.6%) was removed as an outlier.PLS Model In general, the optimal number of latent fac-tors of the PLS model can be determined by the lowest root mean squared error of cross-validation (RMSECV) in the leave-one-out cross validation procedure. However, in many practical analyses, an apparent minimal RMSECV value can not be achieved. I n the present study, we have used the change of the regression vector’s shape depending on the number of latent factors. When the number of factors is small and each additional factor accounts for signiﬁcant vari-ation, the vector’s shape changes dramatically with the num-ber of factors. The point at which the changes are much less striking and appear random signaling is determined as the optimal number of latent factors of the PLS model. From ob-servation of the regression vector’s shape, four PLS factors were used in this study. The results of the correlation of measured COX-2 inhibitory activities and predicted in-hibitory values, and the regression vector of the model are shown in Figs. 2 and 3, respectively. As shown in Fig. 2, the correlation of practically measured inhibitory activities and predicted values is quite good (correlation coefﬁcient ϭ0.9935) and the possibility of transforming chromatographic information into a biological activity is conﬁrmed.Prediction of the activity from a chromatogram is carried out by the calculation of the product of two vectors, the regression vector and chromatogram (Fig. 3). The chro-matogram involves the quantitative information of the chemi-cal components and the regression vector involves the infor-mation on the degree of the contribution strength of the com-pounds to the activity. Thus, it can be considered that the positive peaks indicate the positive effects of the compounds in biological activities. Generally, the earlier PLS factors in the model are most likely to be the ones related to the con-stituents of interest, while later PLS factors have less infor-mation that is useful for predicting concentration. However,if too few PLS factors are used to construct the model, the prediction accuracy for unknown samples will suffer since not enough terms are being used to model all the chromato-graphic variations that compose the constituents of interest.Therefore, it is very difﬁcult to eliminate the effects from the noise or outlying peaks in the chromatogram completely, depending on the number of PLS factors used. Consequently,detailed evaluation of the regression vector at the every PLS factor numbers and the relationship between regression vec-tor and corresponding peak intensities in the chromatogram is required. As shown in Figs. 3, 6 major peaks (peak 1, 21.5min; peak 2, 21.9min; peak 3, 22.3min; peak 4, 25.2min;peak 5, 25.4min; peak 6, 25.7min) were observed. In the re-gression vector shown in Fig. 3, there are 4 positive peaks at 20.1min of the retention time and the points of peaks 1, 3and 6. The peak at 20.1min and peak 1 appear at the point of third PLS factor, and they correspond to the very weak peaks in the chromatograms. I n addition, methanol extract and hexane fraction of sample 25036, having strong inhibition ac-tivity, provides the peaks at 20.1min and peak 1 in the chro-matogram, exceptionally. Thus, it is considered that the 2peaks arise from outlaying observations in the chromatogram of sample 25036 and do not indicate the positive effects of the compounds in biological activities. On the other hand, the strong positive peaks at 22.1 and 25.7min of retention time (corresponding to peaks 3 and 6) in the regression vector were observed from the point of ﬁrst PLS factor, and it is predicted that peaks 3 and 6 contribute strongly to the COX-938V ol. 56, No. 7Fig.2.The Results of the Correlation of Measured COX-2 Inhibitory Ac-tivities and Predicted Inhibitory ValuesJuly 2008939 Fig.3.Superimposed Chromatograms of the Extracts and Solvent Partitioned Fractions (A) and the Regression Vector of the PLS Model (B)the regression vector could be utilized for effective investiga-tion of active constituents in herbal drugs.Acknowledgments This work was supported in part by a grant from the Bio-cluster Program of Toyama prefecture, a research grant from the Japan Health Sciences Foundation, by a Grant-in-Aid for Scientiﬁc Research (B), No. 17406004 in 2005—2007 form the Japan Society for the Promotion of Science and for the 21st Century COE Program from the Ministry of Educa-tion, Culture, Sports, Science and Technology of Japan.References1)WHO, “General Guidelines for Methodologies on Research and Eval-uation of Traditional Medicines,” 2000, p. 1.2)Beek T. A., J. Chromatogr. A, 967, 21—55 (2002).3)Sticher O., Planta Med., 59, 2—11 (1993).4)Chan K., Anal. Sci., 17, a409—a412 (2001).5)EMEA, “Final Proposal for Revision of the Note for Guidance onQuality of Herbal Remedies,” The European Agency for Evaluation of Medicinal Products, London, U.K., 1999.6)FDA, “Guidance for Industry—Botanical Drug Products (Draft Guid-ance),” US Food and Drug Administration, Maryland, U.S.A., 2000. 7)Philipsom J. D., “British Herbal Pharmacopoeia,” British Herbal Medi-cine Association, Forward, U.K., 1996.8)Nederkassel A. M., Daszykowski M., Massart D. L., Heyden Y. V., J.Chromatogr. A, 1096, 177—186 (2005).9)Tohda C., Nakayama N., Hatanaka F., Komatsu K., Evid. Based Com-plement. Alternat. Med., 3, 255—260 (2006).10)Dekebo A., Dagne E., Hansen L. K., Gautun O. R., Aasen A. J., Tetra-hedron Lett., 41, 9875—9878 (2000).11)Hikono H., Sakurai Y., Numabe S., Takemoto T., Chem. Pharm. Bull.,16, 39—42 (1968).12)Hikono H., Agatsuma K., Takemoto T., Tetrahedron Lett., 24, 2855—2858 (1968).13)Hikino H., Konno C., Agatsuma K., Takemoto T., J. Chem. Soc.,Perkin Trans. 1, 1975, 478—474 (1975).14)Y oshihara M., Shibuya H., Kitano E., Y anagi K., Kitagawa I., Chem.Pharm. Bull., 32, 2059—2062 (1984).15)Shiobara Y., Asakawa Y., Kodama M., Y asuda K., Takemoto T., Phyto-chemistry, 24, 2629—2633 (1985).16)Shaftel S. S., Olschowka J. A., Hurley S. D., Moore A. H., O’BanionM. K., Brain Res. Mol. Brain Res., 119, 213—215 (2003).17)Somvanshi R. K., Kumar A., Kant S., Gupta D., Singh S. B., Das U.,Srinivasan A., Singh T. P., Dey S., Biochem. Biophys. Res. Commun., 361, 37—42 (2007).18)Jüni P., Nartey L., Reichenbach S., Sterchi R., Dieppe P., Egger M.,Lancet, 364, 2021—2029 (2004).940V ol. 56, No. 7。

红芪药材防治肝纤维化的谱效关系

乳糖所致大鼠肝损伤及四氯化碳所致小鼠肝损伤具有保护作用，但关于红芪发挥该疗效的物质基础并未研究。为此，本研究以四氯化碳一花生油（体积比
为４：６）溶液造成小鼠肝纤维化，将红芪的ＨＰＬＣ指纹图谱与药效学实验相结合，运用灰色关联度及偏
效。其化学成分主要有多糖、黄酮、皂苷、
昆明种小鼠，雄性，体重１８～２５ｇ，兰州大学动
微量元素及氨基酸。胡芳弟等利用ＨＰＬＣ测
定了红芪中两种异黄酮类成分的含量；邵晶等利
制药公司）；ｌ０批甘肃省不同产地红芪药材，自采
或购买，编号及来源见表１。经兰州大学生药学研究院马志刚教授鉴定为多序岩黄芪Ｈｅｄｙｓａｒｕｍ
ｐｏｌｙｂｏｔｒｙｓＨａｎｄ．Ｍａｚｚ．的干燥根。
３ＳｈｏｕｙａｎｇＴｏｗｎｏｆＬｏｎｇｘｉＣｏｕｎｔｙ
陇西首阳镇
４ＣｈｅｎｇｇｕａｎＴｏｗｎｏｆＴａｎｃｈａｎｇＣｏｕｎｔｙ宕昌城关镇
武都米仓山礼县
酮一硫酸分光光度法、ＨＰＬＣ一蒸发光散射检测器（ＥＬＳＤ）检测法；而对于红芪根中微量元素和氨

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