石榴皮鞣质对大鼠尿液内源性物质代谢的影响

摘 要 目的： 采用代谢组学方法考察石榴皮鞣质对正常大鼠尿液内源性物质代谢的影响。方法: 将大鼠分为空白对照组、石榴皮鞣质给药组,每组按 78.5 mg·ml^-1剂量对大鼠连续灌胃给药4周,收集尿样,用于代谢组学研究。HPLC-MS采集大鼠尿样的数据信息,运用SIMCA-P软件中的PCA 法与PLS DA 法对比分析。结果:石榴皮鞣质给药组与空白对照组得分点达到很好的区分效果, 连续给药22 d 时, 尿液内源性物质代谢改变最为明显。结论: 石榴皮鞣质提取物显著影响大鼠尿样中与药理作用相关内源性物质的代谢, 为深入研究石榴皮鞣质提供依据。     

基于代谢组学的天舒胶囊质量评价与控制

目的:建立基于代谢组学的天舒胶囊高效液相色谱(HPLC)指纹图谱考察方法。方法:采用代谢组学的主成分分析(PCA)及偏最小二乘法(PLS)模型对54份天舒胶囊内容物提取液的HPLC指纹图谱进行整体观察和评价,找出并初步确证可能与相似度强相关的标志物,最后探讨代谢组学用于中药质量评价的可行性和价值。结果:该方法能从代谢组学的层面上区分各样品的质量情况,并以超HPLC-串联质谱法找到并确证标志物为洋地黄内酯Ⅰ,其可作为天舒胶囊的质量控制重点。结论:该HPLC中药指纹图谱-代谢组学平台能较好地应用于中药及中药材的质量标准研究。     

基于液相色谱-质谱技术的乳腺癌转移相关 代谢标志物的筛选

摘要：目的 应用代谢组学技术筛选与乳腺癌转移相关的代谢标志物。方法 收集100例乳腺癌患者和50例 健康志愿者的血清标本,采用高效液相色谱-轨道离子阱质谱联用（HPLC-LTQ Orbitrap XL MS）代谢组学研究平台分 析乳腺癌未转移患者、乳腺癌转移患者和健康人群血清标本的代谢轮廓,并通过模式识别方法结合非参数检验对数 据进行分析。结果 由乳腺癌未转移组、乳腺癌转移组和健康对照组的代谢轮廓构建的正交偏最小二乘判别分析 （OPLS-DA）模型具有很好的判别能力（R2=95.2%,Q2=86.7%）,可以鉴别出用于区分乳腺癌转移与否的8个代谢标志 物,包括溶血磷脂酸［18∶1（9Z）/0∶0］、溶血磷脂酰胆碱（18∶0）、溶血磷脂酰胆碱［20∶3（5Z,8Z,11Z）］、胆碱、磷酸二羟 丙酮（18∶0e）、2R,3S-番石榴酸、芥酸、L-氢化乳清酸。结论 利用代谢组学方法获得的血清代谢轮廓可以用来构建 区分模型和寻找乳腺癌转移相关的代谢标志物,为乳腺癌的早期诊治、预后评估和药物治疗靶点的选择提供支持和 依据。     

偏最小二乘回归与主成分回归的比较

多元回归的建模与分析中 ,变量间存在多元共线性的现象十分普遍。另外 ,实际工作中由于种种原因会造成自变量个数 p较多而观察时点数 n并不多 ,甚至出现 n相似文献   

剔除相关性的最小二乘理论研究

提出了一种新的回归模型,剔除相关性的最小二乘,并给出建模步骤。该模型能有效的克服变量间的相关性,兼顾到变量的筛选并克服回归系数反常的现象。     

虎杖的功效成分组研究Ⅳ-纤维蛋白原谱效分析的研究

目的在HPLC指纹图谱和降低急性血瘀大鼠纤维蛋白原药效的基础上进行谱效分析,以此确定虎杖降低急性血瘀大鼠纤维蛋白原药效的"药效成分组"。方法采用偏最小二乘回归法(partial least squares regression,PLSR),对虎杖HPLC指纹图谱数据和降低急性血瘀大鼠纤维蛋白原药效数据之间的内在联系进行谱效分析。结果指纹图谱上的X1、X2(白藜芦醇苷)、X4、X5、X7、X12、X14、X15这8个成分组成的"药效成分组"对虎杖降低纤维蛋白原药效的贡献最大。结论中医用药基于正确的辩证沦治,是根据中药的功效对患者进行治疗,因此,反映中药功效的"功效成分组"是评价中药质量的关键,"功效成分组"需最终得到药效验证才具有代表性。     

男性高尿酸血症患者血浆代谢组学的研究

目的:研究男性高尿酸血症(HUA)患者血浆成分的变化,为探讨HUA病因、HUA与其他代谢综合征(MS)组分的关系提供参考。方法:运用氢核磁共振(1H-NMR)代谢组学方法分析高尿酸血症患者(HUA组,n=5)和同期体检健康者(健康对照组,n=5)血浆中饱和脂肪酸、N-乙酰糖蛋白、极低密度脂蛋白(VLDL)等代谢物的变化,结合主成分分析(PCA)和偏最小二乘判别分析法(PLS),分析2组这些代谢物差异。结果:HUA组较健康对照组的饱和脂肪酸、N-乙酰糖蛋白、VLDL、乳酸、乙醇的水平上升,卵磷脂、谷氨酰胺、葡萄糖的水平下降。结论:HUA患者较健康对照组存在脂类代谢异常、糖酵解加剧、谷氨酰胺水平下降等问题,可能与HUA的发病、病程转归有关。     

基于脂质代谢组学评价五味子素B诱导的急性高甘油三酯血症小鼠模型

目的利用脂质代谢组学技术对五味子素B(schisandrin B,Sch B)诱导的小鼠高甘油三酯血症模型进行评价和提供新的实验依据。方法♂ICR小鼠分为4组:正常饮食(ND)组; ND+Sch B组;高脂高糖饮食(HFFD)组; HFFD+Sch B组。生化法检测血清甘油三酯(TG)和总胆固醇水平;利用超高效液相色谱-四极杆飞行时间质谱联用仪(UPLCQ-TOF/MS)的代谢组学技术方法测定各组小鼠血清中脂类代谢物的变化。结果 ND+Sch B组与ND组比,筛选出27个差异代谢物,分别为TG类18个、磷脂酰胆碱(PC) 7个、磷脂酰乙醇胺(PE) 2个; HFFD组与ND组比,筛选出27个差异代谢物,分别为神经鞘磷脂6个、PC 13个、胆甾醇酯(CE) 2个、TG类5个、磷脂酰肌醇1个; HFFD+Sch B组与HFFD组比,筛选出25个差异代谢物,分别为TG类14个、CE 1个、PC 6个、PE 4个。结论 Sch B诱导的高甘油三酯血症动物模型涉及血清脂质代谢组学的改变。     

不同产地泽泻煮散的含量测定及其化学计量学分析

目的: 建立超高效液相色谱法(UPLC)测定不同产地泽泻煮散中7种成分的含量, 包括环氧泽泻烯、23-乙酰泽泻醇C、泽泻醇A、泽泻烯醇、泽泻醇B、23-乙酰泽泻醇B和11-去氧泽泻醇B。方法: 采用Waters CORTECS C₁₈色谱柱(2.1 mm×100 mm, 1.6 μm), 流动相为水(A)-乙腈(B), 采用梯度洗脱, 流速0.25 mL·min^-1, 柱温35 ℃。含量测定结果采用聚类分析(HCA)、主成分分析(PCA)、正交-偏最小二乘投影判别分析(OPLS-DA)和Pearson相关性分析方法进行分析。结果: 在所设定的色谱条件下, 7种成分在考察的浓度范围内呈良好的线性关系(r＞0.997 8), 平均回收率和RSD分别在99.28%~100.27%和1.27%~2.26%范围内, 精密度、重复性、稳定性考察也符合分析要求。经过Perason分析, 《中国药典》中泽泻指标性成分23-乙酰泽泻醇C和23-乙酰泽泻醇B含量均与煮散煎出率呈递增趋势。同时通过HCA、PCA和OPLS-DA分析, 福建、江西、四川和广西的4个产地的泽泻煮散存在产地差异, 可以显著区分。结论: 本实验建立一种测定泽泻煮散的含量的方法, 并计算和分析不同产地泽泻煮散中7种成分的含量和煎出率, 且利用化学计量学对样品数据进行分析, 为泽泻煮散的进一步研究和临床推广应用提供科学支持。     

基于多元化学模式识别分析的生姜UPLC指纹图谱研究

摘要：目的:建立生姜UPLC指纹图谱研究方法,通过多元化学模式识别分析,为生姜药材的质量控制提供参考。方法:采用Acclaim C18（2.1 mm×100 mm, 2.2μm）色谱柱,流动相为甲醇-乙腈-水,梯度洗脱,检测波长为280 nm,流速为0.4 ml·min-1,柱温为30℃,建立不同产地的生姜指纹图谱,利用总量统计矩分析、相似度评价、聚类分析、主成分分析和偏最小二乘判别分析等多元化学计量方法对生姜进行质量评价。结果:建立了18批生姜样品指纹图谱,确定了13个共有峰,18批样品相似度均大于0.9。经过聚类分析,18批生姜样品可聚为两类,采用主成分分析确定3个主成分为影响药材样品质量评价的主要因子;13个共有峰中对样品组分起关键作用的成分为峰3（6-姜辣素）、峰7、峰9、峰10、峰11、峰12。结论:利用UPLC法建立的生姜药材指纹图谱准确,可有效评价生姜药材的质量。     

Applications of NIR in early stage formulation development. Part II. Content uniformity evaluation of low dose tablets by principal component analysis

A near infrared method based on principal component analysis (PCA) was developed for predicting content uniformity of low dose tablets manufactured by a direct compression process. The work was conducted in early stage formulation development. NIR spectra of one hundred and eighty tablets from three feasibility batches were used as the pseudo-calibration set. A correlation was established between PCA scores and a set of reference values obtained by HPLC analysis. The reference values were also used to define a concentration range for the active pharmaceutical ingredient to facilitate content uniformity prediction by PCA. Analyses of unknown samples were conducted by forming a prediction set that included the calibration and unknown samples, followed by PCA. Samples from two development batches were predicted using the PCA model and the results were consistent with the reference HPLC values. Remarkably, the model was able to predict CU for tablets that were prepared using different grades of lactose (anhydrous versus monohydrate). Additionally, during this study, the impact of spectrum pretreatments on PCA is demonstrated. A brief discussion is given to highlight the advantages of PCA over partial least squares (PLS) regression for analysis of samples generated in early stage formulation development.     

Revisiting thin-layer chromatography as a lipophilicity determination tool—A comparative study on several techniques with a model solute set

The lipophilicity of a compound is a fundamental property related to pharmaceutical and biomedical activity. As many approaches are mixed together in every-day published studies, the subject needs some standardization. The paper presents a comparative study on several approaches of TLC lipophilicity determination: a single TLC run, extrapolation of a retention, principal component analysis of a retention matrix, PARAFAC on a three-way array and a PLS regression. All techniques were applied to 35 model solutes with simple molecules, using nine concentrations of six modifiers: acetonitrile, acetone, dioxane, propan-2-ol, methanol and tetrahydrofurane. The elaborated comparative analysis formed several general recommendations. Methanol and dioxane were the best modifiers, while acetonitrile gave the worst and inacceptable correlation of retention with lipophilicity. Surprisingly, good correlations were obtained for the single TLC runs and this method is underestimated in the literature. The advanced chemometric processing proposed recently, such as PCA, PARAFAC and PLS did not show a visible advantage comparing to classical methods. A need to use a robust regression and robust correlation measures, due to presence of significant outliers, was also noticed and studied.     

Simultaneous quantitative analysis of mebendazole polymorphs A–C in powder mixtures by DRIFTS spectroscopy and ANN modeling

In the present study, a simple method, based on diffuse reflectance FTIR spectroscopy (DRIFTS) and artificial neural network (ANN) modeling is developed for the simultaneous quantitative analysis of mebendazole polymorphs A–C in powder mixtures. Spectral differences between the polymorphs are elucidated by computationally assisted band assignments on the basis of quantum chemical calculations, and subsequently, the spectra are preprocessed by calculation of 1st and 2nd derivatives. Then ANN models are fitted after PCA compression of the input space. Finally the predictive performance of the ANNs is compared with that of PLS regression. It was found that simultaneous quantitative analysis of forms A–C in powder mixtures is possible by fitting an ANN model to the 2nd derivative spectra even after PCA compression of the data (RMSEP of 1.75% for form A, 1.85% for B, and 1.65% for C), while PLS regression, applied for comparison purposes, results in acceptable predictions only within the 700–1750 cm⁻¹ spectral range and after direct orthogonal signal correction (DOSC), with RMSEP values of 2.69%, 2.68%, and 3.40% for forms A, B, and C, respectively. Application of the ANN to commercial samples of raw material and formulation (suspension) proved its suitability for the prediction of polymorphic content.     

Detection of Lipitor counterfeits: a comparison of NIR and Raman spectroscopy in combination with chemometrics

Research has been carried on the feasibility of near infrared (NIR) and Raman spectroscopy as rapid screening methods to discriminate between genuine and counterfeits of the cholesterol-lowering medicine Lipitor. Classification, based on partial least squares discriminant analysis (PLS-DA) models, appears to be successful for both spectroscopic techniques, irrespective of whether atorvastatine or lovastatine has been used as the active pharmaceutical ingredient (API). The discriminative power of the NIR model, in particular, largely relies on the spectral differences of the tablet matrix. This is due to the relative large sample volume that is probed with NIR and the strong spectroscopic activity of the excipients. PLS-DA models based on NIR or Raman spectra can also be applied to distinguish between atorvastatine and lovastatine as the API used in the counterfeits tested in this study. A disadvantage of Raman microscopy for this type of analysis is that it is primarily a surface technique. As a consequence spectra of the coating and the tablet core might differ. Besides, spectra may change with the position of the laser in case the sample is inhomogeneous. However, the robustness of the PLS-DA models turned out to be sufficiently large to allow a reliable discrimination. Principal component analysis (PCA) of the spectra revealed that the conditions, at which tablets have been stored, affect the NIR data. This effect is attributed to the adsorption of water from the atmosphere after unpacking from the blister. It implies that storage conditions should be taken into account when the NIR technique is used for discriminating purposes. However, in this study both models based on NIR spectra and Raman data enabled reliable discrimination between genuine and counterfeited Lipitor tablets, regardless of their storage conditions.     

Quantitative analysis of diclofenac sodium powder via near-infrared spectroscopy combined with artificial neural network

A method for quantitative analysis of diclofenac sodium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using of orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). 148 batches of different concentrations diclofenac sodium samples were divided into three groups: 80 training samples, 46 validation samples and 22 test samples. The average concentration of diclofenac sodium was 27.80%, and the concentration range of all the samples was 15.01–40.55%. O-PLS method was applied to remove systematic orthogonal variation from original NIR spectra of diclofenac sodium samples, and the filtered signal was used to establish ANN model. In this model, the concentration of diclofenac sodium was determined. The degree of approximation was employed as selective criterion of the optimum network parameters. In order to compare with O-PLS–ANN model, principal component artificial neural network (PC-ANN) model and calibration models that use different preprocessing methods (first derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) of the original spectra were also designed. In addition, partial least squares regression (PLS) models were also established to compare with ANN models. Experimental results show that O-PLS–ANN model is the best.     

Better understanding of dissolution behaviour of amorphous drugs by in situ solid-state analysis using Raman spectroscopy

Amorphous drugs have a higher kinetic solubility and dissolution rate than their crystalline counterparts. However, this advantage is lost if the amorphous form converts to the stable crystalline form during the dissolution as the dissolution rate will gradually change to that of the crystalline form. The purpose of this study was to use in situ Raman spectroscopy in combination with either partial least squares discriminant analysis (PLS-DA) or partial least squares (PLS) regression analysis to monitor as well as quantify the solid-phase transitions that take place during the dissolution of two amorphous drugs, indomethacin (IMC) and carbamazepine (CBZ). The dissolution rate was higher from amorphous IMC compared to the crystalline α- and γ-forms. However, the dissolution rate started to slow down during the experiment. In situ Raman analysis verified that at that time point the sample started to crystallize to the α-form. Amorphous CBZ instantly started to crystallize upon contact with the dissolution medium. The transition from the amorphous form to CBZ dihydrate appears to go through the anhydrate form I. Based on the PLS analysis the amount of form I formed in the sample during the dissolution affected the dissolution rate. Raman spectroscopy combined with PLS-DA was also more sensitive to the solid-state changes than X-ray powder diffraction (XRPD) and was able to detect changes in the solid-state that could not be detected with XRPD.     

Multivariate analysis of nuclear magnetic resonance data--characterization of critical drug substance quality of gentamicin sulfate

Fourteen gentamicin sulfate lots collected from international markets showed high quantities of impurities (30% of studied lots). ¹H NMR spectroscopy as a primary analytical method was applied in order to validate the quantification results obtained from micellar electrokinetic chromatography method (MEKC). In this study, ¹H NMR data of 46 gentamicin sulfate drug substance lots were used to classify the lots by means of principal component analysis (PCA) of 14 ¹H NMR-signals in the 5.0–6.0 ppm region. Three main groups could be classified: high purity (3 lots), average quality (28 lots) and low purity (14 lots); one lot proved to be atypical. The 14 normalized signal heights in the 5.0–6.0 ppm region are predictive for purity quality according to a partial least squares (PLS)-model with sum of all impurities as Y-variable (measured by MEKC).     

Effect of multivariate process instability on principal component analysis: A case study

With the rising use of principal component analysis/partial least squares (PCA/PLS) in the process analytical technology (PAT) initiative of the pharmaceutical industry, it seems appropriate to view that approach from a statistical process control (SPC) perspective. The purpose of this study was to demonstrate the effect of process instability (ie, state of statistical out-of-control) on use of PCA/PLS. The demonstrated differences in results should encourage PCA/PLS users to incorporate SPC as an active part of their process analytical control (PAC) toolkit to check for stability prior to drawing conclusions based on PCA/PLS analysis.     

Near-infrared chemical imaging (NIR-CI) as a process monitoring solution for a production line of roll compaction and tableting

In the present study the application of near-infrared chemical imaging (NIR-CI) supported by chemometric modeling as non-destructive tool for monitoring and assessing the roller compaction and tableting processes was investigated. Based on preliminary risk-assessment, discussion with experts and current work from the literature the critical process parameter (roll pressure and roll speed) and critical quality attributes (ribbon porosity, granule size, amount of fines, tablet tensile strength) were identified and a design space was established. Five experimental runs with different process settings were carried out which revealed intermediates (ribbons, granules) and final products (tablets) with different properties. Principal component analysis (PCA) based model of NIR images was applied to map the ribbon porosity distribution. The ribbon porosity distribution gained from the PCA based NIR-CI was used to develop predictive models for granule size fractions. Predictive methods with acceptable R² values could be used to predict the granule particle size. Partial least squares regression (PLS-R) based model of the NIR-CI was used to map and predict the chemical distribution and content of active compound for both roller compacted ribbons and corresponding tablets. In order to select the optimal process, setting the standard deviation of tablet tensile strength and tablet weight for each tablet batch was considered. Strong linear correlation between tablet tensile strength and amount of fines and granule size was established, respectively. These approaches are considered to have a potentially large impact on quality monitoring and control of continuously operating manufacturing lines, such as roller compaction and tableting processes.