生物分配胶束色谱的药物定量结构保留关系

目的建立药物在生物分配胶束色谱的定量结构保留关系,解析药物在生物分配胶束色谱的保留机理,用于预测新化合物的保留行为,以指导新化合物的设计。方法采用逐步回归与偏最小二乘回归等化学计量学方法,对110个药物的生物分配胶束色谱保留因子与影响药物吸收的基本物理化学参数等进行线性回归,建立药物保留行为的预测模型。结果建立了基于影响药物吸收的物理化学参数的药物生物分配胶束色谱定量结构保留关系,采用测试集进行外部验证显示模型具有良好的预测能力(R2>0.82)。药物的疏水性[正辛醇/水分配系数(P)的对数值的计算值,cal-culated logP,ClogP]、电荷(δ)、分子量(molecular weight,MW)、总表面积(total surface area,TSA)等对其在生物分配胶束色谱的色谱保留作用显著。结论药物在生物分配胶束色谱的定量结构保留关系建立有助于解析药物的保留行为,指导新化合物的设计,提高新药研发的成功率。     

基于UPLC结合化学计量学方法的龙血竭指纹图谱研究

建立不同厂家龙血竭UPLC指纹图谱,为其质量控制提供比较全面的评价方法。采用Phenomenex Kinetex 2.6μC18100A色谱柱,以乙腈-水梯度洗脱,流速1.7 m L·min-1,柱温40℃,检测波长为280 nm。利用化学计量学方法对色谱数据进行相似度分析、聚类分析和主成分分析。采用该方法测定了18批龙血竭,提取15个色谱峰作为指纹图谱共有峰,采用LC-QTOF MS方法指认了13个共有峰;其中15批样品相似度大于0.9;18批样品可大致分为4类。该法重复性好,简便可靠,可以为不同厂家龙血竭的质量控制和评价提供依据。     

HPLC指纹图谱鉴别葶苈子与车前子

目的: 鉴别葶苈子与车前子. 方法: 采用高效液相色谱法建立南葶苈子指纹图谱,并使用相似度评价软件,系统聚类分析和偏最小二乘法判别分析对两者的HPLC图谱进行识别研究. 结果: 葶苈子与车前子的HPLC色谱峰和相对峰面积存在较大的差异,经统计分析,所有样品被分为葶苈子、车前子和混淆品三类. 结论: HPLC指纹图谱结合系统聚类分析和偏最小二乘法判别分析等化学计量学方法可用来鉴别葶苈子和车前子,该方法快速、简便、准确、有效,可为葶苈子和车前子的质量评价提供依据.     

HPLC fingerprint analysis combined with chemometrics for pattern recognition of ginger

Context: Ginger, the fresh rhizome of Zingiber officinale Rosc. (Zingiberaceae), has been used worldwide; however, for a long time, there has been no standard approbated internationally for its quality control.

Objective: To establish an efficacious and combinational method and pattern recognition technique for quality control of ginger.

Methods: A simple, accurate and reliable method based on high-performance liquid chromatography with photodiode array (HPLC-PDA) detection was developed for establishing the chemical fingerprints of 10 batches of ginger from different markets in China. The method was validated in terms of precision, reproducibility and stability; and the relative standard deviations were all less than 1.57%. On the basis of this method, the fingerprints of 10 batches of ginger samples were obtained, which showed 16 common peaks. Coupled with similarity evaluation software, the similarities between each fingerprint of the sample and the simulative mean chromatogram were in the range of 0.998–1.000. Then, the chemometric techniques, including similarity analysis, hierarchical clustering analysis and principal component analysis were applied to classify the ginger samples.

Results and conclusion: Consistent results were obtained to show that ginger samples could be successfully classified into two groups. This study revealed that HPLC-PDA method was simple, sensitive and reliable for fingerprint analysis, and moreover, for pattern recognition and quality control of ginger.     

Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR

The objective of this study was to identify urinary metabolite profiles that discriminate between high and low intake of dietary protein during a dietary intervention. Seventy-seven overweight, non-diabetic subjects followed an 8-week low-calorie diet (LCD) and were then randomly assigned to a high (HP) or low (LP) protein diet for 6 months. Twenty-four hours urine samples were collected at baseline (prior to the 8-week LCD) and after dietary intervention; at months 1, 3 and 6, respectively. Metabolite profiling was performed by (1)H NMR and chemometrics. Using partial least squares regression (PLS), it was possible to develop excellent prediction models for urinary nitrogen (root mean square error of cross validation (RMSECV) = 1.63 mmol/L; r = 0.89) and urinary creatinine (RMSECV = 0.66 mmol/L; r = 0.98). The obtained high correlations firmly establish the validity of the metabolomic approach since urinary nitrogen is a well established biomarker for daily protein consumption. The models showed that trimethylamine-N-oxide (TMAO) is correlated to urinary nitrogen. Furthermore, urinary creatine was found to be increased by the HP diet whereas citric acid was increased by the LP diet. Despite large variations in individual dietary intake, differentiated metabolite profiles were observed at the dietary group-level.     

指纹图谱结合化学计量学优选经典名方温胆汤的提取工艺

目的： 建立指纹图谱与化学计量学相结合的提取工艺优化方法,并将其应用于经典名方温胆汤水提工艺优化,以充分保证经方的有效性。方法： 以温胆汤为模型药物,采用HPLC法建立指纹图谱,对共有峰峰面积进行主成分分析（PCA）,以PCA总因子得分、干膏收率为评价指标,采用L₉（3⁴）正交设计法考察加水量、浸泡时间、煎煮时间、煎煮次数对提取效果的影响,综合评价优化水提工艺参数。结果： 通过中药色谱指纹图谱相似度评价系统匹配28个共有峰,并确定其中7个化合物。根据综合评分结果,确定该制剂最佳提取工艺为12倍加水量,浸泡30 min,煎煮2次,每次1 h。结论： 优选后的工艺提取率高、稳定性和重复性好,适用于温胆汤复方制剂的工业化生产。     

珠芽蓼止泻颗粒联合布拉氏酵母菌散治疗小儿功能性腹泻的临床研究

目的 建立枳实薤白桂枝汤HPLC指纹图谱分析方法和复方中10种指标成分（辛弗林、槲皮素、桂皮酸、厚朴酚、柚皮苷、腺苷、香豆素、橙皮苷、和厚朴酚、新橙皮苷）含量测定方法,开展相关评价分析。方法 采用HPLC法建立枳实薤白桂枝汤指纹图谱,开展相似度评价研究;测定复方中10个指标成分,分析复方中药材不同配伍对其量的变化影响;采用聚类分析等化学计量学方法,对获取相关数据进行分析,评价枳实薤白桂枝汤的质量控制相关指标的影响和价值。结果 10批样品的相似度在0.376～0.990,部分批次相似度大于0.9（5批）,说明10批样品相似度差异较大。10批样品中S1～S3、S5、S6、S8、S10为一组,S4、S9为一组,S7单独为一组。共标定了30个特征峰,经主成分分析,主成分1～6是影响药材样品质量评价的主要因子;30个特征峰中对样品分组起关键作用的成分为21（新橙皮苷）、26、29（和厚朴酚）、3、23、17、30（厚朴酚）、5、24（香豆素）、28和7。含量测定结果显示,除槲皮素外,其余9种成分在测定的质量浓度范围内线性关系、精密度、稳定性和重复性良好;不同配伍会对药材中相关成分产生增加或抑制溶出的作用。结论 所建立的HPLC方法可用于同时测定枳实薤白桂枝汤中10种化学成分的含量,该方法高效、准确、重复性好,可用于枳实薤白桂枝汤的质量控制和评价。     

基于高光谱技术的灵芝孢子粉破壁率快速检测方法研究

目的 以破壁灵芝孢子粉为研究对象,利用高光谱技术结合化学计量学建立了灵芝孢子粉破壁率快速无损检测方法。方法 首先,采集不同破壁率灵芝孢子粉样品的高光谱图像,选定感兴趣区域后计算获得各样品可见-短波近红外波段(397-1004 nm)范围内的光谱数据;然后,比较了运用标准正态变量变换、多元散射矫正、Savitsky-Golay(SG)平滑、小波变换、SG平滑+标准正态变量变换及SG平滑+多元散射矫正等6种光谱预处理方法,竞争性自适应重加权、连续投影算法、无信息变量选择、最小角回归、遗传算法等5种特征波段提取方法以及偏最小二乘法、支持向量机、极限学习机、多层感知机及LightGBM等5种算法所建立的定量校正模型预测性能。结果 获得最优预测性能的算法组合为：SG平滑+竞争性自适应重加权特征波段选择+偏最小二乘;基于该算法组合建立的定量校正模型在破壁率区间为90%-100%的灵芝孢子粉样品预测集决定系数为0.8682、均方根误差为0.0117;进一步,将选定的最优算法组合应用于构建样品破壁率区间为0-100%的定量校正模型,计算测试集决定系数为0.9731,均方根误差为0.0493,表现出良好的泛化能力。结论 所建立的定量检测模型可以实现对灵芝孢子粉破壁率的快速、无损检测,为破壁灵芝孢子粉及其产品的质量控制提供了技术支撑。     

基于多指标结合化学计量学的龙葵果质量评价研究

目的 采用多指标结合化学计量学的方法综合分析龙葵Solanumnigrum果质量及关键指标,为其质量评价提供科学依据。方法 采用HPLC法建立龙葵果特征指纹图谱及澳洲茄碱、澳洲茄边碱含量测定方法,并测定浸出物含量,使用SAS 14.0、SIMCA14.1软件对上述指标进行聚类分析（hierarchical clustering analysis,HCA）、主成分分析（principal component analysis,PCA）及偏最小二乘判别分析（discriminant analysis of partial least squares analysis,PLS-DA）,通过对吉林产地25批不同产区、不同采收期药材的分析,综合评价龙葵果质量及标志性成分。结果 建立的特征指纹图谱及澳洲茄碱、澳洲茄边碱含量测定方法稳定性较好。HCA分析可将25批样品聚为2大类,分类主要与产区有关,I类药材为铁东区及梨树县的孤家子、榆树台产区;II类药材为梨树县的杏山、北夏家、泉眼沟、獾子洞产区。用3个主成分对龙葵果药材进行综合评价,结果表明I类药材综合得分及排名较高,质量较优。根据PLS-DA分析...