基于质量源于生产的广藿香质量标志物的确立

目的建立基于质量源于生产(quality by production,Qb P)的广藿香质量标志物(Q-Marker),为规范广藿香饮片生产,合理提升广藿香饮片质量提供依据。方法对广藿香饮片生产过程中的质量风险点和饮片质量进行分析,发现饮片加工过程中存在广藿香叶加入不足的不规范生产现象,致使广藿香饮片质量下降。采用GC法同时测定广藿香饮片中挥发性成分百秋李醇和广藿香酮的含量,利用指纹图谱结合化学计量学对不同比例广藿香叶的样品中差异性成分进行筛选。结果百秋李醇含量与饮片中广藿香叶比例呈显著正相关,可作为控制饮片中广藿香叶比例的关键标志物之一。正交偏最小二乘-判别分析(OPLS-DA)结果显示,黄酮类成分对不同广藿香叶比例的饮片样品划分具有显著影响。通过同时测定雷杜辛黄酮醇和藿香黄酮醇2种主要黄酮类成分的含量并结合方差分析,结果显示雷杜辛黄酮醇和藿香黄酮醇含量在不同广藿香叶比例样品中具有显著性差异(P0.05),并与广藿香叶比例呈显著性正相关,亦可作为广藿香生产规范性的Q-Marker。考虑饮片加工过程中广藿香叶的损失,以15%广藿香叶比例为标准,建议百秋李醇不低于0.24%,雷杜辛黄酮醇和藿香黄酮醇总量不低于0.045%。结论基于Qb P理念,确立百秋李醇、藿香黄酮醇和雷杜辛黄酮醇为广藿香生产规范化Q-Marker,为广藿香质量标准提升和保障饮片质量提供实验依据。     

硝酸咪康唑栓剂近红外快速定量方法的建立

目的：利用近红外漫反射光谱分析技术和化学计量学方法对硝酸咪康唑栓剂进行快速定量分析。方法以9个厂家生产的42批硝酸咪康唑栓剂分析对象,首先采集样品的近红外漫反射光谱,并以法定方法测定其含量,最后采用偏最小二乘法建立定量模型。结果采用内部交叉验证建立模型。含量范围为7.21%～19.92%;建模谱段为9994～4551 cm-1;预处理方法为一阶导结合矢量归一化,13点平滑;维数为7;内部交叉验证相关系数为96.674%;均方根误差分别为0.509%。结论所建立的释放度快速分析定量模型可对硝酸咪康唑栓剂进行准确、快速定量分析,方法快速简便,可用于药品的快速分析和检验。     

指纹图谱结合化学计量学用于香橼品种鉴别的可行性分析

该文拟探讨利用指纹图谱结合化学计量学手段进行香橼品种鉴别的可行性.共有23批香橼样本,涵盖《中国药典》收载的香圆和枸橼品种,采用高效液相色谱法采集色谱图.对部分特征峰进行初步鉴别,建立共有模式,用相似度评价、二维聚类分析、主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)进行考察.结果表明由25个特征峰组成的共有模式可以有效将香圆和枸橼区分,稳定性好,并且模式识别也初步验证了结果的可靠性.进一步利用绝对峰面积(APA)进行相对定量,体现出品种之间的量化差异,对今后质控所选择合适的指标提供了一定基础.     

色谱指纹图谱结合化学计量学用于3种乳香的鉴别和质量评价

 目的 提出一种中药鉴别和质量评价的整体策略,并遵循该策略建立乳香的HPLC指纹图谱分析方法。方法 该策略包括5个步骤,步骤1为多品种收集,步骤2建立统一条件的色谱指纹图谱鉴别和含量测定分析法,步骤3借助对照品和液质联用法进行成分鉴别,步骤4使用多元统计分析法对中药进行真伪(或不同种)鉴别,步骤5采用相似度阈值评价结合含量测定区间值评价中药质量的优劣。结果 HPLC指纹图谱包含26个色谱峰,其中14个色谱峰进行了鉴别,数据进行了聚类热图分析、主成分分析、偏最小二乘判别分析和自组织映射人工神经网络等多元统计分析,并选择了支持向量机分类器用于模式识别,分别建立了3种药用乳香的共有模式,得出了11-羰基-β-乳香酸(KBA)和11-羰基-β-乙酰乳香酸(AKBA)的含量区间。结论 本方法专属、准确,可用于精确区分和系统评价3种药用乳香。     

双标线性校正结合PDA辅助色谱峰定性用于印度獐牙菜指纹图谱分析

 目的 建立印度獐牙菜指纹图谱,探讨和验证双标线性校正法结合PDA辅助法用于色谱峰定性的可行性。方法 采用HPLC建立印度獐牙菜指纹图谱,使用相似度分析和二维聚类分析进行质量评价,并使用双标线性校正结合PDA辅助法进行色谱峰定性。结果 10批次印度獐牙菜药材选定10个色谱峰作为指纹图谱的共有峰,经化学计量学评价,可对其质量进行初步评价;双标线性校正法预测保留时间的精度优于相对保留时间法;双标线性校正法结合PDA辅助法可更好的对色谱峰进行定性。结论 与相对保留时间法相比,双标线性校正法预测保留时间的准确度更高,色谱柱的适用范围更广。双标线性校正结合PDA对色谱法进行辅助定性可扩大双标线性校正的使用范围。     

基于气味客观化的黄连及其炮制品鉴别研究

气味是鉴别黄连炮制品的重要评价指标,其不同炮制品具有各自独特的气味。该研究以气味客观化为切入点,采用电子鼻技术,建立适合黄连的电子鼻检测方法,分别对生黄连、酒黄连、姜黄连及萸黄连的气味进行检测,依据获得的客观化气味信息,结合化学计量学方法对黄连及其不同炮制品进行区分判别。结果表明,黄连及其不同炮制品在气味特征上存在显著差异,统计质量控制分析(SQC)与软独立建模分析(SIMCA)模型能够实现黄连炮制品与生品的区分,主成分分析(PCA)分析可明显区分黄连及其不同炮制品;另外,对黄连及不同炮制品的区分识别,判别因子分析(DFA)模型正确判别率为100%,线性判别分析(LDA)模型的初始判别率以及交叉验证识别率分别为100%,94.4%。该研究采用电子鼻技术实现了黄连及其炮制品的气味特征差异表征,与化学计量学方法结合实现了黄连及其不同炮制品的鉴别区分;该研究为中药气味客观化鉴别研究提供了思路与方法,有利于传统气味鉴别经验的传承与发展。     

Molecular Factor Computing for Predictive Spectroscopy

Purpose The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Methods Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0–14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. Results In engineering simulations, four MFC filters gave an adequate calibration model (r² = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r² = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. Conclusion MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.     

Near Infrared Spectrometry for the Quantification of Human Dermal Absorption of Econazole Nitrate and Estradiol

Purpose The purpose of this study was to demonstrate the use of near-infrared (NIR) spectrometry for the in vitro quantification of econazole nitrate (EN) and estradiol (EST) in human skin.Methods NIR spectra were collected from EN and EST powders to verify the presence of NIR chromophores. One percent EN cream, a saturated solution of EN, or 0.25% EST solution was applied to human skin. NIR spectra were collected and one-point net analyte signal (NAS) multivariate calibration was used to predict the drug concentrations. NIR results were validated against known skin concentrations measured by high-pressure liquid chromatography (HPLC) analysis of solvent extracts.Results NIR spectroscopy measured dermal absorption from saturated solutions of EN on human skin with an r² = 0.990, standard error of estimation (SEE) = 2.46%, and a standard error of performance (SEP) = 3.55%, EN cream on skin with an r² = 0.987, SEE = 2.30%, and SEP = 2.66%, and 0.25% solutions of EST on skin with an r² = 0.987, SEE = 3.30%, and SEP = 5.66%. Despite low permeation amounts of both drugs through the stratum corneum into human tissue, the NIR signal-to-noise ratio was greater than three, even for the lowest concentrations.Conclusion NIR analyses paralleled the results obtained from HPLC, and thus could serve as a viable alternative for measuring the topical bioavailability/bioequivalence of different EN and EST formulations. Because these experiments were conducted in human tissue, this research suggests an all-optical in vivo method of measurement for dermal absorption could be developed.     

Multivariate evaluation of pharmacological responses in early clinical trials – a study of rIL-21 in the treatment of patients with metastatic melanoma

AIMS

Evaluation of the utility of multivariate data analysis in early clinical drug development.

METHODS

A multivariate chemometric approach was developed and applied for evaluating clinical laboratory parameters and biomarkers obtained from two clinical trials investigating recombinant human interleukin-21 (rIL-21) in the treatment of patients with malignant melanoma. The Phase I trial was an open-label, first-human dose escalation safety and tolerability trial with two separate dosing regimens; six cycles of thrice weekly (3/w) vs. three cycles of daily dosing for 5 days followed by 9 days of rest (5+9) in a total of 29 patients. The Phase II trial investigated efficacy and safety of the ‘5+9’ regimen in 24 patients.

RESULTS

From the Phase I trial, separate pharmacological patterns were observed for each regimen, clearly reflecting distinct properties of the two regimens. Relations between individual laboratory parameters were visualized and shown to be responsive to rIL-21 dosing. In particular, novel systematic pharmacological effects on liver function parameters as well as a bell-shaped dose–response relationship of the overall pharmacological effects were depicted. In validation of the method, multivariate pharmacological patterns discovered in the Phase I trial could be reproduced by the dataset from the Phase II trial, but not from univariate exploration of the Phase I trial.

CONCLUSIONS

The new data analytical approach visualized novel correlations between laboratory parameters that points to specific pharmacological properties. This multivariate chemometric data analysis offers a novel robust, comprehensive and intuitive tool to reveal early pharmacological responses and guide selection of dose regimens.