陈皮水提物指纹图谱的化学计量学分析及与糖尿病认知功能障碍的谱效关系

目的:建立陈皮水提物的HPLC指纹图谱,并研究其与糖尿病大鼠认知功能的谱效关系。方法:采用HPLC建立21批陈皮水提物的指纹图谱,流动相乙腈(A)-0.1%的磷酸(B)梯度洗脱,检测波长270 nm;采用指纹图谱软件中的相似度评价,SPSS 24.0二维聚类分析和SIMCA 14.1主成分分析对结果进行分析;采用腹腔注射链脲佐菌素(STZ,50 mg·kg^-1)复制糖尿病大鼠模型,模型复制成功后给予陈皮水提物灌胃,定期检测空腹血糖值,诱导第12周Morris水迷宫检测实验大鼠的空间学习记忆能力,灰色关联度分析和偏最小二乘回归分析研究谱效关系。结果:确定了陈皮水提物中25个共有峰,鉴定出5种物质,并且将21个不同品种、产地的陈皮分成了2类;与模型组比较,灌胃陈皮水提物的各组大鼠空腹血糖下降,认知能力得到改善;灰色关联分析及最小偏二乘回归分析谱效关系表明,19,15,4,17,6号峰与糖尿病大鼠认知功能关联性较大。结论:不同品种、产地之间的陈皮水提物存在一定的差异,通过化学计量学可以方便、快捷的对不同产地的陈皮水提取物质进行全面的质量评价,并且陈皮水提物可降低糖尿病大鼠的空腹血糖值及改善认知能力,其发挥作用是多种有效成分共同起效的结果,其中19,15,4,17,6号峰是与糖尿病大鼠认知功能密切相关的成分。     

枳实薤白桂枝汤HPLC指纹图谱及10种指标成分含量测定研究

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HPLC指纹图谱、多指标成分含量测定及化学计量学的湿热痹片质量评价

目的 建立指纹图谱和多指标定量与化学计量学相结合的湿热痹片质量评价方法。方法 采用Waters Symmetry C₁₈色谱柱（250 mm×4.6 mm，5 μm），柱温30℃；检测波长分别为303 nm（检测桑皮苷A、桑皮苷F、桑辛素M）和270 nm（检测连翘酯苷B、连翘酯苷A、连翘苷、苍术素醇、白术内酯II、苍术素）；流动相为乙腈-0.2%磷酸水溶液，梯度洗脱，体积流量1.0 mL/min。利用中药色谱指纹图谱相似度评价系统（2012.130723版）建立湿热痹片的HPLC指纹图谱，确定共有峰并进行相似度评价；并对桑皮苷A、桑皮苷F、桑辛素M、连翘酯苷B、连翘酯苷A、连翘苷、苍术素醇、白术内酯II、苍术素的含量测定方法进行方法学验证；基于指纹图谱共有峰峰面积测定结果，采用聚类分析和主成分分析等化学计量学方法对不同批次的湿热痹片进行质量评价。结果 湿热痹片HPLC指纹图谱确认了16个共有峰，指认9个共有峰，10批湿热痹片样品相似度均大于0.95，相似度良好；9种成分在各自的质量浓度范围内线性关系良好（r² ≥ 0.999 1），平均加样回收率分别为98.87%、97.44%、97.94%、98.39%、100.13%、99.06%、96.80%、98.44%、99.15%，RSD分别为1.42%、1.17%、1.30%、0.91%、0.86%、1.23%、1.08%、1.37%、0.79%；10批次样品中桑皮苷A、桑皮苷F、桑辛素M、连翘酯苷B、连翘酯苷A、连翘苷、苍术素醇、白术内酯II、苍术素的质量浓度分别在0.192~0.289、0.057~0.095、0.113~0.158、0.309~0.375、1.537~1.916、0.478~0.596、0.049~0.072、0.279~0.354、0.629~0.759 mg/g。10批湿热痹片聚为2类；主成分1~6是影响湿热痹片质量评价的主要因子。结论 所建立的方法操作便捷、结果准确、重复性好，可用于湿热痹片的质量控制和评价。     

Differential sensing for the regio- and stereoselective identification and quantitation of glycerides

Glycerides are of interest to the areas of food science and medicine because they are the main component of fat. From a chemical sensing perspective, glycerides are challenging analytes because they are structurally similar to one another and lack diversity in terms of functional groups. Furthermore, because animal and plant fat consists of a number of stereo- and regioisomeric acylglycerols, their components remain challenging analytes for chromatographic and mass spectrometric determination, particularly the quantitation of species in mixtures. In this study, we demonstrated the use of an array of cross-reactive serum albumins and fluorescent indicators with chemometric analysis to differentiate a panel of mono-, di-, and triglycerides. Due to the difficulties in identifying the regio- and stereochemistry of the unsaturated glycerides, a sample pretreatment consisting of olefin cross-metathesis with an allyl fluorescein species was used before array analysis. Using this simple assay, we successfully discriminated 20 glycerides via principal component analysis and linear discriminant analysis (PCA and LDA, respectively), including stereo- and regioisomeric pairs. The resulting chemometric patterns were used as a training space for which the structural characteristics of unknown glycerides were identified. In addition, by using our array to perform a standard addition analysis on a mixture of triglycerides and using a method introduced herein, we demonstrated the ability to quantitate glyceride components in a mixture.Glycerides are the primary component of animal fats and vegetable oils (1). They consist of one, two, or three fatty acids esterified on glycerol, and hence are referred to as mono-, di-, and triglycerides, respectively. The structural diversity of glycerides derives in part from their fatty acid alkyl groups, which can differ in carbon number (i.e., chain length), the degree of unsaturation, the position of olefins, and the configuration of the olefins (i.e., cis/trans). Furthermore, these fatty acid alkyl groups can be connected to the sn-1, -2, or -3 carbons on glycerol. Hence, a variety of regio- and stereoisomers can exist for glycerides, posing a challenge for mass spectrometry (2). Further, because the differences in chain length primarily result from the presence of greater or fewer methylene groups, NMR spectroscopy can be ambiguous (3).The analysis of glycerides is primarily important to the food and nutrition industries for tasks such as authenticating edible oils (4), designing foods with certain physical properties (5), and studying how fats are digested and absorbed (6). In particular, classifying all of the various kinds of regio- and stereoisomers of glycerides is biologically important because lipases, enzymes that catalyze the hydrolysis of glycerides into fatty acids and glycerol, exhibit selectivity based on these features of the glyceride substrates. As examples, the position and configuration of olefins, the identity of fatty acid alkyl groups, as well as their position on glycerol (i.e., sn-1,3 versus sn-2), all contribute to differing biological activity (7, 8). Studying the selectivity of these lipases has applications in understanding diseases, including fat malabsorption disorders, hypercholesterolemia, atherosclerosis, and diabetes (9, 10). Research on metabolic disorders has shown that fatty acid accumulation can exert a toxic or a protective effect on a tissue, depending on the specific tissue type (e.g., liver, cardiac, or skeletal muscle) (11, 12) and health state (e.g., diabetic) (13, 14) as well as on the fatty acids (e.g., saturated or unsaturated) (15). Sequestration of fatty acids by esterification to glycerides is one pathway by which these effects are regulated (16). Thus, a deeper understanding of the distinct roles of the cellular storage of structurally different glycerides in normal and disease states is a desirable avenue of research (17). However, currently only limited information is available about the composition of glycerides in adipose and nonadipose tissue.The most common method of glyceride identification is mass spectrometry (MS) (2, 18). However, as alluded to above, this approach has drawbacks. Because glycerides are neutral molecules, they must be ionized to be analyzed by MS. Saponification can be used to obtain the fatty acids, which are both volatile and charged, thereby facilitating MS analysis, but information about the glyceride structure is lost in this process (18). Electrospray ionization and atmospheric pressure chemical ionization are used to ionize glycerides directly; however, the ion yields are low compared with preionized lipids (19, 20). Furthermore, the ability of a glyceride to be ionized using these methods often varies. For example, ion abundance generally increases with increasing number of double bonds in the fatty acid alkyl chain and can also depend on fatty acid alkyl chain length (21). These significant variations in ion abundance mean that ionization methodologies must be developed and tailored to a specific application to satisfactorily detect each glyceride of interest (19). Finally, these variations render the quantification of glycerides, particularly in a complex mixture, quite challenging when using MS (22).Regio- and stereoisomers further confound the discrimination of glycerides by MS, because isomers share the same mass. Other techniques such as chemical derivitization of the glycerides, ion fragmentation, and specialized HPLC must be coupled with MS to effect differentiation of isomeric species. For example, ozonolysis has been used to cleave the double bonds in unsaturated glycerides before ionization to deduce the positions of double bonds (23). Nonaqueous reverse-phase (NARP)-HPLC can resolve cis/trans isomers of triacylglycerols and double-bond positional isomers after treatment of the olefins with bromine (24). Silver ion chromatography has been used to separate triacylglycerol positional isomers under specifically developed solvent and column temperature conditions (25). Silver cationization as a postcolumn treatment in conjunction with NARP-HPLC and ion fragmentation has also been used for triglyceride positional isomer determination (26, 27) Thus, although these current approaches to glyceride isomer analysis have been successful, they are complicated, labor intensive, time-consuming, and at times inconsistent in their results (26).Because glycerides are structurally very similar to one another, we believed that a differential sensing array-based approach would be most suitable for their classification. Our hypothesis was that if a cross-reactive array could be created that was responsive to the subtle structural differences inherent in glycerides, it could be used to pattern individual glycerides, identify structural features of unknown glycerides, and potentially quantitate glycerides in a mixture. Cross-reactive arrays have been successfully used in a number of sensing applications (28–33). Differential sensing mimics the mammalian senses of olfaction and gustation by detecting the pattern of response of an analyte to a collection of semiselective receptors (34, 35). In mammals, the characteristic pattern for a scent or taste is interpreted and stored by the brain (36). In the laboratory, chemometric routines such as principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the relevant information from the array. Both PCA and LDA are multivariate methods that reduce the dimensionality of a data set. PCA does so by finding unbiased orthogonal axes that describe decreasing extents of variance in the data derived from different samples (classes) and repetitions of the samples (37). Any grouping of like samples represents intrinsic similarities between the sample datasets whereas separate classification represents differences in that variable space. LDA classifies samples by calculating discriminant functions that maximize the separation between predetermined classes and minimizes the separation within these classes (38, 39). Thus, LDA is a supervised method, meaning that the classes are provided as inputs into the algorithm. For this reason, a validation method called a leave-one-out cross-validation is used to test the predictive value of the model. Further, LDA can be used to predict the identity of unknowns by identifying which classes in the training set the unknowns most resemble.Therefore, the goal of this project was to develop an array of cross-reactive receptors that could discriminate glycerides. The glycerides selected are shown in Fig. 1. The panel includes commercially available mono-, di-, and triacylglycerols with fatty acid alkyl groups that are relevant to mammalian biology (40). Moreover, the panel consists of examples of each of the following stereo- and regioisomers: (i) cis/trans olefins (D1 and D2; T2 and T3), (ii) differing position of the olefin (T3 and T4), and (iii) differing position of the fatty acid alkyl groups on the glycerol (D5 and D6). Clearly, it would be extremely challenging to create highly selective receptors for each individual glyceride, and thus a differential sensing method seems the only reasonable approach to creating an optical sensing routine to identify and classify these structures.Open in a separate windowFig. 1.Glyceride panel with structures and names.Because glycerides are extremely hydrophobic analytes, we postulated that serum albumins (SAs) would be suitable cross-reactive receptors with which to test our hypothesis. SA is a common plasma protein that binds hydrophobic molecules to transport them through the hydrophilic environment of blood plasma (41). The protein binds a number of endogenous compounds: long-chain fatty acids (K_a = 10⁶–10⁷ M⁻¹) (42), bile acids (K_a = 10³–10⁵ M⁻¹) (43), and steroids (K_a = 10³–10⁵ M⁻¹) (44–46), as well as many drugs, toxins, and fluorophores (41). Despite being composed of fatty acid alkyl groups, glycerides bind less tightly to SAs and in a different location than their fatty acid counterparts (47). The primary sequence of SAs differs between species, which thus exhibit differences in ligand binding (41). Previously, we have used arrays of SAs for the differentiation of other hydrophobic analytes including fatty acids (48), terpenes (49), and plasticizers (50). However, none of these previous studies involved differences between the analyte structures as subtle as glycerides do, nor had we challenged our methods to identify structural aspects of an unknown. Furthermore, we had never implemented a quantitation assay in a complex mixture. Because the binding of ligands to SAs is known to depend on subtle differences in their structure (41), we anticipated that success could be achieved but would be highly dependent upon the signaling modality and potentially analyte prederivitization.Thus, herein we describe a method using SAs to fingerprint glycerides that classifies them as mono-, di-, or triglycerides. The glycerides were further classified based on fatty acid chain length, ester positions on glycerol, and olefin regio- and stereochemistry. For the unsaturated glycerides in the panel, differentiation based on olefin position and stereochemistry was achieved by the use of a pretreatment olefin metathesis. Using the protocols described herein, structural features of unknown glycerides could be identified. Furthermore, the quantitation of trilinolein in a mixture of triglycerides was achieved by application of the standard addition method using a net analyte signal technique (SANAS) presented herein.     

Rapid Determination of the Chemical Composition of Ethylene/Butadiene Copolymers Using FTIR Spectroscopy and Chemometrics

Fourier transform infrared spectroscopy by attenuated total reflection (ATR‐FTIR), combined with the partial least square (PLS) method provides a fast characterization of ethylene/butadiene copolymers' intricate composition. The PLS regression method is constructed to quantify ethylene, 1,2‐butadiene (vinyl), trans‐1,4‐butadiene, and 1,2‐cyclohexane units in the copolymer. These rings are formed by intramolecular cyclization during polymerization. The performance of PLS models is evaluated by comparing the result obtained by ¹³C NMR and the model for three unknown samples. It is shown that the proposed method allows to accurately estimate the chemical composition of ethylene/butadiene copolymers in a much shorter time than NMR.     

基于HPLC指纹图谱的浙贝母质量评价

目的:建立浙贝母药材HPLC指纹图谱,为其质量控制提供比较全面的评价方法。方法:Welch Xtimate^TM C₁₈色谱柱(4.6 mm×250 mm,5 μm);流动相乙腈-10 mmol·L^-1乙酸铵溶液(氨水调pH至10.0),梯度洗脱;流速0.8 mL·min^-1,柱温25℃,蒸发光散射检测器。利用化学计量学方法,包括相似度分析、聚类分析、主成分分析,对色谱数据进行分析。结果:该方法精密度、稳定性、重复性良好。测定了16批浙贝母药材,提取7个色谱峰作为指纹图谱共有峰,采用相似度评价、聚类分析和主成分分析等方法,对所收集的16批样品进行系统比较与归类,16批样品分为2类。结论:该法重复性好,简便可靠,可以为浙贝母的质量控制和评价提供依据。     

Cocaine profiling method retrospectively developed with nontargeted discovery of markers using liquid chromatography with time-of-flight mass spectrometry data

Illicit drug profiling performed by forensic laboratories assists law enforcement agencies through providing information about chemical and/or physical characteristics of seized specimens. In this article, a model was developed for the comparison of seized cocaine based on retrospective analysis of data generated from ultrahigh performance liquid chromatography with time-of-flight mass spectrometry (UHPLC-TOF-MS) comprehensive drug screening. A nontargeted approach to discover target compounds was employed, which generated 53 potential markers using data from cocaine positive samples. Twelve marker compounds were selected for the development of the final profiling model. The selection included a mixture of commonly used cocaine profiling targets and other cocaine-related compounds. Combinations of pretreatments and comparison metrics were assessed using receiver operating characteristic curves to determine the combination with the best discrimination between linked and unlinked populations. Using data from 382 linked and 34,519 unlinked distances, a classification model was developed using a combination of the standardization and normalization transformations with Canberra distance, resulting in a linked cut-off with a 0.5% false positive rate. The present study demonstrates the applicability of retrospectively developing a cocaine profiling model using data generated from UHPLC-TOF-MS nontargeted drug screening without pre-existing information about cocaine impurities. The developed workflow was not specific to cocaine and thus could potentially be applied to any seized drug in which there are both sufficient data and impurities present.     

基于化学计量学的麸炒枳实与烫枳实比较研究

目的 利用指纹图谱与化学计量学进行麸炒枳实和烫枳实的质量评价及比较研究，为临床选择使用枳实2种饮片提供部分科学依据。方法 依据中国药典2020年版和《北京市中药饮片炮制规范》（2008年版）炮制麸炒枳实与烫枳实2种饮片，采用超高效液相色谱法分别建立2种饮片的指纹图谱，采用中药色谱指纹图谱相似度评价系统软件进行相似度分析，利用化学计量学进行2种饮片指纹图谱中14个共有成分及辛弗林的比较研究，并对2种饮片中已指认的9个成分进行定量分析与比较。结果 麸炒枳实和烫枳实与各自对照指纹图谱相似度>0.9，同一批次麸炒枳实与烫枳实相似度>0.9。与对照品比对指认出芸香柚皮苷、柚皮苷、野漆树苷、橙皮苷、新橙皮苷、柚皮素、橙皮素、川陈皮素8个共有峰。聚类分析结果显示2种饮片无明显聚类。PCA分析显示麸炒枳实与烫枳实不能完全区分为2类。VIP法筛选出对区分麸炒枳实和烫枳实影响较大的7个成分，分别是柚皮素、柚皮苷、野漆树苷、X8、橙皮素、川陈皮素、X1。定量分析结果显示，与麸炒枳实组比较，烫枳实组中柚皮苷含量较低，野漆树苷、柚皮素含量较高，且有显著性差异。结论 本研究建立了麸炒枳实与烫枳实的定量指纹图谱及黄酮类多成分含量测定的质量评价方法，研究结果可为临床选择使用枳实2种饮片提供部分科学依据。     

The CSD Drug Subset: The Changing Chemistry and Crystallography of Small Molecule Pharmaceuticals

We report the generation and statistical analysis of the CSD drug subset: a subset of the Cambridge Structural Database (CSD) consisting of every published small-molecule crystal structure containing an approved drug molecule. By making use of InChI matching, a CSD Python API workflow to link CSD entries to the online database Drugbank.ca has been produced. This has resulted in a subset of 8632 crystal structures, representing all published solid forms of 785 unique drug molecules. We hope that this new resource will lead to improvements in targeted cheminformatics and statistical model building in a pharmaceutical setting. In addition to this, as part of the Advanced Digital Design of Pharmaceutical Therapeutics collaboration between academia and industry, we have been given the unique opportunity to run comparative analysis on the internal crystal structure databases of AstraZeneca and Pfizer, alongside comparison to the CSD as a whole.     

Chemometric Models for Quantification of Carbamazepine Anhydrous and Dihydrate Forms in the Formulation

Carbamazepine (CBZ) exists in anhydrous and dihydrate forms. These forms differ in their solubility, dissolution rate, and subsequently in their oral bioavailability. The objective of this study is to develop multivariate chemometric models for estimation of the low level of carbamazepine dihydrate (CBZ-DH) in the CBZ formulations containing excipients of the commercial formulation. The selected excipients were mixed in proportions to make sample matrices ranging from 0% to 50% CBZ-DH. Fourier transform infrared (FTIR), near infrared (NIR), and hyperspectral imaging data were mathematically pretreated before the development of partial least square and principal component analysis regression models. The developed partial least squares regression and principal component analysis models demonstrated predictability of CBZ and CBZ-DH by multiple scattering correction and standard normal variate processing methods. Among the spectroscopic techniques used the model performance parameters such as root-mean-square error, standard error, and bias were found to be low for NIR compared to FTIR. The treated data have shown better model fitting than without treatment, which was demonstrated by correlation coefficient of 0.9778, 0.9824, and 0.9852 for FTIR, NIR, and hyperspectral imaging, respectively. Furthermore, the predicted values were found to be very close to the selected low level of independent samples having 5% CBZ-DH in tablet formulation.