A note on scale estimates based on the empirical characteristic function and their application to test for normality

We present a test for the composite hypothesis of normality against general alternatives. The test statistic is essentially the difference of two estimates of scale based on the empirical characteristic function. The test is simple to apply and its performance is comparable with that of several other well-known tests of normality.     

Comparing dendritic and self-assembly strategies to multivalency--RGD peptide-integrin interactions

This paper compares covalent and non-covalent approaches for the organisation of ligand arrays to bind integrins. In the covalent strategy, linear RGD peptides are conjugated to first and second generation dendrons, and using a fluorescence polarisation competition assay, the first generation compound is demonstrated to show the most effective integrin binding, with an EC(50) of 125 μM (375 μM per peptide unit). As such, this dendritic compound is significantly more effective than a monovalent ligand, which does not bind integrin, even at concentrations as high as 1 mM. However, the second generation compound is significantly less effective, demonstrating that there is an optimum ligand density for multivalency in this case. In the non-covalent approach to multivalency, the same RGD peptide is functionalised with a hydrophobic C12 chain, giving rise to a lipopeptide which is demonstrated to be capable of self-assembly. This lipopeptide is capable of effective integrin binding at concentrations of 200 μM. These results therefore demonstrate that covalent (dendritic) and non-covalent (micellar self-assembly) approaches have, in this case, comparable efficiency in terms of achieving multivalent organisation of a ligand array.     

Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data

Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D (1)H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10-5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D (1)H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.     

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their <Superscript>1</Superscript>H NMR spectra

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of ¹H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0–10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0–2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that ¹H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.     

微波消解ICP-MS测定唐古特乌头中的多种微量元素

采用HNO3-H2O2混合酸体系微波消解及电感耦合等离子体-质谱法,消解唐古特乌头样品,测定其中Mn、Fe、Co、As、Ni、Se、Cu、Zn、V和Cr共10种微量元素。探讨了样品消解的条件,通过加标回收实验,回收率为94.08%—103.50%,验证了分析数据的可靠性。结果表明,该法具有快速、简便、准确度高、能同时分析多元素的优点,结果令人满意。     

Comparison of Estrogen Receptor α and β Subtypes Based on Comparative Molecular Field Analysis (CoMFA)

Abstract

A substantial body of evidence indicates that both humans and wildlife suffer adverse health effects from exposure to environmental chemicals that are capable of interacting with the endocrine system. The recent cloning of the estrogen receptor β subtype (ER-β) suggests that the selective effects of estrogenic compounds may arise in part by the control of different subsets of estrogen-responsive promoters by the two ER subtypes, ER-α and ER-β. In order to identify the structural prerequisites for ligand-ER binding and to discriminate ER-α and ER-3 in terms of their ligand-binding specificities, Comparative Molecular Field Analysis (CoMFA) was employed to construct a three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model on a data set of 31 structurally-diverse compounds for which competitive binding affinities have been measured against both ER-α and ER-β. Structural alignment of the molecules in CoMFA was achieved by maximizing overlap of their steric and electrostatic fields using the Steric and Electrostatic ALignment (SEAL) algorithm. The final CoMFA models, generated by correlating the calculated 3D steric and electrostatic fields with the experimentally observed binding affinities using partial least-squares (PLS) regression, exhibited excellent self-consistency (r ² > 0.99) as well as high internal predictive ability (q ² > 0.65) based on cross-validation. CoMFA-predicted values of RBA for a test set of compounds outside of the training set were consistent with experimental observations. These CoMFA models can serve as guides for the rational design of ER ligands that possess preferential binding affinities for either ER-α or ER-β. These models can also prove useful in risk assessment programs to identify real or suspected EDCs.