Study of multiple binding constants of dexamethasone with human serum albumin by capillary electrophoresis–frontal analysis and multivariate regression

Studies into the interactions between drugs and human serum albumin (HSA) are extremely important for drug discovery, since HSA behaves as a carrier for external drugs and internal biological molecules. In this paper, to evaluate the pharmacokinetic and pharmacodynamic properties of dexamethasone (DXM), the interaction between DXM and HSA was studied by capillary electrophoresis–frontal analysis (CE-FA). According to the Klotz equation, four binding sites between DXM and HSA were obtained, and the average binding constant was 1.05 × 10³ M⁻¹. Furthermore, according to multiple equilibrium theory, based on the assumption that there are two types of binding site, the binding constant at one site was calculated to be 3.539 × 10³ M⁻¹, and the average of the other three was 1.234 × 10³ M⁻¹. In addition, to obtain the detailed binding information at each binding site, new equations were deduced by multivariate regression. The four binding constants of DXM and HSA were calculated to be 5.558 × 10¹ M⁻¹, 2.158 × 10⁴ M⁻¹, 7.312 × 10³ M⁻¹ and 2.043 × 10³ M⁻¹, respectively, which is helpful for detailed studies into the interactions between drugs and proteins with multiple binding sites. Figure Electropherograms of DXM sodium phosphate and HAS mixtures for different protein to drug concentration ratios, obtained by CE-FA     

Comparison of performance of partial least squares regression,secured principal component regression,and modified secured principal component regression for determination of human serum albumin, γ-globulin and glucose in buffer solutions and in vivo blood glucose quantification by near-infrared spectroscopy

The performances of three multivariate analysis methods—partial least squares (PLS) regression, secured principal component regression (sPCR) and modified secured principal component regression (msPCR)—are compared and tested for the determination of human serum albumin (HSA), γ-globulin, and glucose in phosphate buffer solutions and blood glucose quantification by near-infrared (NIR) spectroscopy. Results from the application of PLS, sPCR and msPCR are presented, showing that the three methods can determine the concentrations of HSA, γ-globulin and glucose in phosphate buffer solutions almost equally well provided that the prediction samples contain the same spectral information as the calibration samples. On the other hand, when some potential spectral features appear in new measurements, sPCR and msPCR outperform PLS significantly. The reason for this is that such spectral features are not included during calibration, which leads to a degradation in PLS prediction performance, while sPCR and msPCR can improve their predictions for the concentrations of the analytes by removing the uncalibrated features from the original spectra. This point is demonstrated by successfully applying sPCR and msPCR to in vivo blood glucose measurements. This work therefore shows that sPCR and msPCR may provide possible alternatives to PLS in cases where some uncalibrated spectral features are present in measurements used for concentration prediction.     

A consensus successive projections algorithm – multiple linear regression method for analyzing near infrared spectra

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.     

Effect of γ-radiation on radionuclide retention in compacted bentonite

Compacted bentonite is proposed as an engineered barrier in many concepts for disposal of high level nuclear waste. After the initial deposition however, the bentonite barrier will inevitably be exposed to ionizing radiation (mainly γ) under anoxic conditions. Because of this, the effects of γ-radiation on the apparent diffusivity values and sorption coefficients in bentonite for Cs⁺ and Co²⁺ were tested under different experimental conditions. Radiation induced effects on sorption were in general more noticeable for Co²⁺ than for Cs⁺, which generally showed no significant differences between irradiated and unirradiated clay samples. For Co²⁺ however, the sorption to irradiated MX80 was significantly lower than to the unirradiated clay samples regardless of the experimental conditions. This implies that γ-radiation may alter the surface characteristics contributing to surface complexation of Co²⁺. With the experimental conditions used, however, the effect of decreasing sorption was not large enough to be reflected on the obtained D_a values.     

Implicit characteristic parameter of a programmed temperature retention index database

In a programmed temperature retention index (PTRI) database, there exists a characteristic parameter rt0/β that can be calculated if the experimental parameters are clearly given. This characteristic parameter can be used to flexibly reproduce the original PTRI data under chromatographic conditions different from those originally given. As this characteristic parameter is not explicitly given, it is suggested to name this parameter as the implicit characteristic parameter (TCP) of a PTRI database. The ICP in White's PTRI database was easily found and used satisfactorily to reproduce PTRI of some test compounds using either a Hewlett-Packard ultra-performance OV-1 column or a self-coated OV-1 column. The reproduction of PTRI could not be realized on columns of different materials. The fact that several PTRI databases measured on glass capillary columns could not satisfactorily be reproduced on fused silica column is explained.     

Estimation of Kováts retention indices using group contributions

We have constructed a group contribution method for estimating Kováts retention indices by using observed data from a set of diverse organic compounds. Our database contains observed retention indices for over 35,000 different molecules. These were measured on capillary or packed columns with polar and nonpolar (or slightly polar) stationary phases under isothermal or nonisothermal conditions. We neglected any dependence of index values on these factors by averaging observations. Using 84 groups, we determined two sets of increment values, one for nonpolar and the other for polar column data. For nonpolar column data, the median absolute prediction error was 46 (3.2%). For data on polar columns, the median absolute error was 65 (3.9%). While accuracy is insufficient for identification based solely on retention, it is suitable for the rejection of certain classes of false identifications made by gas chromatography/mass spectrometry.     

Assessment of β-lactams retention in hydrophilic interaction chromatography applying Box-Behnken design

In this paper, the retention prediction models for mixture of β-lactam antibiotics analyzed by hydrophilic interaction chromatography (HILIC) are presented. The aim of the study was to investigate the retention behavior of some organic acids and amphoteric compounds including cephalosporins (cefotaxime, cefalexin, cefaclor, cefuroxime, and cefuroxime axetil) and penicillins (ampicillin and amoxicillin). Retention of substances with acidic functional group in HILIC is considered to be interesting since the majority of publications in literature are related to basic compounds. In the beginning of the study, classical silica columns were chosen for the retention analysis. Then, preliminary study was done and factors with the most significant influence on the retention factors were selected. These factors with the impact on the retention factors were investigated employing Box-Behnken design as a tool. On the basis of the obtained results the mathematical models were created and tested using ANOVA test and finally verified. This approach enables the presentation of chromatographic retention in many ways (three-dimensional (3-D) graphs and simple two-dimensional graphical presentations). All of these gave the possibility to predict the chromatographic retention under different conditions. Furthermore, regarding the structure of the analyzed compounds, the potential retention mechanisms in HILIC were suggested.     

Stereoelectrochemistry of calixarenes – Molecules with multiple redox centers

1. Download : Download high-res image (131KB)
2. Download : Download full-size image

     

Transition metal-catalyzed addition of C-, N- and O-nucleophiles to unactivated C–C multiple bonds

1. Download : Download full-size image

     

Multicomponent analyses of chiral samples by use of regression analysis of UV–visible spectra of cyclodextrin guest–host complexes

We report the first combined use of analytical spectroscopy, guest–host chemistry, and multivariate regression analysis for determination of enantiometric composition of multicomponent samples of chiral analytes. Sample solutions containing multicomponent analytes of ephedrine, tryptophan, propranolol, and proline of varying enantiomeric composition with beta-cyclodextrin (BCD) or methyl-beta-cyclodextrin (Me-BCD) as chiral host molecules were investigated using ultraviolet (UV)–visible spectroscopy. The interactions of enantiomers of chiral analytes with chiral hosts resulted in the formation of transient diastereomeric inclusion complexes with varying spectral properties. Multivariate analysis using partial-least-square (PLS) regression was used to correlate subtle changes in the UV–visible spectra of the guest–host complexes with the enantiomeric composition of the calibration samples. These PLS regressions were carefully optimized and then used to predict the enantiomeric composition of multicomponent chiral analytes of validation samples. The results of these validation studies demonstrate the predictive ability of the regression models for determination of future enantiomeric composition of samples. The accuracy of the models to correctly predict the enantiomeric composition of samples, evaluated by use of the root mean square percent relative error (RMS%RE) was analyte and chiral host dependent. In general, better prediction of enantiomeric composition of samples and low RMS%RE values were obtained when Me-BCD was used as the chiral host. The analyses procedure reported here is simple, rapid, and inexpensive. In addition, this approach does not require prior separation of chiral analytes, thus reducing analysis time and eliminating the need for expensive chiral columns.     

Spectrophotometric simultaneous determination of ceratine, creatinine, and uric acid in real samples by orthogonal signal correction–partial least squares regression

Abstract  This work describes a quantitative spectroscopic method for the analysis of ternary mixtures of ceratine (CER), creatinine (CRE), and uric acid (UA) using multivariate data models based upon ultraviolet spectroscopy. By multivariate calibration methods, such as partial least squares regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. In this study, the calibration model is based on absorption spectra in the 200–260 nm range for 36 different mixtures of CER, CRE, and UA. The unrelated information was removed by the orthogonal signal correction (OSC) method and the results were proved. Evaluation of the prediction errors for the prediction set reveals the OSC-treated data give substantially lower root mean square error of prediction (RMSEP) values than original data. The RMSEP for CER, CRE, and UA with OSC were 1.1686, 0.2195, and 0.3726, and without OSC were 1.9057, 0.3482, and 0.6164, respectively. This procedure allows the simultaneous determination of CER, CRE, and UA in synthetic and real samples. Graphical abstract        

Polarographic immunoassay coupled with catalysis of non-radioactive multiple iodine label

A now polarographic immunoassay was developed In this assay,human serum albumin (HSA) as the model antigen was covalently labeled with organic compound erythrosin B(EB) containing four non-radioactive iodides through Ⅰ step chemical reaction The labeling procedure is simple and the conditions needed are moderate.The molar labeling ratio of KB HSA was 12 Ⅰ The content of iodine in the conjugate obtained by the proposed procedure is ninth higher than that by the other existing methods.A heterogeneous competitive immunoassay was established by compling the catalysis of the conjugate to substrate As(Ⅲ)-Ce(Ⅳ) reaction with the linear-sweep polarographic detec-tion of As(Ⅲ) amount HSA can be determined in the HSA concentration range from 1 to 200μg/mL,with the de-tection hum of 0 66μg/ml.