Haixia Zhang

Prediction of pKa for Neutral and Basic Drugs Based on Radial Basis Function Neural Networks and the Heuristic Method

PurposesQuantitative structure–property relationships (QSPR) were developed to predict the pKa values of a set of neutral and basic drugs via linear and nonlinear methods. The ability of the models to predict pKa was assessed and compared.MethodsThe descriptors of 74 neutral and basic drugs in this study were calculated by the software CODESSA, which can calculate constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. Linear and nonlinear QSPR models were developed based on the heuristic method (HM) and radial basis function neural networks (RBFNN), respectively. The heuristic method was also used for the preselection of appropriate molecular descriptors.ResultsThe obtained linear model had a correlation coefficient of r = 0.884, F = 37.72 with a root-mean-squared (RMS) error of 0.482 for the training set, and r = 0.693, F = 11.99, and RMS = 0.987 for the test set. The RMS in predicting the overall data set is 0.619. The nonlinear model gave better results; for the training set, r = 0.886, F = 202.314, and RMS = 0.458, and for the test set r = 0.737, F = 15.41, and RMS = 0.613. The RMS error in prediction for overall data set is 0.493. Prediction results from nonlinear model are in good agreement with experimental values.ConclusionsIn present study, we developed a QSPR model to predict the important parameter (pKa) of neutral and basic drugs. The model is useful in predicting pKa during the discovery of new drugs when experimental data are unknown.

Molecularly imprinted matrix solid-phase dispersion for extraction of chloramphenicol in fish tissues coupled with high-performance liquid chromatography determination

The synthesis and evaluation of a molecularly imprinted polymer (MIP) as a selective matrix solid-phase dispersion (MSPD) sorbent, coupled with high-performance liquid chromatography for the efficient determination of chloramphenicol (CAP) in fish tissues are studied. The polymer was prepared using CAP as the template molecule, vinylpyridine as the functional monomer and ethylene glycol dimethacrylate as the cross-linking monomer, and sodium dodecyl sulfate as the surfactant in the presence of water as a solvent by miniemulsion polymerization. The CAP-imprinted polymers and nonimprinted polymers (NIPs) were characterized by Fourier transform IR spectroscopy, scanning electron microscopy, and static adsorption experiments. The CAP-imprinted material prepared showed high adsorption capacity, significant selectivity, and good site accessibility. The maximum static adsorption capacity of the CAP-imprinted and the NIP material for CAP was 78.4 and 59.9xa0mg g-1, respectively. The relative selectivity factors of this CAP-imprinted material were larger than 1.9. Several parameters influencing the MSPD process were optimized. Finally, the CAP-imprinted polymers were used as the sorbent in MSPD to determine CAP in three kinds of fishes and resulted in satisfactory recovery in the range 89.8–101.43%. CAP-imprinted polymer as a sorbent in MSPD is better than C18 and attapulgite in terms of both recovery and percent relative standard deviation. The baseline noise was measured from a chromatogram of a blank fish sample which was treated after the MSPD procedure using CAP-imprinted polymer as a sorbent. Signal values of 3 times the noise (signal-to-noise ratio of 3) and 10 times the noise (signal-to-noise ratio of 10) were used to calculate the limit of detection and the limit of quantitation of the calibration curve. The limit of detection for CAP was 1.2xa0ng g-1 and the limit of quantitation was 3.9xa0ng g-1.

Elimination of matrix effects in the determination of bisphenol A in milk by solid-phase microextraction–high-performance liquid chromatography

Solid-phase microextraction coupled to high-performance liquid chromatography (SPME–HPLC) with fluorescence detection was employed to determine bisphenol A (BPA) in milk samples. The potential influence of the milk matrix on the determination of BPA by SPME–HPLC were investigated. Optimal conditions to eliminate any matrix effects were as follows: milk samples were deproteinized with trichloroacetic acid, diluted 20-fold with BPA-free Ultrapure water, dissolved in methanol, the precipitated protein was filtered out, rinsed with methanol and evaporated to remove the methanol. Then, a 40.0-ml solution was used for SPME extraction and HPLC analysis. Satisfactory recoveries (milk: 93.1–101%; soybean milk: 93.9–102%) were achieved. The proposed method was successfully applied to real samples, BPA being detected within the range 1.6–2.6 ng ml−1 in four brands of commercial milk but not in soybean milk.

Analysis of sulfamerazine in pond water and several fishes by high-performance liquid chromatography using molecularly imprinted solid-phase extraction

The synthesis and evaluation of a molecularly imprinted polymer (MIP) used as a selective solid-phase extraction sorbent and coupled to high-performance liquid chromatography (HPLC) for the efficient determination of sulfamerazine (SMR) in pond water and three fishes are reported. The polymer was prepared using SMR as the template molecule, methacrylic acid as the functional monomer and ethylene glycol dimethacrylate as the crosslinking monomer in the presence of tetrahydrofuran as the solvent. The SMR-imprinted polymers and nonimprinted polymers were characterized by FT-IR and static adsorption experiments. The prepared SMR-imprinted material showed a high adsorption capacity, significant selectivity and good site accessibility. The maximum static adsorption capacities of the SMR-imprinted and nonimprinted materials for SMR were 108.8 and 79.6xa0mg g−1, respectively. The relative selectivity factor of this SMR-imprinted material was 1.6. Several parameters influencing the solid-phase extraction process were optimized. Finally, the SMR-imprinted polymers were used as the sorbent in solid-phase extraction to determine SMR in pond water and three fishes with satisfactory recovery. The average recoveries of the MIP-SPE method were 94.0% in ultrapure water and 95.8% in pond water. Relative standard deviations ranging from 0.3% to 5.2% in MIP were acquired. The results for the SMR concentrations in crucian, carp and wuchang fish were 66.0, 127.1 and 51.5xa0ng g−1, respectively. The RSDs (nu2009=u20095) were 3.51%, 0.53% and 5.08%, respectively. The limit of detection (LOD) for SMR was 1xa0ng g−1 and the limit of quantitation (LOQ) was 3.5xa0ng g−1.

Prediction of milk/plasma drug concentration (M/P) ratio using support vector machine (SVM) method.

PurposeDevelopment of reliable computational models to predict/classify milk-to-plasma (M/P) drug concentration ratio remains a challenging object. Support vector machine (SVM) method, as a new algorithm, was constructed to distinguish the potential risk of drugs to nursing infants.MethodsEach drug was represented by a large pool of descriptors, of which five were found to be most important for constructing the predictive models. Next, two classification models, linear discriminant analysis (LDA) and SVM, were developed with bootstrapping validation based on the selected molecular descriptors.Results and ConclusionsThe classification accuracy of training set and test set for SVM was 90.63 and 90.00%, respectively. The total accuracy for SVM was 90.48%, which was higher than that of LDA (77.78%). Comparison of the two methods shows that the performance of SVM was better than that of LDA, which implies that the SVM method is an effective tool in evaluating the risk of drugs when experimental M/P ratios have not been investigated.

Accurate quantitative structure–property relationship model of mobilities of peptides in capillary zone electrophoresis

The aim of this work was to predict electrophoretic mobilities of peptides in capillary zone electrophoresis (CZE) using the linear heuristic method (HM) and a nonlinear radial basis function neural network (RBFNN). Two data sets, consisting of 125 peptides ranging in size between 2 and 14 amino acids and 58 peptides ranging in size between 2 and 39 amino acids, are researched to test applicability of the QSPR methods. In this study, the root mean squared (RMS) errors of the training set, the test set and the whole set of data set 1 are 1.3766, 1.5608 and 1.4157 and the correlation coefficients (R2) are 0.9740, 0.9671 and 0.9724 predicted by RBFNN, respectively. While the RMS errors of the training set, the test set and the whole set of data set 2 are 0.6279, 0.8145 and 0.6673 and the correlation coefficients (R2) are 0.9773, 0.9489 and 0.9732, respectively. So the Offord charge-over-mass term (Q/M(2/3)) combined with descriptors calculated by CODESSA represents the structural features of the peptides appropriately. The electrophoretic mobilities of peptides can be accurately predicted by the linear and nonlinear model. Furthermore, the results of nonlinear model are closer to the experimental data than those of linear model.

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as logu2009(RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure–activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 logu2009(RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

Analysis of Insulin by High Performance Liquid Chromatographic Method with Precolumn Derivatization with 4‐Chloro‐7‐Nitrobenzo‐2‐Oxa‐1,3‐Diazole

Abstract A high performance liquid chromatographic method (HPLC) with precolumn derivatization and fluorescence detection for insulin was developed and applied for the quantification of insulin in spiked serum. To covalence couple with insulin, 4‐chloro‐7‐nitrobenzo‐2‐oxa‐1,3‐diazole (NBD‐Cl) was selected as fluorescent reagent. The optimal derivatization conditions were as follows: temperature 50; time 2 h, in the dark; 0.1 M phosphate buffer (pH 9.0). Analytical separation was carried out on a C18 column and the mobile phase including acetonitrile‐water containing 0.1% trifluoroacetic acid (TFA) (v/v∶ 30/70). The excitation/emission wavelengths were 470/540 nm. Under the conditions, the retention time and capacity factor of the adduct of insulin‐NBD were 10.03 min (flow rate 1 mL/min) and 3, respectively. The recovery of insulin in serum was 95.06% and the detection limit was 90 nM. In the investigated concentration ranges (0.46 µM∼16.10 µM), R2 was 0.9934, which indicated the potential for the application of NBD‐Cl derivatization to the analysis of insulin in the biological matrices, although with the shortcoming of long analytical time.

DEVELOPMENT OF HPLC METHOD FOR THE DETERMINATION OF GASTRODIN AND PARA-HYDROXYBENZYL ALCOHOL IN TALL GASTRODIA TUBER ON THE POLYGLYCOL-C8 COLUMN

Compared with the C8 and C18 reversed phases the polyglycol-C8 bonded phase synthesized in this laboratory exhibits strong retention and different selectivity to hydrophilic solutes. Their characteristic advantages were used to develop a HPLC method for the analysis of the active components, gastrodin and p-hydroxybenzyl alcohol, in tall gastrodia tuber. The chromatographic conditions were optimized by means of computer-assisted method development technique. DryLab software was used to model the retention behavior of the compounds as a function of gradient conditions, based on the data from two scouting gradient runs. Under the optimized conditions: column, polyglycol-C8, 5 μm, 25 × 0.46-cm; solvent A, 25 mM phosphate buffer, pH 2.7/ methanol, 95/ 5 (V/ V); solvent B, methanol; gradient, 0/ 0/ 70% B at 0/ 10/ 40 min; flow rate, 0.5 mL/ min; temperature, ambient, the quality of tall gastrodia tuber from different sources were evaluated. Meanwhile, the three methods for sample preparation were discussed. When the sample is prepared by refluxing in water, the analytical results reflect the sum of gastrodin and p-hydroxybenzyl alcohol existing in the free form and produced from the hydrolysis of gastrodioside.

Analysis of vicine in bitter melon with high performance liquid chromatography

Abstract Vicine in leaves, seeds and fruits of bitter melon were determined with HPLC for the first time. Vicine was extracted with water by ultrasonic, and separated with other substances with C18 reverse phase column. The mobile phase was methanol/0.025 mol/L phosphate buffer (pH 3.0)(10/90, v/v). The effects of the content of methanol and pH on the analysis of vicine were discussed. The recovery of vicine in each sample was between 93% and 105%. The content of vicine in seeds, leaves, and fruits of bitter melon were 0.524%, 0.0456%, 0.115%, respectively.