Mancang Liu

Lanzhou University, Kao-lan-hsien, Gansu Sheng, China

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Publications (78)218.93 Total impact

  • Yueying Ren · Jin Qin · Huanxiang Liu · Xiaojun Yao · Mancang Liu ·
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    ABSTRACT: A Quantitative Structure–Property Relationship (QSPR) study was carried out to model the melting points for a diverse set of 288 potential Ionic Liquids (ILs) including pyridinium bromides, imidazolium bromides, benzimidazolium bromides, and 1-substituted 4-amino-1,2,4-triazolium bromides. Based on the calculated descriptors by CODESSA program, a Principal Component Analysis (PCA) was performed on the whole data to detect the homogeneities in the dataset and to assist the separation of the data into representative training and test sets. Heuristic Method (HM) and Projection Pursuit Regression (PPR) were used to develop linear and nonlinear models between the descriptors and the melting points. The PPR model gave a high predictive correlation coefficient (R2) of 0.810 and an Average of Absolute Relative Deviation (AARD) of 17.75%, which are better than those by HM model (R2=0.712, AARD=24.33%) indicating that PPR is better for the prediction of the melting points. In addition, the descriptors selected by HM can give some insight into factors that can affect the melting points, i.e., benzene ring structure, rotatable bonds, branching, symmetry, and intramolecular electronic effects. This information would be very useful in the design of the potential ILs with desired melting points.
    QSAR & Combinatorial Science 12/2009; 28(11‐12):1237 - 1244. DOI:10.1002/qsar.200710073 · 1.55 Impact Factor
  • Jin Qin · Huanxiang Liu · Jiazhong Li · Yueying Ren · Xiaojun Yao · Mancang Liu ·
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    ABSTRACT: Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 68 inhibitors of AP-1 and NF-kappaB mediated transcriptional activations. The CoMFA model produced statistically significant results with the cross-validated q(2) of 0.594 and the conventional correlation coefficient r(2) of 0.968. The best CoMSIA model was obtained by the combination use of steric, electrostatic, hydrogen-bond donor and acceptor fields. The corresponding q(2) and r(2) of CoMSIA model were 0.703 and 0.932, respectively. From the cross-validated results, it can be seen that the CoMSIA model has a better predictive ability than CoMFA model due to the importance of the hydrogen bonds for the activity of these inhibitors. The predictive abilities of the two models were further validated by a test set of 15 compounds. The models gave predicted correlation coefficient r(pred)(2) of 0.891 for CoMFA model and 0.810 for CoMSIA model. Based on the above results, we identified the key structural features that may help to design potent inhibitors with improved activities: (1) the NH linker at the position R(4) acts as important hydrogen-bond donor and any group on phenyl or 2-thienyl ring of R(1) substituent decreases inhibitory activity, (2)further structural modification of compound 50 on the phenyl ring of the quinazoline ring considering steric, electrostatic and hydrogen-bond acceptor properties will influence the inhibitory activity.
    European Journal of Medicinal Chemistry 07/2009; 44(7):2888-95. DOI:10.1016/j.ejmech.2008.12.006 · 3.45 Impact Factor
  • Hongying Du · Jie Wang · Zhide Hu · Mancang Liu · Xiaojun Yao ·
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    ABSTRACT: Quantitative structure–activity relationship (QSAR) models were successfully developed for predicting the relative sensitivities odor detection thresholds (ODTs) and nasal pungency thresholds (NPTs) for the olfaction and nasal trigeminal chemosensory systems of a set of volatile organic compounds (VOCs). The best multi-linear regression (BMLR) method was used to select the most important molecular descriptors and build a linear regression model. The methods support vector machine (SVM) and local lazy regression (LLR) were also used to build regression models. By comparing the results of these methods for the test set of ODTs and NPTs, the LLR model gave better results for the VOCs with the coefficient of determination R2 (0.9171, 0.9609, respectively) and root mean square error (RMSE) (0.3861, 0.2152, respectively). At the same time, this study identified some important structural information which was strongly correlated to the relative sensitivities of these VOCs. Such information can be used to select and manufacture chemical sensors. As it could predict accurately the relative sensitivities of the olfaction and nasal chemesthesis, the LLR method is a promising approach for QSAR modeling, and it also could be used to model the other similar chemical sensors.
    Sensors and Actuators B Chemical 04/2009; 138(1-138):55-63. DOI:10.1016/j.snb.2009.02.042 · 4.10 Impact Factor
  • Jiazhong Li · Juan Du · Lili Xi · Huanxiang Liu · Xiaojun Yao · Mancang Liu ·
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    ABSTRACT: In this study, the quantitative structure-activity relationship (QSAR) of a series of 2-aminothiazole based Lck inhibitors was investigated. The key structural features responsible for the inhibition activities were discussed in detail. A population of 100 rigorously validated linear QSAR models were established based on the descriptors calculated in DRAGON program and selected by genetic algorithm (GA). A total of 36 descriptors were involved in all the QSAR models. Then the common descriptors appeared in all the models were extracted to build the final QSAR model. As a result, the final 8-parameter QSAR model was established. After analysis of the eight descriptors, some advice was proposed to help the design of possible novel inhibitors with higher bioactivity.
    Analytica chimica acta 02/2009; 631(1):29-39. DOI:10.1016/j.aca.2008.10.026 · 4.51 Impact Factor
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    ABSTRACT: In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model.
    Journal of Computational Chemistry 01/2009; 31(5):973-85. DOI:10.1002/jcc.21383 · 3.59 Impact Factor
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    ABSTRACT: Quantitative structure-activity relationship (QSAR) of a series of structural diverse malonyl-CoA decarboxylase (MCD) inhibitors have been investigated by using the predictive single model as well as the consensus analysis based on a new strategy proposed by us. Self-organizing map (SOM) neural network was employed to divide the whole data set into representative training set and test set. Then a multiple linear regressions (MLR) model population was built based on the theoretical molecular descriptors selected by Genetic Algorithm using the training set. In order to analyze the diversity of these models, multidimensional scaling (MDS) was employed to explore the model space based on the Hamming distance matrix calculated from each two models. In this space, Q(2) (cross-validated R(2)) guided model selection (QGMS) strategy was performed to select submodels. Then consensus modeling was built by two strategies, average consensus model (ACM) and weighted consensus model (WCM), where each submodel had a different weight according to the contribution of model expressed by MLR regression coefficients. The obtained results prove that QGMS is a reliable and practical method to guide the submodel selection in consensus modeling building and our weighted consensus model (WCM) strategy is superior to the simple ACM.
    Journal of Computational Chemistry 12/2008; 29(16):2636-47. DOI:10.1002/jcc.21002 · 3.59 Impact Factor
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    Xiaoyan Liu · Xiaoyun Zhang · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: A sensitive method for the analysis of bisphenol A and 4-nonylphenol is developed by means of the optimization of solid-phase microextraction using Uniform Experimental Design methodology followed by high-performance liquid chromatographic analysis with fluorescence detection. The optimal extraction conditions are determined based on the relationship between parameters and the peak area. The curve calibration plots are linear (r2>or=0.9980) over the concentration range of 1.25-125 ng/mL for bisphenol A and 2.59-202.96 ng/mL for 4-nonylphenol, respectively. The detection limits, based on a signal-to-noise ratio of 3, are 0.097 ng/mL for bisphenol A and 0.27 ng/mL for 4-nonylphenol, respectively. The validity of the proposed method is demonstrated by the analysis of the investigated analytes in real water samples and sensitivity of the optimized method is verified by comparing results with those obtained by previous methods using the same commercial solid-phase microextraction fiber.
    Journal of chromatographic science 09/2008; 46(7):596-600. DOI:10.1093/chromsci/46.7.596 · 1.36 Impact Factor
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    Xiaoyan Liu · Yongsheng Ji · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: 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.
    Food Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment 07/2008; 25(6):772-8. DOI:10.1080/02652030701713921 · 1.80 Impact Factor
  • Xiaoman Jiang · Chuande Zhao · Na Jiang · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: A simple imprinted amino-functionalized silica gel material was synthesized by combining a surface molecular imprinting technique with a sol-gel process for solid-phase extraction-high performance liquid chromatography (SPE-HPLC) determination of diethylstilbestrol (DES). Activated silica gel was used as the supporter and non-imprinted silica sorbent was synthesized without the addition of DES using the same procedure as that of DES-imprinted silica sorbent. Compared with non-imprinted polymer particles, the prepared DES-imprinted silica sorbent showed high adsorption capacity, significant selectivity, good site accessibility and fast binding kinetics for DES. The maximum static adsorption capacity of the DES-imprinted and non-imprinted silica sorbent for DES was 62.58mgg(-1) and 19.89mgg(-1), respectively. The relatively selective factor value of this DES-imprinted silica sorbent was 61.7 at the level of 50mgL(-1). And the uptake kinetics was fairly rapid so that the adsorbent equilibrium was achieved within 10min. Furthermore, the DES-imprinted polymers were used as the sorbent in solid-phase extraction to determine DES in fish samples. The MIP-SPE-HPLC method showed higher selectivity and good recoveries higher than 87.5% (R.S.D. 11.6%). Copyright © 2007 Elsevier Ltd. All rights reserved.
    Food Chemistry 06/2008; 108(3-108):1061-1067. DOI:10.1016/j.foodchem.2007.11.039 · 3.39 Impact Factor
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    Huayu Huang · Cailing Yang · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: Octyl (C8) or octadecyl (C18)-modified mesoporous SBA-15 silica molecular sieves have been prepared by adding SBA-15 silica molecular sieves to octyltrimethoxysilane or octadecyltrimethoxysilane in toluene at 100 °C, and characterized by Fourier transform infrared (FTIR) spectroscopy, powder X-ray diffraction (XRD), nitrogen adsorption–desorption measurements, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). FTIR spectra shows the presence of methylene (–CH2–) and methyl (–CH3) bands on the modified SBA-15. Powder XRD data indicate the structure of modified SBA-15 with octyl or octadecyl groups still remains two-dimensional hexagonal mesostructrure. Brunauer–Emmett–Teller (BET) surface area analysis presents that surface area of octyl- and octadeyl-SBA-15 changed from 647 to 449 and 321 m2 g−1, respectively, and SEM images show the decreased size of modified SBA-15 particles. TEM images of modified materials with alkyl groups show the structures remain the same as the parent SBA-15 silica. We also have studied the adsorption capacity of the materials to phthalate esters (dimethyl and diethyl phthalate) by dynamic adsorption experiments on high performance liquid chromatography (HPLC). It is found that the modified materials can increase the adsorption of phthalate esters compared to SBA-15 particles, and the adsorption capacity increased with the increased length of alkyl chain on SBA-15. The maximum dynamic adsorption capacity for diethyl phthalate was 3.9 (C8-SBA-15) or 4.3 (C18-SBA-15) times higher than that of SBA-15 particles, respectively. The results indicate that alkyl SBA-15 particles could be used for enrichment of phthalate esters in water samples before the further analysis.
    Microporous and Mesoporous Materials 04/2008; 111(1-3):254-259. DOI:10.1016/j.micromeso.2007.07.038 · 3.45 Impact Factor
  • Jiazhong Li · Jin Qin · Huanxiang Liu · Xiaojun Yao · Mancang Liu · Zhide Hu ·
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    ABSTRACT: A predictive nonlinear model for the inhibition activities for a set of pyrazine–pyridine biheteroaryls, inhibitors of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) was developed based on Least Squares Support Vector Machines (LS-SVMs) using molecular descriptors calculated from the molecular structure alone as inputs. Each compound was described by the structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. Five relevant descriptors selected by heuristic method were used to build linear and nonlinear Quantitative Structure–Activity Relationship (QSAR) models using Multiple Linear Regression (MLR) and LS-SVMs. Better results were obtained by the nonlinear LS-SVMs model which gave the correlation coefficients of 0.921 and the MSE of 0.046 for the training set. The corresponding correlation coefficient and MSE for the test set are 0.877 and 0.041, respectively. The good performance of LS-SVMs proved this method to be a reliable and promising tool in QSAR analysis and computer aided molecular design. The models developed can be used for further screening of potential VEGFR-2 inhibitors.
    QSAR & Combinatorial Science 02/2008; 27(2):157 - 164. DOI:10.1002/qsar.200630154 · 1.55 Impact Factor
  • Xiuyong Li · Feng Luan · Hongzong Si · Zhide Hu · Mancang Liu ·
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    ABSTRACT: Quantitative structure-retention relationship (QSRR) studies were performed for predicting the retention times (RTs) of 110 kinds of pesticides or toxicants. Chemical descriptors were calculated from the molecular structure of the compounds alone. The QSRR models were built using the heuristic method (HM) and support vector machine (SVM), respectively. The obtained linear model of HM had a square of a correlation coefficient: R(2)=0.913, F=116.70 with a root mean square error (RMS) error of 0.0387 for the training set, while R(2)=0.907, F=195.49, and RMS=0.0408 for the test set. The non-linear model by SVM gave better results: for the training set R(2)=0.966, F=2420.5, RMS=0.0231 and for the test set R(2)=0.944, F=339.7, RMS=0.0313. The prediction results are in good agreement with the experimental values. And the proposed model could identify and provide some insight into what structural features are related to retention time of these compounds.
    Toxicology Letters 01/2008; 175(1-3):136-44. DOI:10.1016/j.toxlet.2007.10.005 · 3.26 Impact Factor
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    ABSTRACT: A microemulsion electrokinetic chromatography method was used to separate arctiin and arctigenin in Fructus Arctii and its herbal preparations. The separation of arctiin and arctigenin was performed using a 1-butanol-SDS-ethyl acetate-water microemulsion in 10mM sodium tetraborate buffer. The analytes were baseline-resolved within 4 min. In the concentration range 5-500 microg/mL, the calibration curves reveal linear relationships between the peak area for each analyte and its concentration (correlation coefficients: 0.9993 for arctiin and 0.9998 for arctigenin). The method was applied to the analysis of arctiin and arctigenin in herbal preparations, and the recoveries were 98.7-103.1% for arctiin and 97.6-103.2% for arctigenin, respectively.
    Journal of Chromatography B 01/2008; 860(1):127-33. DOI:10.1016/j.jchromb.2007.10.017 · 2.73 Impact Factor
  • Cailing Yang · Xiaoman Jiang · Linyuan Guo · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: A simple high-performance liquid chromatographic method with pre-column derivatization and fluorescence detection was developed and used for the analysis of free amino acids in islets of Langerhans; 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole (NBD-Cl) served as pre-column derivatization reagent. Islets of Langerhans were separated from the pancreas of normal and obese rats, treated with pre-cooling methanol-water (80:20, v/v), and ultrasonicated to fragmentize the islets and effect deproteination. Several parameters influencing the derivatization reaction and chromatographic separation were optimized. Amino acid derivatives obtained under optimal conditions were separated on a C18 column with acetonitrile-acetate buffer as mobile phase and detected at 470 nm/540 nm (Ex/Em). Matrix effects were investigated and good linearities with correlation coefficients better than 0.9972 were obtained over a wide range of 0.42-42.11 microM for most of the amino acids. The detection limits (S/N = 3) were within the range of 6.1-51 nM. The precision of the method and recoveries were in the ranges of 1.43-10.76% (RSD%) and 85.07-108.82%, respectively. The analytical results showed that the serine content was markedly higher in normal rats than in obese rats, whereas methionine was of relatively lower content in both normal and obese rats.
    Journal of Separation Science 12/2007; 30(18):3154-63. DOI:10.1002/jssc.200700160 · 2.74 Impact Factor
  • Yueying Ren · Huanxiang Liu · Xiaojun Yao · Mancang Liu · Botao Fan ·
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    ABSTRACT: A quantitative structure–property relationship (QSPR) study was performed on 42 homogeneous thermotropic liquid crystals (TLCs) to predict their nematic transition temperatures (TN) using molecular descriptors calculated by CODESSA software. A heuristic method (HM) was applied to select descriptors and generate models. The selected five descriptors reflect the constitutional, steric and electronic characteristics that affect the nematic transition phenomena. The high correlation coefficient, R , of 0.9881 for the training set indicates that a linear relationship exists between the structural information and TN in TLCs. Satisfactory results were obtained. For the test set, this model gave an R of 0.96, a root mean squared error of 6.3654 and an absolute average relative deviation of 9.2017%. The results provide a simple, practical and effective method for the prediction of TN of TLCs and the approach can be extended to other QSPR investigations.
    Liquid Crystals 11/2007; 34(11):1291-1297. DOI:10.1080/02678290701663712 · 2.49 Impact Factor
  • Wenjuan Lü · Yonglei Chen · Mancang Liu · Xingguo Chen · Zhide Hu ·
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    ABSTRACT: The logarithmic n-octanol/water partition coefficient (logK(ow)) is a very important property which concerns water-solubility, bioconcentration factor, toxicity and soil absorption coefficient of organic compounds. Quantitative structure-property relationship (QSPR) model for logK(ow) of 133 polychlorinated biphenyls (PCBs) is analyzed using heuristic method (HM) implemented in CODESSA. In order to indicate the influence of different molecular descriptors on logK(ow) values and well understand the important structural factors affecting the experimental values, three multivariable linear models derived from three groups of different molecular descriptors were built. Moreover, each molecular descriptor in these models was discussed to well understand the relationship between molecular structures and their logK(ow) values. The proposed models gave the following results: the square of correlation coefficient, R(2), for the models with one, two and three molecular descriptors was 0.8854, 0.9239 and 0.9285, respectively.
    Chemosphere 10/2007; 69(3):469-78. DOI:10.1016/j.chemosphere.2007.04.044 · 3.34 Impact Factor
  • Xiaoman Jiang · Na Jiang · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: Molecular imprinting is a technique for preparing polymeric materials that are capable of recognizing and binding the desired molecular target with a high affinity and selectivity. The materials can be applied to a wide range of target molecules, even those for which no natural binder exists or whose antibodies are difficult to raise. The imprinting of small organic molecules (e.g., pharmaceuticals, pesticides, amino acids, steroids, and sugars) is now almost routine. In this review, we pay special attention to the synthesis and application of molecular imprinted polymer (MIPs) imprinted with small organic molecules, including herbicides, pesticides, and drugs. The advantages, applications, and recent developments in small organic molecular imprinted technology are highlighted.
    Analytical and Bioanalytical Chemistry 10/2007; 389(2):355-68. DOI:10.1007/s00216-007-1336-6 · 3.44 Impact Factor
  • Xiaoyan Liu · Yongsheng Ji · Yonghui Zhang · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: A simple and environmentally friendly method for determination of seven phenols using solid-phase microextraction (SPME) coupled to high-performance liquid chromatography (HPLC) has been developed. Several materials were used as stationary phase of SPME fibers and an oxidized multiwalled carbon nanotubes material was found to be effective in carrying out simultaneous extraction of phenols in aqueous samples. Compared with the widely used commercially available SPME fibers, this proposed fiber had much lower cost, longer lifetime (over 150 times), shorter analysis time (30 min of extraction and 3 min of desorption time) and comparable or superior extraction efficiency for the investigated analytes. The extraction and desorption conditions were evaluated and the calibration curves of seven phenols were linear (R(2)> or =0.9908) in the range from 10.2 to 1585 ng mL(-1). The limits of detection at a signal-to-noise (S/N) ratio of 3 were 0.25-3.67 ng mL(-1), and the limits of quantification calculated at S/N=10 were 0.83-12.25 ng mL(-1) for these compounds. The possibility of applying the proposed method to environmental water samples analysis was validated.
    Journal of Chromatography A 09/2007; 1165(1-2):10-7. DOI:10.1016/j.chroma.2007.07.057 · 4.17 Impact Factor
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    Cailing Yang · Linyuan Guo · Xiaoyan Liu · Haixia Zhang · Mancang Liu ·
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    ABSTRACT: Tetrandrine (TET) and fangchinoline (FAN) are basic and highly hydrophobic drugs with logP>5.7. In this work, a simple, inexpensive and efficient liquid-phase microextraction (LPME) technology combined with high-performance liquid chromatography (HPLC) was developed for the simultaneous analysis of tetrandrine and fangchinoline in plasma samples. Tetrahydropalmatine was used as internal standard. Several parameters influencing the efficiency of LPME were investigated and optimized including organic solvent, stirring rate, extraction time, salt concentration, organic modifier and pH. Under the optimal conditions, extraction recoveries from plasma samples were 46% for tetrandrine and 50% for fangchinoline, corresponding to the drugs enriched by a factor of 23 and 25 by LPME, respectively. Excellent sample clean-up was observed and good linearities with correlation coefficients (r) of 0.9979 (FAN) and 0.9995 (TET) were obtained in the range of 15-1000 ngmL(-1). The limits of detection (LOD, S/N=3) were 3.0 ngmL(-1) for FAN and 2.0 ngmL(-1) for TET.
    Journal of Chromatography A 09/2007; 1164(1-2):56-64. DOI:10.1016/j.chroma.2007.07.019 · 4.17 Impact Factor
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    ABSTRACT: The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure-activity relationship (QSAR) models for predicting the binding affinity of 152 nonapeptides, which can bind to class I MHC HLA-A*201 molecule. Each peptide was represented by a large pool of descriptors including constitutional, topological descriptors and physical-chemical properties. The heuristic method (HM) was then used to search the descriptor space for selecting the proper ones responsible for binding affinity. The four descriptors were obtained to build linear models based on HM and nonlinear models based on SVM method. The best results are found using SVM: root mean-square (RMS) errors for training, test and whole data set were 0.383, 0.385 and 0.384, respectively. This paper allow the prediction of the binding affinity of new, untested peptides and, through the analysis of contribution of each parameter of different residue at specific position of peptidic ligands, to understand nature of the forces governing binding behavior and suggest new ideas for further synthesis of high-affinity peptides.
    Journal of Molecular Graphics and Modelling 08/2007; 26(1):246-54. DOI:10.1016/j.jmgm.2006.12.002 · 1.72 Impact Factor