Simultaneous extraction and analysis of clozapine and lorazepam from human plasma using dual solvent-stir bar microextraction with different acceptor phases followed by high-performance liquid chromatography ultra-violet detection.

A new design of dual solvent stir bar microextraction (DSSBME) was developed and combined with HPLC-UV for the simultaneous extraction of clozapine (CLZ) and lorazepam (LRP) from human plasma with different acceptor phases. Two short hollow fibers immobilized with an organic extraction solvent were used as the solvent bars for microextraction of CLZ and LRP from the sample solution. The solvent bars were fixed with a staple pin which served as the stirrer. The target analytes were simultaneously and selectively extracted from the sample solution into their corresponding solvent bar. Extraction parameters such as organic solvent type, pH of the sample solution, the acceptor phase concentration, salt incorporation into the solution, stirring rate, and extraction time were optimized to achieve the best extraction results. Under the optimum conditions (1-undecanol as extraction solvent, pH of sample solution = 9.0, 10% w/v NaCl, concentration of HCl = 10 mM, concentration of NaOH = 100 mM, stirring rate of 1400 rpm and extraction time of 30 min at ambient temperature) the limit of detection for CLZ was 0.4 ng mL-1 and for LRP it was 1.1 ng mL-1. The linear range for CLZ was 1.3-1000.0 ng mL-1 (R2 = 0.9991) and for LRP it was 3.6-800.0 ng mL-1 (R2 = 0.9993). Extraction recovery and the enrichment factor for CLZ were 95.4% and 343 and for LRP they were 74.3% and 263, respectively. Finally, the method developed was successfully applied for the simultaneous determination of CLZ and LRP in human plasma samples.

Extraction and determination of flavonoids in fruit juices and vegetables using Fe3 O4 /SiO2 magnetic nanoparticles modified with mixed hemi/ad-micelle cetyltrimethylammonium bromide and high performance liquid chromatography.

Extraction and determination of three flavonoids (morin, quercetin, and kaempferol) were performed by dispersive magnetic solid phase extraction based on mixed hemi/ad-micelles and high-performance liquid chromatography with UV detection. The Fe3 O4 /SiO2 nanoparticles were synthesized and characterized by X-ray diffraction, FTIR, scanning electron microscopy, and thermogravimetric analysis. Fe3 O4 /SiO2 nanoparticles coated with mixed hemi/ad-micelles cetyltrimethyl ammonium bromide was applied as a sorbent and used for extraction of flavonoids. Effective parameters on the extraction recovery such as amount of magnetic nano particles, volume of cetyltrimethyl ammonium bromide solution with specific concentration, pH of sample solution, adsorption equilibrium time, volume of desorption solvent, and desorption times were evaluated and optimized using fractional factorial design and central composite design. Under the optimum condition limit of detection and linearity were 0.83, 2.7-500.0 for morin, 0.18, 0.7-500.0 for quercetin and, 0.37, 1.3-500.0 µg/L for kaempferol. The extraction recovery with relative standard deviation were 97.88, 1.94 for morin, 95.77, 0.80 for quercetin, and 93.35, 1.45 for kaempferol. The proposed method was applied for simultaneous extraction and determination of flavonoids in several fruit juices and vegetable samples.

Probing the binding mechanism of capecitabine to human serum albumin using spectrometric methods, molecular modeling, and chemometrics approach.

Capecitabine as a prodrug of 5-Fluorouracil plays an important role in the treatment of breast and gastrointestinal cancers. Herein, in view of the importance of this drug in chemotherapy, interaction mechanism between Capecitabine (CAP) and human serum albumin (HSA) as a major transport protein in the blood circulatory system has been investigated by using a combination of spectroscopic and molecular modeling methods. The fluorescence spectroscopic results revealed that capecitabine could effectively quench the intrinsic fluorescence of HSA through a static quenching mechanism. Evaluation of the thermodynamic parameters suggested that the binding process was spontaneous while hydrogen bonds and van der Waals forces played a major role in this interaction. The value of the binding constant (Kb = 1.820 × 104) suggested a moderate binding affinity between CAP and HSA which implies its easy diffusion from the circulatory system to the target tissue. The efficiency of energy transfer and the binding distance between the donor (HSA) and acceptor (CAP) were determined according to forster theory of nonradiation energy transfer as 0.410 and 4.135 nm, respectively. Furthermore, UV-Vis spectroscopic results confirmed that the interaction was occurred between HSA and CAP and caused conformational and micro-environmental changes of HSA during the interaction. Multivariate curve resolution-alternating least square (MCR-ALS) methodology as an efficient chemometric tool was used to separate the overlapped spectra of the species. The MCR-ALS result was exploited to estimate the stoichiometry of interaction and to provide concentration and structural information about HSA-CAP interactions. Molecular docking studies suggested that CAP binds mainly to the subdomain IIA of HSA, which were compatible with those obtained by experimental data. Finally, molecular dynamics simulation (MD) was performed on the best docked complex by considering the permanence and flexibility of HSA-CAP complex in the binding site. MD result showed that CAP could steadily bind to HSA in the site I based on the formation of hydrogen bond and π-π stacking interaction in addition to hydrophobic force.

Comparative molecular field analysis of chromatographic hydrophobicity indices for some coumarin analogs.

The chromatographic hydrophobicity index (CHI) is an HPLC-based parameter that provides reliable guidance in optimization of pharmacological efficiency and adsorption, distribution, metabolism and exertion (ADME) profile of drug candidates. In the present work, classical and three-dimensional quantitative structure-property relationship (QSPR) models were developed for prediction of CHI values of some 4-hydroxycoumarin analogs on immobilized artificial membrane column. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) as 3D-QSPR methods were performed to gain insight into the key structural factors affecting on the chromatographic hydrophobicity of interested chemicals. The calculated parameters of Q2 , R2 and standard error were 0.545, 0.996 and 0.773 for CoMFA model and 0.815, 0.986 and 1.44 for CoMSIA model, respectively. The contour maps for steric fields of the CoMFA model illustrate that the hydrophobicity of chemicals will be higher when the positions of R6, R7 and R8 in the 4-hydroxycuomarin ring are substituted by alkyl groups. Moreover, by the analysis of the plots of electrostatic fields, it was concluded that the CHI value greatly increases if one hydrogen on coumarin ring is substituted by the F, Cl, Br, OH or OCH3 group.

Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones.

In this study, the dipeptidyl peptidase-IV (DPP-IV) inhibition activities of a series of novel aminomethyl-piperidones were investigated by molecular docking studies and modeled by quantitative structure-activity relationship (QSAR) methodology. Molecular docking studies were used to find the best conformations of the studied molecules in the active site of DPP-IV protein. Then the best docking-derived conformation for each molecule was applied for calculating the molecular descriptors. Multiple linear regression (MLR) and Levenberg-Marquardt artificial neural network (LM-ANN) were conducted on descriptors derived by docking. The results of these models revealed the superiority of LM-ANN model over MLR which showed the nonlinear relationship between the selected molecular descriptors and DPP-IV inhibition activities of studied molecules. The correlation coefficient (R) and standard error (SE) of ANN model were 0.983 and 0.103 for the training set and 0.966 and 0.168 for the external test set. These results showed a close agreement between the experimental and calculated values of pIC50 which demonstrated the robustness of LM-ANN model in modeling of aminomethyl-piperidones. Applicability domain analysis and sensitivity analysis were applied on the obtained models. This study gives useful information for further experimental studies on DPP-IV inhibitors. The results of this work reveal the applicability of hybrid docking-QSAR methodology in ligand-protein studies.

Determination of Montelukast in Plasma Using ß - Cyclodextrins Coated on CoFe2O4 Magnetic Nanoparticles in Luminol-H2O2 Chemiluminescence System Optimized by Doehlert Design.

A novel chemiluminescence method using ß - cyclodextrins coated on CoFe2O4 magnetic nanoparticles is proposed for the chemiluminometric determination of montelukast in plasma. The effect of coated ß - cyclodexterinon CoFe2O4 magnetic nanoparticles in the chemiluminescence of luminol-H2O2 system was investigated. It was found that ß - cyclodexterin coated on CoFe2O4 magnetic nanoparticles could greatly enhance the chemiluminescence of the luminol-H2O2 system. Doehlert design was applied in order to optimize the number of experiments to be carried out to ascertain the possible interactions between the parameters and their effects on the chemiluminescence emission intensity. This design was selected because the levels of each variable may vary in a very efficient way with few experiments. Doehlert design and response surface methodology have been employed for optimization pH and concentrations of the components. Results showed under the optimized experimental conditions, the relative CL intensity (ΔI) is increased linearly in the concentration range of 0.003-0.586 µgml(-1) of montelukast with limit of detection (LOD) 1.09 × 10(-4) µgml(-1) at S/N ratio of 3, limit of quantitative (LOQ) 3.59 × 10(-4) µgml(-1) and the relative standard deviation 2.63 %. The method has been successfully applied to the determination of montelukast in plasma of human body. Results specified that relative chemiluminescence intensity (ΔI) has good proportional with the montelukast concentration with R(2) = 0.99979. The test of the recovery efficiency for known amounts of montelukast was also performed, the recoveries range obtained from 98.2 to 103.3 %, with RSDs of <4 % indicated that the proposed method was reliable.

Developing an alcoholic-assisted dispersive liquid-liquid microextraction for extraction of pentachlorophenol in water.

Optimization of alcoholic-assisted dispersive liquid-liquid microextraction of pentachlorophenol (PCP) and determination of it with high-performance liquid chromatography (UV-Vis detection) was investigated. A Plackett-Burman design and a central composite design were applied to evaluate the alcoholic-assisted dispersive liquid-liquid microextraction procedure. The effect of seven parameters on extraction efficiency was investigated. The factor studied were type and volume of extraction and dispersive solvents, amount of salt, and agitation time. According to Plackett-Burman design results, the effective parameters were type and volume of extraction solvent and agitation time. Next, a central composite design was applied to obtain optimal condition. The optimized conditions were obtained at 170-µL 1-octanol and 5-min agitation time. The enrichment factor of PCP was 242 with limits of detection of 0.04 µg L(-1). The linearity was 0.1-100 µg L(-1) and the extraction recovery was 92.7%. RSD for intra and inter day of extraction of PCP were 4.2% and 7.8%, respectively for five measurements. The developed method was successfully applied for the determination of PCP in environmental water samples.

Quantitative structure-property prediction of ion-molecule rate constants for proton transfer reaction between H3O+ and volatile organic compound.

A quantitative structure-property relationship (QSPR) study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the ion-molecules rate constants for proton transfer reaction between hydronuim ion (H(3)O(+)) and some important volatile organic compounds (VOCs). A collection of 50 VOCs was chosen as data set that was randomly divided into three groups, training, internal and external test sets consist of 40, 5 and 5 molecules, respectively. A total of five independent variables selected by stepwise multilinear regression are electronic, geometric, topological type descriptors. The ANN model was developed by using the five descriptors appearing in the MLR model as inputs. Among developed models, the best QSPR model was the ANN model that produced a reasonable level of mean square error MSE(train) = 0.021, MSE(external) = 0.186, MSE(internal) = 0.110. The rate constants calculated by this model are in very good agreement with experimental values. The result of this study reveals the applicability of QSPR approaches in prediction of ion-molecules rate constants for proton transfer reaction of VOCs from their molecular structural descriptors.

Prediction of water-phosphatidylcholine membrane partition coefficient of some drugs from their molecular structures.

In this work, the phosphatidylcholine membrane-water partition coefficients (MA) of some drugs were estimated from their theoretical derived molecular descriptors by applying quantitative structure-activity relationship (QSAR) methodology. The data set consisted of 46 drugs where their log MA were determined experimentally. Descriptors used in this work were calculated by DRAGON (version 1) package, on the basis of optimized molecular structures, and the most relevant descriptors were selected by stepwise multilinear regressions (MLRs). These descriptors were used to developing linear and nonlinear models by using MLR and artificial neural networks (ANNs), respectively. During this investigation, the best QSAR model was identified when using the ANN model that produced a reasonable level of correlation coefficients (R(train) = 0.995, R(test) = 0.948) and low standard error (SE(train) = 0.099, SE(test) = 0.326). The built model was fully assessed by various validation methods, including internal and external validation test, Y-randomization test, and cross-validation (Q(2) = 0.805). The results of this investigation revealed the applicability of QSAR approaches in the estimation of phosphatidylcholine membrane-water partition coefficients.

Prediction of gas chromatographic retention indices of some amino acids and carboxylic acids from their structural descriptors.

In this work, quantitative structure-retention relationship (QSRR) approaches were applied for modeling and prediction of the retention index of 282 amino acids (AAs) and carboxylic acids (CAs). Descriptors that were used to encode structural features of molecules in a data set were calculated by using the Dragon software. The genetic algorithm (GA) and stepwise multiple linear regression (MLR) methods were used to select the most relevant descriptors. Then support vector machine (SVM), artificial neural network (ANN) and multiple linear regression were utilized to construct nonlinear and linear quantitative structure-retention relationship models. The obtained results using these techniques revealed that nonlinear models were much better than other linear ones. The GA-ANN model has the average absolute relative errors (AARE) of 0.054, 0.059 and 0.100 for training, internal and external test set. Applying the tenfold cross-validation procedure on GA-AAN model obtained the statistics of Q(2)=0.943, which revealed the reliability of this model.

Predictions of retention factors for some organic nucleuphiles in complexation gas chromatography.

In the present work, the quantitative structure-retention relationship (QSRR) was used to predict the gas chromatographic retention factors of some organic nucleuphile on chemically modified stationary phase by complexes of Cu (II) with amino groups. The gravitation index, relative negative charge surface area, C component of moment of inertia and weighted negative charged partial surface area are selected as the most relevant descriptors from the pool of descriptors. These descriptors were used for developing multiple linear regression (MLR) and artificial neural network (ANN) models as linear and nonlinear feature mapping techniques. The root mean square errors (RMES) in calculation of retention factors for training, internal and external test set are 0.242, 0.295, and 0.240, respectively for MLR model, and for ANN model the RMSE for training, internal and external test set are; 0.084, 0.108, and 0.176. The ANN and MLR model were further examined by cross validation test, which obtained statistics of Q2 = 0.82 and SPRESS = 0.22 for MLR model and Q2 = 0.97, SPRESS = 0.07 for ANN model. Comparison between these results and other statistics of ANN and MLR models revealed the superiority of ANN over MLR model.

Prediction of immobilized artificial membrane-liquid chromatography retention of some drugs from their molecular structure descriptors and LFER parameters.

In this work multiple linear regression (MLR) was carried out for the prediction of immobilized artificial membrane (IAM) retention factors of 40 basic and neutral drugs in two mobile phase compositions. We developed some MLR models by using linear free energy relationships (LFER) parameters and also theoretically derived molecular descriptor. Root mean square error of MLR model in prediction of log k(wPBS)(IAM) and k(wMOPS)(IAM) are 0.332 and 0.351, respectively, while these values are 0.371 and 0.500 for LFER models. Inspections to these values indicate that the statistical parameters of MLR models are better than LFER models. The credibility of MLR models was evaluated by using leave-many-out cross-validation and y-scrambling procedures. The results of these tests indicate the applicability of theoretically derived molecular descriptors and LFER parameters prediction of IAM retention of drugs.

Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography.

Multiple linear regression (MLR) and artificial neural network (ANN) were used to predict the migration factors of benzene derivatives in MEKC. Some topological and electronic descriptors were calculated for each solute in the data set, and then the stepwise MLR method was used to select more significant descriptors and MLR model development. The selected descriptors are: Kier & Hall index (order1), relative negative charge surface area, HA dependent HDSA-2/TMSA, C component of moment of inertia, Y component of dipole moment and SDS to decanol ratio in mobile phase. In the next step these descriptors were used as input of an ANN. After optimization and training of ANN it was used to predict the migration factors of external test set as well as internal and training sets. The root mean square errors for ANN predicted migration factors of training, internal and external test set were 0.110, 0.231 and 0.228, respectively, while these values are 0.200, 0.240 and 0.247 for the MLR model, respectively. Comparison between these values and other statistical parameters for these two models revealed that there were not any significant differences between ANN and MLR in prediction of solute migration factors in MEKC.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

A novel quantitative structure-activity relationship model for prediction of biomagnification factor of some organochlorine pollutants.

The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative structure-activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity test reveals that the structure of the training and test sets can represent those of the whole ones. After calculation and screening of a large number of molecular descriptors, the methods of stepwise multiple linear regression and genetic algorithm (GA) were used for the selection of most important and significant descriptors which were related to BMF. Then multiple linear regression and artificial neural network (ANN) techniques were applied as linear and non-linear feature mapping techniques, respectively. By comparison between statistical parameters of these methods it was concluded that an ANN model, which used GA selected descriptors, was superior over constructed models. Descriptors which were used by this model are: topographic electronic index, complementary information content, XY shadow/XY rectangle and difference between partial positively and negatively charge surface area. The standard errors for training and test sets of this model are 0.03 and 0.20, respectively. The degree of importance of each descriptor was evaluated by sensitivity analysis approach for the nonlinear model. A good results (Q (2) = 0.97 and SPRESS = 0.084) is obtained by applying cross-validation test that indicating the validation of descriptors in the obtained model in prediction of BMF for these compounds.

A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine.

In this work some chemometrics methods were applied for modeling and prediction of the induction of apoptosis by 4-aryl-4-H-chromenes with descriptors calculated from the molecular structure alone. The genetic algorithm (GA) and stepwise multiple linear regression methods were used to select descriptors which are responsible for the apoptosis-inducing activity of these compounds. Then support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure-activity relationship models. The obtained results using SVM were compared with ANN and MLR; it revealed that the GA-SVM model was much better than other models. The root-mean-square errors of the training set and the test set for GA-SVM model are 0.181, 0.241 and the correlation coefficients were 0.950, 0.924, respectively, and the obtained statistical parameters of cross validation test on GA-SVM model were Q(2)=0.71 and SRESS=0.345 which revealed the reliability of this model. The results were also compared with previous published model and indicate the superiority of the present GA-SVM model.