Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints.

Black tea, a widely popular non-alcoholic beverage, is renowned for its unique aroma and has attracted significant attention due to its complex composition. However, the chemical profile of Iranian tea remains largely unexplored. In this research, black tea samples from key tea cultivation regions in four geographical areas in northern Iran were firstly analyzed using headspace solid-phase microextraction followed by gas chromatography-mass spectrometry (HS-SPME-GC-MS) to separate, identify, and quantify their volatile organic compounds.

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.

Extraction and determination of flavonoids in fruit juices and vegetables using Fe O /SiO 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 Fe O /SiO nanoparticles were synthesized and characterized by X-ray diffraction, FTIR, scanning electron microscopy, and thermogravimetric analysis. Fe O /SiO nanoparticles coated with mixed hemi/ad-micelles cetyltrimethyl ammonium bromide was applied as a sorbent and used for extraction of flavonoids.

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.

Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR studies on a series of novel HIV-1 protease inhibitors.

Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR as three efficient methods of QSAR have been performed on 40 newly synthesized inhibitors against HIV-1 protease. Molecular alignment was performed by aid of crystallographic structure of template inhibitor (indirect alignment) and also by the molecular mechanic (MM)-minimized structure. Both alignment methods produced satisfactory statistics for training set, but indirect alignment had more predictive power.

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.

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.

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

A novel chemiluminescence method using β - cyclodextrins coated on CoFe2O4 magnetic nanoparticles is proposed for the chemiluminometric determination of montelukast in plasma. The effect of coated β - cyclodexterinon CoFe2O4 magnetic nanoparticles in the chemiluminescence of luminol-H2O2 system was investigated. It was found that β - cyclodexterin coated on CoFe2O4 magnetic nanoparticles could greatly enhance the chemiluminescence of the luminol-H2O2 system.

QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors.

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease.

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.

In-silico prediction of gas chromatographic retention indices of some terpenols.

A quantitative structure-retention relationship study based on multiple linear regression technique was carried out to investigate the gas chromatographic retention indices (RIs) of some terpenols on the HP 5 ms fused silica column. A collection of 75 terpene alcohols was chosen as dataset. The data were divided into two groups; a training set and a prediction set consist of 60 and 15 molecules, respectively.

In silico prediction of dermal penetration rate of chemicals from their molecular structural descriptors.

The dermal penetration rate of some volatile and non-volatile organic compounds was estimated by quantitative structure-activity relationship approaches by using interpretable molecular descriptors. Linear and nonlinear models were developed using multiple linear regressions (MLR) and artificial neural network (ANN) methods. Robustness and reliability of the constructed MLR and ANN models were evaluated by using the leave-one-out cross-validation method, which produces the statistics of Q(MLR)(2)=0.

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.

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).

Extraction optimization of polycyclic aromatic hydrocarbons by alcoholic-assisted dispersive liquid-liquid microextraction and their determination by HPLC.

A sensitive method for the extraction and determination of polycyclic aromatic hydrocarbons (PAHs) using alcoholic-assisted dispersive liquid-liquid microextraction (AA-DLLME) and HPLC was developed. The extraction procedure was based on alcoholic solvents for both extraction and dispersive solvents. The effective parameters (type and volume of extraction and dispersive solvents, amount of salt and stirring time) on the extraction recovery were studied and optimized utilizing factorial design (FD) and central composite design (CCD).

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.

Quantitative and qualitative prediction of corneal permeability for drug-like compounds.

A set of 69 drug-like compounds with corneal permeability was studied using quantitative and qualitative modeling techniques. Multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) were used to develop quantitative relationships between the corneal permeability and seven molecular descriptors selected by stepwise MLR and sensitivity analysis methods. In order to evaluate the models, a leave many out cross-validation test was performed, which produced the statistic Q(2)=0.

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.

Cytotoxicity estimation of ionic liquids based on their effective structural features.

Cytotoxicity of a diverse set of 227 ionic liquids (taken from UFT/Merck Ionic Liquids Biological Effects Database) containing 94 imidazolium, 53 pyridinium, 23 pyrrolidinium, 22 ammonium, 15 piperidinium, 10 morpholinium, 5 phosphanium, and 5 quinolinium cations in combination with 25 different types of anions to Leukemia Rat Cell Line (IPC-81) was estimated from their structural parameters using quantitative structure - toxicity relationship "QSTR" methodology. Linear and nonlinear models were developed using genetic algorithm (GA), multiple linear regressions (MLR) and multilayer perceptron neural network (MLP NN) approaches. Robustness and reliability of the constructed models were evaluated through internal and external validation methods.

Classification of drugs according to their milk/plasma concentration ratio.

The classification of drugs was done according to their milk/plasma concentration ratio (M/P) by using counter propagation artificial neural network (CP-ANN). The features of each drug were encoded by linear free energy relationship (LFER) parameters. These descriptors were used as inputs for developing linear discriminant analysis, quadratic discriminant analysis, least square support vector machine and CP-ANN models to distinguish the potential risk of 154 drugs as high risk (with M/P > 1) and low risk (with M/P < 1) for lactating women.

QSAR prediction of D2 receptor antagonistic activity of 6-methoxy benzamides.

Quantitative structure-activity relationship (QSAR) method was used to predict the pIC(50) value of 58 derivatives of 6-methoxy benzamides in this work. The artificial neural network (ANN) and multiple linear regressions (MLR) were used to construct the non-linear and linear QSAR models, respectively. The standard errors in the prediction of pIC(50) for training, internal and external test sets, are; 0.

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.

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 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.

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.