Application of Rank Annihilation Factor Analysis for Antibacterial Drugs Determination by Means of pH Gradual Change-UV Spectral Data.

The main objective of this study was to develop a simple and efficient spectrophotometric technique combined with chemometrics for the simultaneous determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in drug formulations. Specifically, we sought: (i) to evaluate the potential use of rank annihilation factor analysis (RAFA) to pH gradual change spectrophotometric data in order to provide sufficient accuracy and model robustness; and (ii) to determine SMX and TMP concentration in drug formulations without tedious pre-treatments such as derivatization or extraction techniques which are time-consuming and require hazardous solvents. In the proposed method, the spectra of the sample solutions at different pH values were recorded and the pH-spectra bilinear data matrix was generated. On these data, RAFA was then applied to estimate the concentrations of SMX and TMP in synthetic and real samples. Applying RAFA showed that the two drugs could be determined simultaneously with concentration ratios of SMX to TMP varying from 1:30 to 30:1 in the mixed samples (concentration range is 1-30 µg mL-1 for both components). The limits of detection were 0.25 and 0.38 µg mL-1 for SMX and TMP, respectively. The proposed method was successfully applied to the simultaneous determination of SMX and TMP in some synthetic, pharmaceutical formulation and biological fluid samples. In addition, the means of the estimated RSD (%) were 1.71 and 2.18 for SMX and TMP, respectively, in synthetic mixtures. The accuracy of the proposed method was confirmed by spiked recovery test on biological samples with satisfactory results (90.50-109.80%).

Fatty Acids-Based Quality Index to Differentiate Worldwide Commercial Pistachio Cultivars.

The fatty acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi, and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty acid data yielded three significant PCs, which together account for 80.0% of the total variance in the dataset. A linear discriminant analysis (LDA) model that was evaluated with cross-validation correctly classified almost all of the samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty acid chromatographic fingerprint.

Reversed-phase single drop microextraction followed by high-performance liquid chromatography with fluorescence detection for the quantification of synthetic phenolic antioxidants in edible oil samples.

The reversed-phase mode of single drop microextraction has been used as a preparation method for the extraction of some phenolic antioxidants from edible oil samples. Butylated hydroxyl anisole, tert-butylhydroquinone and butylated hydroxytoluene were employed as target compounds for this study. High-performance liquid chromatography followed by fluorescence detection was applied for final determination of target compounds. The most interesting feature of this study is the application of a disposable insulin syringe with some modification for microextraction procedure that efficiently improved the volume and stability of the solvent microdrop. Different parameters such as the type and volume of solvent, sample stirring rate, extraction temperature, and time were investigated and optimized. Analytical performances of the method were evaluated under optimized conditions. Under the optimal conditions, relative standard deviations were between 4.4 and 10.2%. Linear dynamic ranges were 20-10 000 to 2-1000 µg/g (depending on the analytes). Detection limits were 5-670 ng/g. Finally, the proposed method was successfully used for quantification of the antioxidants in some edible oil samples prepared from market. Relative recoveries were achieved from 88 to 111%. The proposed method had a simplicity of operation, low cost, and successful application for real samples.

Mechanistic-based descriptors for QSAR study of psychotropic drug toxicity.

A quantitative structure-activity relationship model has been developed for three important classes of psychotropic drugs including phenothiazines, antidepressants and anxiolitics. Toxicity was expressed as the oral LD(50) values for mice. Acute toxicity of drugs correlated with a proposed descriptor based on the oxidation mechanism of bioactivation. The new descriptor was named hydrogen charge (HC) descriptor, and it is the sum of the HCs of amine moiety, carbons in the alpha-position of the heteroatoms, carbonyl groups and unsaturated bonds. The results showed that the model with the descriptor of ratio of the HC over volume of the molecules, named the effective hydrogen charge (EHC) were superior to the model with the HC descriptor. Multiple linear regression was derived for the three categories of drugs separately predicting the LD(50) values. The linear models were found to describe the system with statistical parameters of RMSECV and RMSEP as follows: 41, 49 for phenothiazines, 111, 190 for antidepressants and 151, 276 for anxiolitics. The performance of the constructed models was examined using RMSECV (leave-one-out and leave-group-out), standard error, coefficient of determination (R(2)), R(2) cross-validated (q(2)), the average chance correlation and residual error plots. Finally, the LD(50) for a variety of compounds, with unknown experimental values, were predicted.

Multi-way analysis of quantum topological molecular similarity descriptors for modeling acidity constant of some phenolic compounds.

Three-way analyses of quantum topological molecular similarity descriptors were used for quantitative structure property relationship modeling of the acidity constant of some phenol derivatives. A three-way data was built for different molecules by constructing a data matrix for each molecule. The matrix was produced by considering different bonds in each molecule and different descriptors in each bond. The three-way models parallel factor analysis and N-way partial least squares, and two-way models including partial least squares were used for modeling structure-acidity relationships. Comparison of the results showed that the three-way arrays produced more predictive models with lower over-fitting. The bilinear partial least square model resulted in a biased estimation of acidity constant of prediction set with average relative error of prediction of 1.87%, whereas that obtained by parallel factor analysis and N-way partial least squares was near to zero (i.e. -0.41 and -0.33, respectively). Additionally, the three-way methods allowed investigating the significant impact of different bonds and different descriptors using leverages of the parallel factor analysis loadings.