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Razavi A.E.,Isfahan Pharmaceutical science Research Center | Ani M.,Isfahan Pharmaceutical science Research Center | Pourfarzam M.,Isfahan Pharmaceutical science Research Center | Naderi G.A.,Isfahan University of Medical Sciences
Journal of Research in Medical Sciences | Year: 2012

Background: High density lipoprotein (HDL) particles are heterogeneous in composition, structure, size, and may differ in conferring protection against cardiovascular disease. HDL associated enzyme, paraoxonase-1 (PON1), has an important role in attenuation of atherogenic low density lipoprotein (LDL) oxidation. The aim of this study was to investigate the associations between HDL particle size and PON1 activity in relation to serum HDL cholesterol (HDL-C) levels. Materials And Methods: One hundred and forty healthy subjects contributed to this study. HDL was separated by sequential ultracentrifugation and its size was estimated by dynamic light scattering. Paraoxonase activity was measured spectrophotometrically using paraoxon as substrate. Results: Results of this study showed that PON1 activity had negative correlations with HDL mean particle size (r = -0.22, P < 01), HDL2/HDL3 ratio, and serum HDL-C levels (r = -0.25, P < 0.01). HDL mean particle size and HDL2/HDL3 ratio had negative correlation with body mass index (BMI), waist hip ratio (WHR), and serum triglyceride (TG) levels, and positive correlation with serum HDL-C levels. Serum HDL-C levels had significant positive correlations with age, total cholesterol (TC), and apolipoprotein A-I (apo A-I) and significant negative correlation with BMI, WHR, and TG. Conclusion: Based on the results of this study, determination of HDL mean particle size beside the serum PON1 activity may help to better understand the CAD risks, pathogenesis, and prognosis, and may also help to design therapeutic protocols toward beneficial modifications of HDL characteristics. Source


Sabet R.,Isfahan University of Medical Sciences | Mohammadpour M.,Isfahan University of Medical Sciences | Sadeghi A.,Isfahan University of Medical Sciences | Fassihi A.,Isfahan University of Medical Sciences | Fassihi A.,Isfahan Pharmaceutical science Research Center
European Journal of Medicinal Chemistry | Year: 2010

Quantitative structure activity relationships (QSAR) of anti-cancer isatin derivatives were discovered by multiple linear regressions (MLR) and genetic algorithm partial least squares (GA-PLS) methods. Topological, chemical, geometrical and functional groups descriptors were found to be effective parameters on the cytotoxic activity. The positive effects of the number of halogen atoms and the number of total secondary carbons, and the negative effects of the number of secondary amides, and the number of ketones on the anti-cancer activity were in agreement with previous SAR studies. Hansch analysis showed the importance of lipophilic R3 and R5 substituents. Between MLR and GA-PLS, MLR represented superior results with a high statistical quality (R2 = 0.92 and Q2 = 0.90) for predicting the activity of the compounds. © 2009 Elsevier Masson SAS. All rights reserved. Source


Shahlaei M.,Kermanshah University of Medical Sciences | Shahlaei M.,Isfahan University of Medical Sciences | Sabet R.,Isfahan University of Medical Sciences | Ziari M.B.,Islamic Azad University at Shahreza | And 4 more authors.
European Journal of Medicinal Chemistry | Year: 2010

Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R 2=0.91 and R CV 2=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules. © 2010 Elsevier Masson SAS. All rights reserved. Source


Saghaie L.,Isfahan University of Medical Sciences | Shahlaei M.,Isfahan University of Medical Sciences | Shahlaei M.,Kermanshah University of Medical Sciences | Fassihi A.,Isfahan University of Medical Sciences | And 4 more authors.
Chemical Biology and Drug Design | Year: 2011

Quantitative relationships between calculated molecular structure and 26 diaryl-substituted pyrazoles CCR2 inhibitors were investigated by GA-stepwise multiple linear regression. In multiple linear regression analysis, the quantitative structure-activity relationship models were constructed by grouping descriptors and also dual selection of variables using genetic algorithm and stepwise selection methods from each group of the pool of all calculated descriptors. The accuracy of the proposed multiple linear regression model was demonstrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomization. Furthermore, the domain of applicability that shows the area of reliable predictions was defined. The prediction results were in good agreement with the experimental values. © 2010 John Wiley & Sons A/S. Source


Saghaie L.,Isfahan University of Medical Sciences | Saghaie L.,Isfahan Pharmaceutical science Research Center | Shahlaei M.,Isfahan University of Medical Sciences | Shahlaei M.,Kermanshah University of Medical Sciences | And 3 more authors.
Journal of Molecular Graphics and Modelling | Year: 2010

The detailed application of multivariate image analysis (MIA) method for the evaluation of quantitative structure activity relationship (QSAR) of some cyclin dependent kinase 4 inhibitors is demonstrated. MIA is a type of data mining methods that is based on data sets obtained from 2D images. The purpose of this study is to construct a relationship between pixels of images of investigated compounds as independent and their bioactivities as a dependent variable. Partial least square (PLS) and principal components-radial basis function neural networks (PC-RBFNNs) were developed to obtain a statistical explanation of the activity of the molecules. The performance of developed models were tested by several validation methods such as external and internal tests and also criteria recommended by Tropsha and Roy. The resulted PLS model had a high statistical quality (R 2 = 0.991 and RCV2=0.993) for predicting the activity of the compounds. Because of high correlation between values of predicted and experimental activities, MIA-QSAR proved to be a highly predictive approach. © 2010 Elsevier Inc. All rights reserved. Source

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