Vesna Rastija

SAR and QSAR of the Antioxidant Activity of Flavonoids

Flavonoids are a group of naturally occurring phytochemicals abundantly present in fruits, vegetables, and beverages such as wine and tea. In the past two decades, flavonoids have gained enormous interest because of their beneficial health effects such as anti-inflammatory, cardio-protective and anticancer activities. These findings have contributed to the dramatic increase in the consumption and use of dietary supplements containing high concentrations of plant flavonoids. The pharmacological effect of flavonoids is mainly due to their antioxidant activity and their inhibition of certain enzymes. In spite of abundant data, structural requirements and mechanisms underlying these effects have not been fully understood. This review presents the current knowledge about structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs) of the antioxidant activity of flavonoids. SAR and QSAR can provide useful tools for revealing the nature of flavonoid antioxidant action. They may also help in the design of new and efficient flavonoids, which could be used as potential therapeutic agents.

QSAR study of antioxidant activity of wine polyphenols

Quantitative structure activity relationships (QSAR) were obtained describing the antioxidant activity of the main pharmacologically active polyphenols of wine, using molecular properties and descriptors derived from the 2D and 3D representations of molecules. The best models for the prediction of the ability to scavenge the ABTS radical cation were obtained by polynomial regression analysis using zero-order connectivity index and molar refractivity. Statistically, significant models for lipid peroxidation inhibiting effects of flavonoids were obtained by polynomial and multiple regression using lipophilicity, Balaban index, Balaban-type index and 3D GETAWAY descriptor. The 3D descriptors possess the ability for discrimination of stereoisomers, like catechin and epicatechin. We demonstrated that a size and shape of molecules, as well as steric properties, play an important role in the antioxidant activity of polyphenols.

CoMSIA and POM analyses of anti-malarial activity of synthetic prodiginines

In present work, 53 synthetic prodiginines were selected to establish thriving CoMSIA (Comparative Molecular Similarity Indices Analysis) model to explore the structural features influencing their anti-malarial activity. POM (Petra/Osiris/Molinspiration) was carried out to get insight into requirements that can lead to the improvement of the activity of these molecules. The CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor/donor effects, is with R(2)(cv)=0.738 and R(2)=0.911. The analyses reveal that lipophilicity, hydrogen donor/acceptor and steric factors play crucial role. The study with constructive propositions could be useful for the design of new analogues with enhanced activity.

Analysis of phenolic components in Croatian red wines by thin-layer chromatography

A thin-layer chromatographic (TLC) method has been used for determination of phenolic compounds in two samples of wine commercially available in Croatia. Sample preparation was by liquid—liquid extraction with diethyl ether at pH 2.0. Extracts and standard solutions were applied to 20 cm × 20 cm silica gel 60 F254 TLC plates. After treatment of the developed plates with ammonia vapor, phenolic compounds were detected on chromatograms by their colors or by fluorescence in UV light at λ= 244 nm and at λ= 366 nm before and after spraying with 1% ethanolic AlCl3. The efficiency of eleven mobile phases was tested by three mathematical techniques—calculation of the information content (I) derived from Shannon’s equation, determination of discriminating power (DP), and formation of clusters and dendrogram. It was shown that the best mobile phase for separation of phenolic compounds in the wine extracts was benzene—ethyl acetate—formic acid, 30 + 15 + 5 (v/v). Six phenolic compounds were identified in the tested samples.

Effect of information leakage and method of splitting (rational and random) on external predictive ability and behavior of different statistical parameters of QSAR model

Quantitative Structure–Activity Relationship not only provides guidelines regarding structural features responsible for biological activity but it can be used also for prediction of desired activity prior to synthesis of untested chemicals. Therefore, an appropriate validation of any QSAR is of utmost importance to judge its external predictive ability. Generally, internal and external validations (preferred by many) are used in the absence of a true external dataset. The model developed using external method may not be reliable as it may not capture all essential features required for the particular SAR due to omission of some compounds, especially for small datasets. In external validation, the splitting is done either rationally or in random manner before descriptor selection. In the present study, rational splitting of dataset was performed using a novel method and its effect on statistical parameters was analyzed. The analysis reveals that the predictive ability of a QSAR model is sensitive toward (1) the method of splitting and (2) distribution of the training and the prediction sets. In addition, purposeful selection can be used to influence the statistical parameters; therefore, external validation based on single split is insufficient to guarantee the true predictive ability of a QSAR model. Besides, it appears that the selection of descriptors prior to splitting (information leakage) has little role to play in deciding external predictivity of the model. The present study reveals that as many as possible statistical parameters should be examined along with boot-strapping instead of single external validation.

Two-dimensional quantitative structure–activity relationship study on polyphenols as inhibitors of α-glucosidase

Polyphenols and their derivates have been reported to exhibit inhibitory activity against α-glucosidase. The relationship between structure and inhibitory activity of polyphenols was studied by means of multiple linear regression analysis with use of various descriptors derived from the 2D and 3D representations of molecules and physicochemical parameters calculated by DRAGON. The best model for the prediction of inhibitory activity was obtained using combination of topological charge index (JGI2), information index (CIC2) and number of exo-conjugated C atoms (nCconjR). This study revealed that an enhanced inhibitory activity of polyphenols is mainly conditioned by stabilization of molecule due to intramolecular electron and charge delocalization.

QSAR modeling of anthocyanins, anthocyanidins and catechins as inhibitors of lipid peroxidation using three-dimensional descriptors

This work describes the quantiative structure–activity relationship (QSAR) study of lipid peroxidation inhibitory effect of catechins, anthocyanidins and anthocyanins using molecular descriptors and physicochemical parameters derived from optimised three-dimensional (3D) structure, since this set of studied compounds contains stereoisomers with different activities. Six groups of 3D descriptors have been used to generate QSAR models: geometrical, 3D molecule representation of structures based on electron diffraction (3D-MoRSE); Randic molecular profiles; geometry, topology and atom weights assembly (GETAWAY); radial distribution function (RDF); and weigthed covariance matrices (WHIM) descriptors. The 3D molecular descriptors and physicochemical parameters have been calculated applying the online software Parameter Client and HyperChem 8.0. The primary selection of 3D molecular descriptors and physicochemical parameters was based on their ability to discriminate stereoisomers. Further selection of predictor variables for multiple regression was performed by the best-subset and forward stepwise method. The best-developed QSAR models consisted of geometrical, RDF and Randic molecular profiles descriptors. Those descriptors could be used for the prediction of the biological activity of catechin stereoisomers and their derivates. The obtained models suggest that the inhibitory effect of studied compounds is related to the shape of the molecule and the three-dimensional distribution of atomic mass in the molecule.

QSAR of antitrypanosomal activities of polyphenols and their analogues using multiple linear regression and artificial neural networks.

In order to find a thriving quantitative structure-activity relationship for antitrypanosomal activities (against Trypanosma brucei rhodesiense) of polyphenols that belong to different structural groups, multiple linear regression (MLR) and artificial neural networks (ANN) were employed. The analysis was performed on two different-sized training sets (59% and 78% molecules in the training set), resulting in relatively successful MLR and ANN models for the data set containing the smaller training set. The best MLR model obtained using the five descriptors (R3m(+), GAP, DISPv, HATS2m, JGI2) was able to account only for 74% of the variance of antitrypanosomal activities of the training set and achieved a high internal, but low external prediction. Nonlinearities of the best ANN model compared with the linear model improved the coefficient of determination to 98.6%, and showed a better external predictive ability. The obtained models displayed relevance of the distance between oxygen atoms in molecules of polyphenols, as well as stability of molecules, measured by the difference between the energy of the highest occupied molecular orbital and the energy of the lowest unoccupied molecular orbital (GAP) for their activity.

Quantitative structure–activity relationships (QSARs) and pharmacophore modeling for human African trypanosomiasis (HAT) activity of pyridyl benzamides and 3-(oxazolo[4,5-b]pyridin-2-yl)anilides

In the present work, quantitative structure–activity relationship and pharmacophore modeling analysis were performed for human African trypanosomiasis healing activity of pyridyl benzamides (dataset-1) and 3-(oxazolo[4,5-b]pyridin-2-yl)anilides (dataset-2). For quantitative structure–activity relationship analysis, a pool of descriptors (mono-dimensional to three-dimensional) was generated, followed by descriptor reduction using objective and subjective feature selection. Multiple splitting was employed for the generation of multiple quantitative structure–activity relationship models to get maximum information about the descriptors that have correlation with the HAT activity of pyridyl benzamides and 3-(oxazolo[4,5-b]pyridin-2-yl)anilides. The genetic algorithm-multilinear regression quantitative structure–activity relationship models have excellent statistical robustness with good external predictive ability. The pharmacophore model and quantitative structure–activity relationship analyses furnished complementary and consensus results to each other.

Effects of early leaf removal on grape yield, chemical characteristics, and antioxidant activity of grape variety Cabernet Sauvignon and wine from eastern Croatia

ABSTRACT The aim of this study was to determine the impact of two different treatments of early defoliation performed before blooming on: grape yield, chemical parameters, polyphenols content, and antioxidant activity of grape and red wine cv. Cabernet Sauvignon from the vineyard located in Ilok, the eastern continental region of Croatia. Two different treatments of leaf removal (LR) were performed: removal of 3 leafs (T1) and 6 leafs (T2) before blooming, together with control (no leaf removal) (T3) during two years (2013 and 2014). Crop yield and average cluster weights per vine were determined. Density, pH and titratable acidity were measured in must, while the total phenols, total anthocyanins and antioxidant activity were measured in the extract of grape skin and produced wine. The analysis of individual anthocyanins in wine was performed by HPLC method. T2 treatment significantly lowered the crop yield and the average cluster weights, and increased total phenols, total anthocyanins, antioxidant activity and most abundant individual anthocyanins in wine. Defoliation did not affect the other chemical parameters in must, grape skin extract and wine. Vintage year is statistically the most significant source of variability for density of must, antioxidant activity in grape skin extract, as well as pH and titratable acidity in wine. This study has showed that the early leaf removal treatment in eastern continental part of Croatia could be used for the production of smaller quantity of high quality Cabernet Sauvignon red wine abundant with anthocyanins.