Željko Debeljak

Chemical composition of the ethanolic propolis extracts and its effect on HeLa cells.

ETHNOPHARMACOLOGICAL RELEVANCE Propolis is a resinous hive product collected by honeybees from various plant sources. It is widely used in traditional medicine and is reported to have a broad spectrum of pharmacological effects (antibacterial, antihepatoxic, antioxidative, anti-inflammatory, etc.). Thus the aim of this study was to assess cytotoxic effect of various ethanol propolis extractions on the cervical tumor cell line (HeLa) and compare it with their phenolic acids and flavonoids composition. MATERIALS AND METHODS Twenty samples of raw propolis were collected from 17 localities of Croatia (I-XVII), 2 of Bosnia and Hercegovina (XVIII, XIX) and 1 of Macedonia (XX). Reverse phase HPLC was used to determine the chemical composition of polyphenols. Biological experiments were carried out in vitro on cervix adenocarcinoma cell line (HeLa). RESULTS Phenolic acids (ferulic acid, p-coumaric acid, caffeic acid) and flavonoids (tectochrysin, galangin, pinocembrin, pinocembrin-7-methylether, chrysin, apigenin, kaempferol, quercetin) have been determined using HPLC analysis in 20 ethanolic propolis extracts. All samples contain tectochrysin in ranges of 0.1988 mg/g (XVIII) to 1.2004 mg/g (III), while caffeic acid and quercetin have not been found. Flavonoid that is most abundant is galangin in ranges from 0.3706 mg/g (XVII) to 47.4879 mg/g (IX). The samples of propolis numbers I, VI and X applied in the investigated concentration range manifested significant reduction of cell growth. GI(50) value as indicator of cytotoxicity among propolis samples showed that propolis number VII is the most effective (GI(50) =76 μg/ml) followed by propolis nos. XV, XVIII and I. CONCLUSION Antiproliferative and cytotoxic effect of propolis on the HeLa cells is not correlating with the concentration of particular components but on establishing the possible synergistic antiproliferative activity of individual phenolic acid and flavonoids. Differences in the chemical composition lead to diversity in biological activity of propolis samples.

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixons outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.

Evaluation of antiaggregatory activity of flavonoid aglycone series

BackgroundAmong natural compounds, present in every day diet, flavonoids have shown beneficial effect in prevention of cardiovascular diseases that can be attributed, at least partially to the described antiaggregatory activity i.e. antiplatelet effects of flavonoids. Due to the ever increasing pharmacological interest in antiplatelet agents a systematic experimental evaluation of large flavonoid series is needed.MethodsA set of thirty flavonoid aglycones has been selected for the evaluation. All measurements of aggregation were done under standardized and firmly controlled in vitro conditions. The whole blood samples, multiple platelet functional analyzer and adenosine diphosphate (ADP) as a weak agonist of aggregation were selected for this purpose.ResultsThe results were expressed as minimal concentration of flavonoid that can significantly lower the platelet aggregation compared to the corresponding untreated sample (minimal antiaggregatory concentration - MINaAC). All analyzed flavonoids exhibited antiaggregatory activity MINaAC ranging from 0.119 μM to 122 μM, while the most potent representatives were 3,6-dihydroxyflavone (0.119 μM) and syringetin (0.119 μM).ConclusionsMeasurable antiplatelet activity established at submicromolar flavonoid concentrations suggests that even a dietary consumption of some flavonoids can make an impact on in vivo aggregation of platelets. These findings also point out a therapeutical potential of some flavonoids.

QSAR Study of Antimicrobial Activity of Some 3-Nitrocoumarins and Related Compounds†

A new class of antimicrobial agents, 3-nitrocoumarins and related compounds, has been chosen as a subject of the present study. In order to explore their activity and molecular properties that determine their antimicrobial effects, QSAR models have been proposed. Most of the 64 descriptors used for the development were extracted from semiempirical and density functional theory (DFT) founded calculations. For this study literature data containing results of microbiological activity screening of 33 coumarin derivatives against selected clinical isolates of C. albicans (CA) and S. aureus (SA) have been selected. Multivariate predictive models based on random forests (RF) and two hybrid classification approaches, genetic algorithms (GA) associated with either support vector machines (SVM) or k nearest neighbor (kNN), have been used for establishment of QSARs. An applied feature selection approach enabled two-dimensional linear separation of active and inactive compounds, which was a necessary tool for rational candidate design and descriptor relevance interpretation. Candidate molecules were checked by cross-validated models, and selected derivatives have been synthesized. Their antimicrobial activities were compared to antimicrobial activities of the representative derivatives from the original set in terms of minimal inhibitory concentration (MIC) against chosen SA and CA ATCC strains. High ranking of descriptors consistent with the degree of hydrolytic instability of selected compounds is common to models of antimicrobial activity against both microorganisms. However, descriptor ranking indicates different antimicrobial mechanisms of action of chosen coumarin derivatives against selected microbial species.

Relationship between cannabinoids content and composition of fatty acids in hempseed oils

Hempseed oils acquired on the Croatian markets were characterised by cannabinoid content and fatty acid composition. The new method for determination of cannabinoid content was developed and validated in the range of 0.05-60 mg/kg, and the content of tetrahydrocannabinol varied between 3.23 and 69.5 mg/kg. Large differences among the samples were obtained for phenotype ratio suggesting that not all of analysed hempseed oils were produced from industrial hemp. Sample clustering based on cannabinoid content assigned samples to two groups closely related to the phenotype ratios obtained. The results of this study confirm that hempseed oil is a good source of polyunsaturated fatty acids, especially γ-linolenic and stearidonic acid, but the content varies a lot more than the omega-6/omega-3 ratio. The grouping of samples on fatty acid content assigned samples to two groups which were consistent with the groups obtained based on cannabinoid content clustering.

Warfarin and Flavonoids Do Not Share the Same Binding Region in Binding to the IIA Subdomain of Human Serum Albumin

Human serum albumin (HSA) binds a variety of xenobiotics, including flavonoids and warfarin. The binding of another ligand to the IIA binding site on HSA can cause warfarin displacement and potentially the elevation of its free concentration in blood. Studies dealing with flavonoid-induced warfarin displacement from HSA provided controversial results: estimated risk of displacement ranged from none to serious. To resolve these controversies, in vitro study of simultaneous binding of warfarin and eight different flavonoid aglycons and glycosides to HSA was carried out by fluorescence spectroscopy as well as molecular docking. Results show that warfarin and flavonoids do not share the same binding region in binding to HSA. Interactions were only observed at high warfarin concentrations not attainable under recommended dosing regimes. Docking experiments show that flavonoid aglycons and glycosides do not bind at warfarin high affinity sites, but rather to different regions within the IIA HSA subdomain. Thus, the risk of clinically significant warfarin–flavonoid interaction in binding to HSA should be regarded as negligible.

Interference of selected flavonoid aglycons in platelet aggregation assays.

Abstract Background: Flavonoids are widely distributed across the plant kingdom and are therefore common ingredients in an everyday diet. Some flavonoids have a potential to affect platelet aggregation; most often antiaggregatory effects of flavonoids are observed. The objective of this research was to evaluate the in vitro effect of a selected set of flavonoids on platelet aggregation in whole blood. Methods: The effect of five selected flavonoids (pinocembrin-7-methylether, epicatechin, hesperetin, 6-hydroxyflavone and 3,6-dihydroxyflavone) on platelet aggregation was studied in the citrated whole blood samples collected from 75 healthy volunteers. A Multiplate® impedance analyzer and five different aggregation inducers (ADP, arachidonic acid, collagen, ristocetin and TRAP-6) were utilized for the analysis of samples. Results: Minimal antiaggregatory concentrations (MINaAC) of flavonoids in individual tests were reported in the following ranges: 0.12–1.91 μM; 15.26–244.14 μM; 15.26–122.07 μM; and 0.06–15.26 μM for ADP, collagen, TRAP-6 and ristocetin aggregation-inducers, respectively. When arachidonic acid was used for induction of platelet aggregation, a proaggregatory effect was observed for pinocembrin-7-methylether, epicatechin, hesperetin and 3,6-dihydroxyflavone, while the expected antiaggregatory effect was observed only for 6-hydroxyflavone (MINaAC=7.63 μM). Conclusions: Flavonoids interfere with in vitro platelet aggregation assays exhibiting either anti- or proaggregatory effects in concentration ranges that can be achieved in circulation by dietary intake. Thus, dietary intake of flavonoids should be taken into account when interpreting the results of whole blood platelet aggregation.

Application of self-organizing maps for the classification of chromatographic systems and prediction of values of chromatographic quantities

The applicability of self-organizing maps (SOM) for the classification of chromatographic systems or components of chromatographic systems based on data taken from literature is shown. The SOM approach is compared to dendrogram and principal components analysis (PCA) approaches. It has been shown that the distance between classified objects could reveal linear correspondence with quantity to be optimized, e.g. resolution, so it can be applied in the chromatographic method development. SOMs can also be applied for prediction of chromatographic quantities. It is shown that SOM-based response surface modeling is comparable to triangular presentation of mobile phase composition response surfaces.

Purity assessment of recombinant human granulocyte colony-stimulating factor in finished drug product by capillary zone electrophoresis

Current methods for determination of impurities with different charge‐to‐volume ratio are limited especially in terms of sensitivity and precision. The main goal of this research was to establish a quantitative method for determination of impurities with charges differing from that of recombinant human granulocyte colony‐stimulating factor (rhG‐CSF, filgrastim) with superior precision and sensitivity compared to existing methods. A CZE method has been developed, optimized, and validated for a purity assessment of filgrastim in liquid pharmaceutical formulations. Optimal separation of filgrastim from the related impurities with different charges was achieved on a 50 μm id fused‐silica capillary of a total length of 80.5 cm. A BGE that contains 100 mM phosphoric acid adjusted to pH 7.0 with triethanolamine was used. The applied voltage was 20 kV while the temperature was maintained at 25°C. UV detection was set to 200 nm. Method was validated in terms of selectivity/specificity, linearity, precision, LOD, LOQ, stability, and robustness. Linearity was observed in the concentration range of 6–600 μg/mL and the LOQ was determined to be 0.3% relative to the concentration of filgrastim of 0.6 mg/mL. Other validation parameters were also found to be acceptable; thus the method was successfully applied for a quantitative purity assessment of filgrastim in a finished drug product.

Novel approach to evolutionary neural network based descriptor selection and QSAR model development

SummaryCapability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh’s filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance.