Innovative Strategy for Aroma Stabilization Using Green Solvents: Supercritical CO2 Extracts of Satureja montana Dispersed in Deep Eutectic Solvents.

The aim of this work was to establish the potential of natural deep eutectic solvents (NADES) for the stabilization of aroma volatile organic compounds from a natural source. Satureja montana was used as a source of volatile components, as it is rich in terpenes of great commercial and biological importance, such as carvacrol, thymol, and thymoquinone, among others. Supercritical CO2 was used to extract the lipophilic fraction of S. montana, which was further directly dispersed in NADES. The stabilizing capacity of seven different NADES based on betaine and glycerol was analyzed. The stability of the components in NADES was monitored by analyzing the headspace profile during 6 months of storage at room temperature. The changes in the headspace profile over time were analyzed by using different statistical and chemometric tools and the Wilcoxon matched pair test. It was determined that alterations over time occurred such as degradation and oxidation, and they were the most prominent in the control. In addition, the indicator of decreased stability of the control was the formation of the new compounds that could compromise the quality of the product. In the stabilized NADES samples, the changes were significantly less prominent, indicating that the NADES had a stabilizing effect on the volatile compounds. According to Wilcoxon matched pair test, the most efficient stability was achieved by using betaine/ethylene glycol, glycerol/glucose, and betaine/sorbitol/water. Therefore, by applying two green solvents, a sustainable approach for obtaining pure and high-quality S. montana extracts with extended stability at room temperature was established.

Chemometrics of anisotropic lipophilicity of anticancer androstane derivatives determined by reversed-phase ultra high performance liquid chromatography with polar aprotic and protic modifiers.

The research of the use of steroid compounds in the treatment of various types of cancer has a history of more than 50 years. Numerous steroid derivatives have expressed significant anticancer activity, however the thorough analysis of their physicochemical and toxicological properties is required prior to their clinical application. The present study is focused on the characterization of physicochemical properties of a series of previously synthesized new androstane derivatives (16E-hydroxyimino derivatives, D-homo lactones and D-seco dinitriles) with anticancer activity applying reversed-phase ultra high performance liquid chromatography (RP-UHPLC) system under isocratic conditions and chemometric tools, including in silico determination of their absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. The chromatographic analysis was carried out applying two two-component and one ternary mobile phases with aprotic (acetonitrile) and protic (water and methanol) solvents. The conducted quantitative structure-retention relationship modeling resulted in two univariate and nine multivariate linear mathematical models that successfully correlate the retention parameters (logk) with descriptors of molecular polarity/lipophilicity (tPSA, REF, Average LogP, ClogP, ALOGP, LogS-SILICOS-IT, AClogS), descriptors that mainly depend on intermolecular interactions (BP, CP, CT, DE, HL) and ADMET descriptors (SWISSLogKpSP, GPCR, HIA, EI). The quality of the models was proved by the internal and external validation, while the robustness of the models was confirmed by Y-randomization test. Considering the established meaningful relationships between physicochemical/ADMET properties and retention parameters, the determined logk parameters of the analyzed series of steroid derivatives can be considered to be a biopharmaceutical property from the perspective of lipophilicity.

Comparative chemometric and quantitative structure-retention relationship analysis of anisotropic lipophilicity of 1-arylsuccinimide derivatives determined in high-performance thin-layer chromatography system with aprotic solvents.

Numerous structurally different amides and imides including succinimide derivatives exhibit diverse bioactive potential. The development of new compounds requires rationalization in the design in order to provide structural changes that guarantee favorable physico-chemical properties, pharmacological activity and safety. In the present research, a comprehensive study with comparison of the chromatographic lipophilicity and other physico-chemical properties of five groups of 1-arylsuccinimide derivatives was conducted. The chemometric analysis of their physico-chemical properties was carried out by using unsupervised (hierarchical cluster analysis and principal component analysis) and supervised pattern recognition methods (linear discriminant analysis), while the correlations between the in silico molecular features and chromatographic lipophilicity were examined applying linear and non-linear Quantitative Structure-Retention Relationship (QSRR) approaches. The main aim of the conducted research was to determine similarities and dissimilarities among the studied 1-arylsuccinimides, to point out the molecular features which have significant influence on their lipophilicity, as well as to establish high-quality QSRR models which can be used in prediction of chromatographic lipophilicity of structurally similar 1-arylsuccinimides. This study is a continuation of analysis and determination of the physico-chemical properties of 1-arylsuccinimides which could be important guidelines in further in vitro and eventually in vivo studies of their biological potential.

Chromatographic and computational screening of anisotropic lipophilicity and pharmacokinetics of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimides.

The present study is focused on a series of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimide derivatives, as potential anticonvulsants. The retention behavior of eleven succinimide derivatives was determined by using reversed phase high performance liquid chromatography (RP-HPLC) and reversed phase high performance thin layer chromatography (RP-HPTLC). The estimated retention behavior was correlated with partition (logP) and distribution coefficients (logD). These high correlations pointed out that the determined retention parameters (logk0 and RM0) can be considered chromatographic (anisotropic) lipophilicity of the studied succinimide derivatives. The structural properties, which dominantly affect the chromatographic lipophilicity, were determined as well. The significant correlations between the chromatographic lipophilicity and plasma protein binding (PPB), Madin-Darby Canine Kidney (MDCK) cells permeability, volume of distribution (Vd) and absorption constant (Ka) indicate the strong influence of lipophilicity on pharmacokinetics of 1-aryl-3-ethyl-3-methylsuccinimide derivatives. These derivatives have also been tested applying Comprehensive Medicinal Chemistry (CMC) drug-like rules which confirmed their drug-like properties. Besides, their blood-brain penetration (BBB) ability has been estimated applying the set of Clark's rules and by using Pre-ADMET software. Regarding toxicity, it was predicted that only one compound from the set might have toxic effects by blocking the hERG potassium channel. The present study reveals which molecular features in the structure of novel succinimide derivatives could be crucial for their lipophilicity, and consequently for their pharmacokinetic properties. The results indicate that the newly synthesized series of succinimide derivatives should be further considered in design of novel anticonvulsants.

Toward identification of the risk group of food products: Chemometric assessment of heavy metals content in confectionery products.

The main raw material for cookies production is wheat flour (white or wholemeal/integral) as a nutritionally highly valuable component. In addition to the benefits it brings, this raw material is also a potential source of contamination with residues of heavy metals originating from soil and plant protection agents. Therefore, it is necessary to analyze their concentrations in both wheat flour and final products, since the wheat flour is often present in cookies and related confectionery products in a proportion >60 or 70%. The aim of this paper was to determine the content of heavy metals, including highly toxic ones (As, Cd, Hg and Pb) and essential metals with potential toxic effects (Fe, Cu, Cr, Co, Mn, Ni, Zn) in cookies, waffles and crackers available in local markets in the Autonomous Province of Vojvodina, Republic of Serbia. The present study is focused on chemometric estimation of the risk group of confectionery products containing wheat flour in an easy and efficient way. Hierarchical cluster analysis, principal component analysis, linear discriminant analysis and the sum of ranking differences analysis were applied for this purpose as chemometric tools with substantially different theoretical bases. The obtained results indicated that there is a specific group of cookies with a relatively high content of heavy metals. The group of crackers contained lower concentrations of heavy metals than the other groups of studied products. The results of the sum of ranking differences analysis indicate that there is no strict separation between the samples regarding the ratio among heavy metals contents.

On the characterization of novel biologically active steroids: Selection of lipophilicity models of newly synthesized steroidal derivatives by classical and non-parametric ranking approaches.

In this paper, the guidelines for the interpretation of the results of quantitative structure-retention relationship (QSRR) modeling, comparison and assessment of the established models, as well as the selection of the best and most consistent QSRR model were presented. Various linear and non-linear chemometric regression techniques were used to build QSRR models for chromatographic lipophilicity prediction of a series of triazole, tetrazole, toluenesulfonylhydrazide, nitrile, dinitrile and dione steroid derivatives. Linear regression (LR) and multiple linear regression (MLR) were used as linear techniques, while artificial neural networks (ANNs) were applied as non-linear modeling techniques. Generated models were statistically evaluated applying different approaches for model comparison and ranking. Two non-parametric methods (generalized pair correlation method - GPCM and sum of ranking differences - SRD) were used for model ranking and assessment of the best model for chromatographic lipophilicity prediction using experimentally obtained logk values and row average as a reference ranking. Both, GPCM and SRD, provided highly similar model choice regardless on a different background. These results are in agreement with the classical approach.

New protic ionic liquids for fungi and bacteria removal from paper heritage artefacts.

In this work, new protic ionic liquids (PILs) with 1-ammonium-2-propanol cation and nine different anions: formate (For), acetate (Ac), lactate (Lac), trifluoroacetate (TFA), chloroacetate (ClA), trichloroacetate (TClA), 3-chloropropionate (3-ClP), 4-chlorobutyrate (4-ClB) and mandelate (Man) were prepared in order to study their antimicrobial activity and possible application for fungi and bacteria removal from deteriorated paper heritage. Ten filamentous fungal strains isolated from specific pigmented area of the damaged books: Trichoderma sp., Cladosporium sp., Penicillium sp.(1-3), Penicillium citrinum, Aspergillus sp.(1,2), Aspergillus flavus, Fusarium graminearum, eight Gram positive and Gram negative ATCC bacterial strains: B. subtilis (6633), S. aureus (6538), E. faecalis (19433), K. rhizophila (9341), E. coli (11229), S. enteritidis (13076), P. mirabilis (12453), P. aeruginosa (15692) and two yeast Candida strains: Candida albicans (ATCC 10231) and Candida albicans (L) were used in this study. The results indicated that antimicrobial activity of selected ionic liquids is significantly affected by the size and specific functional groups in the anion structure. These facts opened the possibility for molecular design of new ionic liquids with strong inhibition properties against the specific bacterial, mould and yeast strains. The significant antimicrobial properties observed in this research suggest that studied PILs may have potential applications in the paper art and artefact cleaning and conservation replacing thus, conventional solvents and organic substances that are toxic for humans and environment.

Toward steroidal anticancer drugs: Non-parametric and 3D-QSAR modeling of 17-picolyl and 17-picolinylidene androstanes with antiproliferative activity on breast adenocarcinoma cells.

The present study is aimed to analyze lipophilicity and ADMET profiles, and to develop field based 3D-QSAR and ligand-based pharmacophore hypothesis for a series of 17α-picolyl and 17(E)-picolinylidene androstane derivatives in order to give detailed structural insights and to highlight important binding features of novel androstane derivatives, as compounds with antiproliferative activity toward breast adenocarcinoma cells. This study can provide guidelines for the rational design of novel potent compounds. Sum of ranking differences (SRD), as a non-parametric method, was applied for compounds ranking. 3D-QSAR methods, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were applied to predict the antiproliferative effect on breast adenocarcinoma cells and provide the regions in space where interactive fields may influence the activity. The compounds are ranked so the compounds with the most favorable ADME and lipophilicity features together with significant anticancer activity can be distinguished. The established 3D-QSAR model could be used for design of new compounds with antiproliferative activity on the human ER- breast adenocarcinoma cells. The pharmacophore model is able to accurately predict antiproliferative activity. Generally, the present study provides significant guidelines for further selection, synthesis and rational design of new highly potential androstane derivatives as anticancer drugs.

Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans.

The results presented in this study include the prediction of the antifungal activity of 24 oxazolo derivatives based on their topological and electrostatic molecular descriptors, derived from the 2D molecular structures. The artificial neural network (ANN) method was applied as a regression tool. The input data for ANN modeling were selected by stepwise selection (SS) procedure. The ANN modeling resulted in three networks with the outstanding statistical characteristics. High predictivity of the established networks was confirmed by comparisons of the predicted and experimental data and by the residuals analysis. The obtained results indicate the usefulness of the formed ANNs in precise prediction of minimum inhibitory concentrations of the analyzed compounds towards Candida albicans. The Sum of Ranking Differences (SRD) method was used in this study to reveal possible grouping of the compounds in the space of the variables used in ANN modeling. The obtained results can be considered to be a contribution to development of new antifungal drugs structurally based on oxazole core, particularly nowadays when there is a lack of highly efficient antimycotics.

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrPC) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome.

Chemometrics approach based on chromatographic behavior, in silico characterization and molecular docking study of steroid analogs with biomedical importance.

Physicochemical characterization of steroid analogs (triazole, tetrazole, toluenesulfonylhydrazide, nitrile, dinitrile and dione) is considered to be a very important step in further drug selection. This study applies to the determination of lipophilicity of previously synthesized steroid derivatives using reversed-phase high-performance liquid chromatography (RP HPLC). Chemometric aspect of chromatographic lipophilicity is given throughout multiple linear regression (MLR) quantitative structure-retention relationships (QSRR) approach. Minimal inhibitory concentration (MIC) is determined for two steroid derivatives possessing antimicrobial activity against Staphylococcus aureus. Molecular docking study was performed in order to identify the compound with the most promising potential as human cytochrome P450 CYP17A1inhibitor. Identified 3ß-hydroxyandrost-5-eno[16,17-d]-1,2,3-triazole (I.2.) could be recommended for further trials for anticancer drugs and subjected to the absorption, distribution, metabolism, excretion and toxicity (ADMET) evaluation.

A comparative study of chromatographic behavior and lipophilicity of selected natural styryl lactones, their derivatives and analogues.

This study is based on the analyses of the retention behavior of selected natural styryl lactones and their synthetic analogues in reversed-phase high-performance liquid chromatography. Chromatographic separations were achieved applying ZORBAX SB-C18 column and two different mobile phases: methanol-water and acetonitrile-water. Chromatographic lipophilicity of the analyzed compounds was defined by logk0 constant and correlated with in silico molecular descriptors. According to the statistical validation parameters, obtained results indicate that the presented linear and multiple quantitative structure-retention relationship models can successfully predict the chromatographic lipophilicity of structurally similar compounds. Hierarchical cluster analyses (HCA) was applied in order to group similar compounds according to their chromatographic and in silico lipophilicity. It can be concluded that chromatographic systems with methanol-water were better for modelling of logk0. Modelling was performed in order to characterize compounds regarding their lipophilicity profiles as future drug candidates.

Lipophilicity estimation and characterization of selected steroid derivatives of biomedical importance applying RP HPLC.

The present paper deals with chromatographic lipophilicity determination of twenty-nine selected steroid derivatives using reversed-phase high-performance liquid chromatography (RP HPLC) combined with two mobile phase, acetonitrile-water and methanol-water. Chromatographic behavior of four groups (triazole and tetrazole, toluenesulfonylhydrazide, nitrile and dinitrile and dione) of selected steroid derivatives was studied. Investigated compounds were grouped using principal component analysis (PCA) according to their logk values for both mobile phases. Grouping was in the very good accordance with the polarity and lipophilicity of the investigated compounds. QSRR (quantitative structure-retention relationship) approach was used to model chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods. The most influential molecular descriptors were lipophilicity descriptors that are important for molecules ability to pass through biological membranes and geometrical descriptors. All established LR-QSRR and MLR-QSRR models were statistically validated by standards, cross- and external validation parameters as well as with two graphical methods. According to all these assessments, MLR models were better for chromatographic lipophilicity prediction. It was shown that chromatographic systems with methanol-water were better for modeling of logk than systems with acetonitrile-water, as well as the systems that contained lower volume fractions of organic component in mobile phase. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates of biomedical importance.

Preselection of A- and B- modified d-homo lactone and d-seco androstane derivatives as potent compounds with antiproliferative activity against breast and prostate cancer cells - QSAR approach and molecular docking analysis.

The problem with trial-and-error approach in organic synthesis of targeted anticancer compounds can be successfully avoided by computational modeling of molecules, docking studies and chemometric tools. It has been proven that A- and B- modified d-homo lactone and d-seco androstane derivatives are compounds with significant antiproliferative activity against estrogen-independent breast adenocarcinoma (ER-, MDA-MB-231) and androgen-independent prostate cancer cells (AR-, PC-3). This paper presents the quantitative structure-activity relationship (QSAR) models based on artificial neural networks (ANNs) which are able to predict whether d-homo lactone and/or d-seco androstane-based compounds will express antiproliferative activity against breast cancer cells (MDA-MB-231) or not. Also, the present paper describes the molecular docking study of 3ß-acetoxy-5α,6α-epoxy- (3) and 6α,7α-epoxy-1,4-dien-3-one (24) d-homo lactone androstane derivatives, as well as 4-en-3-one (15) d-seco androstane derivative, which are compounds with strong or moderate antiproliferative activity against prostate cancer cells (PC-3), and compares them with commercially available medicament for prostate cancer - abiraterone. The obtained promising results can be used as guidelines in further syntheses of novel d-homo lactone and d-seco androstane derivatives with antiproliferative activity against breast and prostate cancer cells.

Comprehensive QSRR modeling as a starting point in characterization and further development of anticancer drugs based on 17α-picolyl and 17(E)-picolinylidene androstane structures.

The selection of the most promising anticancer compounds from the pool of the huge number of synthesized molecules is a quite complex task. There are many compounds characterization approaches which can suggest the best structural features of a molecule with the highest antiproliferative effect on the certain type of cancer cell lines. One of these approaches is the lipophilicity determination of compounds and the analysis of its correlation with the anticancer activity. Since the importance of the lipophilicity is underlined in many earlier studies, this study is focused on determination of lipophilicity of previously synthesized 17α-picolyl and 17(E)-picolinylidene androstane derivatives by using reversed-phase high performance liquid chromatography (RP-HPLC) as a very fast, effective and relatively cheap method. Determination of the chromatographic lipophilicity of the studied androstanes can be considered as the part of their physicochemical characterization, which is a very important step in their further selection as drug candidates. The present study does not neglect the in silico approach. The determined chromatographic lipophilicity was analyzed by quantitative structure-retention relationship (QSRR) approach in order to reveal which molecular characteristics contribute mostly to the typical behavior of the androstanes in the applied chromatographic system, and thus to their lipophilicity. Classical statistical approach and Sum of Ranking Differences method were used for selection of the best QSRR models which should be used in prediction of chromatographic lipophilicity of studied androstane derivatives.

Multivariate Chemometrics with Regression and Classification Analyses in Heroin Profiling Based on the Chromatographic Data.

The purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. The article is based on the gas chromatography analysis of heroin samples seized from three different locations in Serbia. Chemometric approach with appropriate statistical tools (multiple-linear regression (MLR), hierarchical cluster analysis (HCA) and Wald-Wolfowitz run (WWR) test) were applied on chromatographic data of heroin samples in order to correlate and examine the geographic origin of seized heroin samples. The best MLR models were further validated by leave-one-out technique as well as by the calculation of basic statistical parameters for the established models. To confirm the predictive power of the models, external set of heroin samples was used. High agreement between experimental and predicted values of acetyl thebaol and diacetyl morphine peak ratio, obtained in the validation procedure, indicated the good quality of derived MLR models. WWR test showed which examined heroin samples come from the same population, and HCA was applied in order to overview the similarities among the studied heroine samples.

QSRR Analysis in Characterization of Some Benzimidazole Derivatives.

In this paper, quantitative structure-retention relationship study has been applied in order to correlate obtained retention parameter R(M)(0) and two groups of molecular descriptors, for eleven investigated benzimidazole derivatives. Principal component analysis (PCA), followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR), was applied in order to identify the most important molecular descriptors. Mathematical models were established and the best models were further validated by leave-on-out (LOO) technique as well as by the calculation of the statistical parameters. Statistically significant models were established.

Chemometric approach to texture profile analysis of kombucha fermented milk products.

In the present work, relationships between the textural characteristics of fermented milk products obtained by kombucha inoculums with various teas were investigated by using chemometric analysis. The presented data which describe numerically the textural characteristics (firmness, consistency, cohesiveness and index of viscosity) were analysed. The quadratic correlation was determined between the textural characteristics of fermented milk products obtained at fermentation temperatures of 40 and 43 °C, using milk with 0.8, 1.6 and 2.8% milk fat and kombucha inoculums cultivated on the extracts of peppermint, stinging nettle, wild thyme and winter savory. Hierarchical cluster analysis (HCA) was performed to identify the similarities among the fermented products. The best mathematical models predicting the textural characteristics of investigated samples were developed. The results of this study indicate that textural characteristics of sample based on winter savory have a significant effect on textural characteristics of samples based on peppermint, stinging nettle and wild thyme, which can be very useful in the determination of products texture profile.

Artificial neural network approach to modelling of metal contents in different types of chocolates.

The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.

Prediction of In-silico ADME Properties of 1,2-O-Isopropylidene Aldohexose Derivatives.

Retention behaviour of molecules mostly depends on their chemical structure. Retention data of biologically active molecules could be an indirect relationship between their structure and biological or pharmacological activity, since the molecular structure affects their behaviour in all pharmacokinetic stages. In the present paper, retention parameters (R M (0)) of biologically active 1,2-O-isopropylidene aldohexose derivatives, obtained by normal-phase thin-layer chromatography (NP TLC), were correlated with selected physicochemical properties relevant to pharmacokinetics, i.e. absorption, distribution, metabolism, and elimination (ADME) properties. Conducted correlation analysis showed high dependence between R M (0) and blood brain barrier penetration, skin permeability, enzyme inhibition, binding affinity to nuclear receptor ligand and G protein-coupled receptors, as well as lipophilicity (expressed as Hansh-Leo's parameter Clog P). The statistical validity of the established polynomial dependence of the second degree between R M (0) and mentioned ADME properties was confirmed by standard statistical measures and leave-one-out cross-validation method. On the basis of in-silico calculated ADME properties and retention data, the similarity between studied molecules was examined using principal component analysis (PCA). The obtained results indicate the possibility of predicting ADME properties of studied compounds on the basis of their retention data (R M (0)). These preliminary results could be treated as guideline for selecting new 1,2-O-isopropylidene aldohexose derivatives as drug candidates.