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.

In vitro antitumor activity, ADME-Tox and 3D-QSAR of synthesized and selected natural styryl lactones.

Prostate cancer is a common cause of death in men and a novel treating methods should be developed. In order to find a new drug for prostate cancer, a series of novel conformationally constrained analogues of (+)-goniofufurone and 7-epi-(+)-goniofufurone, as well as the newly synthesized styryl lactones containing the cinnamic acid ester groups were evaluated for in vitro cytotoxicity against prostate cancer cell (PC-3). Furthermore, prediction of physicochemical characteristics and drugability as well as in silico ADME-Tox tests of investigated compounds were performed. The 3D-QSAR model was established using the comparative molecular field analysis method. According to obtained results, the tricyclic compounds 9 and 10 had the highest potency with IC50 < 20 µM. Evaluation of structural features through 3D-QSAR model identified steric field feature on the cinnamic acid ester groups at C-7 as a crucial for the cytotoxic activity. This research suggests that most of the analysed compounds have desirable properties for drug candidates and high potential in drug development, which recommend them for further research in treatment of prostate cancer. Furthermore, obtained 3D-QSAR model is able to successfully identify styryl lactones that have significant cytotoxic activity and provide information for screening and design of novel inhibitors against PC-3 cell line that could be used as drugs in treatment of the prostate cancer.

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.

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.

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.

Chemometric approach to prediction of antibacterial agent production by Streptomyces hygroscopicus.

The nutritional requirements for antimicrobial agent production using Streptomyces hygroscopicus were analyzed in shake flask experiments. Antimicrobial activity was tested against Staphylococcus aureus and Bacillus cereus. The mathematical models have been generated with relative high complexity in order to give an adequate fit to the data. All the results suggest a high dependence of produced antimicrobial agent quantities on the amount of carbon, nitrogen, and phosphorus in cultivation medium. The statistical results of the generated models reflect the high predictive ability. The derived models were validated using leave-one-out cross-validation technique, and from statistical point of view, they have significantly high values of the cross-validation parameters.

Non-linear assessment of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives--chemometric guidelines for further syntheses.

The present paper deals with prediction of cytotoxic activity of 17-picolyl and 17-picolinylidene androstane derivatives toward androgen receptor negative prostate cancer cell line (PC-3). The prediction was achieved applying artificial neural networks (ANNs) method on the basis of molecular descriptors. The most important descriptors (skin permeability (SP), Madin-Darby canine kidney cell permeability (MDCK) and universal salt solubility factor (S+SF)) were selected by using stepwise selection coupled with partial least squares method. The ANN modelling was carried out in order to obtain reliable models which can facilitate further synthesis of androstane derivatives with high antiproliferative activity toward PC-3 cell line. The modelling procedure resulted in three ANN models with the best statistical performance. The obtained results show that the established ANN models can be applied for required purpose.

RP-HPTLC Retention Data in Correlation with the In-silico ADME Properties of a Series of s-triazine Derivatives.

The properties relevant to pharmacokinetics and pharmacodynamics of four series of synthesized s-triazine derivatives have been studied by Quantitative structure-retention relationship (QSRR) approach. The chromatographic behavior of these compounds was investigated by using reversed-phase high performance thin-layer chromatography (RP-HPTLC). Chromatographic retention (R M (0)) was correlated with selected physicochemical parameters relevant to pharmacokinetics, i.e. ADME (absorption, distribution, metabolism and excretion). In addition, the ability to act as kinase inhibitors and protease inhibitors was predicted for all investigated triazine classes. Also, in order to confirm similarities/dissimilarities between series of examined compounds, principal component analysis (PCA) based on calculated ADME properties was conducted. The R M (0) values of the s-triazine derivatives have been recommended for description and evaluation of pharmacokinetic properties. According to results of this study, the synthesized s-triazine derivatives meet pharmacokinetic criteria of preselection for drug candidates.

QSRR modeling of retention behavior of some s-triazine derivatives.

The properties relevant to lipophilicity of four series of synthesized s-triazine derivatives have been studied by quantitative structure-retention relationship (QSRR) approach. Examination of chromatographic behavior revealed a linear correlation between RM values and the volume fraction of mobile phase modifier. Furthermore, a reliable relationship was defined between the retention constants, RM0, and theoretically calculated bioactivity descriptors for lipophilicity and solubility. Principal component analysis (PCA) followed by multiple linear regression (MLR) and hierarchical cluster analysis (HCA) was performed to identify the most important factors, to quantify their influences, and to select descriptors that best describe the behavior of the compounds investigated. The best QSRR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. The RM0 values of the investigated s-triazine derivatives have been recommended for description of their lipophilicity and evaluation of pharmacokinetic properties.

Multivariate regression modelling of antifungal activity of some benzoxazole and oxazolo[4,5-b]pyridine derivatives.

In the present study, principal component analysis (PCA) followed by principal component regression (PCR) and partial least squares (PLS) method was applied in order to identify the most important in silico molecular descriptors and quantify their influence on antifungal activity (expressed as minimal inhibitory concentration) of selected benzoxazole and oxazolo[4,5-b]pyridine derivatives against Candida albicans. PLS regression showed the best statistical performance, according to the lowest value of the standard error (root mean square errors of calibration of 0.02526 and cross-validation of 0.04533), while PCR model was characterized by root mean square errors of calibration of 0.03176 and cross-validation of 0.05661. The most important descriptors in both PLS and PCR model are solubility in water, expressed as AClogS and ABlogS, and lipophilicity, expressed as XlogP2 and ABlogP. Very good predictive ability of the established models, confirmed by corresponding statistical parameters, allows us to estimate antifungal activity of structurally similar compounds.