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.

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.

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 3β-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.

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.

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.

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.

Quantitative structure-activity relationship (QSAR) study of a series of benzimidazole derivatives as inhibitors of Saccharomyces cerevisiae

A quantitative structure activity relationship (QSAR) has been carried out on a series of benzimidazole derivatives to identify the structural requirements for their inhibitory activity against yeast Saccharomyces cerevisiae. A multiple linear regression (MLR) procedure was used to model the relationships between various physicochemical, steric, electronic, and structural molecular descriptors and antifungal activity of benzimidazole derivatives. The QSAR expressions were generated using a training set of 16 compounds and the predictive ability of the resulting models was evaluated against a test set of 8 compounds. The best QSAR models were further validated by leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. Therefore, satisfactory relationships between antifungal activity and molecular descriptors were found. QSAR analysis reveals that lipophilicity descriptor (logP), dipole moment (DM) and surface area grid (SAG) govern the inhibitory activity of compounds studied against Saccharomyces cerevisiae.

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.

Quantitative structure-retention relationship analysis of some xylofuranose derivatives by linear multivariate method

The relationship between retention behavior of eight 1,2-O-cyclohexylidene xylofuranose derivatives and their molecular characteristics was studied using chemometric Quantitative Structure-Retention Relationships (QSRR) approach. QSRR analysis was carried out on the retention parameter RM0, obtained by normal-phase thin-layer chromatography, by using molecular descriptors, as well as partition coefficient for n-octanol/water bi-phase system (logP). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the retention behavior of the compounds investigated, and to determine the similarities between molecules. MLR equations, that represent the retention measure RMo as a function of the in silico molecular descriptors were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. Obtained results indicate that previously mentioned mathematical models are statistically significant and can successfully predict retention behavior of examined xylofuranose derivatives.