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.

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.