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 Structural Diversity and Biological Activity of Steroid Oximes

Steroids and their derivatives have been the subject of extensive research among investigators due to their wide range of pharmacological properties, in which steroidal oximes are included. Oximes are a chemical group with the general formula R₁R₂C=N-OH and they exist as colorless crystals and are poorly soluble in water. Oximes can be easily obtained through the condensation of aldehydes or ketones with various amine derivatives, making them a very interesting chemical group in medicinal chemistry for the design of drugs as potential treatments for several diseases. In this review, we will focus on the different biological activities displayed by steroidal oximes such as anticancer, anti-inflammatory, antibacterial, antifungal and antiviral, among others, as well as their respective mechanisms of action. An overview of the chemistry of oximes will also be reported, and several steroidal oximes that are in clinical trials or already used as drugs are described. An extensive literature search was performed on three main databases-PubMed, Web of Science, and Google Scholar.

Quantitative structure-chromatographic retention relationship of synthesized peptides (HGRFG, NPNPT) and their derivatives

Carapax Trionycis extract peptides (HGRFG, NPNPT) are able to protect against CCl₄-induced liver fibrosis. Therefore, this study applies to deal with chromatographic lipophilicity determination of synthesized peptides (HGRFG, NPNPT) and their derivatives using reversed-phase high performance liquid chromatography (RP-HPLC) combined with methanol-water mobile phase and two reversed-phase chromatographic columns (COSMOISL 5C18-MS-II and SHIMADZU-C18). The chromatographic lipophilicity of the analyzed compounds was expressed as logk_w constant and correlated with lipophilicity descriptors. Quantitative structure-retention relationships (QSRR) analysis was performed to imitate chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). The most influential molecular descriptors were lipophilicity descriptors, which are important for molecules ability to pass through biological membranes. All established QSRR models were statistically validated by standards, cross- and external validation parameters. According to these statistical validation parameters, MLR models (R² > 0.856) were better for chromatographic lipophilicity prediction of peptide compounds. It can be concluded that chromatographic systems with COSMOISL 5C18-MS-II column were better for modeling of logk_w than systems with SHIMADZU-C18 column. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates.

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.

Estimation of Retention Time in GC/MS of Volatile Metabolites in Fragrant Rice Using Principle Components of Molecular Descriptors

A quantitative structure-retention relationship (QSRR) study was applied for an estimation of retention times of secondary volatile metabolites in Thai jasmine rice. In this study, chemical components in rice seed were extracted using solvent extraction, then separated and identified by gas chromatography-mass spectrometry (GC-MS). A set of molecular descriptors was generated for these substances obtained from GC-MS analysis to numerically represent the molecular structure of such compounds. Principal component analysis (PCA) and principal component regression analysis (PCR) were used to model the retention times of these compounds as a function of the theoretically derived descriptors. The best fitted regression model was obtained with R-squared of 0.900. The informative chemical properties related to retention time were elucidated. The results of this study demonstrate clearly that the combination of molecular weight and autocorrelation functions of two dimensional interatomic distance, which are molecular polarizability, atom identity, sigma charge, sigma electronegativity and polarizability, can be considered as comprehensive factors for predicting the retention times of volatile compounds in rice.