Structure-retention and mobile phase-retention relationships for reversed-phase high-performance liquid chromatography of several hydroxythioxanthone derivatives in binary acetonitrile-water mixtures

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.

Influence of High pH on the Organization of Acetonitrile at the Silica/Water Interface Studied by Sum Frequency Generation Spectroscopy

The acetonitrile-water mixture is one of the most commonly used solvents in hydrophilic interaction chromatography, which contains silica as the solid phase. As such, the silica/acetonitrile-water interface plays a large role in the separation of compounds. Varying the pH is one way to influence retention times, particularly of ionizable solutes, yet the influence of high pH is often unpredictable. To determine how the structure of this interface changes with pH, we utilized the surface specific technique sum frequency generation (SFG). Previous SFG studies at neutral pH have suggested the existence of acetonitrile bilayers at the aqueous silica interface even at low acetonitrile mole fractions. Here we find that the SFG signal from 2900 to 3040 cm^-1 at the silica/acetonitrile-water interface increased as we adjusted the aqueous pH from near neutral to high values. This increase in signal was attributed to a greater amount of aligned water which is consistent with an increase in silica surface charge at high pH. In contrast, complementary measurements of the silica/acetonitrile-deuterium oxide interface revealed that the acetonitrile methyl mode nearly vanished as the aqueous pH was increased. This loss of methyl mode signal is indicative of a decrease in the number density of acetonitrile molecules at the interface, as orientation analysis indicates no significant change in the net orientation of the outer leaflet of the acetonitrile bilayer over the pH range studied.

Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance.