Micellar versus hydro-organic reversed-phase liquid chromatography: A solvation parameter-based perspective

Solute–Solvent Interactions in Hydrophilic Interaction Liquid Chromatography: Characterization of the Retention in a Silica Column by the Abraham Linear Free Energy Relationship Model

The Abraham linear free energy relationship model has been used to characterize a hydrophilic interaction liquid chromatography (HILIC) silica column with acetonitrile/water and methanol/water mobile phases. Analysis by the model for acetonitrile/water mobile phases points to solute volume and hydrogen bond basicity as the main properties affecting retention, whereas solute hydrogen bond acidity, dipolarity and polarizability practically do not affect it. Formation of a cavity is easier in acetonitrile-rich mobile phases than in the aqueous stationary phase, and hence increase of solute volume decreases retention. Conversely, hydrogen bond acidity is stronger in the aqueous stationary phase than in the acetonitrile-rich mobile phase and thus an increase of solute hydrogen bond basicity increases retention. Results are similar for methanol/water mobile phases with the difference that solute hydrogen bond acidity is significant too. Increase in hydrogen bond acidity of the solute decreases retention showing that methanol mobile phases must be better hydrogen bond acceptors than acetonitrile ones, and even than water-rich stationary phases. The results are like the ones obtained in zwitterionic HILIC columns bonded to silica or polymer supports for acetonitrile/water mobile phases, but different for solute hydrogen bond acidity for a polymer bonded zwitterionic column with methanol/water mobile phases, indicating that bonding support plays an important role in HILIC retention. Comparison to RPLC characterized systems confirms the complementarity of HILIC systems to RPLC ones because the main properties affecting retention are the same but with reversed coefficients. The least retained solutes in RPLC are the most retained in HILIC.

Linear free energy relationship models for the retention of partially ionized acid-base compounds in reversed-phase liquid chromatography

The LFER model of Abraham is applied to the retention of the neutral and ionic forms of 94 solutes in a C18 column and 40% v/v acetonitrile/water mobile phase. The results show that polarizability and cavity formation interactions increase retention, whereas dipole and hydrogen bonding interactions favours partition to the mobile phase and thus, they decrease retention. The coefficients of the ionic descriptors measure the effect of the electrostatic interactions and their contribution to partition of the cation or anion between the two mobile and stationary chromatographic phases. A new LFER model for application to the retention of partially dissociated acids and bases is derived averaging the descriptors of the neutral and ionic forms according to their degrees of ionization in the mobile phase. This new LFER model is satisfactorily compared to other literature modified Abraham models for a set of 498 retention data of partially dissociated acids and bases. All tested models require the calculation of the ionization degrees of the compounds at the measuring pH. Calculation of the ionization degrees in the chromatographic mobile phase (i.e. from pH and pK_a in the eluent) give good correlations for all tested models. However, estimation of these ionization degrees from pH - pK_a data in pure water gives biased estimations of the retention of the partially ionized solutes.

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography

In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes' retention in MLC mode becomes a challenging task. Mixed Quantitative Structure - Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes' retention. This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN). A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q²) values. Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.

Partitioning of Aromatic Constituents into Water from Jet Fuels

A comprehensive study of the most commonly used jet fuels (i.e., Jet A-1 and JP-8) was performed to properly assess potential contamination of the subsurface environment from a leaking underground storage tank occurred in an airport. The objectives of this study were to evaluate the concentration ranges of the major components in the water-soluble fraction of jet fuels and to estimate the jet fuel-water partition coefficients (K fw) for target compounds using partitioning experiments and a polyparameter linear free-energy relationship (PP-LFER) approach. The average molecular weight of Jet A-1 and JP-8 was estimated to be 161 and 147 g/mole, respectively. The density of Jet A-1 and JP-8 was measured to be 786 and 780 g/L, respectively. The distribution of nonpolar target compounds between the fuel and water phases was described using a two-phase liquid-liquid equilibrium model. Models were derived using Raoult's law convention for the activity coefficients and the liquid solubility. The observed inverse, log-log linear dependence of the K fw values on the aqueous solubility were well predicted by assuming jet fuel to be an ideal solvent mixture. The experimental partition coefficients were generally well reproduced by PP-LFER.

Evaluating the interactions of organic compounds with multi-walled carbon nanotubes by self-packed HPLC column and linear solvation energy relationship

Understanding the interactions between organic pollutants and carbon nanotubes (CNTs) is critical for fate assessment of both CNTs and organic pollutants. In this study, the chromatographic approach was introduced based on CNTs as stationary phase for the evaluation of such interactions. The pristine multi-walled carbon nanotubes (MWCNTs) were packed into columns of high-pressure liquid chromatography (HPLC) and the retention factors (k') were determined to characterize the adsorption affinity of organic compounds onto MWCNTs. Nine compounds were tested. The results showed that their lnk' values followed the order: benzene < toluene < phenol < chlorobenzene < bromobenzene < aniline < sulfamethoxazole < sulfadiazine ≈ sulfadimidine. The linear solvation energy relationship (LSER) theory was adopted to correlate lnk' with the molecular solvatochromic parameters. We found that lnk' of the studied compounds correlate positively with molecular polarizability (E) significantly, suggesting that the π-/n-electrons-dependent polarizable interactions play a major role for the adsorption. Moreover, the thermodynamic parameters calculated from van't Hoff equations revealed that the interactions between the compounds and MWCNTs were spontaneous and exothermic processes.

The use of biopartitioning micellar chromatography and immobilized artificial membrane column for in silico and in vitro determination of blood-brain barrier penetration of phenols

Biopartitioning Micellar Chromatography (BMC) is a mode of micellar liquid chromatography that uses C18 stationary phases and micellar mobile phases of Brij35 under adequate experimental conditions and can be useful to mimic human drug absorption, blood-brain barrier distribution or partitioning processes in biological systems. BMC system can be useful in constructing good predictive models because the characteristics of the BMC system are similar to biological barriers and extracellular fluids. Immobilized Artificial Membrane (IAM) chromatography uses stationary phase which consists of a monolayer of phosphatidylcholine covalently immobilized on an inert silica support. IAM columns are thought to mimic very closely a membrane bilayer and are used in a HPLC system with a physiological buffer as eluent. In this paper the usefulness of BMC and IAM system for in silico and in vitro determination of blood-brain barrier (BBB) penetration of phenols has been demonstrated. The most important pharmacokinetic parameters of brain have been obtained for the determination of BBB penetration, i.e. BBB permeability - surface area product (PS), usually given as a logPS, brain/plasma equilibration rate (log(PS×fu,brain)) and fraction unbound in plasma (Fu). Moreover, the relationships between retention of eighteen phenols and different parameters of molecular size, lipophilicity and BBB penetration were studied. Extrapolated to pure water values of the logarithms of retention factors (logkw) have been compared with the corresponding octanol-water partition coefficient (logPo-w) values of the solutes. In addition, different physicochemical parameters from Foley's equation for BMC system have been collated with the chromatographic data. The Linear Solvation Energy Relationship (LSER) using Abraham model for the describing of phenols penetration across BBB has been used. Four equations were developed as a multiple linear regression using retention data from IAM and BMC system (QRAR models) and molecular volume parameter (Vm) and Abraham descriptors to correlate the logBB values. Moreover, in order to establish the relationships between different variables, the principal components analysis (PCA) has been done. The results of PCA were obtained using chromatographic data from IAM and BMC systems as well as from the structures of tested phenols. The four parameters: logkwIAM(exp), logkwBMC(exp), analyte-micelle association constant (Kma) and logPo-w have been checked.

Recent theoretical and practical applications of micellar liquid chromatography (MLC) in pharmaceutical and biomedical analysis

Micellar liquid chromatography (MLC) is an analytical technique belonging to the wide range of reversed-phase liquid chromatographic (RP-LC) separation techniques. MLC with the use of surfactant solutions above its critical micellar concentration (CMC) and the addition of organic modifiers is currently an important analytical tool with still growing theoretical considerations and practical applications in pharmaceutical analysis of drugs and other biologically active compounds. The use of MLC as an alternative, relatively much faster in comparison to conventional chromatographic separation techniques has several advantages, especially as being suitable for screening pharmaceutical analysis. The analytical data received from MLC analysis are considered a useful source of information to predict passive drug absorption, drug transport and other pharmacokinetics and physicochemical measures of pharmaceutical substances. In the review several MLC assays for determination of drugs and other active compounds in biological samples were compared and critically discussed. The presented overview provides information on recent applications and achievements connected with the practical use of MLC. The review covers fields of interest related to theory and mechanism of MLC separation, direct applications of MLC in pharmaceutical analysis, including optimization and efficiency of separation with the use of modification of stationary phase and mobile phase compositions as well as the determination of physicochemical characteristics of drugs by MLC.

Selectivity of Brij-35 in Micellar Liquid Chromatographic Separation of Positional Isomers

Implementation of Brij-35, a nonionic surfactant, as a mobile phase for separation of positional isomers is investigated. Chromolith C-18 SpeedROD is used as a stationary phase. The effect of surfactant and organic modifier (propanol) concentration on the separation of some selected isomers is studied and evaluated in terms of linear solvation energy relationship (LSER). Shape selectivity is assessed by α value of sorbic and benzoic acid, which is found to be 1.339 by using mobile phase composed of 0.5% aqueous solutions of Brij-35 and propanol in 9 : 1. Isomers of parabens, nitroanilines, nitrophenols, and quinolinols are successfully separated using mobile phases composed of various percentages of surfactant and propanol. System constants for nonionic MLC using LSER analysis show that hydrogen bond basicity and dipolarity may be major contributors to selectivity, while excess molar refraction helps fine-tuning the separation which also imparts unique selectivity to nonionic surfactants as compared to ionic ones.

Separation and mechanism elucidation for six structure-like matrine-type alkaloids by micellar liquid chromatography

A mixed micellar liquid chromatography (MLC) method, the mobile phase consisting of anionic surfactant SDS and nonionic surfactant Brij35, was firstly developed for the separation and determination of six structure-like matrine-type alkaloids, including matrine, oxymatrine, sophocarpine, oxysophocarpine, sophoridine, and oxysophoridine. The factors influencing the resolution of the six alkaloids were systematically investigated and optimized, including the micellar composition and concentration, column temperature, the type and amount of organic solvent, and the pH values in the mobile phases. Under the optimized separation conditions, the six matrine-type alkaloids could be easily isocratically eluted with a baseline separation within 22 min. Under the designated conditions (SDS concentration from 10 to 50 mM, Brij35 from 5 to 30 mM, pH 3 and 5% 1-propanol), the hydrophobic selectivity was negatively correlated with the concentration of Brij35 but not with SDS. The functional group selectivity of the carbonyl group, double bond, and diastereomers, all decreased with the increase in percentage of SDS in the mixed micellar phase, because the strong electrostatic force masks other molecular forces which can discriminate the retention of the analytes. Therefore, such a combination in surfactants of MLC is a powerful strategy to increase the selectivity by adjusting the balance among the various molecular interaction forces influencing analytes' retention. Finally, the developed method was successfully used to separate and determine the contents of main alkaloids in Sophora medicinal plants, S. flavescens Ait. In summary, the mixed MLC is a valuable approach to separate and determine the structure-like multi-component natural samples.

Retention mechanisms in micellar liquid chromatography

Micellar liquid chromatography (MLC) is a reversed-phase liquid chromatographic (RPLC) mode with mobile phases containing a surfactant (ionic or non-ionic) above its critical micellar concentration (CMC). In these conditions, the stationary phase is modified with an approximately constant amount of surfactant monomers, and the solubilising capability of the mobile phase is altered by the presence of micelles, giving rise to diverse interactions (hydrophobic, ionic and steric) with major implications in retention and selectivity. From its beginnings in 1980, the technique has evolved up to becoming a real alternative in some instances (and a complement in others) to classical RPLC with hydro-organic mixtures, owing to its peculiar features and unique advantages. This review is aimed to describe the retention mechanisms (i.e. solute interactions with both stationary and mobile phases) in an MLC system, revealed in diverse reports where the retention behaviour of solutes of different nature (ionic or neutral exhibiting a wide range of polarities) has been studied in a variety of conditions (with ionic and non-ionic surfactants, added salt and organic solvent, and varying pH). The theory is supported by several mechanistic models that describe satisfactorily the retention behaviour, and allow the measurement of the strength of solute-stationary phase and solute-micelle interactions. Suppression of silanol activity, steric effects in the packing pores, anti-binding behaviour, retention of ionisable compounds, compensating effect on polarity differences among solutes, and the contribution of the solvation parameter model to elucidate the interactions in MLC, are commented.