Modeling of retention of adrenoreceptor agonists and antagonists on polar stationary phases in hydrophilic interaction chromatography: a review.

Retention prediction models for reversed-phase liquid chromatography (RPLC) have been extensively studied owing to the fact that RPLC remains the most widely used chromatographic technique especially in the field of pharmaceutical and biomedical analyses. However, RPLC is not always the method of choice for the analysis of some compounds that have high polarity. Hydrophilic interaction chromatography (HILIC) has been gaining interest in the last few years as an alternative option to RPLC for the analysis of polar and hydrophilic analytes. HILIC is a variant of normal-phase liquid chromatography, but utilizes water in a water-miscible organic solvent as the eluent in conjunction with a hydrophilic stationary phase. The present review aims to summarize recent contributions on the development of retention prediction models for a group of basic analytes, namely, the adrenoreceptor agonists and antagonists, on different polar stationary phases. The use of multiple linear regression and artificial neural networks in model building is highlighted.

Effects of alcohols on CE enantioseparation of basic drugs with native gamma-CD as chiral selector.

The effects of alcohol on the CE enantioseparation of selected basic drugs with gamma-CD as the chiral selector was investigated. The enantioseparation behavior of the analytes with gamma-CD in the absence and presence of different alcohols specifically methanol, ethanol, 2-propanol (IPA), and 2-methyl-2-propanol (TBA), the relationship of enantiomeric resolution (R(s)) values with either hydrophobicity or bulkiness of the alcohols, as well as the effect of these alcohols on interaction of the analytes with gamma-CD were studied. Results showed that hydrophobicity and/or bulkiness of alcohols have an influence on the enantioresolution of most of the analytes based on the relatively high correlation coefficients (R) obtained between R(s) versus log P and between R(s) versus ovality (i.e., parameter to indicate bulkiness of a molecule). Comparison of the values of the average binding constants obtained for each enantiomeric pair in the presence and absence of 5% IPA showed that alcohols can increase, decrease, or give a minimal effect on the analyte-gamma-CD interaction depending on the analyte. Furthermore, the significant enhancement in the enantioresolution of both propranolol and pindolol in the presence of either IPA or TBA led to the baseline enantioresolution of both drugs using 35 mM gamma-CD.

Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography.

The development of retention prediction models for the seven ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2, and Rb1 on a polyamine-bonded stationary phase in hydrophilic interaction chromatography (HILIC) is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable for four temperature conditions (0, 10, 25, and 40 degrees C). Using stepwise MLR, the retention of the analytes in all the temperature conditions was satisfactorily described by a two-predictor model wherein the predictors were the percentage of ACN (%ACN) in the mobile phase and local dipole index (LDI) of the compounds. These predictors account for the contribution of the solute-related variable (LDI) and the influence of the mobile phase composition (%ACN) on the retention behavior of the ginsenosides. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R(2) values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.

Development of retention prediction models for adrenoreceptor agonists and antagonists on a polyvinyl alcohol-bonded stationary phase in hydrophilic interaction chromatography.

Retention prediction models based on multiple linear regression (MLR) and artificial neural network (ANN) for adrenoreceptor agonists and antagonists chromatographed on a polyvinyl alcohol-bonded stationary phase under hydrophilic interaction chromatography were described. The models showed the combined effects of solute structure and mobile phase composition on the retention behavior of the analytes. Using stepwise MLR, the retentions of the studied compounds were satisfactorily described by a five-predictor model; the predictors being the %ACN, the logarithm of the partition coefficient (log D), the number of hydrogen bond donors (HBD), the desolvation energy for octanol (FOct), and the total absolute atomic charge (TAAC). The inclusion of the solute-related descriptors suggested that hydrophilic interactions such as hydrogen bonding and also ionic interactions are possible mechanisms by which analytes are retained on the studied system. ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architectures were found to be 5-3-1 for the datasets at pH 3.0 and 4.0, and 5-4-1 for the dataset at pH 5.0. The optimized ANNs showed better predictive properties than the MLR models for both training and test sets under all pH conditions.

Retention prediction modeling of ginsenosides on a polyvinyl alcohol-bonded stationary phase at subambient temperatures using multiple linear regression and artificial neural network.

The development of retention prediction models for the seven ginsenosides (Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1) on a polyvinyl alcohol (PVA)-bonded stationary phase at subambient temperatures is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable. Using stepwise MLR, the retention of the analytes under all temperature conditions was satisfactorily described by a three-predictor model; the predictors being the percentage of acetonitrile (%MeCN) in the mobile phase, the number of hydrogen bond donors (HBD) and the ovality (Ov) of the compounds. These predictors account for the contribution of the solute-related variables (HBD and Ov) and the influence of the mobile phase composition (%MeCN) on the retention behavior of the ginsenosides. The MLR models produced adequate fits, as proven by the high calibration R2 values of the predicted versus the observed log k (> 0.95) and good predictive properties, as indicated by the high cross-validated q2 (> 0.93) and high R2 (> 0.95) values obtained from the test set. ANN modeling was also conducted using the predictors that were derived from MLR as inputs and log k as the output. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R2 values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.

Retention behavior of ginsenosides on a poly(vinyl alcohol)-bonded stationary phase in hydrophilic interaction chromatography.

The influences of the organic component of the mobile phase and the column temperature on the retention of ginsenosides on a poly(vinyl alcohol) (PVA) bonded stationary phase operated under hydrophilic interaction chromatographic mode were investigated. The retention of the ginsenosides was found to increase with increasing amount of acetonitrile (MeCN) in the mobile phase, which is typical of hydrophilic interaction chromatographic behavior. It was also found that the retention of the analytes was highly affected by the type of the organic modifier used. Aqueous MeCN (75-90%) gave the most satisfactory retention and separation of ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1 compared with aqueous methanol, isopropyl alcohol or tetrahydrofuran at the same composition levels. The effects of the different types of organic modifiers on the retention of the analytes were attributed to their solvent strength and hydrogen-bond accepting/donating properties. The effect of temperature on the retention of ginsenoside on the PVA-bonded phase was assessed by constructing van't Hoff plots for two temperature ranges: subambient (273-293 K) and ambient-elevated (298-333 K) temperatures. van't Hoff plots for all analytes were linear at the two temperature intervals; however, the slopes of the lines corresponding to ginsenosides Rg1 and Re were completely different from those for the rest of the analytes especially in the subambient temperature range. Enthalpy-entropy compensation (EEC) studies were conducted to verify the difference in thermodynamics observed for ginsenosides Rg1 and Re compared with the other analytes. EEC plots showed that Rf, Rd, Rc, Rb2 and Rb1 were possibly retained by the same retention mechanism, which was completely different from that of Rg1 and Re at subambient temperatures. Retention prediction models were derived using multiple linear regression to identify solute attributes that affected the retention of the analytes on the PVA-bonded phase. The mathematical models derived revealed that the number of hydrogen-bond donors and the ovality of the molecules are important molecular properties that govern the retention of the compounds on the chromatographic system.

Simultaneous enantioseparation and sensitivity enhancement of basic drugs using large-volume sample stacking.

Simultaneous enantioseparation with sensitive detection of four basic drugs, namely methoxamine, metaproterenol, terbutaline and carvedilol, using a 20-mum ID capillary with native beta-CD as the chiral selector was demonstrated by the large-volume sample stacking method. The procedure included conventional sample loading either hydrodynamically or electrokinetically at longer injection times without polarity switching and EOF manipulation. In comparison to conventional injections, depending on the analyte, about several hundred- and a thousand-fold sensitivity enhancement was achieved with the hydrodynamic and the electrokinetic injections, respectively. The simple method developed was applied to the analysis of racemic analytes in serum samples and better recovery was achieved using hydrodynamic injection than electrokinetic injection.

Retention prediction of adrenoreceptor agonists and antagonists on a diol column in hydrophilic interaction chromatography.

Retention prediction models using multiple linear regression (MLR) and artificial neural networks (ANN) were developed for adrenoreceptor agonists and antagonists chromatographed on a diol column under hydrophilic interaction chromatographic (HILIC) mode at three pH conditions (3.0, 4.0 and 5.0). Using stepwise MLR, the retention behavior of the analytes was satisfactorily described by a five-predictor model; the predictors being the percentage of acetonitrile in the mobile phase (% ACN), the logarithm of partition coefficient (log D), the number of hydrogen bond donor (HBD), the number of hydrogen bond acceptor (HBA), and the total absolute atomic charge of the molecule (TAAC). Among the five descriptors, % ACN had the strongest effect on the retention as indicated by its relatively higher standardized coefficient compared to the other four predictors. The inclusion of the four predictors which are related to the properties of the compounds (log D, HBD, HBA and TAAC), suggested hydrophilic interaction, hydrogen bonding and ionic interaction as possible mechanisms of retention of the analytes on the studied system. The models derived from MLR also showed adequate fit as proven by the high correlation (R2 as high as 0.9667) between observed and predicted log k values for the training set and good predictive power on the test set (R2 greater than 0.97). ANN analyses of the data were also conducted using the five predictors derived from MLR as inputs and log k as output. The trained ANNs showed better predictive abilities as compared to MLR models as indicated by relative higher R2 and lower root mean square error of predictions (RMSEP) for both training and test sets. The derived models can be used as references for method development and optimization of chromatographic conditions for the separation of adrenoreceptor agonists and antagonists.

Retention prediction of adrenoreceptor agonists and antagonists on unmodified silica phase in hydrophilic interaction chromatography.

The development of retention prediction models for adrenoreceptor agonists and antagonists chromatographed on an unmodified silica stationary phase under the hydrophilic interaction chromatographic (HILIC) mode at three pH conditions (3.0, 4.0 and 5.0) is described. The models were derived using multiple linear regression (MLR) and an artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable. In addition to the effects of the solute-related variables (molecular descriptors), the percentage of acetonitrile (%ACN) was also used as a predictor to gauge the influence of the mobile phase on the retention behavior of the analytes. Using stepwise MLR, the retention behavior of the studied compounds at pH 3.0 were satisfactorily described by a four-predictor model; the predictors being the %ACN, the logarithm of the partition coefficient (log D), the number of hydrogen bond acceptors (HBA), and the magnitude of the dipole moment (DipolMag). In addition to these four predictors, the total absolute atomic charge (TAAC) was found to be a significant predictor of retention at pH 4.0 and 5.0. Among the five descriptors, %ACN had the strongest effect on the retention, as indicated by its higher standardized coefficient than those obtained for the other four predictors. The inclusion of these four predictors which are related to the molecular properties of the compounds (log D, HBA, DipolMag, and TAAC) suggested that hydrophilic interactions, hydrogen bonding and ionic interactions are possible mechanisms by which analytes are retained on the studied system. The reliability and predictive ability of the derived MLR equations were tested using cross-validation and a test set which was not used when fitting the model. The models derived from MLR produced adequate fits, as proven by the high R2 values obtained for all calibration and training sets (0.9497 and above), and their good predictive power, as indicated by the high cross-validated q2 (0.9465 and above) and high R2 (0.9305 and above) values obtained for the test sets. ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as output. A comparison of the models derived from both ANN and MLR revealed that the trained ANNs showed better predictive abilities than the MLR models, as indicated by their higher R2 values and their lower root mean square error of predictions (RMSEP) for both training and test sets under all pH conditions. The derived models can be used as references and they provide a useful tool for method development and the optimization of chromatographic conditions for the separation of adrenoreceptor agonists and antagonists.

Optimization of capillary electrophoretic enantioseparation for basic drugs with native beta-CD as a chiral selector.

This study presents the advantages of the 20 microm inner diameter (id) capillary for the enantioseparation of ten basic drugs with native beta-CD as the chiral selector. The apparent binding constants of each enantiomeric pair were determined to calculate the optimum beta-CD concentration ([beta-CD]opt) and the optimization was subsequently carried out. Comparison of the 20 microm id with 50 microm id were made in terms of the results obtained in the optimization and detection limits. Applying the optimum conditions for each compound, reproducible results (RSD from 0-3; n>5) were obtained for the 20 microm id capillary. Although the sensitivity is lower in the 20 microm id capillary, the LOD determined using this capillary is still found to be acceptable for the ten basic drugs studied. Enhanced resolution and faster analysis times were the main advantages observed with the use of this capillary in enantioseparation.