Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC

Retention prediction models for a group of pyrazines chromatographed under reversed-phase mode were developed using multiple linear regression (MLR) and artificial neural networks (ANNs). Using MLR, the retention of the analytes were satisfactorily described by a two-predictor model based on the logarithm of the partition coefficient of the analytes (log P) and the percentage of the organic modifier in the mobile phase (ACN or MeOH). ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architecture was found to be 2-2-1 for both ACN and MeOH data sets. The optimized ANNs showed better predictive properties than the MLR models especially for the ACN data set. In the case of the MeOH data set, the MLR and ANN models have comparable predictive performance.     

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.     

Cytotoxicity and 2D-QSAR study of some heterocyclic compounds

Herein we have studied the cytotoxicity and quantitative structure–activity relationship (QSAR) of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydantoin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC₅₀ values (Log of LC₅₀) against brine shrimp lethality bioassay in order to derive the 2D-QSAR models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and ANN methods gave highly significant square correlation coefficient (R²) values of 0.83, 0.81 and 0.91 respectively. The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient Q² (0.74).     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

Prediction of human serum albumin-drug binding affinity without albumin

A new liquid chromatographic system was developed to measure protein-drug binding affinity indirectly without albumin and was evaluated using log nK values of drugs measured by a modified Hummel-Dreyer method using purified human serum albumin. The retention factors of acidic and basic drugs were measured by reversed-phase and ion-exchange liquid chromatography in sodium phosphate buffer, pH 7.40, containing 50 vol.% methanol at 37 °C. The bonded phases were pentyl, guanidino and carboxyl phases. The combined retention factors were correlated with the log nK values measured by a modified Hummel-Dreyer method because glycosylation of human serum albumin did not significantly affect log nK value. The correlation coefficients were 0.949 (n=7) for acidic drugs and 0.978 (n=5) for basic drugs. The log nK values of 26 acidic and 18 basic drugs were predicted from their retention factors measured by reversed-phase and ion-exchange liquid chromatography.     

Investigation of the chromatographic behaviour of some selenium species—Comparison with their octanol-water partitioning

The retention behaviour of selenites (Se(IV)), selenates (Se(VI)), seleno-dl-methionine (Se-Met), selenocystine (Se-Cyst), selenocystamine (Se-CM) and selenourea (Se-U) was investigated using a Discovery end-capped reversed-phase column as stationary phase and different mobile phase conditions. Extrapolated to 100% aqueous mobile phase retention factors (log k_w) of the investigated Se species, determined using different methanol fractions (φ) as organic modifier, were compared with the corresponding actual values. The proper operation of this column even at 100% aqueous phase proved to be valuable for the accurate determination of log k_w values of Se-CM and Se-Cyst, presenting a convex curvature log k = f(φ) at low MeOH fractions, often neglected in the extrapolation procedure. The effect of the presence of n-decylamine as well as saturation of the mobile phase with n-octanol was also studied. For ampholytic Se-Met and Se-Cyst the effect of n-decylamine in retention reflected the predominance of zwitterionic nature in the case of Se-Met in contrary to the non-zwitterionic species found in the case of Se-Cyst, in accordance with our previous findings concerning partitioning experiments in the n-octanol/water system. Finally, an attempt was made to correlate log k_w values with the logarithm of n-octanol/water distribution coefficient, log D, of the investigated Se species and an indicative log D value of Se-U was derived.     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

Selectivity patterns in micellar electrokinetic chromatography: Characterization of fluorinated and aliphatic alcohol modifiers by micellar selectivity triangle

The usefulness of the micellar selectivity triangle (MST) for prediction and interpretation of separation patterns in micellar electrokinetic chromatography (MEKC) separations is presented. In addition, we demonstrate the capability of controlling selectivity properties of micelles through addition of organic modifiers with known solvation properties as predicted by MST. The examples are modification of the hydrogen bond donor (HBD) micelle of lithium perfluorooctanesulfonate, the hydrogen bond acceptor (HBA) micelle of tetradecyltrimethylammonium bromide, and the sodium dodecyl sulfate micelles with intermediate hydrogen bonding properties with two hydrophobic organic modifiers. One is an aliphatic alcohol, n-pentanol that can act as both a HBA and a HBD; by contrast, the other organic modifier is a fluorinated alcohol, hexafluoroisopropanol that is a strong HBD modifier and would enhance the hydrogen bond donor strength of micelles. A test sample composed of 20 small organic solutes representing HBA, HBD, and non-hydrogen bond aromatic compounds was carefully selected. The trends in retention behavior of these compounds in different micelles are consistent with the selectivity patterns predicted by the MST scheme. To the best of our knowledge, this is the first report on the unique selectivity of fluorinated alcohols as modifiers in MEKC. These results demonstrate the usefulness of the MST scheme for identifying pseudo-phases with highly similar or different selectivities and can serve as a guide for judicious selection of modifiers to create pseudo-phases with desired selectivity behavior on a rational basis.