Application of planar and column micellar liquid chromatography to the prediction of physicochemical properties and biological activity of compounds

APPLICATION OF MICELLAR LIQUID CHROMATOGRAPHY TO MODEL ECOTOXICITY OF PESTICIDES. COMPARISON WITH IMMOBILIZED ARTIFICIAL MEMBRANE CHROMATOGRAPHY AND N-OCTANOL-WATER PARTITIONING

The potential of Micellar Liquid Chromatography (MLC) to model ecotoxicological endpoints for a series of pesticides was investigated. To exploit the flexibility in MLC conditions, different surfactants were employed and retention mechanism was tracked and compared to Immobilized Artificial Membrane (IAM) chromatographic retention and n-octanol- water partitioning, logP. Neutral polyoxyethylene (23) lauryl ether (Brij-35), anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) were used in presence of PBS at pH=7.40 and acetonitrile as organic modifier when necessary. Similarities/ dissimilarities between MLC retention and IAM or logP were investigated by Principal Component Analysis (PCA) and Liner Solvation Energy Relationships (LSER). LSER revealed that hydrogen bonding acidity is the most important factor for differentiation between MLC and IAM or logP. The impact of hydrogen bonding is exemplified in the relationships of MLC retention factors with IAM or logP, which necessitate the inclusion of a relevant descriptor. PCA further revealed that MLC retention factors are clustered together with IAM indices and logP within a broader ellipse formed by ecotoxicological endpoints, involving LC₅₀/ EC₅₀ values of six aquatic organisms namely Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values of Honey Bee, thus justifying their use to construct relevant models. Satisfactory specific models for individual organisms, as well as general fish models, were obtained, in most cases, upon combination of MLC retention factors with Molecular Weight (MW) or/ and hydrogen bond parameters. All models were evaluated and compared to previously reported IAM and logP based models using an external validation data set. Predictions with Brij-35 and SDS based models were comparable, although slightly inferior than those obtained with IAM, while they were in all cases better than those obtained with logP. CTAB led to a satisfactory prediction model for Honey Bee, but it was found less suitable for aquatic organisms.

Analysis of canthin-6-one alkaloids derived from Eurycoma spp. by micellar liquid chromatography and conventional high-performance liquid chromatography: a comparative evaluation

Extracts of Eurycoma longifolia Jack (EL) and Eurycoma harmandiana Pierre (EH) contain numerous bioactive compounds and varying matrices that are challenging to separate using chromatographic techniques. Herein, micellar liquid chromatography (MLC) was used to analyze canthin-6-one alkaloids contained in these extracts, and the achieved performance was compared with that of a conventional high-performance liquid chromatography (HPLC) method. The optimal mobile phase of MLC corresponded to 15 : 85 (v/v) acetonitrile : water (pH 3) containing 110 mM sodium dodecyl sulfate and 10 mM NaH₂PO₄. The retention times of canthin-6-one-9-O-β-d-glucopyranoside, 9-hydroxycanthin-6-one, canthin-6-one, and 9-methoxycanthin-6-one were 4.78/15.42, 17.64/24.11, 32.84/38.27, and 39.04/39.86 min, respectively, in the cases of isocratic MLC and conventional HPLC. In both cases, the analyte resolution exceeded 1.5. The MLC elution behavior of the examined analytes was largely determined by their hydrophobicity and ionization. The sensitivity, precision, accuracy, and per-run acetonitrile consumption of the MLC method were comparable to those of the conventional HPLC method. However, the latter method exhibited higher performance for application to EL and EH samples, particularly those with low analyte concentrations and varying sample matrices. Overall, the analysis of canthin-6-one alkaloids using MLC was limited to trace analytes due to interference by the matrix.

RP-18 TLC retention data and calculated physico-chemical parameters as predictors of soil-water partition and bioconcentration of organic sunscreens

RP-18 TLC chromatography was used to evaluate the impact on the environment (mobility in soil expressed as soil-water partition coefficient, log K_oc; bioconcentration factor in aquatic organisms, log BCF) of several cosmetic raw materials - sunscreens, preservatives and vitamins. The retention parameters R_M⁰ (R_M extrapolated to zero concentration of an organic modifier in a mobile phase), S (slope), PC₁ (1st principal component) and R_M^75% (single TLC run parameter for mobile phases containing 75% (v/v) of an organic modifier) obtained for six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physico-chemical properties (PSA - polar surface area; M_W - molecular weight; V_M - molar volume) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' mobility in soil or the affinity for aquatic organisms. The parameters R_M⁰, S and R_M^75% obtained for THF, dioxane and acetone may be used to investigate even very lipophilic compounds. R_M^75% is of a little bit limited use but it should be considered a time- and cost-effective alternative to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some environmental parameters investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

Application of RP-18 TLC Retention Data to the Prediction of the Transdermal Absorption of Drugs

Several chromatographic parameters (R_M⁰ and S obtained from RP-18 TLC with methanol—pH 7.4 phosphate buffer mobile phases by extrapolation to zero concentration of methanol; R_f and R_M obtained from RP-18 TLC with acetonitrile—pH 7.4 phosphate buffer 70:30 v/v as a mobile phase) and calculated molecular descriptors (molecular weight—M_W; molar volume—V_M; polar surface area—PSA; total count of nitrogen and oxygen atoms—(N+O); H-bond donor count—HD; H-bond acceptor count—HA; distribution coefficient—log D; total energy—E_T; binding energy—E_b; hydration energy—E_h; energy of the highest occupied molecular orbital—E_HOMO; energy of the lowest unoccupied orbital—E_LUMO; electronic energy—E_e; surface area—S_a; octanol-water partition coefficient—log P; dipole moment—DM; refractivity—R, polarizability—α) and their combinations (R_f/PSA, R_M/M_W, R_M/V_M) were tested in order to generate useful models of solutes’ skin permeability coefficient log K_p. It was established that neither R_M⁰ nor S obtained in the conditions used in this study is a good predictor of the skin permeability coefficient. The chromatographic parameters R_f and R_f/PSA were also unsuitable for this purpose. A simple and potentially useful, purely computational model based on (N+O), log D and HD as independent variables and accounting for ca. 83% of total variability was obtained. The evaluation of parameters derived from R_M (R_M, R_M/M_W, R_M/V_M) as independent variables in log K_p models proved that R_M/V_M is the most suitable descriptor belonging to this group. In a search for a reliable log K_p model based on this descriptor two possibilities were considered: a relatively simple model based on 5 independent variables: (N+O), log D, R_M/V_M, E_T and E_h and a more complex one, involving also E_b, M_W and PSA.

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.