Comparison of supercritical fluid chromatographic methods to predict the skin permeability of pharmaceutical and cosmetic compounds.

Supercritical fluid chromatography (SFC) was explored as an alternative for liquid chromatography to predict the skin permeability of pharmaceutical and cosmetic compounds. Nine dissimilar stationary phases were applied to screen a test set of 58 compounds. The experimental retention factors (log k), in addition to two sets of theoretical molecular descriptors, were applied to model the skin permeability coefficient. Different modelling approaches, i.e. multiple linear regression (MLR) and partial least squares (PLS) regression, were used. In general, the MLR models performed better than the PLS models for a given descriptor set. The results obtained on a cyanopropyl (CN) column provided the best correlation with the skin permeability data. The retention factors obtained on this column were included in a simple MLR model, together with the octanol-water partition coefficient and the number of atoms (r² = 0.81, RMSEC = 0.537 or 20.5% and RMSECV = 0.580 or 22.1%). The overall best MLR model included the chromatographic descriptor from a phenyl column and 18 descriptors (r² = 0.98, RMSEC = 0.167 or 6.2% and RMSECV = 0.238 or 8.9%). This model showed a good fit, on top of very good predictive features. However, stepwise MLR models with a reduced complexity could also be determined, with the best performance parameters obtained with the CN-column based retention and eight descriptors (r² = 0.95, RMSEC = 0.282 or 10.7% and RMSECV = 0.353 or 13.4%). SFC thus provides a suitable alternative to the liquid chromatographic techniques previously applied to model the skin permeability.

Predicting skin permeability of pharmaceutical and cosmetic compounds using retention on octadecyl, cholesterol-bonded and immobilized artificial membrane columns.

In this study, the retention on three types of columns, an immobilized artificial membrane (IAM), a cholesterol-bonded and an octadecyl (C18) column, was applied for the prediction of skin permeability. The first two columns are biomimicking ones, which have certain components of the skin bound to the stationary phase, and were applied in HPLC, while the sub-2 µm C18 column was studied in UHPLC because of its fast features. Fifty-eight compounds were analyzed applying different mobile-phase compositions, with varying percentages of organic modifier on every column, to extrapolate the retention factor to a theoretically purely aqueous mobile phase (log kw). The retention factors, along with two sets of theoretical molecular descriptors, were used to model the skin permeability coefficient (log Kp) using multiple linear regression (MLR) and partial least squares (PLS) regression modelling. Although the retention factors (log k) on the IAM column showed a better correlation with the skin permeability, the overall best model was obtained by applying a stepwise MLR approach on the UHPLC parameters combined with some theoretical descriptors. This model showed a good fit, and on top has potential to accurately predict skin permeability values. Furthermore, the UHPLC method has the advantage of being fast and can thus be classified as a high-throughput approach.

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules.

A micellar liquid chromatographic method was developed to assist in the modeling of the skin permeability of pharmaceutical and cosmetic compounds. The composition of the mobile phase was determined by means of a two-factor central composite design, after which it was tested on both a particle-based and monolithic column. The latter provided the opportunity to increase the flow rate from 1 to 8 mL/min without reaching too high backpressures. The micellar conditions allowed analyzing a large test set of compounds with diverse characteristics with just one mobile-phase composition. The obtained experimental chromatographic descriptors besides two sets of theoretical molecular descriptors were used to model the skin permeability coefficient log Kp, applying multiple linear regression and partial least squares regression approaches. The micellar method on the monolithic column provided useful models with similar or even slightly better performance parameters than the method on the particle-based column. Furthermore, a much faster analysis can be achieved when applying a flow rate of 8 mL/min, making the micellar monolithic method ideal to estimate skin permeability.

Comparison of in-silico modelling and reversed-phase liquid chromatographic retention on an octadecyl silica column to predict skin permeability of pharmaceutical and cosmetic compounds.

This study focuses on the in-silico modelling of the skin permeability using a test set of pharmaceutical and cosmetic compounds. Two sets of theoretical molecular descriptors, obtained from the E-Dragon and Vega ZZ software programs, were used in the models. Different linear regression methods, i.e. Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression, were applied for modelling and estimating the skin permeability. The best model was obtained using a stepwise MLR approach on the E-Dragon descriptor set. In a second step, the retention of the test set compounds was measured on a C18 column at two pH levels: pH 5.5 and pH 7. Different organic-modifier fractions were applied in the mobile phase to be able to extrapolate the retention factors to a log kw value, with kw the estimated retention factor in an aqueous mobile phase without organic modifier. Thereafter it was examined whether combining this chromatographic descriptor with the theoretical descriptors could improve the modelling of the skin permeability. The chromatographic descriptor often did not show an added value compared to the models containing only theoretical descriptors. Therefore, the in-silico models were preferred, and these models could be useful to predict the skin permeability of pharmaceutical and cosmetic compounds.

Stationary-phase optimized selectivity in supercritical fluid chromatography using a customized Phase OPtimized Liquid Chromatography kit: comparison of different prediction approaches.

The use of stationary-phase optimized selectivity in liquid chromatography (SOS-LC) was shown to be successful for HPLC to analyze complex mixtures using a Phase OPtimized Liquid Chromatography (POPLC) kit. This commercial kit contains five stationary-phase types of varying lengths, which can be coupled to offer an improved separation of compounds. Recently, stationary-phase optimized selectivity supercritical fluid chromatography (SOS-SFC) has been introduced, transferring the methodology to SFC. In this study, the applicability of a customized POPLC expert kit for isocratic SFC runs was explored. Five stationary-phase chemistries were selected as potentially most suitable for achiral separations of polar compounds: aminopropyl (amino), cyanopropyl (CN), diol, ethylpyridine (EP), and silica. The retention factors (k) on the individual stationary phases were used for the prediction of the best stationary-phase combination, based on the POPLC algorithm (via the included software). As an alternative, the best column combination was predicted using multiple linear regression (MLR) models on the results obtained from a simplex centroid mixture design with only three stationary-phase types (amino, silica, and EP). A third approach applied the isocratic POPLC algorithm on the same three stationary-phase data. The proposed combinations were assembled and tested. The predicted and experimental retention factors were compared. The predictions based on the POPLC algorithm provided a stationary phase showing a complete separation of the mixture. The stationary phase suggested by the MLR models, on the other hand, showed co-elution of two compounds, due to an unexpected experimental retention shift. Overall, the customized POPLC kit showed good potential to be applied in SFC. Graphical abstract.

VLDL Induced Modulation of Nitric Oxide Signalling and Cell Redox Homeostasis in HUVEC.

High levels of circulating lipoprotein constitute a risk factor for cardiovascular diseases, and in this context, the specific role of the very-low-density lipoproteins (VLDL) is poorly understood. The response of human umbilical vein endothelial cells (HUVEC) to VLDL exposure was studied, especially focusing on the pathways involved in alteration of redox homeostasis and nitric oxide (NO) bioavailability. The results obtained by the analysis of the expression level of genes implicated in the NO metabolism and oxidative stress response indicated a strong activation of inducible NO synthase (iNOS) upon 24 h exposure to VLDL, particularly if these have been preventively oxidised. Simultaneously, both mRNA and protein expression of endothelial NO synthase (eNOS) were decreased and its phosphorylation pattern, at the key residues Tyr495 and Ser1177, strongly suggested the occurrence of the eNOS uncoupling. The results are consistent with the observed increased production of nitrites and nitrates (NOx), reactive oxygen species (ROS), 3-nitrotyrosine (3-NT), and, at mitochondrial level, a deficit in mitochondrial O2 consumption. Altogether, these data suggest that the VLDL, particularly if oxidised, when allowed to persist in contact with endothelial cells, strongly alter NO bioavailability, affecting redox homeostasis and mitochondrial function.