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