RESUMO
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.