Prediction of skin penetration using artificial neural network (ANN) modeling

Artificial neural network (ANN) analysis was used to predict the skin permeability of selected xenobiotics. Permeability coefficients (log k(p)) were obtained from various literature sources. A previously reported equation, which was shown to be useful in the prediction of skin permeability, uses the partial charges of the penetrants, their molecular weight, and their calculated octanol water partition coefficient (log K(oct)). The equation was used to predict the skin permeability for the set of 40 compounds (r(2) = 0.672). A successful ANN was developed and the ANN produced log k(p) values that correlated well with the experimental ones(r(2) = 0.997). The penetration properties of a selection of compounds through human skin that have not been previously investigated, etodolac, famotidine, nimesulide, nizatidine, ranitidine, were investigated. Their permeability coefficients were determined. It was then possible to compare the experimental data with that predicted using the partial charge equation and the trained ANN. ANN modeling for predicting skin permeability was found to be useful for predicting skin permeability coefficients of compounds. In conclusion, the developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of skin penetration for new drugs or toxic penetrants.