Human Skin Permeability Prediction by Machine Learning for Transdermal Drug Delivery

Background and Objectives: The dose a transdermal patch delivers is governed by the drug's permeability coefficient (Kp) across skin, and the reference Potts-Guy equation assumes log Kp is linear in lipophilicity and molecular weight. Whether that form holds across the whole of the public human data, and how far such a model generalises to a new therapeutic class, is unknown. Materials and Methods: Two open human skin permeation databases were merged and standardised, yielding 732 permeability records for 327 compounds, 40 of them analgesics. Fifteen two-dimensional descriptors were computed. Linear, ridge, random forest and gradient boosting models were compared by cross-validation, a held-out test set and leave-analgesics-out extrapolation, with every fold and split defined on compounds.

Results