Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework

Skin serves as a drug administration
route, and skin permeability
of chemicals is of significant interest in the pharmaceutical and
cosmetic industries. An aggregated conformal prediction (ACP) framework
was used to build models for predicting the permeation rate (log Kp) of chemical compounds through human skin.
The conformal prediction method gives as an output the prediction
range at a given level of confidence for each compound, which enables
the user to make a more informed decision when, for example, suggesting
the next compound to prepare. Predictive models were built using both
the random forest and the support vector machine methods and were
based on experimentally derived permeability data on 211 diverse compounds.
The derived models were of similar predictive quality as compared
to earlier published models but have the extra advantage of not only
presenting a single predicted value for each compound but also a reliable,
individually assigned prediction range. The models use calculated
descriptors and can quickly predict the skin permeation rate of new
compounds.