In silico prediction of dermal absorption from non-dietary exposure to plant protection products

Abstract

An in silico model for predicting skin penetration of active ingredients formulated in plant protection products (PPP) has been developed using random forests (machine learning technique) that were trained with data from in vitro human skin studies taken from the EFSA dermal absorption database and in-house data from Bayer. In addition to the applied dose, various physicochemical properties were considered as model parameters. The model has been linked to a novel percentile approach in order to make the results usable for regulatory purposes. Application to an external validation data set demonstrated that the tool is ready for use. Finally, we propose to follow a tiered decision tree approach for non-dietary risk assessments including the use of the in silico dermal absorption prediction model as part of a safety assessment of a PPP.