A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria

Abstract

A decision tree-based defined approach (DA) has been designed using exclusion criteria based on applicability domain knowledge of in chemico/in vitro information sources covering key events 1–3 in the skin sensitisation adverse outcome pathway and an in silico tool predicting the adverse outcome (Derek Nexus). The hypothesis is that using exclusion criteria to de-prioritise less applicable assays and/or in silico outcomes produces a rational, transparent, and reliable DA for the prediction of skin sensitisation potential. Five exclusion criteria have been established: Derek Nexus reasoning level, Derek Nexus negative prediction, metabolism, lipophilicity, and lysine-reactivity. These are used to prioritise the most suitable information sources for a given chemical and results from which are used in a ‘2 out of 3’ approach to provide a prediction of hazard. A potency category (and corresponding GHS classification) is then assigned using a k-Nearest Neighbours model containing human and LLNA data. The DA correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% of a dataset with reference LLNA and human data. The correct potency category was identified for 59% and 68% of chemicals, and the GHS classification accurately predicted for 73% and 76% with reference LLNA and human data, respectively.