Prediction of skin sensitization potency using machine learning approaches.

ABSTRACT: The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categori

SUBMITTER: Zang Q 

PROVIDER: S-EPMC5435511 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature