Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides

Adverse outcome pathway (AOP)-based computational models provide state-of-the-art pre-diction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evalu-ation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be in-appropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, re-spectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesti-cides by 19.6%.

Automated summary

Adverse outcome pathway (AOP)-based computational models are promising alternatives to animal testing, but their applicability in the field of pesticides needs further investigation. This study identified a consensus chemical space by comparing the predicted results with animal testing data, aiming to reduce animal testing for pesticides.