Evaluation and improvement of QSAR predictions of skin sensitization for pesticides

Abstract

In vivo skin sensitization assays have to be provided by applicants to the competent authorities in the European Union for the approval of active substances (AS) in pesticides. This study aimed to test the practicability of in silico predictions for AS by freely available (Q)SAR tools to evaluate their use as a time- and cost-effective alternative to animal testing in the context of the 3R concept. Predictions of skin sensitization for 48 selected sensitizing and non-sensitizing AS by the software programs CAESAR, Toxtree, OECD (Q)SAR Toolbox, CASE Ultra, Leadscope and SciQSAR were collected and compared. Different data evaluation methodologies (score definition, mean, weighted mean, threshold score definition) were applied to optimize the predictions. The calculation methods were internally cross-validated and further validated with an additional validation set of 80 AS. Although the presented calculation methodologies are not suitable as a stand-alone method, this study has shown weaknesses and strengths of some prominent (Q)SAR programs and diverse combinatorial options in the prediction of skin sensitization by pesticidal AS. The present study will help to foster discussions on in silico alternatives to animal testing in the pesticide area.