Non-animal assessment of skin sensitization is a global trend. Recently, scientific efforts have been focused on the integration of multiple evidence for decision making with the publication of OECD Guideline No. 497 for defined approaches to skin sensitization. The integrated testing strategy (ITS) methods reported by the guideline integrates in chemico, in vitro, and in silico testing to assess both hazard and potency of skin sensitization. The incorporation of in silico methods achieved comparable performance with fewer experiments compared to the traditional two-out-of-three (2o3) method. However, the direct application of current ITSs to agrochemicals can be problematic due to the lack of agrochemicals in the training data of the incorporated in silico methods. To address the issue, we present ITS-SkinSensPred 2.0 for agrochemicals and agrochemical formulations using a reconfigured in silico model SkinSensPred for pesticides. Compared to ITSv2, the proposed ITS-SkinSensPred 2.0 achieved an 11% and 16% improvement in the accuracy and correct classification rate for hazard identification and potency classification, respectively. In addition, an online ITS tool was implemented and available on the SkinSensDB website. The tool is expected to be useful for evaluating skin sensitization of substances.
Keywords: 3R; SkinSensDB; SkinSensPred; adverse outcome pathway; machine learning; pesticides; skin sensitization.