Mechanism‐informed read‐across assessment of skin sensitizers based on SkinSensDB

ABSTRACT Integrative testing strategies using adverse outcome pathway (AOP)‐based alternative assays for assessing skin sensitizers show the potential for replacing animal testing. However, the application of alternative assays for a large number of chemicals is still time‐consuming and expensive. In order to facilitate the assessment of skin sensitizers based on integrative testing strategies, a mechanism‐informed read‐across assessment method was proposed and evaluated using data from SkinSensDB. First, the prediction performance of two integrated testing strategy models was evaluated giving the highest area under the receiver operating characteristic curve (AUC) values of 0.928 and 0.837 for predicting human and LLNA data, respectively. The proposed read‐across prediction method achieves AUC values of 0.957 and 0.802 for predicting human and LLNA data, respectively, with interpretable activation statuses of AOP events. As data grows, a better prediction performance is expected. A user‐friendly tool has been constructed and integrated into SkinSensDB that is publicly accessible at http://cwtung.kmu.edu.tw/skinsensdb. HighlightsA mechanism‐informed read‐across assessment method was proposed for the assessment of skin sensitizers based on SkinSensDB.The majority vote method based on three key events performs best for predicting human and LLNA data.The proposed method is useful for predicting human data with an AUC value higher than 0.9.A user‐friendly tool is publicly accessible at http://cwtung.kmu.edu.tw/skinsensdb.