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• W2270295941 abstract "There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays." @default.
• W2270295941 created "2016-06-24" @default.
• W2270295941 creator A5003107599 @default.
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• W2270295941 date "2016-04-01" @default.
• W2270295941 modified "2023-10-16" @default.
• W2270295941 title "Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays" @default.
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• W2270295941 doi "https://doi.org/10.1016/j.yrtph.2016.01.009" @default.
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