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• W2610808267 abstract "Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70–0.81 and 0.72–0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass prediction of murine skin sensitization; and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure–activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal (http://labmol.com.br/predskin/), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available." @default.
• W2610808267 created "2017-05-12" @default.
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• W2610808267 date "2017-05-10" @default.
• W2610808267 modified "2023-10-15" @default.
• W2610808267 title "Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals" @default.
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• W2610808267 doi "https://doi.org/10.1021/acs.jcim.7b00194" @default.
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