Computational approaches for skin sensitization prediction
نویسندگان
چکیده
منابع مشابه
Prediction of skin sensitization potency using machine learning approaches.
The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potenc...
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INTRODUCTION Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage. RE...
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One of the Interagency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluat...
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Cutaneous irritation presents a major health problem with serious social and occupational impact. The interaction between an irritant and the human skin depends on multiple factors: the intrinsic properties and the nature of the irritant itself, and specific individual- and environment-related variables. The main pathological mechanisms of irritancy include skin barrier disruption, induction of...
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ژورنال
عنوان ژورنال: Critical Reviews in Toxicology
سال: 2018
ISSN: 1040-8444,1547-6898
DOI: 10.1080/10408444.2018.1528207