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首页> 外文期刊>Applied in vitro toxicology. >Alternative Integrated Testing for Skin Sensitization: Assuring Consumer Safety
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Alternative Integrated Testing for Skin Sensitization: Assuring Consumer Safety

机译：替代集成测试皮肤敏化作用:保证消费者的安全

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摘要

Cosmetics legislation in Europe has driven the validation and acceptance of non-animal alternatives, most recently in the area of skin sensitization. Despite use of these methods to meet regulatory needs, it is also essential that they allow evaluation regarding human safety. For cosmetic product safety, it is necessary to understand how they can be used and with what limitations, and thereby reveal what remains to be addressed. A dataset of 165 ingredients (137 cosmetic ingredients +28 reference substances) has been identified, curated, and subjected to testing using accepted in vitro methods, with additional information, including physicochemical data and in silico results. The inputs from multiple determinants of skin sensitizing activity have been used in five individual supervised classification models (or machine learning approaches), which were then collated in a robust statistical manner, a stacking meta-model, to deliver a prediction with an optimized level of confidence. For the training set, with the probability cutoffs at 70% and 30%, predictive sensitivity was 97%, specificity was 88%, the overall accuracy was 93%, and kappa was 85%. A further 52 substances were used to test the effectiveness of the model: the predictive sensitivity was 89%, specificity was 95%, overall accuracy was 91%, and kappa was 82%. In conclusion, this stacking meta-model delivers improved performance and therefore enhanced confidence in the discrimination of skin sensitizers from nonsensitizers. The key remaining gap, prediction of skin sensitization potency, may benefit from a similar approach, maximizing use of evidence from individual strands of prediction, while minimizing the impact of the limitations from any particular one.
机译：化妆品在欧洲推动立法验证和验收无动物选择,最近一次是在皮肤的面积敏化。满足监管的需要,也是必不可少的他们允许评估关于人类安全。化妆品产品安全,它是必要的了解如何使用他们什么的局限性,从而揭示是什么得到解决。化妆品成分+ 28参考物质)已被确定、策划和接受吗测试使用公认的体外方法,额外的信息,包括物理化学数据在网上的结果。多个皮肤敏化的决定因素在五个人活动使用监督分类模型(或机器学习方法),然后整理一个健壮的统计方式,堆积元模型,提供一个预测优化水平的信心。设置,中断概率在70%和30%,预测敏感性为97%,特异性88%,总体精度为93%,卡帕85%。预测模型的有效性敏感性为89%,特异性为95%,整体准确性为91%,和卡巴是82%。结论,这堆积模型提供改进的性能,因此提高对皮肤的歧视的信心从nonsensitizers增敏剂。剩下的差距,预测皮肤敏化效力,可能受益于类似的方法,从个人最大化的使用证据链的预测,而最小化从任何特定限制的影响一个。

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