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首页> 外文期刊>Journal of applied toxicology >Prediction of skin sensitization potency using machine learning approaches
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Prediction of skin sensitization potency using machine learning approaches

机译：采用机器学习方法预测皮肤敏感效力

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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 potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens (TM) assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright (C) 2017 John Wiley & Sons, Ltd.
机译：更换用于测试皮肤敏感的监管分类的动物用途是美国联邦机构的优先事项，这些机构使用这些测试的数据。已经开发出并评估了将物质作为敏感剂或非敏感剂分类物质的机器学习模型已经开发和评估。由于某些监管机构要求敏感剂进一步分为效力类别，因此我们开发了统计模型，以预测鼠局部淋巴结测定（LLNA）和人类结果的皮肤致敏效力。我们模型的输入变量包括来自三种非动物试验方法的六种物理化学性质和数据：直接肽反应性测定;人体细胞系活化试验;和角蛋白酶（TM）测定。模型建立以预测使用四种机器学习方法的三个效力类别，并使用外部测试集进行验证，并留出次级交叉验证。一个单层策略在一起建模了所有三类响应，而双层策略模型敏感剂/非敏化剂反应，然后将敏感剂分类为强或弱敏感剂。双层模型使用所有测定的支持向量机和物理化学数据输入提供了最佳性能，屈服精度为88％，用于预测LLNA结果（120种物质）和81％，用于预测人类试验结果（87种物质）。最好的单层模型预测LLNA结果，精度78％和人类成果，精度为75％。相比之下，LLNA预测69％精度（正确分类的87种物质中的60％）的人力效力类别。这些结果表明，使用非动物方法的计算模型可以提供评估皮肤敏感效力的有价值的信息。版权所有（c）2017 John Wiley＆Sons，Ltd。

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