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Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization

机译:基于人工神经网络风险评估模型的体外敏感试验描述仪的组合评价

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摘要

The skin sensitization potential of chemicals has been determined with the use of the murine local lymph node assay (LLNA). However, in recent years public concern about animal welfare has led to a requirement for non-animal risk assessment systems for the prediction of skin sensitization potential, to replace LLNA. Selection of an appropriate in vitro test or in silico model descriptors is critical to obtain good predictive performance. Here, we investigated the utility of artificial neural network (ANN) prediction models using various combinations of descriptors from several in vitro sensitization tests. The dataset, collected from published data and from experiments carried out in collaboration with the Japan Cosmetic Industry Association (JCIA), consisted of values from the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA), SH test and antioxidant response element (ARE) assay for chemicals whose LLNA thresholds have been reported. After confirming the relationship between individual in vitro test descriptors and the LLNA threshold (e.g.EC3 value), we used the subsets of chemicals for which the requisite test values were available to evaluate the predictive performance of ANN models using combinations of h-CLAT/DPRA (N = 139 chemicals), the DPRA/ARE assay (N = 69), the SH test/ARE assay (N = 73), the h-CLAT/DPRA/ARE assay (N = 69) and the h-CLAT/SH test/ARE assay (N = 73). The h-CLAT/DPRA, h-CLAT/DPRA/ARE assay and h-CLAT/SH test/ARE assay combinations showed a better predictive performance than the DPRA/ARE assay and the SH test/ARE assay. Our data indicates that the descriptors evaluated in this study were all useful for predicting human skin sensitization potential, although combinations containing h-CLAT (reflecting dendritic cell-activating ability) were most effective for ANN-based prediction. Copyright (c) 2015 John Wiley & Sons, Ltd.
机译:通过使用鼠局部淋巴结测定(LLNA)确定了化学品的皮肤敏化潜力。然而,近年来对动物福利的公众关注导致非动物风险评估系统对皮肤致敏潜力预测,取代LLNA。选择适当的体外测试或在Silico模型描述符中是至关重要的,以获得良好的预测性能。在这里,我们研究了使用来自几种体外敏化测试的各种描述符的各种组合的人工神经网络(ANN)预测模型的效用。从已发布的数据收集的数据集以及与日本化妆品行业协会(JCIA)合作进行的实验组成,由人体细胞系活化试验(H-CLAT),直接肽反应性测定(DPRA),SH检测组成抗氧化剂反应元件(AS)用于报告LLNA阈值的化学物质的测定。在确认单个体外测试描述符和LLNA阈值(EGEC3值)之间的关系之后,我们使用了使用H-Clat / DPRA的组合来评估所需测试值的化学物质的子集来评估ANN模型的预测性能(n = 139化学品),DPRA /是测定(n = 69),SH检测/是测定(n = 73),H-CLAT / DPRA /是测定(n = 69)和H-CLAT / Sh测试/是测定(n = 73)。 H-CLAT / DPRA,H-CLAT / DPRA /是测定和H-CLAT / SH检测/是测定组合显示比DPRA /是测定和SH检测/测定的更好的预测性能。我们的数据表明,在该研究中评估的描述符全部用于预测人体皮肤致敏潜力,尽管含有H-CLAT(反射树突细胞活化能力)的组合对于基于安基的预测最有效。版权所有(c)2015 John Wiley&Sons,Ltd。

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