A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces
A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces
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DOI：
10.1002/jat.3448
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发表时间：
2017-08-01
影响因子：
3.3
通讯作者：
Parakhia, Rahul
中科院分区：
文献类型：
--
作者：
Canipa, Steven J.;Chilton, Martyn L.;Parakhia, Rahul
皮肤接触化学品可能会导致炎症反应，称为过敏性接触性皮炎。因此，必须评估新的和现有的化学品的皮肤致敏潜力，并相应地减少接触。迫切需要开发定量非动物方法，以更好地预测潜在致敏剂的效力，这主要是由欧盟（EU）法规1223/2009驱动的，该法规禁止对在欧盟销售的化妆品成分使用动物试验。使用1096项小鼠局部淋巴结（LLNA）研究的内部数据集开发了计算机模拟最近邻模型。使用相同机理空间（通过激活相同Derek皮肤致敏警报定义）内最多10种最相似化合物的EC 3值的加权平均值，预测给定化学品的EC 3值（与对照品相比，刺激指数增加3倍的供试品有效浓度）。该模型使用以前看不见的内部（n=45）和外部（n=103）数据进行验证，并使用三倍误差，五倍误差，欧洲化学品生态毒理学和毒理学中心（ECETOC）和全球化学品统一分类和标签制度（GHS）分类评估预测的准确性。特别是，该模型很好地预测了GHS皮肤致敏类别的化合物，预测了64%的化学品在正确的类别内的外部测试集。在以前未见过的数据集中的其余化学品中，25%被过度预测（GHS 1A预测：GHS 1B实验），11%被预测不足（GHS 1B预测：GHS 1A实验）。版权所有（c）2017 John Wiley & Sons，Ltd.使用1096个鼠局部淋巴结研究的内部数据集开发了计算机模拟最近邻模型。使用相同机理空间内最多10种最相似化合物的EC 3值的加权平均值预测给定化学品的EC 3值（通过激活相同Derek皮肤致敏警报定义）。该模型使用先前未见过的内部（n=45）和外部（n=103）数据进行了验证，具有良好的EC 3预测性。
Dermal contact with chemicals may lead to an inflammatory reaction known as allergic contact dermatitis. Consequently, it is important to assess new and existing chemicals for their skin sensitizing potential and to mitigate exposure accordingly. There is an urgent need to develop quantitative non-animal methods to better predict the potency of potential sensitizers, driven largely by European Union (EU) Regulation 1223/2009, which forbids the use of animal tests for cosmetic ingredients sold in the EU. A Nearest Neighbours in silico model was developed using an in-house dataset of 1096 murine local lymph node (LLNA) studies. The EC3 value (the effective concentration of the test substance producing a threefold increase in the stimulation index compared to controls) of a given chemical was predicted using the weighted average of EC3 values of up to 10 most similar compounds within the same mechanistic space (as defined by activating the same Derek skin sensitization alert). The model was validated using previously unseen internal (n=45) and external (n=103) data and accuracy of predictions assessed using a threefold error, fivefold error, European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) and Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classifications. In particular, the model predicts the GHS skin sensitization category of compounds well, predicting 64% of chemicals in an external test set within the correct category. Of the remaining chemicals in the previously unseen dataset, 25% were over-predicted (GHS 1A predicted: GHS 1B experimentally) and 11% were under-predicted (GHS 1B predicted: GHS 1A experimentally). Copyright (c) 2017 John Wiley & Sons, Ltd.A Nearest Neighbours in silico model was developed using an in-house dataset of 1096 murine local lymph node studies. The EC3 value of a given chemical was predicted using the weighted average of EC3 values of up to 10 most similar compounds within the same mechanistic space (as defined by activating the same Derek skin sensitization alert). The model was validated using previously unseen internal (n=45) and external (n=103) data with good EC3 predictivity.