Asturiol D, Casati S, Worth A
Joint Research Centre, Via Enrico Fermi 2749, Ispra 21027, VA, Italy.
Joint Research Centre, Via Enrico Fermi 2749, Ispra 21027, VA, Italy.
Toxicol In Vitro. 2016 Oct;36:197-209. doi: 10.1016/j.tiv.2016.07.014. Epub 2016 Jul 22.
Since March 2013, it is no longer possible to market in the European Union (EU) cosmetics containing new ingredients tested on animals. Although several in silico alternatives are available and achievements have been made in the development and regulatory adoption of skin sensitisation non-animal tests, there is not yet a generally accepted approach for skin sensitisation assessment that would fully substitute the need for animal testing. The aim of this work was to build a defined approach (i.e. a predictive model based on readouts from various information sources that uses a fixed procedure for generating a prediction) for skin sensitisation hazard prediction (sensitiser/non-sensitiser) using Local Lymph Node Assay (LLNA) results as reference classifications. To derive the model, we built a dataset with high quality data from in chemico (DPRA) and in vitro (KeratinoSens™ and h-CLAT) methods, and it was complemented with predictions from several software packages. The modelling exercise showed that skin sensitisation hazard was better predicted by classification trees based on in silico predictions. The defined approach consists of a consensus of two classification trees that are based on descriptors that account for protein reactivity and structural features. The model showed an accuracy of 0.93, sensitivity of 0.98, and specificity of 0.85 for 269 chemicals. In addition, the defined approach provides a measure of confidence associated to the prediction.
自2013年3月起，在欧盟（EU）销售含有经动物试验的新成分的化妆品已不再可行。尽管有几种计算机模拟替代方法可用，并且在皮肤致敏非动物试验的开发和监管采用方面已取得进展，但尚未有能完全替代动物试验需求的普遍接受的皮肤致敏评估方法。这项工作的目的是建立一种明确的方法（即基于来自各种信息源读数的预测模型，该模型使用固定程序生成预测），以使用局部淋巴结试验（LLNA）结果作为参考分类来预测皮肤致敏危害（致敏剂/非致敏剂）。为了推导该模型，我们构建了一个数据集，其中包含来自化学方法（DPRA）和体外方法（KeratinoSens™和h-CLAT）的高质量数据，并用几个软件包的预测结果进行了补充。建模结果表明，基于计算机模拟预测的分类树能更好地预测皮肤致敏危害。该明确方法由基于描述符的两个分类树的共识组成，这些描述符考虑了蛋白质反应性和结构特征。对于269种化学物质，该模型的准确率为0.93，灵敏度为0.98，特异性为0.85。此外，该明确方法还提供了与预测相关的置信度度量。
