Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy.
Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland.
ALTEX. 2018;35(2):169-178. doi: 10.14573/altex.1707171. Epub 2017 Sep 18.
Food contamination due to unintentional leakage of chemicals from food contact materials (FCM) is a source of increasing concern. Since for many of these substances, only limited or no toxicological data are available, the development of alternative methodologies to establish rapidly and cost-efficiently level of safety concern is critical to ensure adequate consumer protection. Computational toxicology methods are considered the most promising solutions to cope with this data gap. In particular, mutagenicity assessment has a particular relevance and is a mandatory requirement for all substances released from plastic FCM, regardless how low migration and exposure are. In the present work, a strategy integrating a number of (Quantitative) Structure Activity Relationship ((Q)SAR) models for Ames mutagenicity predictions is proposed. A list of chemicals representing likely migrating moieties from FCM was selected to test the value of the newly defined strategy and the possibility to combine predictions given by the different algorithms was evaluated. In particular, a scheme to integrate mutagenicity estimations into a single final assessment was developed resulting in an increased domain of applicability. In most cases, a deeper analysis of experimental data, where available, allowed fixing misclassification errors, highlighting the importance of data curation in the development, validation and application of in silico methods. The high accuracy of the strategy provided the rationales for its application for toxicologically uncharacterized chemicals. Finally, the overall strategy of integration will be automated through its implementation into a freely available software application.
食品接触材料（FCM）中化学物质的非故意泄漏导致的食品污染是一个日益令人关注的问题。由于这些物质中的许多物质只有有限的或没有毒理学数据，因此开发替代方法来快速且经济高效地建立安全关注水平对于确保充分保护消费者至关重要。计算毒理学方法被认为是应对这一数据缺口的最有前途的解决方案。特别是，致突变性评估具有特别的相关性，并且是所有从塑料 FCM 中释放的物质的强制性要求，无论迁移和暴露水平有多低。在本工作中，提出了一种整合多种（定量）构效关系（QSAR）模型用于 Ames 致突变性预测的策略。选择了一组代表 FCM 中可能迁移部分的化学物质来测试新定义策略的价值，并评估不同算法的预测结果的组合可能性。特别是，开发了一种将致突变性估计纳入单一最终评估的方案，从而扩大了适用范围。在大多数情况下，对现有实验数据进行更深入的分析允许纠正错误分类，突出了在开发、验证和应用计算方法时数据管理的重要性。该策略的高精度为其应用于毒理学未表征的化学物质提供了依据。最后，将通过将其实现到一个免费的可用软件应用程序中，实现集成的整体策略的自动化。
