Nestlé Research Centre, Vers-Chez-Les-Blanc, Lausanne, Switzerland.
Istituto Superiore di Sanità, Rome, Italy.
Regul Toxicol Pharmacol. 2014 Mar;68(2):275-96. doi: 10.1016/j.yrtph.2013.08.018. Epub 2013 Sep 5.
There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.
对于那些没有毒理学数据的化学物质，人们需要建立一种方法来确定与饮食暴露相关的安全关注水平。在这种情况下，使用计算方法似乎很有前景。要做出安全声明，需要对毒理学参考点进行定量预测，例如对 DNA 反应性化学物质的无观察到的不良效应水平和致癌效力。已经开发了决策树 (DT) 来帮助整合暴露信息和通过定量构效关系 ((Q)SAR) 软件和阅读技术获得的预测毒理学参考点。将预测的毒理学值与暴露进行比较，以获得暴露量的余地 (MoE)。MoE 的大小定义了安全关注的水平，并且应该考虑到许多不确定性，例如经典的种间和个体间变异性以及其他根据具体情况确定的不确定性。对计算方法的不确定性的分析以及案例研究的结果表明，基于 DT 的应用来建立安全关注不太可能比基于实验数据的应用具有更大的不确定性。DT 充分利用了所有可用的数据，确保了足够的保守性。当需要快速决策时，可以使用它。
