Luijten Mirjam, van Benthem Jan, Morita Takeshi, Corvi Raffaella, Escobar Patricia A, Fujita Yurika, Hemmerich Jennifer, Honarvar Naveed, Kirkland David, Koyama Naoki, Lovell David P, Mathea Miriam, Williams Andrew, Dertinger Stephen, Pfuhler Stefan, Pennings Jeroen L A
Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
National Institute of Technology and Evaluation, Tokyo, Japan.
Environ Mol Mutagen. 2024 Dec 3. doi: 10.1002/em.22640.
In human health risk assessment of chemicals and pharmaceuticals, identification of genotoxicity hazard usually starts with a standard battery of in vitro genotoxicity tests, which is needed to cover all genotoxicity endpoints. The individual tests included in the battery are not designed to pick up all endpoints. This explains why resulting data can appear contradictory, thereby complicating accurate interpretation of the findings. Such interpretation could be improved through application of mathematical modeling. One of the advantages of mathematical modeling is that the strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and convey the associated uncertainties. This approach was explored by the working group "Predictivity of In Vitro Genotoxicity Testing," convened in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). Specifically, we applied mathematical modeling to a database with publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide strong predictive weight-of-evidence for evaluating genotoxic hazard of a substance, although they are better in predicting absence of genotoxic potential than in predicting presence of genotoxic potential. Remarkably, the bacterial reverse mutation (Ames) test did not significantly change these predictions when used in combination with in vitro mutagenicity and clastogenicity tests using cells of mammalian origin. However, in case only data from a bacterial reverse mutation test are available for the assessment of genotoxic potential, these do bear weight of evidence and thus can be used. Genotoxicity assays are generally executed in tiers, in which the bacterial reverse mutation test often is the starting point. Thus, it is reasonable to suspect that early in development test results from the bacterial reverse mutation test have influenced the composition of the database studied here. We performed several tests on the robustness of the database used for the analyses presented here, and the forthcoming results do not indicate a strong bias. Further research comparing in vitro genotoxicity data with in vivo data for additional compounds will provide more insights whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.
在化学品和药品的人体健康风险评估中，遗传毒性危害的识别通常始于一系列标准的体外遗传毒性试验，这对于涵盖所有遗传毒性终点是必要的。该试验组合中包含的各个试验并非旨在检测所有终点。这就解释了为什么所得数据可能看似相互矛盾，从而使对研究结果的准确解读变得复杂。通过应用数学建模可以改进这种解读。数学建模的优点之一是考虑到了每个试验的优缺点。此外，生成的预测是客观的，并传达了相关的不确定性。“体外遗传毒性试验的预测性”工作组在第8届遗传毒性试验国际研讨会（IWGT）的背景下召开会议，探讨了这种方法。具体而言，我们将数学建模应用于一个包含公开可用的体外和体内遗传毒性数据的数据库。结果表明，哺乳动物体外致断裂试验和哺乳动物细胞基因突变试验共同为评估物质的遗传毒性危害提供了有力的预测证据权重，尽管它们在预测无遗传毒性潜力方面比预测有遗传毒性潜力方面表现更好。值得注意的是，当与使用哺乳动物来源细胞的体外诱变性和致断裂试验结合使用时，细菌回复突变（Ames）试验并没有显著改变这些预测。然而，如果仅可获得细菌回复突变试验的数据用于评估遗传毒性潜力，这些数据确实具有证据权重，因此可以使用。遗传毒性试验通常分阶段进行，其中细菌回复突变试验通常是起点。因此，有理由怀疑在开发早期细菌回复突变试验的结果影响了此处研究的数据库的构成。我们对用于此处分析的数据库的稳健性进行了多项测试，即将得出的结果并未表明存在强烈偏差。进一步比较更多化合物的体外遗传毒性数据与体内数据的研究将提供更多见解，以确定是否确实到了重新考虑标准体外遗传毒性试验组合构成的时候。
