Differences between in vitro and in vivo genotoxicity due to metabolism: The role of kinetics

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-05-01 DOI:10.1016/j.comtox.2022.100222
P.I. Petkov , H. Ivanova , M. Honma , T. Yamada , T. Morita , A. Furuhama , S. Kotov , E. Kaloyanova , G. Dimitrova , O. Mekenyan
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引用次数: 2

Abstract

Traditional QSAR models predict mutagenicity solely based on structural alerts for the interaction of parent chemicals or their metabolites with target macromolecules. In the present work, it is demonstrated that the presence of an alert is necessary to identify damage but it is not always sufficient to assess mutagenic potential. This is addressed by accounting for the kinetics of simulating metabolism and formation of adducts with macromolecules. The mutagenic potential of chemicals is related to the degree to which selected macromolecules are altered. This extent is estimated by the amount of formed DNA/protein adducts. Here the effect of modelling kinetic factors is investigated for chemicals having documented in vitro negative and in vivo positive data in mutagenicity and clastogenicity tests of similar capacity - in vitro Ames vs in vivo TGR and in vitro CA vs in vivo MN tests. Two factors justify the conflict in mutagenicity data: the differences in enzyme expression in the in vitro vs in vivo metabolism and the difference in exposure time for in vitro and in vivo tests. Addressing these factors required simulating the formation of DNA/protein adducts and introducing empirically-defined thresholds for the amounts of the adducts leading to mutagenic potential.

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代谢引起的体外和体内遗传毒性的差异:动力学的作用
传统的QSAR模型仅基于母体化学物质或其代谢物与目标大分子相互作用的结构警报来预测突变性。在目前的工作中,它证明了警报的存在是必要的,以确定损害,但它并不总是足以评估致突变的潜力。这是通过计算模拟代谢和形成加合物与大分子的动力学来解决。化学物质的致突变潜能与所选择的大分子被改变的程度有关。这个程度是通过形成的DNA/蛋白质加合物的数量来估计的。在这里,对在类似容量的致突变性和致裂性试验(体外Ames与体内TGR试验和体外CA与体内MN试验)中记录了体外阴性和体内阳性数据的化学物质,研究了建模动力学因素的影响。两个因素证明了致突变性数据的冲突:体外和体内代谢中酶表达的差异以及体外和体内试验暴露时间的差异。解决这些因素需要模拟DNA/蛋白质加合物的形成,并引入经验定义的导致突变潜力的加合物数量的阈值。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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