Mekenyan Ovanes, Todorov Milen, Serafimova Rossitsa, Stoeva Stoyanka, Aptula Aynur, Finking Robert, Jacob Elard
Laboratory of Mathematical Chemistry, Bourgas As. Zlatarov University, Bulgaria.
Chem Res Toxicol. 2007 Dec;20(12):1927-41. doi: 10.1021/tx700249q. Epub 2007 Dec 4.
Modeling the potential of chemicals to induce chromosomal damage has been hampered by the diversity of mechanisms which condition this biological effect. The direct binding of a chemical to DNA is one of the underlying mechanisms that is also responsible for bacterial mutagenicity. Disturbance of DNA synthesis due to inhibition of topoisomerases and interaction of chemicals with nuclear proteins associated with DNA (e.g., histone proteins) were identified as additional mechanisms leading to chromosomal aberrations (CA). A comparative analysis of in vitro genotoxic data for a large number of chemicals revealed that more than 80% of chemicals that elicit bacterial mutagenicity (as indicated by the Ames test) also induce CA; alternatively, only 60% of chemicals that induce CA have been found to be active in the Ames test. In agreement with this relationship, a battery of models is developed for modeling CA. It combines the Ames model for bacterial mutagenicity, which has already been derived and integrated into the Optimized Approach Based on Structural Indices Set (OASIS) tissue metabolic simulator (TIMES) platform, and a newly derived model accounting for additional mechanisms leading to CA. Both models are based on the classical concept of reactive alerts. Some of the specified alerts interact directly with DNA or nuclear proteins, whereas others are applied in a combination of two- or three-dimensional quantitative structure-activity relationship models assessing the degree of activation of the alerts from the rest of the molecules. The use of each of the alerts has been justified by a mechanistic interpretation of the interaction. In combination with a rat liver S9 metabolism simulator, the model explained the CA induced by metabolically activated chemicals that do not elicit activity in the parent form. The model can be applied in two ways: with and without metabolic activation of chemicals.
化学物质诱导染色体损伤的潜力建模一直受到决定这种生物学效应的机制多样性的阻碍。化学物质与DNA的直接结合是导致细菌致突变性的潜在机制之一。由于拓扑异构酶的抑制导致DNA合成紊乱以及化学物质与与DNA相关的核蛋白（如组蛋白）相互作用被确定为导致染色体畸变（CA）的其他机制。对大量化学物质的体外遗传毒性数据进行的比较分析表明，超过80% 引起细菌致突变性的化学物质（如Ames试验所示）也会诱导CA；相反，仅发现60% 诱导CA的化学物质在Ames试验中有活性。与这种关系一致，开发了一系列用于CA建模的模型。它结合了已经推导并整合到基于结构指数集的优化方法（OASIS）组织代谢模拟器（TIMES）平台中的细菌致突变性Ames模型，以及一个新推导的考虑导致CA的其他机制的模型。这两个模型都基于反应性警报的经典概念。一些指定的警报直接与DNA或核蛋白相互作用，而其他警报则应用于二维或三维定量构效关系模型的组合中，以评估来自分子其余部分的警报激活程度。对相互作用的机理解释证明了每个警报的使用合理性。与大鼠肝脏S9代谢模拟器相结合，该模型解释了由代谢激活的化学物质诱导的CA，这些化学物质以母体形式不具有活性。该模型可以两种方式应用：化学物质有或没有代谢激活。
