Krishnan K, Pelekis M
Département de médecine du travail et d'hygiène du milieu, Faculté de médecine, Université de Montréal, PQ, Canada.
Toxicology. 1995 Dec 28;105(2-3):355-64. doi: 10.1016/0300-483x(96)83476-7.
The available data on the binary chemical interactions involving hematotoxicants, particularly organic chemicals causing a reduction in either the number of white/red blood cells or the capacity of hemoglobin to transport oxygen, are limited. These observations are limited to investigations in rodents of the enhancement or attenuation of the hematotoxicity of benzene, dichloromethane and dimethylanilines following prior administration of inducers of CYP 2E1 or co-administration of substrates for this isoenzyme. The relevance of these data on interactions for humans exposed at low concentrations can be assessed only when the mechanism of interaction is understood at a quantitative level, and incorporated within a physiological modeling framework. The present study exemplifies the predictability of the magnitude of binary chemical interactions in humans exposed to low concentrations, by developing a physiological model of the modulation by toluene of dichloromethane-induced carboxyhemoglobinemia. Consistent with the basic biochemical principles, this modeling exercise suggests that, with competitive metabolic inhibition mechanism, the threshold for binary chemical interactions will follow a downward trend with increasing number of substrates or structurally-similar substances in a mixture. The use of this kind of mechanistic models, along with data from descriptive chemical interaction studies, will form the very basis of mechanistic risk assessment methods for complex chemical mixtures.
关于涉及血液毒物（特别是导致白细胞/红细胞数量减少或血红蛋白运输氧气能力降低的有机化学物质）的二元化学相互作用的现有数据有限。这些观察仅限于对啮齿动物的研究，即在事先给予CYP 2E1诱导剂或同时给予该同工酶底物后，观察苯、二氯甲烷和二甲基苯胺的血液毒性增强或减弱情况。只有当相互作用机制在定量水平上得到理解，并纳入生理建模框架时，才能评估这些关于低浓度暴露人群相互作用的数据的相关性。本研究通过建立甲苯对二氯甲烷诱导的碳氧血红蛋白血症调节作用的生理模型，例证了低浓度暴露人群中二元化学相互作用强度的可预测性。与基本生化原理一致，该建模研究表明，对于竞争性代谢抑制机制，随着混合物中底物或结构相似物质数量的增加，二元化学相互作用的阈值将呈下降趋势。使用这种机制模型以及描述性化学相互作用研究的数据，将构成复杂化学混合物机制风险评估方法的基础。
