Bos Peter Martinus Jozef, Zeilmaker Marco Jacob, van Eijkeren Jan Cornelis Henri
RIVM (National Institute for Public Health and the Environment), Centre for Substances and Integrated Risk Assessment, 3720 BA Bilthoven, The Netherlands.
Toxicol Sci. 2006 Jun;91(2):576-85. doi: 10.1093/toxsci/kfj176. Epub 2006 Mar 28.
Acute exposure guideline levels (AEGLs) are derived to protect the human population from adverse health effects in case of single exposure due to an accidental release of chemicals into the atmosphere. AEGLs are set at three different levels of increasing toxicity for exposure durations ranging from 10 min to 8 h. In the AEGL setting for methylene chloride, specific additional topics had to be addressed. This included a change of relevant toxicity endpoint within the 10-min to 8-h exposure time range from central nervous system depression caused by the parent compound to formation of carboxyhemoglobin (COHb) via biotransformation to carbon monoxide. Additionally, the biotransformation of methylene chloride includes both a saturable step as well as genetic polymorphism of the glutathione transferase involved. Physiologically based pharmacokinetic modeling was considered to be the appropriate tool to address all these topics in an adequate way. Two available PBPK models were combined and extended with additional algorithms for the estimation of the maximum COHb levels. The model was validated and verified with data obtained from volunteer studies. It was concluded that all the mentioned topics could be adequately accounted for by the PBPK model. The AEGL values as calculated with the model were substantiated by experimental data with volunteers and are concluded to be practically applicable.
急性暴露指导水平(AEGLs)旨在保护人群在化学品意外释放到大气中导致单次暴露时免受健康不良影响。AEGLs针对暴露持续时间从10分钟到8小时设定了三个毒性逐渐增加的不同水平。在二氯甲烷的AEGL设定中,必须解决特定的其他问题。这包括在10分钟到8小时的暴露时间范围内,相关毒性终点从母体化合物引起的中枢神经系统抑制转变为通过生物转化为一氧化碳形成碳氧血红蛋白(COHb)。此外,二氯甲烷的生物转化包括一个饱和步骤以及所涉及的谷胱甘肽转移酶的基因多态性。基于生理学的药代动力学建模被认为是充分解决所有这些问题的合适工具。将两个可用的PBPK模型进行了合并,并使用额外的算法进行扩展,以估计最大COHb水平。该模型通过志愿者研究获得的数据进行了验证和核实。得出的结论是,PBPK模型可以充分考虑所有上述问题。用该模型计算的AEGL值得到了志愿者实验数据的证实,并被认为实际适用。