根据静脉血浓度和不确定的生理药代动力学模型重建挥发性有机化合物的暴露量

IF 3.1 Q2 TOXICOLOGY
L. Simon, M.K. Prakasha
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引用次数: 0

摘要

以生理为基础的药代动力学模型用于确定挥发性有机化合物的暴露量,特别侧重于间二甲苯。被动扩散被用来描述通过皮肤的渗透。所提出的模型与实验数据一致,研究人员可以监测不同分区的浓度曲线。研究还关注了参数不确定性对模型预测的影响。局部和全局敏感性分析评估了分区参数、皮肤扩散系数和代谢参数对血液浓度的影响。两种方法都表明,Michaelis-Menten 动力学和瘦组织:血液分配系数对总变异性的影响最大。反向剂量测定方法利用测量到的生物标志物水平来估算四小时内的暴露剂量。在使用平均值百分之二十五以内的随机参数进行模拟时,结果与实验数据一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstruction of exposure to volatile organic compounds from venous blood concentration and an uncertain physiologically-based pharmacokinetic model
Physiologically-based pharmacokinetic modeling was applied to determine exposures to volatile organic compounds, specifically focusing on m-xylene. Passive diffusion was used to describe permeation through the skin. The proposed model agreed with the experimental data and allowed researchers to monitor the concentration profiles in different compartments. The study also focused on the impact of parameter uncertainty on the model predictions. Local and global sensitivity analyses evaluated the influence of partition parameters, diffusion coefficients in the skin, and metabolic parameters on the blood concentration. Both methods show that the Michaelis-Menten kinetics and the lean tissue:blood partition coefficients contributed the most to the total variability. A reverse dosimetry approach used the measured biomarker level to estimate the exposure dose in four hours. The results aligned with experimental data when simulations were conducted using random parameters selected within twenty-five percent of the mean.
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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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