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评估毒代动力学的体外-体内外推。

Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics.

机构信息

National Center for Computational Toxicology.

National Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711.

出版信息

Toxicol Sci. 2018 May 1;163(1):152-169. doi: 10.1093/toxsci/kfy020.

Abstract

Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks.

摘要

在环境中存在的数千种潜在化学物质中,优先考虑风险需要暴露、毒性和毒代动力学(TK)数据,而这些数据往往是不可用的。相对高通量、体外 TK(HTTK)测定和体外到体内外推(IVIVE)方法已被开发用于预测 TK,但可用于基准这些方法的大多数体内 TK 数据来自于药品。在这里,我们报告了 26 种具有环境相关性的非药品化学品的新的体内大鼠 TK 实验。静脉内和口服给药均用于计算生物利用度。这些化学品以及先前发表的体内大鼠研究中的另外 19 种化学品(包括一些药品)被系统地分析以估计体内 TK 参数(例如,分布体积[Vd],消除率)。对于每种化学品,都有大鼠特异性的 HTTK 数据可用,并对关键 TK 预测进行了检查:口服生物利用度、清除率、Vd 和不确定性。对于非药品化学品,生物利用度的预测效果不佳。虽然没有一种药品的吸收率低于 10%,但非药品的生物利用度低至 0.3%。对于非药品,总清除率通常被低估,而校准到药品的 Vd 方法可能不适合其他化学品。然而,非药品的稳态、峰值和时间积分血浆浓度可以被预测到合理的准确度。当纳入生物利用度的实验测量时,血浆浓度预测得到了改善。总之,HTTK 和 IVIVE 方法足够稳健,可以应用于基于人类健康风险进行优先排序的具有环境相关性的化学品的高通量体外毒性筛选数据。

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本文引用的文献

1
httk: R Package for High-Throughput Toxicokinetics.
J Stat Softw. 2017 Jul 17;79(4):1-26. doi: 10.18637/jss.v079.i04.
2
In vitro to in vivo extrapolation for high throughput prioritization and decision making.
Toxicol In Vitro. 2018 Mar;47:213-227. doi: 10.1016/j.tiv.2017.11.016. Epub 2017 Dec 5.
3
Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.
J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):549-565. doi: 10.1007/s10928-017-9548-7. Epub 2017 Oct 14.
4
High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling.
Sci Total Environ. 2018 Feb 15;615:150-160. doi: 10.1016/j.scitotenv.2017.09.033. Epub 2017 Sep 29.
6
An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library.
Environ Sci Technol. 2017 Sep 19;51(18):10786-10796. doi: 10.1021/acs.est.7b00650. Epub 2017 Sep 6.
7
Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.
Environ Int. 2017 Sep;106:105-118. doi: 10.1016/j.envint.2017.06.004. Epub 2017 Jun 16.
8
Modeling Exposure in the Tox21 in Vitro Bioassays.
Chem Res Toxicol. 2017 May 15;30(5):1197-1208. doi: 10.1021/acs.chemrestox.7b00023. Epub 2017 Apr 24.
9
10
ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology.
Chem Res Toxicol. 2016 Aug 15;29(8):1225-51. doi: 10.1021/acs.chemrestox.6b00135. Epub 2016 Jul 20.

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