Pearce Robert G, Setzer R Woodrow, Strope Cory L, Wambaugh John F, Sipes Nisha S
U.S. Environmental Protection Agency 109 T.W. Alexander Dr. Mail Code D143-02 Research Triangle Park, NC 27711, United States of America
Division of the National Toxicology Program National Institute of Environmental Health Sciences 111 T.W. Alexander Dr., ML: K2-17 Research Triangle Park, NC 27709, United States of America URL: http://www.niehs.nih.gov/research/atniehs/labs/bmsb/.
J Stat Softw. 2017 Jul 17;79(4):1-26. doi: 10.18637/jss.v079.i04.
Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package . These models are designed to be parameterized using high-throughput data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific data. This package is structured to be augmented with additional chemical data as they become available. Package enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.
通过诸如ToxCast和Tox21等高通量筛选程序，已经对数千种化学物质进行了分析；对这些化学物质进行测试的部分原因是，它们中的大多数在危害、暴露或毒代动力学方面的数据有限或没有数据。毒代动力学模型有助于预测化学物质暴露后产生的组织浓度，并且可以使用“反向剂量测定”方法来预测足以导致通过高通量筛选确定为具有生物活性的组织浓度的暴露剂量。我们在R软件包中创建了四个毒代动力学模型。这些模型设计为使用高通量数据（血浆蛋白结合和肝脏清除率）以及结构衍生的物理化学性质和物种特异性生理数据进行参数化。该软件包包含用于蒙特卡罗采样和反向剂量测定的工具，以及用于分析浓度与时间模拟的函数。该软件包目前可以使用人类数据对人类、大鼠、小鼠、狗和兔子体内的553种化学物质进行预测，包括94种药物和415种ToxCast化学物质。对于其中67种化学物质，该软件包包括大鼠特异性数据。该软件包的结构设计为随着更多化学数据的获得而进行扩充。该软件包能够将毒代动力学纳入正在进行高通量筛选的化学物质的统计分析中。
