Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
Oak Ridge Institute for Science and Education (ORISE) fellow at the Center for Computational Toxicology and Exposure, Office of Research and Development, Research Triangle Park, NC, USA.
Expert Opin Drug Metab Toxicol. 2021 Aug;17(8):903-921. doi: 10.1080/17425255.2021.1935867. Epub 2021 Jun 15.
Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.
This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and toxicokinetic (TK) data.
HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.
商业和环境中存在成千上万种化学物质,其毒性数据尚不可用。因此,风险评估人员需要快速筛选这些化学品,以评估它们对公众健康的潜在风险。当与高通量毒代动力学(HTTK)数据和模型结合使用时,高通量生物活性筛选(HTS)可用于这些数千种化学物质的特征描述。
本文综述了通用基于生理学的毒代动力学(PBTK)模型和高通量 PBTK 外推(IVIVE)的高通量建模。我们重点介绍了“httk”,这是一组公共的、开源的计算建模工具和毒代动力学(TK)数据。
HTTK 凭借其支持快速化学筛选/优先级划分、进行 IVIVE 以及仅使用有限的化学特异性数据为大量化学物质提供临时 TK 建模的能力,使化学风险评估人员受益。尽管通用 TK 模型设计会增加预测不确定性,但这些模型通过提高模型实施准确性提供了补偿性好处。此外,模型和数据的公开分发增强了可重复性。对于 httk 包,模块化和开源设计可以使该工具能够被广泛的用户社区使用和不断改进,以支持高通量化学优先排序和快速剂量估算的迫切需求,从而促进快速危害评估。