Sensitivity Analysis of the Inputs for Bioactivity-Exposure Ratio Calculations in a NAM-Based Systemic Safety Toolbox

基于NAM的系统安全性工具箱中生物活性-暴露比计算输入参数的敏感性分析

阅读:1

Abstract

To support regulatory decision-making without animal testing, Next-Generation Risk Assessment (NGRA) frameworks leverage New Approach Methodologies (NAM). A widely used strategy in such frameworks is to calculate Bioactivity-Exposure Ratios (BERs), which compare NAM assay-derived points of departure (PODs) and estimated human internal exposures. However, key methodological choices in NGRA, such as toxicokinetic modeling software, POD sources, and adjustments for in vivo and in vitro free concentrations, may introduce uncertainty. To determine the robustness of our previously reported NGRA workflow, we conducted a comprehensive sensitivity analysis of these variables as applied to 35 chemicals covering a range of end uses including consumer, pesticide and pharmaceutical. Physiologically based kinetic (PBK) modeling using both commercial (GastroPlus) and open-source (httk) software was evaluated across three levels of parameterization. The httk model produced individual and population C(max) estimates that are comparable to GastroPlus when the chemical-specific input parameters are aligned. BERs were calculated using C(max) estimates, previously reported in vitro PODs from human-relevant cell-based assays, ToxCast, and fixed values based on internal threshold of toxicological concern (iTTCs), with and without adjustment for unbound fractions in plasma and media. We found that BERs from human-relevant cell-based PODs and nominal concentrations best matched risk classifications, with improved predictive performance over iTTC or ToxCast-derived PODs. Consideration of population variability in PBK had limited impact on risk prediction. Adjustments for free concentrations did not enhance classification or in vitro/in vivo concordance. These findings support the potential of human-relevant in vitro PODs and nominal dose metrics in NGRA, and demonstrate that accessible, well-parameterized PBK tools can enhance efficiency and reproducibility.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。