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基于体外固有清除率的鱼类生物浓缩评估:经验模型与体外-体内外推模型的预测能力比较。

Bioconcentration Assessment in Fish Based on In Vitro Intrinsic Clearance: Predictivity of an Empirical Model Compared to In Vitro-In Vivo Extrapolation Models.

机构信息

Fragrances S&T, Givaudan Schweiz AG, Kemptthal 8310, Switzerland.

Regulatory Affairs & Product Safety, Fragrance & Beauty, Givaudan UK Ltd, Ashford, Kent, TN24 OLT, United Kingdom.

出版信息

Environ Sci Technol. 2023 Sep 12;57(36):13325-13335. doi: 10.1021/acs.est.3c02216. Epub 2023 Aug 29.

Abstract

To estimate the bioconcentration factor (BCF), the in vitro intrinsic clearance (CL) from rainbow trout liver S9 fractions (RT-S9) can be applied to in vitro-in vivo extrapolation (IVIVE) models, yet uncertainties remain in model parameterization. An alternative model approach is evaluated: a regression model was built in the form log BCF = × log + × log CL. The coefficients and were fitted based on a training set of 40 chemicals. A high robustness of the coefficients and good accuracy of BCF prediction were found on independent datasets of neutral organic chemicals (measured log 3.3-6.2). BCF predictions were similar to or in better agreement with in vivo BCFs compared to IVIVE models (2.4- to 2.9- vs 2.8- to 3.6-fold misprediction) for training and test sets. Species-matched models (trout, carp) did not result in improvements. This study presents the largest dataset on CL and BCFs to assess predictivity of the RT-S9 assay. The robustness of the regression statistics on different datasets and the high statistical weight of the CL term illustrate the predictive power of the RT-S9 assay as an important step toward regulatory acceptance to replace animal experiments.

摘要

为了估计生物浓缩因子(BCF),可以将虹鳟鱼肝脏 S9 级分(RT-S9)的体外固有清除率(CL)应用于体外-体内外推(IVIVE)模型,但模型参数化仍存在不确定性。评估了一种替代模型方法:以 log BCF = × log + × log CL 的形式构建了回归模型。根据 40 种化学物质的训练集拟合系数 和 。在独立的中性有机化学品数据集(实测 log 3.3-6.2)上发现,系数具有很高的稳健性,BCF 预测的准确性也很好。与 IVIVE 模型相比,BCF 预测(训练集和测试集分别为 2.4-至 2.9-与 2.8-至 3.6-倍误报)与体内 BCF 更为相似或更吻合。物种匹配模型(鳟鱼、鲤鱼)并没有改善。本研究提供了最大的 CL 和 BCF 数据集,以评估 RT-S9 测定的预测能力。不同数据集的回归统计数据的稳健性以及 CL 项的高统计权重说明了 RT-S9 测定作为替代动物实验的重要步骤具有很强的预测能力,有助于监管部门接受。

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