预测无脊椎动物对化学物质的摄取和消除动力学:残差模型的技术注释

IF 3.1 Q2 TOXICOLOGY
Henk J. van Lingen , Edoardo Saccenti , Maria Suarez-Diez , Marta Baccaro , Nico W. van den Brink
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引用次数: 0

摘要

用于预测纳米材料和其他有毒化学物质含量的毒性动力学模型的拟合通常没有评估剩余方差结构。本研究的目的是在拟合非线性毒性动力学单室模型以预测无脊椎生物体内化学物质的摄取、生物积累和消除时,评估假设均方差或异方差的各种剩余方差结构。描述几种水生和陆生无脊椎动物暴露于特定金属纳米材料和其他化学品的数据可从评估毒性动力学模型的剩余方差函数的实际实验中获得。作为概念的证明,模拟了真正的同方差和异方差性质的数据集。根据数据集的不同,采用不同残差方差假设的模型在很大程度上影响了残差图和参数的误差范围或预测的化学物质含量。因此,选择最准确的残余方差函数进行毒物动力学建模,无论是均匀方差还是异方差,都可以提高对无脊椎生物中化学物质含量的预测以及对相关吸收和消除率的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting uptake and elimination kinetics of chemicals in invertebrates: A technical note on residual variance modeling
Toxicokinetic models for predicting contents of nanomaterials and other toxic chemicals are often fitted without evaluation of the residual variance structure. The aim of the present study was to evaluate various residual variance structures, assuming either homoscedasticity or heteroscedasticity, when fitting non-linear toxicokinetic one-compartment models for predicting uptake, bioaccumulation and elimination of chemicals in invertebrate organisms. Data describing the exposure of several aquatic and terrestrial invertebrates to specific metal nanomaterials and other chemicals were available from real experiments for evaluating the residual variance functions for toxicokinetic models. As proof of concept, datasets of truly homoscedastic and heteroscedastic nature were simulated. Depending the dataset, applying models with different residuals variance assumption largely affected the residual plots and the error margins of parameters or the predicted content of a chemical. Consequently, selecting the most accurate residual variance functions for toxicokinetic modeling, either homoscedastic or heteroscedastic, improves the prediction of chemical contents in invertebrate organisms and the estimation of the associated uptake and elimination rates.
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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