重复给药动物实验中器官水平效应的可重复性

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-08-09 DOI:10.1016/j.comtox.2023.100287
Katie Paul Friedman , Miran J. Foster , Ly Ly Pham , Madison Feshuk , Sean M. Watford , John F. Wambaugh , Richard S. Judson , R. Woodrow Setzer , Russell S. Thomas
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引用次数: 1

摘要

这项工作基于重复动物研究的可变性,估计了新方法(NAM)在预测成年动物重复给药研究中器官水平效应方面的基准。使用毒性参考数据库(v2.1)中肾上腺、肝、肾、脾、胃和甲状腺的体重、大体或组织病理学变化的治疗相关效应值。计算重复研究中器官水平发现的化学一致性率,这些重复研究仅由重复化学定义,化学和物种定义,或化学和研究类型定义。不同器官的一致性为39 ~ 88%,种内一致性最高。治疗相关效应值的方差,包括最低效应水平(LEL)值和基准剂量(BMD)值,按器官计算。采用多线性回归模型,以器官水平效应值的研究描述符为协变量,估计总方差、均方误差(MSE)和根残差均方误差(RMSE)。MSE值被解释为未解释方差的估计值,表明研究描述符占器官水平水平总方差的52-69%。RMSE范围为0.41 ~ 0.68 log10-mg/kg/day。还量化了慢性(CHR)和亚慢性(SUB)给药方案在器官水平上的差异。比值比表明,如果SUB研究为阴性,CHR器官效应不太可能发生。CHR -亚器官水平水平的平均差异范围为−0.38 ~−0.19 log10 mg/kg/day;这些平均差异的大小小于重复研究的均方根误差。最后,采用体外到体内外推法(IVIVE)比较肾脏和肝脏的体外NAMs与水平的生物活性浓度。观察到的水平和平均IVIVE剂量预测之间的平均差异接近0.5 log10-mg/kg/天,但化学物质之间的差异很大。总的来说,重复给药器官水平效应的可变性表明,NAM预测水平的定量准确度至少应为±1 log10-mg/kg/天,定性准确度不超过70%。
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Reproducibility of organ-level effects in repeat dose animal studies

This work estimates benchmarks for new approach method (NAM) performance in predicting organ-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from the Toxicity Reference database (v2.1) for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used. Rates of chemical concordance among organ-level findings in replicate studies, defined by repeated chemical only, chemical and species, or chemical and study type, were calculated. Concordance was 39–88%, depending on organ, and was highest within species. Variance in treatment-related effect values, including lowest effect level (LEL) values and benchmark dose (BMD) values when available, was calculated by organ. Multilinear regression modeling, using study descriptors of organ-level effect values as covariates, was used to estimate total variance, mean square error (MSE), and root residual mean square error (RMSE). MSE values, interpreted as estimates of unexplained variance, suggest study descriptors accounted for 52–69% of total variance in organ-level LELs. RMSE ranged from 0.41 to 0.68 log10-mg/kg/day. Differences between organ-level effects from chronic (CHR) and subchronic (SUB) dosing regimens were also quantified. Odds ratios indicated CHR organ effects were unlikely if the SUB study was negative. Mean differences of CHR - SUB organ-level LELs ranged from − 0.38 to − 0.19 log10 mg/kg/day; the magnitudes of these mean differences were less than RMSE for replicate studies. Finally, in vitro to in vivo extrapolation (IVIVE) was employed to compare bioactive concentrations from in vitro NAMs for kidney and liver to LELs. The observed mean difference between LELs and mean IVIVE dose predictions approached 0.5 log10-mg/kg/day, but differences by chemical ranged widely. Overall, variability in repeat dose organ-level effects suggests expectations for quantitative accuracy of NAM prediction of LELs should be at least ± 1 log10-mg/kg/day, with qualitative accuracy not exceeding 70%.

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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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