In silico predictions of sub-chronic effects: Read-across using metabolic relationships between parents and transformation products

IF 3.1 Q2 TOXICOLOGY
Darina G. Yordanova , Chanita D. Kuseva , Hristiana Ivanova , Terry W. Schultz , Vanessa Rocha , Andreas Natsch , Heike Laue , Ovanes G. Mekenyan
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引用次数: 0

Abstract

Justifying read-across predictions for subchronic effects, such as no observed adverse effect levels (NOAEL), is challenging. The scarcity of suitable experimental data hampers such predictions, such that a conservative approach is often employed where the structural similarity between target and the tested source substances is very high. A less stringent interpretation of structural similarity may be used to expand data gap-filling by read-across if other types of similarity (e.g., toxicokinetic and toxicodynamic consideration) are factored into the justification. Herein, qualitative and quantitative in silico-assisted procedures are described and demonstrated for those instances where no structurally similar analogues are identified. In the qualitative approach, the toxicity classification of the most toxic metabolite is assigned directly to the target compound. While simple, this approach may lead to an over-classification of the target compound and a false positive result. In contrast, the quantitative approach is more complicated. In addition to identifying those metabolites causing toxicity, it examines the quantitative information for the amount of the most toxic metabolite. The maximum dose of the parent chemical is estimated which will not result in the generation of toxic metabolites sufficient to cause harmful effects. This quantitative approach permits a calculation of the margin of exposure, is noteworthy for industrial assessment purposes.

亚慢性效应的硅学预测:利用亲本和转化产物之间的代谢关系进行交叉阅读
对亚慢性效应(如无观测不良效应水平 (NOAEL))进行横向预测是一项具有挑战性的工作。由于缺乏合适的实验数据,因此在目标物质与受测源物质的结构相似性非常高的情况下,通常会采用保守的方法进行预测。如果将其他类型的相似性(例如毒物动力学和毒效学考虑因素)考虑在内,对结构相似性的解释可以不那么严格,从而通过读取交叉来扩大数据缺口。本文介绍了硅辅助定性和定量程序,并针对没有发现结构相似的类似物的情况进行了演示。在定性方法中,将毒性最强的代谢物的毒性分类直接分配给目标化合物。这种方法虽然简单,但可能会导致目标化合物的过度分类和假阳性结果。相比之下,定量方法更为复杂。除了要识别那些导致毒性的代谢物外,它还要检查毒性最强的代谢物数量的定量信息。对母体化学品的最大剂量进行估算,以确定其不会产生足以造成有害影响的有毒代谢物。这种定量方法允许计算暴露的阈值,在工业评估中值得注意。
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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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