Using Read-Across to build Physiologically-Based Kinetic models: Part 2. Case studies for atenolol and flumioxazin

IF 3.1 Q2 TOXICOLOGY
Courtney V. Thompson , Steven D. Webb , Joseph A. Leedale , Peter E. Penson , Alicia Paini , David Ebbrell , Judith C Madden
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引用次数: 0

Abstract

Read-across, wherein information from a data-rich chemical is used to make a prediction for a similar chemical that lacks the relevant data, is increasingly being accepted as an alternative to animal testing. Identifying chemicals that can be considered as similar (analogues) is crucial to the process. Two resources have been developed previously to address the issue of analogue selection and facilitate physiologically-based kinetic (PBK) model development, using read-across. Chemical-specific PBK models, available in the literature, were collated to form a PBK model dataset (PMD) of over 7,500 models. A KNIME workflow was created to accompany this PMD that can aid the selection of appropriate chemical analogues from chemicals within this dataset (i.e. chemicals that are similar to a target of interest and are known to have an existing PBK model). Information from the PBK model for the source chemical can then be used in a read-across approach to inform the development of a new PBK model for the target. The application of these resources is tested here using two case studies (i) for the drug atenolol and (ii) for the plant protection product, flumioxazin. New PBK models were constructed for these two target chemicals using data obtained from source chemicals, identified by the workflow as being similar (analogues). In each case, the published PBK model for the source chemical was initially reproduced, as accurately as possible, before being adapted and used as a template for the target chemical. The performance of the new PBK models was assessed by comparing simulation outputs to existing data on key kinetic properties for the targets. The results demonstrate that a read-across approach can be successfully applied to develop new PBK models for data-poor chemicals, thus enabling their deployment during early-stage risk assessment. This assists prediction of internal exposure whilst reducing reliance on animal testing.

利用 "交叉阅读 "建立基于生理学的动力学模型:第二部分。阿替洛尔和氟米恶嗪的案例研究
从数据丰富的化学物质中获得的信息被用来对缺乏相关数据的类似化学物质进行预测,这种方法越来越多地被接受为动物试验的替代方法。识别可被视为相似(类似物)的化学物质对该过程至关重要。以前已经开发了两个资源来解决类似物选择的问题,并使用读取来促进基于生理的动力学(PBK)模型的开发。对文献中可用的化学特异性PBK模型进行了整理,形成了一个包含7500多个模型的PBK模型数据集(PMD)。KNIME工作流是为了配合PMD而创建的,它可以帮助从该数据集中的化学品中选择适当的化学类似物(即与感兴趣的目标相似并且已知具有现有PBK模型的化学品)。来自源化学物质的PBK模型的信息可以用于跨读方法,为目标物质的新PBK模型的开发提供信息。在此通过两个案例研究(一)对阿替洛尔药物和(二)对植物保护产品氟恶嗪进行测试,对这些资源的应用进行测试。利用从源化学品中获得的数据,为这两种目标化学品构建了新的PBK模型,这些数据由工作流程确定为相似(类似物)。在每种情况下,首先尽可能准确地复制源化学品的已发表的PBK模型,然后进行调整并用作目标化学品的模板。通过将仿真结果与现有目标关键动力学特性数据进行比较,评估了新PBK模型的性能。结果表明,跨读方法可以成功地应用于开发新的PBK模型,用于数据贫乏的化学品,从而使其能够在早期风险评估中部署。这有助于预测内部暴露,同时减少对动物试验的依赖。
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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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