非膳食暴露于植物保护产品的皮肤吸收的计算机预测

IF 3.1 Q2 TOXICOLOGY
Christian J. Kuster , Jenny Baumann , Sebastian M. Braun , Philip Fisher , Nicola J. Hewitt , Michael Beck , Fabian Weysser , Linus Goerlitz , Petrus Salminen , Christian R. Dietrich , Magnus Wang , Matthias Ernst
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引用次数: 0

摘要

使用随机森林(机器学习技术)开发了预测植物保护产品(PPP)中活性成分的皮肤渗透的计算机模型,该模型使用来自EFSA皮肤吸收数据库的体外人体皮肤研究数据和来自拜耳的内部数据进行训练。除了施加剂量外,还考虑了各种物理化学性质作为模型参数。该模型与一种新颖的百分位数方法相关联,以便使结果可用于监管目的。外部验证数据集的应用程序证明该工具已准备好使用。最后,我们建议采用分层决策树方法进行非饮食风险评估,包括使用计算机皮肤吸收预测模型作为PPP安全评估的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In silico prediction of dermal absorption from non-dietary exposure to plant protection products

An in silico model for predicting skin penetration of active ingredients formulated in plant protection products (PPP) has been developed using random forests (machine learning technique) that were trained with data from in vitro human skin studies taken from the EFSA dermal absorption database and in-house data from Bayer. In addition to the applied dose, various physicochemical properties were considered as model parameters. The model has been linked to a novel percentile approach in order to make the results usable for regulatory purposes. Application to an external validation data set demonstrated that the tool is ready for use. Finally, we propose to follow a tiered decision tree approach for non-dietary risk assessments including the use of the in silico dermal absorption prediction model as part of a safety assessment of a PPP.

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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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