Using read-across to build physiologically-based kinetic models: Part 1. Development of a KNIME workflow to assist analogue selection for PBK modelling

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2023-12-01 DOI:10.1016/j.comtox.2023.100292

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: 1

Abstract

Read-across refers to the process by which information from one (source) chemical is used to infer information about another similar (target) chemical. This method can be used to fill data gaps and so inform safety assessment where data are lacking for chemicals of interest. As one chemical cannot be considered as absolutely similar to another, only similar with respect to a given property, it is essential to justify the selection of similar chemicals (analogues) for the purposes of read-across. A previously created dataset of available physiologically-based kinetic (PBK) models (referred to as the PBK modelling dataset or PMD) was used in the development of a KNIME workflow. KNIME is a freely-available, open-source analytics platform that allows users to create workflows to analyse and visualise data. The KNIME workflow described here was designed to identify chemical analogues with a corresponding model in the PMD. The PMD combined with the KWAAS enables PBK model information from source chemical(s) to be used in a read-across approach to help develop new PBK models for target chemicals. This KNIME workflow was applied to six chemicals, representing different types of chemical classes (drugs, cosmetics, botanicals, industrial chemicals, pesticides, and food additives) to assess its applicability across various industries. Information acquired from these PBK models can be used to support safety assessment of chemicals and reduce reliance on animal testing.

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利用 "交叉阅读 "建立基于生理学的动力学模型：第 1 部分.开发 KNIME 工作流程以协助为 PBK 建模选择模拟物

跨读是指利用一种(源)化学物质的信息来推断另一种类似(目标)化学物质的信息的过程。这种方法可用于填补数据空白，从而为缺乏相关化学品数据的安全评估提供信息。由于一种化学物质不能被认为与另一种化学物质绝对相似，只有就某一特定性质而言是相似的，因此必须证明选择相似的化学物质(类似物)是合理的，以便进行解读。先前创建的可用生理动力学(PBK)模型数据集(称为PBK建模数据集或PMD)用于KNIME工作流程的开发。KNIME是一个免费的开源分析平台，允许用户创建工作流来分析和可视化数据。本文描述的KNIME工作流程旨在通过PMD中的相应模型识别化学类似物。PMD与KWAAS相结合，可以将来自源化学品的PBK模型信息用于跨读方法，以帮助开发针对目标化学品的新PBK模型。该KNIME工作流程应用于六种化学品，代表不同类型的化学类别(药物、化妆品、植物、工业化学品、农药和食品添加剂)，以评估其在不同行业的适用性。从这些PBK模型中获得的信息可用于支持化学品的安全评估，并减少对动物试验的依赖。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Toxicology Computer Science-Computer Science Applications

CiteScore

5.50

自引率

0.00%

发文量

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