使用Generalised read-across (GenRA)实现系统的read-across

IF 3.1 Q2 TOXICOLOGY
Grace Patlewicz, Imran Shah
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引用次数: 2

摘要

在分类和模拟方法中,跨读仍然是一种流行的数据缺口填补技术。阻碍阅读接受的主要问题之一是解决和减少不确定性的概念。框架和格式的创建有助于促进对开发、评估和剩余不确定性的理解。然而,通读仍然是一种专家驱动的方法,每项评估都根据自己的优点来决定,没有客观的方法来评估绩效或量化不确定性。在这里,描述了创建一种跨读算法方法的潜在动机,即通用跨读(GenRA)方法。该方法的总体目标是量化绩效和不确定性。讨论了在量化每个相似性上下文的影响方面取得的进展,这些相似性上下文通常作为通读评估的一部分。说明了该方法的基础框架、迄今为止开发的软件工具,以及如何使用GenRA作为筛查级别危险评估决策背景的一部分来做出和解释预测。讨论了未来的方向和该领域仍需解决的一些首要问题,以及GenRA可能在多大程度上促进这些需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards systematic read-across using Generalised Read-Across (GenRA)

Read-across continues to be a popular data gap filling technique within category and analogue approaches. One of the main issues hindering read-across acceptance is the notion of addressing and reducing uncertainties. Frameworks and formats have been created to help facilitate read-across development, evaluation, and residual uncertainties. However, read-across remains an expert-driven approach with each assessment decided on its own merits with no objective means of evaluating performance or quantifying uncertainties. Here, the underlying motivation of creating an algorithmic approach to read-across, namely the Generalised Read-Across (GenRA) approach, is described. The overall objectives of the approach were to quantify performance and uncertainty. Progress made in quantifying the impact of each similarity context commonly relied upon as part of read-across assessment are discussed. The framework underpinning the approach, the software tools developed to date and how GenRA can be used to make and interpret predictions as part of a screening level hazard assessment decision context are illustrated. Future directions and some of the overarching issues still needed in this field and the extent to which GenRA might facilitate those needs are discussed.

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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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