经合组织 (Q)SAR 评估框架:提高计算方法监管普及率的工具

IF 3.1 Q2 TOXICOLOGY
Andrea Gissi , Olga Tcheremenskaia , Cecilia Bossa , Chiara Laura Battistelli , Patience Browne
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引用次数: 0

摘要

在化学品危害评估中使用动物试验的替代方法,包括 (Q) SAR,受到国际关注。监管机构要接受替代方法,就需要有考虑方法及其结果科学严谨性的原则。经合组织 (Q)SAR 评估框架 (QAF) 的制定是为了指导监管机构在化学品评估中考虑 (Q)SAR 模型和预测。QAF 以现有的模型评估原则为基础,汲取了长期以来监管机构在评估 (Q)SAR 预测方面的经验,确立了评估预测和多重预测结果的新原则。为所有原则确定的评估要素规定了评估 (Q)SAR 模型和预测的置信度和不确定性的标准,同时保持必要的灵活性,以适应不同的监管环境和目的。使用 "质量评估框架",评估人员可以一致、透明地评估和决定(质量)SAR 的有效性,而模型开发人员和用户也有了明确的要求。QAF 的发布有望提高 (Q) SAR 在监管方面的使用率和认可度,并可能成为为其他新方法 (NAM) 建立类似规范性框架的范例。本文概述了 "快速评估框架 "指南的主要科学方面,并介绍了该指南如何促进在化学品评估中使用替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The OECD (Q)SAR Assessment Framework: A tool for increasing regulatory uptake of computational approaches

There is international interest in using alternatives to animal testing, including (Q)SARs, in chemical hazard assessments. The regulatory acceptance of alternative methods requires principles for considering the scientific rigour of methods and their results. The OECD (Q)SAR assessment Framework (QAF) was developed as guidance for regulators when considering (Q)SAR models and predictions in chemical evaluation. The QAF builds on existing principles for evaluating models and, learning from the longstanding regulatory experience in assessing (Q)SAR predictions, establishes new principles for evaluating predictions and results from multiple predictions. Assessment elements, identified for all principles lay out criteria for assessing the confidence and uncertainties in (Q)SAR models and predictions, while maintaining the flexibility necessary to adapt to different regulatory contexts and purposes. Using the QAF, assessors can consistently and transparently evaluate and decide on the validity of (Q)SARs, and model developers and users have clear requirements to meet. The publication of the QAF is expected to increase the regulatory use and acceptance of (Q)SARs and may become an example to build similar prescriptive frameworks for other new approach methodologies (NAMs). This article provides an overview of the main scientific aspects of the QAF guidance and provides context for how this guidance can promote the use of alternative methods in chemical assessments.

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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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