使用 AOPs 构建专家评审:根据 ICH S1B(R1)对致癌性进行可靠的证据权重评估

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2024-06-06 DOI:10.1016/j.comtox.2024.100320
Susanne A. Stalford, Alex N. Cayley, Adrian Fowkes, Antonio Anax F. de Oliveira, Ioannis Xanthis, Christopher G. Barber
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引用次数: 0

摘要

人们普遍认为,非动物研究可用于评估化学品对人体的安全性。这些新方法(NAM)通常整合了包括硅学和体外模型在内的多种来源的数据。监管指南正在不断更新,以认识到这些科学进步可以在不影响人体安全的情况下取代动物研究。其中一项法规 ICH S1B(R1) 于 2022 年进行了更新,纳入了致癌性证据权重评估的规定,使用六个因素来确定是否有足够的证据来免除进行大鼠致癌性实验。然而,大量的数据和证据很难组织和解释成一个连贯的评估。为了帮助进行此类评估,我们开发了一款软件,该软件将不良结果路径 (AOP) 与推理相结合,对知识进行组织和上下文关联,并根据现有数据提供结果。利用该框架开发了一个工作流程,用于评估初步结果和组织专家评审,以调查可能导致化合物致癌潜力(或不致癌)的因素和潜在生物机制。该框架用于组织专家审查三个具有不同活性和支持证据水平的实例。这凸显了 AOP 对专家评审的支持,显示了 1) 使用 AOP 分析数据的价值,2) 专家评审对增强结果可信度的重要性,以及 3) 这种方法如何能够准确预测实验结果。因此,使用这种方法评估 ICH S1B(R1)的证据将提供透明、科学可靠和可重复的结果,从而减少对大鼠致癌性研究的需求。
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Structuring expert review using AOPs: Enabling robust weight-of-evidence assessments for carcinogenicity under ICH S1B(R1)

There is widespread acceptance that non-animal studies can be used to assess chemical safety in humans. These New Approach Methodologies (NAMs) typically integrate data from multiple sources including in silico and in vitro models. Regulatory guidelines are being updated to recognise that these scientific advances are allowing animal studies to be replaced without compromising human safety. One such regulation, ICH S1B(R1), was updated in 2022 to include the provision for a weight-of-evidence assessment for carcinogenicity, using six factors to determine if there was sufficient evidence to waive the need to run a rat carcinogenicity assay. The volume of data and evidence, however, can be hard to organise and interpret into a cohesive evaluation. To aid such assessments, software has been developed that combines adverse outcome pathways (AOPs) and reasoning, to organise and contextualise knowledge, and provide an outcome based on the data available. Using this framework, a workflow has been developed to assess the initial outcome and structure expert review to investigate the factors, and potential biological mechanisms which could contribute to a compound’s carcinogenic potential (or lack thereof). The framework was used to structure expert review of three examples of differing activity and levels of supporting evidence. This highlighted where AOPs supported expert review by showing 1) the value in using AOPs to analyse data, 2) the importance of expert review to strengthen confidence in outcomes, and 3) how this approach can accurately predict experimental results. Therefore, using this approach to assess evidence for ICH S1B(R1) will give transparent, scientifically robust, and reproducible calls, and thus reduce the need for rat carcinogenicity studies.

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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
期刊最新文献
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