支持安全评估的发育和生殖毒性不良后果途径网络

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2024-08-08 DOI:10.1016/j.comtox.2024.100325
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引用次数: 0

摘要

发育和生殖毒性(DART)是保护人类健康的关键监管终点。DART 评估需要大量动物,费用昂贵,而且通常在药物开发的后期阶段进行。因此,目前正在开发新的方法(NAM),以取代动物试验。这些新方法(包括硅学模型)可用于在化合物开发的早期阶段筛查 DART 危害,将来还可能用于监管机构的 DART 评估。由于发育毒物定性错误会产生影响,因此需要对使用 NAMs 进行的评估有高度的信心和理解;很可能需要多种 NAMs 来取代目前基于动物的评估。不良后果途径 (AOP) 是记录毒性机制的实用工具。可以将 NAM 与 AOP 沿线的关键事件 (KE) 联系起来,为 NAM 的输出提供背景信息,从而增强使用 NAM 的信心。很可能需要针对特定毒性终点的路径网络,才能有信心地将基于 AOP 的方法应用于安全评估。目前,公共领域中描述的 DART AOP 数量不足;因此,利用基于文献的方法,开发了一个由 340 个关键效应因子(包括 68 个 MIE)组成的网络。通过将相关检测、数据和基于专家规则的结构警报与适当的关键效应因子联系起来,使这一路径基础具有化学意识。评估了该网络作为危险筛选工具的使用情况,并研究了其在辅助 ICH S5 工作流程方面的应用。在该 AOP 网络中获取的知识还可以指导进一步开发和使用与 DART 相关的 NAM 以及测试和评估综合方法 (IATA)。
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A developmental and reproductive toxicity adverse outcome pathway network to support safety assessments

Developmental and reproductive toxicity (DART) are key regulatory endpoints for the protection of human health. DART assessments require large numbers of animals, are expensive and often run at late stages of drug development. Therefore, new approach methodologies (NAMs) are being developed to transition away from animal testing. These NAMs (including in silico models) can be used to screen for DART hazards at the early stages of compound development and may in the future be used for regulatory DART assessments. Due to the implications of a mischaracterised developmental toxicant, both high confidence and understanding of the assessments made using NAMs will be required; it is likely that multiple NAMs will be needed in order to replace the current animal-based assessments. Adverse outcome pathways (AOPs) serve as a pragmatic tool for documenting mechanisms of toxicity. NAMs can be associated to key events (KEs) along an AOP, providing context to their outputs, and therefore increasing confidence in their use. It is likely that networks of pathways will be required for a specific toxicity endpoint in order to confidently apply an AOP-based approach to safety assessments. An insufficient number of DART AOPs are currently described within the public domain; therefore, using a literature-based approach, a network consisting of 340 KEs (including 68 MIEs) was developed. This foundation of pathways was made chemically aware through the association of relevant assays, data and expert rule-based structural alerts to appropriate KEs. The use of the network as a hazard screening tool was assessed, and the application of this to aid an ICH S5 workflow investigated. The knowledge captured within this AOP network can also guide the further development and use of DART-relevant NAMs and integrated approaches to testing and assessments (IATAs).

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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
Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review From model performance to decision support – The rise of computational toxicology in chemical safety assessments Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment The OECD (Q)SAR Assessment Framework: A tool for increasing regulatory uptake of computational approaches A developmental and reproductive toxicity adverse outcome pathway network to support safety assessments
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