集成计算机方法的神经毒性危害评估框架的现状和未来方向

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-05-01 DOI:10.1016/j.comtox.2022.100223
Kevin M. Crofton , Arianna Bassan , Mamta Behl , Yaroslav G. Chushak , Ellen Fritsche , Jeffery M. Gearhart , Mary Sue Marty , Moiz Mumtaz , Manuela Pavan , Patricia Ruiz , Magdalini Sachana , Rajamani Selvam , Timothy J. Shafer , Lidiya Stavitskaya , David T. Szabo , Steven T. Szabo , Raymond R. Tice , Dan Wilson , David Woolley , Glenn J. Myatt
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引用次数: 8

摘要

神经毒理学是研究暴露于化学、生物或物理物质后对发育中或成熟的成人神经系统结构或功能的不良影响的学科。开发更多信息的替代方法来评估由外源性药物引起的发育(DNT)和成人(NT)神经毒性是迫切需要的。使用这些替代方法,包括从化学结构预测DNT或NT的计算机方法(例如,基于统计和基于专家规则的系统),理想情况下是基于对相关生物机制的全面理解。本文讨论了DNT/NT测试中已知的机制以及当前的技术状态。本文回顾了目前可用的基于化学结构知识的神经毒性评估的计算机方法,并提出了集成计算机方法和实验信息的概念框架。建立这一框架对于制定议定书(即标准化方法)至关重要,以确保以透明、一致和可防御的方式对基于化学结构的NT和DNT进行评估。
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Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

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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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