期刊
COMPUTATIONAL TOXICOLOGY
卷 22, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.comtox.2022.100223
关键词
In silico; Computational toxicology; Neurotoxicity; Developmental neurotoxicity; In silico toxicology protocols; Hazard identification; Risk assessment; QSAR; Expert system; Profilers; Read-across
类别
资金
- National Institute of Environmental Health Sciences of the National Institutes of Health [R44ES026909]
Neurotoxicology focuses on studying the adverse effects of exposure to chemical, biological, or physical agents on the nervous system, and there is a need for alternative methods to assess these effects. The research emphasizes on using computer-assisted methods to predict neurotoxicity based on chemical structure, which must be integrated with experimental information to ensure transparency and consistency in assessments.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据