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蒙特卡罗模型用于亚慢性重复剂量毒性:全身和器官特异性毒性。

Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity.

机构信息

Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.

出版信息

Int J Mol Sci. 2022 Jun 14;23(12):6615. doi: 10.3390/ijms23126615.

DOI:10.3390/ijms23126615
PMID:35743059
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9224506/
Abstract

The risk-characterization of chemicals requires the determination of repeated-dose toxicity (RDT). This depends on two main outcomes: the no-observed-adverse-effect level (NOAEL) and the lowest-observed-adverse-effect level (LOAEL). These endpoints are fundamental requirements in several regulatory frameworks, such as the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and the European Regulation of 1223/2009 on cosmetics. The RDT results for the safety evaluation of chemicals are undeniably important; however, the in vivo tests are time-consuming and very expensive. The in silico models can provide useful input to investigate sub-chronic RDT. Considering the complexity of these endpoints, involving variable experimental designs, this non-testing approach is challenging and attractive. Here, we built eight in silico models for the NOAEL and LOAEL predictions, focusing on systemic and organ-specific toxicity, looking into the effects on the liver, kidney and brain. Starting with the NOAEL and LOAEL data for oral sub-chronic toxicity in rats, retrieved from public databases, we developed and validated eight quantitative structure-activity relationship (QSAR) models based on the optimal descriptors calculated by the Monte Carlo method, using the CORAL software. The results obtained with these models represent a good achievement, to exploit them in a safety assessment, considering the importance of organ-related toxicity.

摘要

化学品的风险特征需要确定重复剂量毒性 (RDT)。这取决于两个主要结果:无观察到不良效应水平 (NOAEL) 和最低观察到不良效应水平 (LOAEL)。这些终点是几个监管框架的基本要求,例如《化学品注册、评估、授权和限制》(REACH) 和关于化妆品的 1223/2009 年欧洲法规。RDT 结果对化学品的安全评估是不可否认的重要;然而,体内试验耗时且非常昂贵。基于计算机的模型可以为亚慢性 RDT 研究提供有用的输入。考虑到这些终点的复杂性,涉及可变的实验设计,这种非测试方法具有挑战性和吸引力。在这里,我们构建了八个用于预测 NOAEL 和 LOAEL 的基于计算机的模型,重点关注全身和器官特异性毒性,研究对肝脏、肾脏和大脑的影响。从从公共数据库中检索到的口服亚慢性毒性的 NOAEL 和 LOAEL 数据开始,我们使用 CORAL 软件,基于蒙特卡罗方法计算的最佳描述符,开发并验证了八个定量构效关系 (QSAR) 模型。考虑到与器官相关的毒性的重要性,这些模型的结果是一个很好的成果,可以在安全评估中加以利用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3727/9224506/95607f9708f4/ijms-23-06615-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3727/9224506/95607f9708f4/ijms-23-06615-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3727/9224506/95607f9708f4/ijms-23-06615-g001.jpg

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