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CATMoS: Collaborative Acute Toxicity Modeling Suite
Environmental Health Perspectives ( IF 10.4 ) Pub Date : 2021-4-30
Kamel Mansouri, Agnes L. Karmaus, Jeremy Fitzpatrick, Grace Patlewicz, Prachi Pradeep, Domenico Alberga, Nathalie Alepee, Timothy E.H. Allen, Dave Allen, Vinicius M. Alves, Carolina H. Andrade, Tyler R. Auernhammer, Davide Ballabio, Shannon Bell, Emilio Benfenati, Sudin Bhattacharya, Joyce V. Bastos, Stephen Boyd, J.B. Brown, Stephen J. Capuzzi, Yaroslav Chushak, Heather Ciallella, Alex M. Clark, Viviana Consonni, Pankaj R. Daga, Sean Ekins, Sherif Farag, Maxim Fedorov, Denis Fourches, Domenico Gadaleta, Feng Gao, Jeffery M. Gearhart, Garett Goh, Jonathan M. Goodman, Francesca Grisoni, Christopher M. Grulke, Thomas Hartung, Matthew Hirn, Pavel Karpov, Alexandru Korotcov, Giovanna J. Lavado, Michael Lawless, Xinhao Li, Thomas Luechtefeld, Filippo Lunghini, Giuseppe F. Mangiatordi, Gilles Marcou, Dan Marsh, Todd Martin, Andrea Mauri, Eugene N. Muratov, Glenn J. Myatt, Dac-Trung Nguyen, Orazio Nicolotti, Reine Note, Paritosh Pande, Amanda K. Parks, Tyler Peryea, Ahsan H. Polash, Robert Rallo, Alessandra Roncaglioni, Craig Rowlands, Patricia Ruiz, Daniel P. Russo, Ahmed Sayed, Risa Sayre, Timothy Sheils, Charles Siegel, Arthur C. Silva, Anton Simeonov, Sergey Sosnin, Noel Southall, Judy Strickland, Yun Tang, Brian Teppen, Igor V. Tetko, Dennis Thomas, Valery Tkachenko, Roberto Todeschini, Cosimo Toma, Ignacio Tripodi, Daniela Trisciuzzi, Alexander Tropsha, Alexandre Varnek, Kristijan Vukovic, Zhongyu Wang, Liguo Wang, Katrina M. Waters, Andrew J. Wedlake, Sanjeeva J. Wijeyesakere, Dan Wilson, Zijun Xiao, Hongbin Yang, Gergely Zahoranszky-Kohalmi, Alexey V. Zakharov, Fagen F. Zhang, Zhen Zhang, Tongan Zhao, Hao Zhu, Kimberley M. Zorn, Warren Casey, Nicole C. Kleinstreuer

Abstract

Background:

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

Objectives:

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD5050mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

Methods:

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

Results:

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

Discussion:

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program’s Integrated Chemical Environment tools and data sets ( ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495



中文翻译:

CATMoS:协同急性毒性建模套件

摘要

背景:

人体暴露于成千上万的化学物质中,需要对其潜在毒性进行评估。急性全身毒性试验可作为监管危险性分类,标记和风险管理的基础。但是,使用传统的啮齿动物急性毒性试验评估所有新的和现有的化学药品都是成本和时间的限制。使用现有数据建立的计算机模型可快速预测急性毒性,而无需使用动物。

目标:

美国替代方法验证机构间协调委员会(ICCVAM)急性毒性工作组组织了一项国际合作,开发基于五个不同终点的计算机模拟模型,用于预测急性口腔毒性:致死剂量50(LD50 价值,美国环境保护局的危害(四类),全球分类和标签危害统一分类制度(五种),剧毒化学品[LD50LD5050毫克/公斤)]和无毒化学品(大号d50>2,000毫克/公斤)。

方法:

编制了关于11,992种化学品的急性口腔毒性数据清单,分为培训和评估集,可供35个参与的国际研究小组使用,该小组总共提交了139个预测模型。使用外部验证集对属于所提交模型的适用范围内的预测进行评估。然后将它们合并到共识模型中,以利用各个方法的优势。

结果:

由此产生的共识性预测利用了每个模型的集体优势,形成了协作急性毒性建模套件(CATMoS)。与体内结果相比,CATMoS在准确性和鲁棒性方面显示出高性能。

讨论:

监管机构正在评估CATMoS的用途和适用性,以作为体内大鼠急性口腔毒性研究的潜在替代品。通过国家毒理学计划的综合化学环境工具和数据集(ice.ntp.niehs.nih.gov),可以得到CATMoS对800,000多种化学物质的预测。还可以在免费,独立的开源工具OPERA中实施这些模型,该工具可以预测新的和未经测试的化学品。https://doi.org/10.1289/EHP8495

更新日期:2021-05-02
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