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计算机毒理学方案。

In silico toxicology protocols.

机构信息

Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA.

Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.

出版信息

Regul Toxicol Pharmacol. 2018 Jul;96:1-17. doi: 10.1016/j.yrtph.2018.04.014. Epub 2018 Apr 17.

Abstract

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

摘要

本出版物调查了计算毒理学方法在不同行业和机构中的几种应用。它强调了在进行毒性相关预测时制定标准化方案的必要性。本研究阐述了在多个行业和监管机构中,支持主要毒性终点(如遗传毒性、致癌性、急性毒性、生殖毒性、发育毒性)的计算预测所需的信息。当这些新的计算毒理学(IST)方案得到充分开发和实施时,将确保在不同行业和监管机构中以一致、可重复和记录良好的方式进行计算毒理学评估,以支持更广泛地采用和接受这些方法。IST 方案的开发是通过一个国际联盟的合作发起的,旨在反映用于危害识别和特征描述的计算毒理学的最新进展。本文包括了描述此类方案开发的一般框架,该框架基于针对一系列相关毒性效应或机制的明确定义的计算预测和/或可用的实验数据。本出版物提出了一种确定计算预测与实验数据可靠性的新方法。此外,我们还讨论了如何根据信息的相关性和可靠性来确定评估的置信度水平。

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本文引用的文献

2
Alarms about structural alerts.
Green Chem. 2016 Aug 21;18(16):4348-4360. doi: 10.1039/C6GC01492E. Epub 2016 Jun 28.
3
Progress with Structure-Activity Relationship modelling of occupational chemical respiratory sensitizers.
Curr Opin Allergy Clin Immunol. 2017 Apr;17(2):64-71. doi: 10.1097/ACI.0000000000000355.
4
(Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.
Food Chem Toxicol. 2017 Apr;102:109-119. doi: 10.1016/j.fct.2017.02.002. Epub 2017 Feb 2.
6
Management of organic impurities in small molecule medicinal products: Deriving safe limits for use in early development.
Regul Toxicol Pharmacol. 2017 Mar;84:116-123. doi: 10.1016/j.yrtph.2016.12.011. Epub 2016 Dec 27.
8
Validation of Computational Methods.
Adv Exp Med Biol. 2016;856:165-187. doi: 10.1007/978-3-319-33826-2_6.
9
Quantifying the benefits of using read-across and in silico techniques to fulfill hazard data requirements for chemical categories.
Regul Toxicol Pharmacol. 2016 Nov;81:250-259. doi: 10.1016/j.yrtph.2016.09.004. Epub 2016 Sep 7.
10
In silico toxicology: computational methods for the prediction of chemical toxicity.
Wiley Interdiscip Rev Comput Mol Sci. 2016 Mar;6(2):147-172. doi: 10.1002/wcms.1240. Epub 2016 Jan 6.

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