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一种评估结构警示以预测毒性的方案 - 通过描述不确定性来评估置信度。

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties.

机构信息

School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.

KREATiS SAS, 23 rue du Creuzat, ZAC de St-Hubert, 38080, L'Isle d'Abeau, France.

出版信息

Regul Toxicol Pharmacol. 2022 Nov;135:105249. doi: 10.1016/j.yrtph.2022.105249. Epub 2022 Aug 27.

Abstract

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

摘要

毒理学中的构效关系 (SARs) 使形成结构规则成为可能,这些规则被编码为结构警报,是计算毒理学中的重要工具。虽然其他计算方法有评估方法,但没有正式的流程来评估与结构警报相关的置信度。本研究提出了十二个标准来评估与结构警报相关的不确定性,从而可以对置信度进行评估。这些标准基于声明的目的、化学、毒理学和机制描述、性能和覆盖范围,以及对警报的佐证和支持证据。可以对警报进行置信度评估和评分,从而确定需要更多信息的领域。评估结构警报的方案被置于工业和监管应用的各种用例背景下。对警报的分析以及对评估方案的考虑,确定了警报可能具有的不同特征,例如高度特异性或通用性。这些特征可能决定警报何时可用于特定用途,例如识别用于类推的类似物或进行危害识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7887/9585125/58eda5b666b6/gr1.jpg

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