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基于结构活性关系 (SAR) 的类推选择合适类似物的配对分子对 (MMP) 方法。

A matched molecular pair (MMP) approach for selecting analogs suitable for structure activity relationship (SAR)-based read across.

机构信息

The Procter & Gamble Company, 8700 Mason Montgomery Rd. Mason, OH, 45040, USA.

The Procter & Gamble Company, 8700 Mason Montgomery Rd. Mason, OH, 45040, USA.

出版信息

Regul Toxicol Pharmacol. 2021 Aug;124:104966. doi: 10.1016/j.yrtph.2021.104966. Epub 2021 May 24.

Abstract

One of the most challenging aspects of SAR-based read across is the identification of structurally similar compounds suitable for use as data sources to cover the safety of a target chemical. Matched molecular pair analysis (MMPA) provides a systematic method for mining experimental data for chemical substitutions that may be interpreted in terms of changes in properties. Here we use the relationships between structural substitutions linking a target chemical with an analog determined to be suitable using the expert-judgment based P&G framework of Wu et al. (2010). The relationships are established by applying MMPA to a database of compounds with safety assessed using SAR-based read across to suitable analogs possessing toxicological data. The analysis revealed that only five categories of substitutions per chemical class (aromatic or aliphatic) were necessary to link all molecular pairs. These data are summarized in a workflow outlining a strategy for searching toxicological databases for potential analogs. This approach provides structural comparisons that are interpretable and sensitive to small differences in the local structure of two compounds that may be linked to suitability for read across in contrast to the use of quantitative similarity measures which show little correlation with analog suitability.

摘要

基于 SAR 的 read across 最具挑战性的方面之一是确定结构相似的化合物,这些化合物可作为数据源用于覆盖目标化学物质的安全性。匹配分子对分析(MMPA)为挖掘可能根据性质变化来解释的化学取代的实验数据提供了一种系统的方法。在这里,我们使用与目标化学物质相关的结构取代关系,这些取代关系是通过应用 MMPA 到使用 Wu 等人(2010 年)基于专家判断的 P&G 框架确定为合适的类似物的化合物数据库来建立的。关系是通过将安全性评估基于 SAR 的 read across 到具有毒理学数据的合适类似物的化合物数据库应用 MMPA 来建立的。分析表明,每个化学类别的分子对(芳香族或脂肪族)只需要五个取代类别就可以连接所有的分子对。这些数据总结在概述用于搜索毒理学数据库以寻找潜在类似物的策略的工作流程中。这种方法提供了可解释的结构比较,并且对两个可能与 read across 适用性相关的化合物的局部结构的微小差异敏感,与类似物适用性的定量相似性度量相比,这些度量显示出很少的相关性。

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