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基于高度优化的抗菌药物数据库的化学生物信息学分析的毒理学关注阈值的新范例。

A new paradigm in threshold of toxicological concern based on chemoinformatics analysis of a highly curated database enriched with antimicrobials.

机构信息

Altamira LLC, Columbus, OH, 43235, USA; Molecular Networks GmbH, Nürnberg, Germany; The Ohio State University, OH, 43210, USA.

Steptoe & Johnson LLC, Washington, DC, 20036, USA.

出版信息

Food Chem Toxicol. 2020 Sep;143:111561. doi: 10.1016/j.fct.2020.111561. Epub 2020 Jul 5.

Abstract

A new database of antimicrobial-enriched chemicals for the Threshold of Toxicological Concern (TTC) approach has been compiled, comprising 1357 chemicals with 276, 54, and 1027 substances in Cramer Classes I, II, and III, respectively. To enrich the chemical space of the No-/Lowest-Observed-Adverse Effect Level (NOAEL/LOAEL) database, a reference Antimicrobial (AM) Inventory (681) was established for chemical inclusion. To this database, the three existing TTC datasets were combined via robust data fusion process. From the final AM TTC Dataset, the fifth percentiles were derived to be 2.7, 0.43, and 0.12 mg/kg-bw/day for Cramer Classes I, II, and III, respectively. Considering the high percentage of AMs being Cramer Class III, the thresholds are remarkably stable across various TTC datasets. Based on the AM-enriched database, a set of AM categories stratified across potency were developed to classify AMs beyond the capability of the conventional Cramer Tree approach. Grouping the query chemical within the AM category, further distribution analyses were conducted to identify subclasses and differentiate potency. This study proposes a new framework for potential assessment of chronic toxicity made possible with the power of modern reliable databases and chemoinformatic methods.

摘要

一个新的抗菌增强化学品数据库已经为毒理学关注阈值(TTC)方法编译完成,包含 1357 种化学品,其中 Cramer 分类 I、II 和 III 分别有 276、54 和 1027 种物质。为了丰富无/最低观察不良效应水平(NOAEL/LOAEL)数据库的化学空间,建立了一个参考抗菌(AM)清单(681)用于化学物质的纳入。将这三个现有的 TTC 数据集通过稳健的数据融合过程进行了合并。从最终的 AM TTC 数据集,得出了第五个百分位数分别为 2.7、0.43 和 0.12 mg/kg-bw/day,用于 Cramer 分类 I、II 和 III。考虑到 AM 高比例为 Cramer 分类 III,因此在不同的 TTC 数据集之间,阈值是非常稳定的。基于 AM 增强数据库,开发了一组分层的 AM 类别,用于对超过传统 Cramer 树方法能力的 AM 进行分类。将查询化学物质归入 AM 类别中,进一步进行分布分析以识别子类和区分效力。本研究提出了一个新的框架,用于通过现代可靠数据库和化学信息学方法的力量来评估慢性毒性的潜力。

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