Nelms Mark D, Lougee Ryan, Roberts David W, Richard Ann, Patlewicz Grace
Oak Ridge Institute for Science and Education (ORISE), 1299 Bethel Valley Road, Oak Ridge, TN 37830, USA.
National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency (US EPA), 109 TW Alexander Dr, Research Triangle Park (RTP), NC 27711, USA.
Comput Toxicol. 2019 Nov 1;12:1-13. doi: 10.1016/j.comtox.2019.100100.
The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each 'consensus' reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.
许多毒理学作用机制的分子起始事件包括外源性亲电试剂与内源性亲核试剂之间的反应性共价结合。亲电试剂的靶位点通常是肽、蛋白质、酶或DNA。其中,与蛋白质和DNA形成共价加合物可能是最确定的,因为它们与皮肤致敏和遗传毒性终点最为密切相关。因此,能够识别化学结构中的亲电特征为表征其反应性概况提供了一个起点。已经开发了许多软件工具来帮助识别指示亲电反应潜力的结构特征,以满足各种目的,包括:1)促进毒性效应(如皮肤致敏潜力)的跨类别形成,以及2)对物质进行剖析,以识别潜在的混杂因素,从而在高通量筛选(HTS)试验中使其活性合理化。在此,对文献中已发表的三种此类方案进行了比较,这些方案是SMARTS模式及其相关化学生物反应域的集合。目标是:1)更好地理解它们的范围和覆盖范围,2)相对于已发表的皮肤致敏数据集评估它们的性能,该数据集中已经有基于专家判断分配可能的机制域的人工注释。然后将这三种方案应用于Tox21库,并报告了共识结果,以突出可能表现出反应性响应的化学物质的比例,该响应特定于机制反应域,但对于基于靶组织的活性是非特异性的。计算了ToxPrint指纹,并计算了活性富集,以比较为皮肤致敏数据集和每个“共识”反应域的Tox21化学物质确定的结构特征。富集的ToxPrints还用于识别可能对反应性有信息价值的ToxCast试验。