The Use of a Chemistry-based Profiler for Covalent DNA Binding in the Development of Chemical Categories for Read-across for Genotoxicity

Abstract

An important molecular initiating event for genotoxicity is the ability of a compound to bind covalently with DNA. However, not all compounds that can undergo covalent binding mechanisms will result in genotoxicity. One approach to solving this problem, when in silico prediction techniques are being used, is to develop tools that allow chemicals to be grouped into categories based on their ability to bind covalently to DNA. For this analysis to take place, compounds need to be placed within categories where the trend in toxicity can be explained by simple descriptors, such as hydrophobicity. However, this can occur only when the compounds within a category are structurally and mechanistically similar. Chemistry-based profilers have the ability to screen compounds and highlight those with similar structures to a target compound, and are thus likely to act via a similar mechanism of action. Here, examples are reported to highlight how structure-based profilers can be used to form categories and hence fill data gaps. The importance of developing a well-defined and robust category is discussed in terms of both mechanisms of action and structural similarity.

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

“…In addition to supporting models such as (Q)SARS, the information from MIEs is a key means of grouping compounds providing a mechanistic basis and transparency. [113][114] In silico 2-D profilers of structural alerts, built on knowledge of MIEs have provided a means to group compounds for a number of adverse outcomes including skin sensitisation; 79 respiratory sensitisation; 46 phospholipidosis; 68-69 mutagenicity; 44,115 hepatotoxicity; 116 reproductive toxicity 59 and testicular toxicity. 117 Successful grouping allows for read-across to fill data gaps for these types of endpoints.…”

A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions

“…ToxCast data has shown promise for providing cost-effective bioactivity information for a large number of chemicals, especially in cases where many related or orthogonal assays exist for a common target-mediated mechanism (Judson et al, 2015, Browne et al, 2015. Cheminformatics approaches are also providing valuable information for chemicals where little or no toxicity data exist (Allen et al, 2016, Enoch et al, 2011, Enoch et al, 2012, Nelms et al, 2015a, Nelms et al, 2015b, Sakuratani et al, 2013b, Sakuratani et al, 2013a, Naven et al, 2013. The present work focused on how in vitro and in silico data can be leveraged to provide the maximal information in data-limited situations.…”

“…Collections of structural alerts associated with a given MIE have been recently termed in silico profilers especially in the context of their practical implementation into software tools such as the OECD Toolbox (Dimitrov et al, 2016). The information held by these profilers can be used to generate chemical clusters centered on the ability to elicit the same MIE (Allen et al, 2016, Enoch et al, 2011, Enoch et al, 2012, Naven et al, 2013, Nelms et al, 2015a, Nelms et al, 2015b, Sakuratani et al, 2013a, Sakuratani et al, 2013b.…”

The Changing Paradigm in Preclinical Toxicology: in vitro and in silico Methods in Liver Toxicity Evaluations

“…Read-across is defined by the European Chemicals Agency (2017, p. 6) as "a technique for predicting endpoint information for one substance (target substance), by using data from the same endpoint from (an)other substance(s), (source substance(s))". A range of in silico tools are available for grouping the chemicals and read-across (Enoch, Cronin and Ellison, 2011). Publicly available software include, toxicity estimation software tool (test), the oecd qsar toolbox, high-throughput virtual molecule docking (htvmd), MetaCore, and the topkat model.…”