The definition of the applicability domain relevant to skin sensitization for the aromatic nucleophilic substitution mechanism

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

Kinetic-Based Reactivity for Michael Acceptors: Structural Activity Relationships and Its Relationship to Excess Acute Fish Toxicity

Acute aquatic toxicity is divided into the "physical" mode governed by weak, non-covalent interactions and the "chemical" mode governed by covalent reactions. The potency of chemical interactions is typically expected to be greater than that for physical ones. This enhanced potency is called "excess" toxicity. As databases have become complex, substances thought to elicit a chemical mode reveal a lack of excess toxicity. One mechanism where the latter is prevalent is Michael-type addition. A series of α-β-unsaturated substances were evaluated for reactivity. Second order rate constants (k') were calculated (M^-1 s^-1) and found to vary from >4000 to <0.0003. The electron-withdrawing capacity of the polar group impacts k' values; the sequence is nitro > carbonyl or sulfone ≫ sulfoxide, nitrile or amide. When the α-carbon and/or the β-carbon of the π-system are substituted, the k' value is sharply reduced. Excess toxicity is associated with k' values >0.01 (M^-1 s^-1).

Validation of Computational Methods

In this chapter, we provide an overview of how (Quantitative) Structure Activity Relationships, (Q)SARs, are validated and applied for regulatory purposes. We outline how chemical categories are derived to facilitate endpoint specific read-across using tools such as the OECD QSAR Toolbox and discuss some of the current difficulties in addressing the residual uncertainties of read-across. Finally we put forward a perspective of how non-testing approaches may evolve in light of the advances in new and emerging technologies and how these fit within the Adverse Outcome Pathway (AOP) framework.

Glutathione metabolism in the HaCaT cell line as a model for the detoxification of the model sensitisers 2,4-dinitrohalobenzenes in human skin

Glutathione (GSH) is the most prominent antioxidant in cells and the co-factor of an important set of enzymes involved in the skin metabolic clearance system, glutathione S-transferases (GST). Here, we describe an LC-MS (liquid chromatography-mass spectroscopy) method to measure GSH and its disulfide form (GSSG) in HaCaT cells and a 3D Reconstructed Human Epidermis (RHE) model. In our assay, the basal level of GSH in both systems was in the low nmol/mg soluble protein range, while the level of GSSG was systematically below our limit of quantification (0.1 μM). We found that 2,4-dinitrohalobenzenes deplete the GSH present in HaCaT cells within the first hour of exposure, in a dose dependent manner. The level of GSH in HaCaT cells treated with a single non-toxic dose of 10 μM of dinitrohalobenzene was also shown to increase after two hours. While cells treated with 1-chloro-2,4-dinitrobenzene (DNCB) and 1-fluoro-2,4-dinitrobenzene (DNFB) repleted GSH to levels similar to untreated control cells within 24h, 1-bromo-2,4-dinitrobenzene (DNBB) seemed to prevent such a repletion and appeared to be the most toxic compound in all assays. A mathematical modelling of experimental results was performed to further rationalise the differences observed between test chemicals. For this purpose the biological phenomena observed were simplified into two sequential events: the initial depletion of the GSH stock after chemical treatment followed by the repletion of the GSH once the chemical was cleared. Activation of the nuclear factor E2-related factor 2 (Nrf2) pathway was observed with all compounds within two hours, and at concentrations less than 10 μM. These data show that GSH depletion and repletion occur rapidly in skin cells and emphasize the importance of conducting kinetic studies when performing in vitro experiments exploring skin sensitization.

Proposal of an in silico profiler for categorisation of repeat dose toxicity data of hair dyes

This study outlines the analysis of 94 chemicals with repeat dose toxicity data taken from Scientific Committee on Consumer Safety opinions for commonly used hair dyes in the European Union. Structural similarity was applied to group these chemicals into categories. Subsequent mechanistic analysis suggested that toxicity to mitochondria is potentially a key driver of repeat dose toxicity for chemicals within each of the categories. The mechanistic hypothesis allowed for an in silico profiler consisting of four mechanism-based structural alerts to be proposed. These structural alerts related to a number of important chemical classes such as quinones, anthraquinones, substituted nitrobenzenes and aromatic azos. This in silico profiler is intended for grouping chemicals into mechanism-based categories within the adverse outcome pathway paradigm.

Experimental verification of structural alerts for the protein binding of sulfur-containing compounds

As often noted by Dr. Gilman Veith, a major barrier to advancing any model is defining its applicability domain. Sulfur-containing industrial organic chemicals can be grouped into several chemical classes including mercaptans (RSH), sulfides (RSR'), disulfides (RSSR'), sulfoxides (RS(=O)R'), sulfones (RS(=O)(=O)R'), sulfonates (ROS(=O)(=O)R') and sulfates (ROS(=O)(=O)OR'). In silico expert systems that predict protein binding reactions from 2D structure sub-divide these chemical classes into a variety of chemical reactive mechanisms and reactions which have toxic consequences. Using the protein binding profilers in version 3.1 of the OECD QSAR Toolbox, a series of sulfur-containing chemicals were profiled for protein binding potential. From these results it was hypothesized which sulfur-containing chemicals would be reactive or non-reactive in an in chemico glutathione assay and whether if reactive they would exhibit toxicity in excess of baseline in the Tetrahymena pyriformis population growth impairment assay. Subsequently, these hypotheses were tested experimentally. The in chemico data show that the in silico profiler predictions were generally correct for all chemical categories, where testing was possible. Mercaptans could not be assessed for GSH reactivity because they react directly with the chromophore 5,5'-dithiobis-(2-nitrobenzoic acid). With some exceptions, the major being disulfides, the in vitro toxicity data supported the in chemico findings.

Approaches for Grouping Chemicals into Categories

This chapter outlines the various methods by which chemical similarity can be defined to allow for chemical category formation. The focus is on three methods: simple analogues, definition of the chemistry associated with molecular initiating events and chemoinformatics. An outline of how each method is used in practice and how they have been developed into in silico tools is presented.

Experimental verification, and domain definition, of structural alerts for protein binding: epoxides, lactones, nitroso, nitros, aldehydes and ketones

This study outlines how a combination of in chemico and Tetrahymena pyriformis data can be used to define the applicability domain of selected structural alerts within the profilers of the OECD QSAR Toolbox. Thirty-three chemicals were profiled using the OECD and OASIS profilers, enabling the applicability domain of six structural alerts to be defined, the alerts being: epoxides, lactones, nitrosos, nitros, aldehydes and ketones. Analysis of the experimental data showed the applicability domains for the epoxide, nitroso, aldehyde and ketone structural alerts to be well defined. In contrast, the data showed the applicability domains for the lactone and nitro structural alerts needed modifying. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in the chemical category in predictive and regulatory toxicology. This study highlights the importance of utilizing multiple profilers in category formation.

The definition of the toxicologically relevant applicability domain for the SNAr reaction for substituted pyridines and pyrimidines

This study outlines how results from a glutathione reactivity assay (so-called in chemico data) can be used to define the applicability domain for the nucleophilic aromatic substitution (SNAr) reaction for nitrogen-containing aromatic compounds. SNAr is one of the six mechanistic domains that have been shown to be important in toxicological endpoints in which the ability to bind covalently to a protein is a key molecular initiating event. This study has analysed experimental data (2 h RC50 values), allowing a clear and interpretable structure-activity relationship to be developed for pyridines and pyrimidines which reside within the SNAr domain. The in-ring nitrogen(s) act as activating groups in the SNAr reaction. The position(s) of the in-ring nitrogen(s) as well as other activating groups, especially in relationship to the leaving group, affect reactive potency. The experimentally defined applicability domain has resulted in a series of structural alerts. These results build on early work on the benzene derivatives residing in the SNAr domain. The definition of the applicability domain for the SNAr reaction and the resulting structural alerts are likely to be beneficial in the development of computational tools for category formation and read-across in hazard identification, and the development of adverse outcome pathways.