Metabolism-based category formation for the prioritisation of genotoxicity hazard assessment for plant protection product residues (Part 4): α-Chloroacetamides.

In dietary risk assessment of plant protection products, residues of active ingredients and their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity, in terms of metabolism, a metabolic similarity profiling scheme has been developed from an analysis of 69 α-chloroacetamide herbicides for which either Ames, chromosomal aberration or micronucleus test results are publicly available. A set of structural space alerts were defined, each linked to a key metabolic transformation present in the α-chloroacetamide metabolic space. The structural space alerts were combined with covalent chemistry profiling to develop categories suitable for chemical prioritisation via read-across. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for metabolism data individual groups of plant protection products as the basis for the development of the structural space alerts.

Development of an adverse outcome pathway network for nephrotoxicity.

Adverse outcome pathways (AOPs) were introduced in modern toxicology to provide evidence-based representations of the events and processes involved in the progression of toxicological effects across varying levels of the biological organisation to better facilitate the safety assessment of chemicals. AOPs offer an opportunity to address knowledge gaps and help to identify novel therapeutic targets. They also aid in the selection and development of existing and new in vitro and in silico test methods for hazard identification and risk assessment of chemical compounds. However, many toxicological processes are too intricate to be captured in a single, linear AOP. As a result, AOP networks have been developed to aid in the comprehension and placement of associated events underlying the emergence of related forms of toxicity-where complex exposure scenarios and interactions may influence the ultimate adverse outcome. This study utilised established criteria to develop an AOP network that connects thirteen individual AOPs associated with nephrotoxicity (as sourced from the AOP-Wiki) to identify several key events (KEs) linked to various adverse outcomes, including kidney failure and chronic kidney disease. Analysis of the modelled AOP network and its topological features determined mitochondrial dysfunction, oxidative stress, and tubular necrosis to be the most connected and central KEs. These KEs can provide a logical foundation for guiding the selection and creation of in vitro assays and in silico tools to substitute for animal-based in vivo experiments in the prediction and assessment of chemical-induced nephrotoxicity in human health.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Metabolism-based category formation for the prioritisation of genotoxicity hazard assessment for plant protection product residues (part 3): Strobilurins.

In dietary risk assessment of plant protection products, residues of active ingredients and their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity, in terms of metabolism, a metabolic similarity profiling scheme has been developed from an analysis of 46 chemicals of strobilurin fungicides and their metabolites for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This profiling scheme consists of a set of ten sub-structures, each linked to a key metabolic transformation present in the strobilurin metabolic space. This metabolic similarity profiling scheme was combined with covalent chemistry profiling and physico-chemistry properties to develop chemical categories suitable for chemical prioritisation via read-across. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for metabolism data and individual groups of plant protection products as the basis for the development of such profiling schemes.

Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues (part 2): Triazoles.

In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 66 triazole agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of ten structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the triazole chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.

Sub-structure-based category formation for the prioritisation of genotoxicity hazard assessment for pesticide residues: Sulphonyl ureas.

In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 74 sulphonyl urea agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of seven structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the sulphonyl urea chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.

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.

Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use.

Computational approaches are increasingly used to predict toxicity due, in part, to pressures to find alternatives to animal testing. Read-across is the "new paradigm" which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities i.e. molecular similarity is integral to read-across. Various of molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset.

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

INTRODUCTION: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31 selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed.

A strategy for structuring and reporting a read-across prediction of toxicity.

Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be used to fill data gaps for regulatory decisions is proposed. There are two major aspects of any read-across exercise, namely assessing similarity and uncertainty. While there can be an over-arching rationale for grouping organic substances based on molecular structure and chemical properties, these similarities alone are generally not sufficient to justify a read-across prediction. Further scientific justification is normally required to justify the chemical grouping, typically including considerations of bioavailability, metabolism and biological/mechanistic plausibility. Sources of uncertainty include a variety of elements which are typically divided into two main issues: the uncertainty associated firstly with the similarity justification and secondly the completeness of the read-across argument. This article focuses on chronic toxicity, whilst acknowledging the approaches are applicable to all endpoints. Templates, developed from work to prepare for the application of new toxicological data to read-across assessment, are presented. These templates act as proposals to assist in assessing similarity in the context of chemistry, toxicokinetics and toxicodynamics as well as to guide the systematic characterisation of uncertainty both in the context of the similarity rationale, the read across data and overall approach and conclusion. Lastly, a workflow for reporting a read-across prediction is suggested.

Screening Chemicals for Receptor-Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow.

Assessing compounds for their pharmacological and toxicological properties is of great importance for industry and regulatory agencies. In this study an approach using open source software and open access databases to build screening tools for receptor-mediated effects is presented. The retinoic acid receptor (RAR), as a pharmacologically and toxicologically relevant target, was chosen for this study. RAR agonists are used in the treatment of a number of dermal conditions and specific types of cancer, such as acute promyelocytic leukemia. However, when administered chronically, there is strong evidence that RAR agonists cause hepatosteatosis and liver injury. After compiling information on ligand-protein-interactions, common substructures and physico-chemical properties of ligands were identified manually and coded into SMARTS strings. Based on these SMARTS strings and calculated physico-chemical features, a rule-based screening workflow was built within the KNIME platform. The workflow was evaluated on two datasets: one with RAR agonists exclusively and another large, chemically diverse dataset containing only a few RAR agonists. Possible modifications and applications of screening workflows, dependent on their purpose, are presented.

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.

Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling.

Nowadays nanotechnology is one of the most promising areas of science. The number and quantity of synthesized nanomaterials increase exponentially, therefore it is reasonable to expect that comprehensive risk assessment based only on empirical testing of all novel engineered nanoparticles (NPs) will very soon become impossible. Hence, the development of computational methods complementary to experimentation is very important. Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models widely used in pharmaceutical chemistry and environmental science can also be modified and adopted for nanotechnology to predict physico-chemical properties and toxicity of empirically untested nanomaterials. All QSPR/QSAR modelling activities are based on experimentally derived data. It is important that, within a given data set, all values should be consistent, of high quality and measured according to a standardized protocol. Unfortunately, the amount of such data available for engineered nanoparticles in various data sources (i.e. databases and the literature) is very limited and seldom measured with a standardized protocol. Therefore, we have proposed a framework for collecting and evaluating the existing data, with the focus on possible applications for computational evaluation of properties and biological activities of nanomaterials.

Experimental verification of structural alerts for the protein binding of cyclic compounds acting as Michael acceptors.

This study outlines how a combination of and in vitro data can be used to define the applicability domain of selected structural alerts within the protein binding profilers of the Organisation for Economic Co-operation (OECD) Quantitative Structure-Activity Relationship (QSAR) Toolbox. Thirty chemicals containing a cyclic moiety were profiled for reactivity using the OECD and Optimised Approach based on Structural Indices Set (OASIS) protein binding profilers. The profiling results identified 22 of the chemicals as being reactive towards proteins. Analysis of the experimentally data showed 19 of these chemicals to be reactive. Subsequent analysis allowed refinements to be suggested to improve the applicability domain of the structural alerts investigated. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in chemical category in predictive and regulatory toxicology.

Hepatotoxicity: a scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action.

The ability of a compound to cause adverse effects to the liver is one of the most common reasons for drug development failures and the withdrawal of drugs from the market. Such adverse effects can vary tremendously in severity, leading to an array of possible drug-induced liver injuries (DILIs). As a result, it is not surprising that drug development has evolved into a complex and multifaceted process including methods aiming to identify potential liver toxicities. Unfortunately, hepatotoxicity remains one of the most complex and poorly understood areas of human toxicity; thus it is a significant challenge to identify potential hepatotoxins. The performance of existing methods to identify hepatotoxicity requires improvement. The current study details a scheme for generating chemical categories and the development of structural alerts able to identify potential hepatotoxins. The study utilized a diverse 951-compound dataset and used structural similarity methods to produce a number of structurally restricted categories. From these categories, 16 structural alerts associated with observed human hepatotoxicity were developed. Furthermore, the mechanism(s) by which these compounds cause hepatotoxicity were investigated and a mechanistic rationale was proposed, where possible, to yield mechanistically supported structural alerts. Alerts of this nature have the potential to be used in the screening of compounds to highlight potential hepatotoxicity, whilst the chemical categories themselves are important in applying read-across approaches. The scheme presented in this study also has the potential to act as a knowledge generator serving as an excellent starting platform from which to conduct additional toxicological studies.

Prediction of immobilised artificial membrane chromatography retention factors using theoretical molecular fragments and structural features.

Many in silico alternatives to aquatic toxicity tests rely on hydrophobicity-based quantitative structure-activity relationships (QSARs). Hydrophobicity is often estimated as log P, where P is the octanol-water partition coefficient. Immobilised artificial membrane (IAM) high performance liquid chromatography (HPLC) may be a more biologically relevant alternative to log P. The aim of this study was to investigate the applicability of a theoretical structural fragment and feature-based method to predict log k IAM (the logarithm of the retention index determined by IAM-HPLC) values. This will allow the prediction of log k IAM based on chemical structure alone. The use of structural fragment values to predict log P was first proposed in the 1970s. The application of a similar method using fragment values to predict log k IAM is a novel approach. Values of log k IAM were determined for 22 aliphatic and 42 aromatic compounds using an optimised and robust IAM-HPLC assay. The method developed shows good predictive performance using leave-one-out cross validation and application to an external validation set not seen a priori by the training set also generated good predictive values. The ability to predict log k IAM without the need for practical measurement will allow for the increased use of QSARs based on this descriptor.

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.

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.

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis.

For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.