Uncertainty in the results from oral repeated dose toxicity tests: Impact on regulatory classifications.

The Lowest Observed (Adverse) Effect Level (LO(A)EL) values are point-of-departure (PoD) values that quantify repeat dose toxicity (RDT). Here, the uncertainty in the regulatory classification of these PoDs is investigated. In the application stage, the dose-response was approximated for a large set of series, giving an account of the possible presence of a hormesis zone. The minimal effect dose (MED) or dose was computed, and the ratio MED/LO(A)EL was used to represent the two components of the experimental uncertainty. The uncertainty estimations were calculated for any combination of gender and reported examination item. Subsequently, how this uncertainty affects the possible classifications was analyzed, and the percentage of the chemicals receiving ambiguous classification was determined. It was shown that more than 40% of the investigated chemicals cannot be classified unambiguously in the Globally Harmonized System (GHS) classification scheme and bear a potential for misclassification when a regulatory decision is based on a single LO(A)EL value. A table containing grey zones for different risk levels and a table with GHS classification distributions for various LO(A)EL values were prepared to facilitate the use of the RDT uncertainty in the practice.

Estimating uncertainty in LLNA EC3 data and its impact on regulatory classifications.

The murine Local Lymph Node Assay (LLNA) is a test that produces numerical results (EC3 values) quantifying the sensitization potency of chemicals. These results are broadly used in toxicology and serve as a basis for various classifications, which determine subsequent regulatory decisions. The continuing interest in LLNA data and the diminished likelihood of new experimental EC3 data being generated sparked this investigation of uncertainty. Instead of using the Gaussian distribution as a default choice for assessing variability in a data set, two strictly positive distributions were proposed and their performance over the available experimental EC3 values was tested. In the application stage, how the uncertainty in EC3 values affects the possible classifications was analyzed, and the percentage of the chemicals receiving ambiguous classification was determined. It was shown that this percentage is high, which increases the risk of improper classification. Two approaches were suggested in regulatory practice to address the uncertainty in the EC3 data: the approaches based on "grey zones" and the classification distribution. If a chemical cannot be classified unambiguously, the latter appears to be an acceptable means to assess the level of sensitization potency of chemicals and helps provide better regulatory decisions.

Selection of Representative Constituents for Unknown, Variable, Complex, or Biological Origin Substance Assessment Based on Hierarchical Clustering.

Many of the newly produced and registered substances are complex mixtures or substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs). The latter often consist of a large number of constituents, some of them difficult-to-identify constituents, which complicates their (eco)toxicological assessment. In the present study, through a series of examples, different scenarios for selection of representatives via hierarchical clustering of UVCB constituents are exemplified. Hierarchical clustering allows grouping of the individual chemicals into small sets, where the constituents are similar to each other with respect to more than one criterion. To this end, various similarity criteria and approaches for selection of representatives are developed and analyzed. Two types of selection are addressed: (1) selection of the most "conservative" constituents, which could be also used to support prioritization of UVCBs for evaluation, and (2) obtaining of a small set of chemical representatives that covers the structural and metabolic diversity of the whole target UVCBs or a mixture that can then be evaluated for their environmental and (eco)toxicological properties. The first step is to generate all plausible UVCB or mixture constituents. It was found that the appropriate approach for selecting representative constituents depends on the target endpoint and physicochemical parameters affecting the endpoint of interest. Environ Toxicol Chem 2021;40:3205-3218. © 2021 SETAC.

Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox.

A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R2. The mechanistic and predictive coverages were >0.85.

Mechanistic relationship between biodegradation and bioaccumulation. Practical outcomes.

According to the REACH Regulation, for all substances manufactured or imported in amounts of 10 or more tons per year, that are not exempted from the registration requirement, a Chemical Safety Assessment (CSA) must be conducted. According to CSA criteria, for these substances persistent, bioaccumulative and toxic (PBT), and very persistent and very bioaccumulative (vPvB) assessment is requested. In order to reduce the number of applications of the expensive bioaccumulation test it seems useful to search thresholds for other related parameters above which no bioaccumulation is observed. Given the known relationship between ready biodegradability and bioaccumulation, one such parameter is biodegradation. This article addresses this relationship in searching for BOD threshold above which no vB and B chemicals could be observed. It was found that the regulatory criteria for persistency could be used for identification of not vB and B chemicals. In addition, fish liver metabolism is determined as the most significant factor in reducing of maximum bioaccumulation potential of the chemicals. It was found that parameters associated with the models simulating fish metabolism could be also used for identification of not vB and B chemicals.

The implementation of RAAF in the OECD QSAR Toolbox.

The Read-Across Assessment Framework (RAAF) was developed by the European Chemicals Agency (ECHA) as an internal tool providing a framework for a consistent, structured and transparent assessment of grouping of chemicals and read-across. Following a RAAF-based evaluation, also developers and users of read-across predictions outside ECHA can judge whether their read-across rationale is sufficiently robust from a regulatory perspective. The aim of this paper is to describe the implementation of RAAF functionalities in the OECD QSAR Toolbox report. These can be activated in the prediction report after performing a readacross prediction. Once the user manually selects the appropriate scenario, the RAAF assessment elements appear and are automatically aligned with the suitable category elements of the Toolbox report. Subsequently, these are evaluated as part of the category consistency assessment functionality. The implementation of the RAAF functionality is illustrated in practice with two examples.

UVCB substances II: Development of an endpoint-nonspecific procedure for selection of computationally generated representative constituents.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) comprise approximately 40% of all registered substances submitted to the European Chemicals Agency. One of the main characteristics of UVCBs is that they have no unique representation. Industry scientists who are part of the scientific community have been working with academics and consultants to address the problem of a lack of a defined structural description. It has been acknowledged that one of the obstacles is the large number of possible structural isomers. We have recently proposed and published a methodology, based on the generic substance identifiers, to address this issue. The methodology allows for the coding of constituents, their generation, calculation of important characteristics of UVCB constituents, and selection of representative constituents. In the present study we introduce a statistical selection of the minimum number of generated constituents representing a UVCB. This representative sample was selected in such a way that the structural variability and the properties of concern of the UVCB were approximated within a predefined tolerable error. The aim of the statistical selection was to enable the assessment of UVCB substances by decreasing the number of constituents that need to be evaluated. The procedure, which was shown to be endpoint-independent, was validated theoretically and on real case studies. Environ Toxicol Chem 2019;38:682-694. © 2019 SETAC.

Validation of the performance of TIMES genotoxicity models with EFSA pesticide data.

This study validates the performance of the TIssue MEtabolism Simulator (TIMES) genotoxicity models with data on pesticide chemicals included in a recently released European Food Safety Authority (EFSA) genotoxicity database. The EFSA database is biased towards negative chemicals. A comparison of substances included in the EFSA database and TIMES genotoxicity databases showed that the majority of the EFSA pesticides is not included in the TIMES genotoxicity databases and, thus, out of the applicability domains of the current TIMES models. However, the EFSA genotoxicity database provides an opportunity to expand the TIMES models. Where there is overlap of substances, consistency between EFSA and TIMES databases for the chemicals with documented data is found to be high (>80%) with respect to the Ames data and lower than the Ames data with respect to chromosomal aberration (CA) and mouse lymphoma assay (MLA) data. No conclusion for consistency with respect to micronucleus test and comet genotoxicity data can be provided due to the limited number of overlapping substances. Specificity of the models is important, given the prevalence of negative genotoxicity data in the EFSA database. High specificity (>80%) is obtained for prediction of the EFSA pesticides with Ames data. Moreover, this high specificity of the TIMES Ames models is not dependant on pesticides being within the domains. Specificity of the TIMES CA and MLA models is lower (>40%) to pesticides for out of domain. Sensitivity of TIMES in vitro and in vivo models cannot be properly estimated due to the small number of positive chemicals in the EFSA database.

Development of a Decision Tree for Mitochondrial Dysfunction: Uncoupling of Oxidative Phosphorylation.

Mitochondrial dysfunction is the result of a number of processes including the uncoupling of oxidative phosphorylation. This study outlines the development of a decision tree-based profiling scheme capable of assigning chemicals to one of six confidence-based categories. The decision tree is based on a set of structural alerts and physicochemical boundaries identified from a detailed study of the literature. The physicochemical boundaries define a chemical relationship with both log P and p Ka. The study also outlines how the decision tree can be used to profile databases through an analysis of the publically available databases in the OECD QSAR Toolbox. This analysis enabled a set of additional structural alerts to be identified that are of concern for protonophoric ability. The decision tree will be incorporated in the OECD QSAR Toolbox V4.3. The intended usage is to group the chemicals into categories of chronic human health and environmental toxicological end points.

The OECD QSAR Toolbox Starts Its Second Decade.

The OECD QSAR Toolbox is a computer software designed to make pragmatic qualitative and quantitative structure-activity relationship methods-based predictions of toxicity, including read-across, available to the user in a comprehensible and transparent manner. The Toolbox, provide information on chemicals in structure-searchable, standardized files that are associated with chemical and toxicity data to ensure that proper structural analogs can be identified. This chapter describes the advantages of the Toolbox, the aims, approach, and workflow of it, as well as reviews its history. Additionally, key functional elements of it use are explained and features new to Version 4.1 are reported. Lastly, the further development of the Toolbox, likely needed to transform it into a more comprehensive Chemical Management System, is considered.

The adverse outcome pathway for skin sensitisation: Moving closer to replacing animal testing.

This article outlines the work of the Organisation for Economic Co-operation and Development (OECD) that led to being jointly awarded the 2015 Lush Black Box Prize. The award-winning work centred on the development of 'The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins'. This Adverse Outcome Pathway (AOP) has provided the mechanistic basis for the integration of skin sensitisation-related information. Recent developments in integrated approaches to testing and assessment, based on the AOP, are summarised. The impact of the AOP on regulatory policy and on the Three Rs are discussed. An overview of the next generation of the skin sensitisation AOP module in the OECD QSAR Toolbox, based on more-recent work at the Laboratory of Mathematical Chemistry, is also presented.

Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity.

We investigated the performance of an integrated approach to testing and assessment (IATA), designed to cover different genotoxic mechanisms causing cancer and to replicate measured carcinogenicity data included in a new consolidated database. Genotoxic carcinogenicity was predicted based on positive results from at least two genotoxicity tests: one in vitro and one in vivo (which were associated with mutagenicity categories according to the Globally Harmonized System classification). Substances belonging to double positives mutagenicity categories were assigned to be genotoxic carcinogens. In turn, substances that were positive only in a single mutagenicity test were assigned to be mutagens. Chemicals not classified by the selected genotoxicity endpoints were assigned to be negative genotoxic carcinogens and subsequently evaluated for their capability to elicit non-genotoxic carcinogenicity. However, non-genotoxic carcinogenicity mechanisms were not currently included in the developed IATA. The IATA is docked to the OECD Toolbox and uses measured data for different genotoxicity endpoints when available. Alternatively, the system automatically provides predictions by SAR genotoxicity models using the OASIS Tissue Metabolism Simulator platform. When the developed IATA was tested against the consolidated database, its performance was found to be high, with sensitivity of 74% and specificity of 83%, when measured carcinogenicity data were used along with predictions falling within the models' applicability domains. Performance of the IATA would be slightly changed to a sensitivity of 80% and specificity of 72% when the evaluation by non-genotoxic carcinogenicity mechanisms was taken into account. Copyright © 2016 John Wiley & Sons, Ltd.

UVCB substances: methodology for structural description and application to fate and hazard assessment.

Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) have been conventionally described in generic terms. Commonly used substance identifiers are generic names of chemical classes, generic structural formulas, reaction steps, physical-chemical properties, or spectral data. Lack of well-defined structural information has significantly restricted in silico fate and hazard assessment of UVCB substances. A methodology for the structural description of UVCB substances has been developed that allows use of known identifiers for coding, generation, and selection of representative constituents. The developed formats, Generic Simplified Molecular-Input Line-Entry System (G SMILES) and Generic Graph (G Graph), address the need to code, generate, and select representative UVCB constituents; G SMILES is a SMILES-based single line notation coding fixed and variable structural features of UVCBs, whereas G Graph is based on a workflow paradigm that allows generation of constituents coded in G SMILES and end point-specific or nonspecific selection of representative constituents. Structural description of UVCB substances as afforded by the developed methodology is essential for in silico fate and hazard assessment. Data gap filling approaches such as read-across, trend analysis, or quantitative structure-activity relationship modeling can be applied to the generated constituents, and the results can be used to assess the substance as a whole. The methodology also advances the application of category-based data gap filling approaches to UVCB substances.

A feasibility study: Can information collected to classify for mutagenicity be informative in predicting carcinogenicity?

Carcinogenicity is a complex endpoint of high concern yet the rodent bioassay still used is costly to run in terms of time, money and animals. Therefore carcinogenicity has been the subject of many different efforts to both develop short-term tests and non-testing approaches capable of predicting genotoxic carcinogenic potential. In our previous publication (Mekenyan et al., 2012) we presented an in vitro-in vivo extrapolation workflow to help investigate the differences between in vitro and in vivo genotoxicity tests. The outcomes facilitated the development of new (Q)SAR models and for directing testing. Here we have refined this workflow by grouping specific tests together on the basis of their ability to detect DNA and/or protein damage at different levels of biological organization. This revised workflow, akin to an Integrated Approach to Testing and Assessment (IATA) informed by mechanistic understanding was helpful in rationalizing inconsistent study outcomes and categorizing a test set of carcinogens with mutagenicity data on the basis of regulatory mutagenicity classifications. Rodent genotoxic carcinogens were found to be correctly predicted with a high sensitivity (90-100%) and a low rate of false positives (3-10%). The insights derived are useful to consider when developing future (non-)testing approaches to address regulatory purposes.

What determines skin sensitization potency-myths, maybes and realities. Part 1. The 500 molecular weight cut-off.

BACKGROUND: It is widely accepted that there is a molecular weight (MW) cut-off of 500, such that single chemicals with MWs higher than 500 cannot be skin sensitizers. If true, this could serve as a useful principle for designing non-sensitizing chemicals. OBJECTIVES: To assess whether the 500 MW cut-off is a myth or a reality. METHODS: A database of 699 chemicals tested for skin sensitization in guinea pigs or mice was analysed to establish the number of tested chemicals with MW > 500, and to establish whether any of these were sensitizers. RESULTS: Only 13 (2%) of the 699 chemicals in the database have MW > 500. Of the 13 tested compounds with MW > 500 in the database, five are sensitizers and eight are non-sensitizers. CONCLUSIONS: The 500 MW cut-off for skin sensitization is a myth, probably derived from the widespread misconception that ability to efficiently penetrate the stratum corneum is a key determinant of sensitization potency. The scarcity of sensitizers with MW > 500 simply reflects the general scarcity of chemicals with MW > 500.

MetaPath: an electronic knowledge base for collating, exchanging and analyzing case studies of xenobiotic metabolism.

The MetaPath knowledge base was developed for the purpose of archiving, sharing and analyzing experimental data on metabolism, metabolic pathways and crucial supporting metadata. The MetaPath system grew out of the need to compile and organize the results of metabolism studies into a systematic database to facilitate data comparisons and evaluations. Specialized MetaPath data evaluation tools facilitate the review of pesticide metabolism data submitted for regulatory risk assessments as well as exchange of results of complex analyses used in regulation and research. Customized screen editors called Composers were developed to automate data entry into MetaPath while also streamlining the production of agency specific study summaries such as the Data Evaluation Records (DER) used by the US EPA Office of Pesticide Programs. Efforts are underway through an Organization for Economic Co-operation and Development (OECD) work group to extend the use of DER Composers as harmonized templates for rat metabolism, livestock residue, plant residue and environmental degradation studies.

Investigating the relationship between in vitro-in vivo genotoxicity: derivation of mechanistic QSAR models for in vivo liver genotoxicity and in vivo bone marrow micronucleus formation which encompass metabolism.

Strategic testing as part of an integrated testing strategy (ITS) to maximize information and avoid the use of animals where possible is fast becoming the norm with the advent of new legislation such as REACH. Genotoxicity is an area where regulatory testing is clearly defined as part of ITS schemes. Under REACH, the specific information requirements depend on the tonnage manufactured or imported. Two types of test systems exist to meet these information requirements, in vivo genotoxicity assays, which take into account the whole animal, and in vitro assays, which are conducted outside the living mammalian organism using microbial or mammalian cells under appropriate culturing conditions. Clearly, with these different broad experimental categories, results for a given chemical can often differ, which presents challenges in the interpretation as well as in attempting to model the results in silico. This study attempted to compare the differences between in vitro and in vivo genotoxicity results, to rationalize these differences with plausible hypothesis in concert with available data. Two proof of concept (Q)SAR models were developed, one for in vivo genotoxicity effects in liver and a second for in vivo micronucleus formation in bone marrow. These "mechanistic models" will be of practical value in testing strategies, and both have been implemented into the TIMES software platform ( http://oasis-lmc.org ) to help predict the genotoxicity outcome of new untested chemicals.

TIMES-SS--a mechanistic evaluation of an external validation study using reaction chemistry principles.

The TImes MEtabolism Simulator platform used for predicting skin sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a consortium comprised of industry and regulators. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic three-dimensional quantitative structure-activity relationships. Here, we describe an external validation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. The LLNA results were evaluated with respect to reaction chemistry principles for sensitization. Additional testing on a further four chemicals was carried out to explore some of the specific reaction chemistry findings in more detail. Improvements for TIMES-SS, where appropriate, were put forward together with proposals for further research work. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization potential under legislative programs such as REACH.

TIMES-SS--a promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity.

The TImes MEtabolism Simulator platform used for predicting Skin Sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a Consortium comprising industry and regulators. The model was developed with the aim of minimizing animal testing and to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. Here, we describe the extent to which the five OECD principles are met and in particular the results from an external evaluation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. Further evaluation of these results highlighted certain inconsistencies which were rationalized by a consideration of reaction chemistry principles for sensitization. Improvements for TIMES-SS were proposed where appropriate. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization hazard under legislative programs such as REACH.

Skin sensitization: modeling based on skin metabolism simulation and formation of protein conjugates.

A quantitative structure-activity relationship (QSAR) system for estimating skin sensitization potency has been developed that incorporates skin metabolism and considers the potential of parent chemicals and/or their activated metabolites to react with skin proteins. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were, significant, weak, or nonsensitizing. Because skin sensitization potential depends upon the ability of chemicals to react with skin proteins either directly or after appropriate metabolism, a metabolic simulator was constructed to mimic the enzyme activation of chemicals in the skin. This simulator contains 203 hierarchically ordered spontaneous and enzyme controlled reactions. Phase I and phase II metabolism were simulated by using 102 and 9 principal transformations, respectively. The covalent interactions of chemicals and their metabolites with skin proteins were described by 83 reactions that fall within 39 alerting groups. The SAR/QSAR system developed was able to correctly classify about 80% of the chemicals with significant sensitizing effect and 72% of nonsensitizing chemicals. For some alerting groups, three-dimensional (3D)-QSARs were developed to describe the multiplicity of physicochemical, steric, and electronic parameters. These 3D-QSARs, so-called pattern recognition-type models, were applied each time a latent alerting group was identified in a parent chemical or its generated metabolite(s). The concept of the mutual influence amongst atoms in a molecule was used to define the structural domain of the skin sensitization model. The utility of the structural model domain and the predictability of the model were evaluated using sensitization potency data for 96 chemicals not used in the model building. The TIssue MEtabolism Simulator (TIMES) software was used to integrate a skin metabolism simulator and 3D-QSARs to evaluate the reactivity of chemicals thus predicting their likely skin sensitization potency.