Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity

This review of QSARs for genotoxicity and carcinogenicity was performed in a broad sense, considering both models available in software tools and models that are published in the literature. The review considered the potential applicability of diverse models to pesticides as well as to other types of regulated chemicals and pharmaceuticals. The availability of models and information on their applicability is summarised in tables, and a range of illustrative or informative examples are described in more detail in the text. In many cases, promising models were identified but they are still at the research stage. For routine application in a regulatory setting, further efforts will be needed to explore the applicability of such models for specific purposes, and to implement them in a practically useful form (i.e. user-friendly software). It is also noted that a range of software tools are research tools suitable for model development, and these require more specialised expertise than other tools that are aimed primarily at end-users such as risk assessors. It is concluded that the most useful models are those which are implemented in software tools and associated with transparent documentation on the model development and validation process. However, it is emphasised that the assessment of model predictions requires a reasonable amount of QSAR knowledge, even if it is not necessary to be a QSAR practitioner.JRC.DG.I.6-Systems toxicolog

Applicability of the Threshold of Toxicological Concern (TTC) approach to cosmetics – preliminary analysis

This report describes the application of chemoinformatic methods to explore the applicability of the Threshold of Toxicological Concern (TTC) approach to cosmetic ingredients. For non-cancer endpoints, the most widely used TTC approach is the Cramer classification scheme, which categorises chemicals into three classes (I, II and III) depending on their expected level of concern for oral systemic toxicity (low, medium, high, respectively). The chemical space of the Munro non-cancer dataset was characterised to assess whether this underlying TTC dataset is representative of the “world” of cosmetic ingredients, as represented by the COSMOS Cosmetics Inventory. In addition, the commonly used Cramer-related Munro threshold values were applied to a toxicological dataset of cosmetic ingredients, the COSMOS TTC dataset, to assess the degree of protectiveness resulting from the application of the Cramer classification scheme. This analysis is considered preliminary, since the COSMOS TTC dataset and Cosmetics Inventory are subject to an ongoing process of extension and quality control within the COSMOS project. The results of this preliminary analysis show that the Munro dataset is broadly representative of the chemical space of cosmetics, although certain structural classes are missing, notably organometallics, silicon-containing compounds, and certain types of surfactants (non-ionic and cationic classes). Furthermore, compared with the Cosmetics Inventory, the Munro dataset has a higher prevalence of reactive chemicals and a lower prevalence of larger, long linear chain structures. The COSMOS TTC dataset, comprising repeat dose toxicity data for cosmetics ingredients, shows a good representation of the Cosmetics Inventory, both in terms of physicochemical property ranges, structural features and chemical use categories. Thus, this dataset is considered to be suitable for investigating the applicability of the TTC approach to cosmetics. The results of the toxicity data analysis revealed a number of cosmetic ingredients in Cramer Class I with No Observed Effect Level (NOEL) values lower than the Munro threshold of 3000 µg/kg bw/day. The prevalence of these “false negatives” was less than 5%, which is the percentage expected by chance resulting from the use of the 5th percentile of cumulative probability distribution of NOELs in the derivation of TTC values. Furthermore, the majority of these false negatives do not arise when structural alerts for DNA-binding are used to identify potential genotoxicants, to which a lower TTC value of 0.0025 µg/kg bw/day is typically applied. Based on these preliminary results, it is concluded that the current TTC approach is broadly applicable to cosmetics, although a number of improvements can be made, through the quality control of the underlying TTC datasets, modest revisions / extensions of the Cramer classification scheme, and the development of explicit guidance on how to apply the TTC approach.JRC.I.5-Systems Toxicolog

The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

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Computational Methods in Support of Chemical Risk Assessment

Chemical risk assessment for human health effects is performed in order to establish safe exposure levels of chemicals to which individuals are exposed. The process of risk assessment traditionally involves the generation of toxicological studies from which health based guidance values are derived for a specific chemical. For low level exposures to chemicals, where there are no or limited chemical specific toxicity data, the application of the Threshold of Toxicological Concern (TTC) approach may estimate whether the exposure levels can be considered safe. The TTC approach has recently gained increasing interest as new requirements, under different regulatory frameworks, emerge for the safety assessment of chemicals and to assess chemicals for which testing is not routinely required. The application of TTC relies heavily on computational (in silico) methods. In silico tools are computer implemented models that, based on commonalities in the toxicity of “similar” chemical structures, may predict hazard. In silico methods are rapidly evolving and gaining importance within the context of Integrated Approaches to Testing and Assessment (IATA) and their acceptance for regulatory purposes is expanding. The work presented in this thesis has focused on the use and applicability of a wide range of computational approaches to assist in the application of the TTC concept. In the TTC approach, the identification of genotoxic chemicals is a primary requirement. In silico approaches apply expert knowledge and/or statistical methods to either predict genotoxicity or to identify structural alerts associated with it. This thesis focused, in part, on a group of important environmental pollutants, nitrobenzenes, to assess the applicability of in silico tools to predict genotoxicity. For this purpose a dataset containing 252 nitrobenzenes including Ames test results was compiled. Based on these test results a case study for sodium nitro-guaiacolate, a pesticide active substance, was developed. The case study demonstrated that (Q)SAR and a category approach incorporating read-across, are applicable for the prediction of genotoxicity and supports their use within a weight of evidence approach. Another aspect of the TTC approach is the evaluation of repeat dose, non-cancer endpoints. For that purpose chemicals are separated into groups related to three levels of concern based on the Cramer classification. For each level, namely the Cramer Classes (I, II and III), a safe exposure level has been established. Therefore, as interest to apply TTC expands to new groups of chemicals, the reliability and conservativeness of the established thresholds relative to Cramer Classes for the new chemistries must be established. In this thesis the TTC approach was evaluated for 385 cosmetic ingredients, 77 biocides and 102 compounds classified as reproductive and developmental toxicants. To support the evaluation at different levels, chemical datasets containing toxicological data were utilised and computational tools were applied to compare datasets. The results indicated, that the historical “Munro” dataset is broadly representative for cosmetics and biocides. In addition, that the threshold levels for Cramer Class III are within the range of Munro’s threshold further supports the validity of the TTC approach and its conservativeness for the groups of chemicals analysed. Cramer Class I thresholds were found to be valid only for classified developmental and reproductive toxicants. The results also supported the validity of the classification of chemicals into Cramer class III. It is foreseen that the TTC approach will gain increasing acceptance in the risk assessment of different groups of chemicals. Therefore it is emphasised that the future work should focus on the identification of the limitations of the application of TTC, including the identification of groups of chemicals to which TTC cannot be applied, the expansion of the underlying toxicological datasets, and the development of tools to support the application of TTC so that is transparent and acceptable for regulatory purposes