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

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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The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development

Prediction of dose-hepatotoxic response in humans based on toxicokinetic/toxicodynamic modeling with or without in vivo data : A case study with acetaminophen

International audienceIn the present legislations, the use of methods alternative to animal testing is explicitly encouraged, to use animal testing only 'as a last resort' or to ban it. The use of alternative methods to replace kinetics or repeated dose in vivo tests is a challenging issue. We propose here a strategy based on in vitro tests and QSAR (Quantitative Structure Activity Relationship) models to calibrate a dose-response model predicting hepatotoxicity. The dose response consists in calibrating and coupling a PBPK (physiologically-based pharmacokinetic) model with a toxicodynamic model for cell viability. We applied our strategy to acetaminophen and compared three different ways to calibrate the PBPK model: only with in vitro and in silico methods, using rat data or using all available data including data on humans. Some estimates of kinetic parameters differed substantially among the three calibration processes, but, at the end, the three models were quite comparable in terms of liver toxicity predictions and close to the usual range of human overdose. For the model based on alternative methods, the good adequation with the two other models resulted from an overestimated renal elimination rate which compensated for the underestimation of the metabolism rate. Our study points out that toxicokinetics/toxicodynamics approaches, based on alternative methods and modelling only, can predict in vivo liver toxicity with accuracy comparable to in vivo methods

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is an important consideration in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available, both for practical reasons and to respect the principle of the 3Rs (Replacement, Reduction and Refinement) of animal use. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or, practically used by various regulatory agencies (e.g. U.S. Food and Drug Administration, U.S. and Danish Environmental Protection Agencies), as well as the other existing programs were analysed, highlighting their benefits and limitations. Both statistically-based and knowledge-based (expert system) tools were analysed. Information on the models’ training sets as well as their applicability domains was retrieved. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that the predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high sensitivity models (low rate of false negatives) with high specificity ones (low rate of false positives), and in vitro assays in an integrated manner.JRC.I.5-Systems Toxicolog

COSMOS : An International Cooperative Project Developing Computational Models for Repeated Dose Toxicity

The COSMOS (Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety) Project is a unique international collaboration developing computational approaches for the prediction of repeated dose toxicity. The project comprises 15 partners from academia, industry, regulatory agencies and SMEs from across Europe and the US. Moreover, COSMOS is part of a cluster of six research projects within the SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing) Research Initiative. Organ level toxicity involves complex mechanisms, thus it cannot be predicted by a single simplified in silico model. Therefore COSMOS is taking an innovative approach integrating different technologies, e.g. the threshold of toxicological concern approach, grouping of chemicals, (Q)SARs for toxicity prediction and modelling of biokinetics. All are being developed with a special emphasis on the mechanistic basis of the models considered. Computational workflows as well as a new comprehensive database with reliable structures and repeated dose toxicity data will be freely available to support safety assessment without the use of animals and will thus contribute to the 3Rs. The international dimension is important for the development and especially regulatory acceptance of the models proposed, the international companies having to assure the safety of their products on a global scale. Therefore industry, regulatory agencies and NGOs in Europe and the US are involved either as project partners or as external experts

Prediction of dose-hepatotoxic response in humans based on toxicokinetic/toxicodynamic modeling with or without in vivo data: A case study with acetaminophen

In the present legislations, the use of methods alternative to animal testing is explicitly encouraged, to use animal testing only 'as a last resort' or to ban it. The use of alternative methods to replace kinetics or repeated dose in vivo tests is a challenging issue. We propose here a strategy based on modeling to analyze in vitro tests and on QSAR (Quantitative Structure Activity Relationship) models to calibrate a dose-response model predicting hepatotoxicity. The dose response consists in calibrating and coupling a PBPK (Physiologically-based pharmacokinetic) model with a toxicodynamic model for cell viability. We applied our strategy to acetaminophen and compared three different ways to calibrate the PBPK model: only with in vitro and in silico methods, using rat data or using all available data including data on humans. Some estimates of kinetic parameters differed substantially among the three calibration processes, but, at the end, the three models were quite comparable in terms of liver toxicity predictions and close to the usual range of human overdose. For the model based on alternative methods, such behaviour resulted from an overestimated renal elimination rate which compensated for the underestimation of the metabolism rate. Our study points out that the coupling of alternative methods is a rather complex issue and that more studies are needed in order to assess the general reliability of these approaches.JRC.I.5-Systems Toxicolog

Molecular Modelling Study of the PPARγ Receptor in Relation to the Mode of Action/Adverse Outcome Pathway Framework for Liver Steatosis

The comprehensive understanding of the precise mode of action and/or adverse outcome pathway (MoA/AOP) of chemicals has become a key step toward the development of a new generation of predictive toxicology tools. One of the challenges of this process is to test the feasibility of the molecular modelling approaches to explore key molecular initiating events (MIE) within the integrated strategy of MoA/AOP characterisation. The description of MoAs leading to toxicity and liver damage has been the focus of much interest. Growing evidence underlines liver PPARγ ligand-dependent activation as a key MIE in the elicitation of liver steatosis. Synthetic PPARγ full agonists are of special concern, since they may trigger a number of adverse effects not observed with partial agonists. In this study, molecular modelling was performed based on the PPARγ complexes with full agonists extracted from the Protein Data Bank. The receptor binding pocket was analysed, and the specific ligand-receptor interactions were identified for the most active ligands. A pharmacophore model was derived, and the most important pharmacophore features were outlined and characterised in relation to their specific role for PPARγ activation. The results are useful for the characterisation of the chemical space of PPARγ full agonists and could facilitate the development of preliminary filtering rules for the effective virtual ligand screening of compounds with PPARγ full agonistic activity

ChemoinformaticaSoluzioni e strumenti per scienze e tecnologie biomediche /

XXII, 184 pagg.online resource