Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity

This evaluation of the non-commercial (Q)SARs for mutagenicity and carcinogenicity consisted of a preliminary survey (Phase I), and then of a more detailed analysis of short listed models (Phase II). In Phase I, the models were collected from the literature, and then assessed according to the OECD principles based on the information provided by the authors-. Phase I provided the support for short listing a number of promising models, that were analyzed more in depth in Phase II. In Phase II, the information provided by the authors was completed and complemented with a series of analyses aimed at generating an overall profile of each of the short listed models. The models can be divided into two families based on their target: a) congeneric; and b) non-congeneric sets of chemicals. The QSARs for congeneric chemicals include most of the chemical classes top ranking in the EU High Production Volume list, with the notable exception of the halogenated aliphatics. They almost exclusively aim at modeling Salmonella mutagenicity and rodent carcinogenicity, which are crucial toxicological endpoints in the regulatory context. The lack of models for in vivo genotoxicity should be remarked. Overall the short listed models can be interpreted mechanistically, and agree with, and/or support the available scientific knowledge, and most of the models have good statistics. Based on external prediction tests, the QSARs for the potency of congeneric chemicals are 30 to 70 % correct, whereas the models for discriminating between active and inactive chemicals have considerably higher accuracy (63 to 100 %), thus indicating that predicting intervals is more reliable than predicting individual data points. The internal validation procedures (e.g., cross-validation, etc...) did not seem to be a reliable measure of external predictivity. Among the non-local, or global approaches for non-congeneric data sets, four models based on the use of Structural Alerts (SA) were short listed and investigated in more depth. The four sets did not differ to a large extent in their performance. In the general databases of chemicals the SAs appear to agree around 65% with rodent carcinogenicity data, and 75% with Salmonella mutagenicity data. The SAs based models do not seem to work equally efficiently in the discrimination between active and inactive chemicals within individual chemical classes. Thus, their main role is that of preliminary, or large-scale screenings. A priority for future research on the SAs is their expansion to include alerts for nongenotoxic carcinogens. A general indication of this study, valid for both congeneric and noncongeneric models, is that there is uncertainty associated with (Q)SARs; the level of uncertainty has to be considered when using (Q)SAR in a regulatory context. However, (Q)SARs are not meant to be black-box machines for predictions, but have a much larger scope including organization and rationalization of data, contribution to highlight mechanisms of action, complementation of other data from different sources (e.g., experiments). Using only non-testing methods, the larger the evidence from QSARs (several different models, if available) and other approaches (e.g. chemical categories, read across) the higher the confidence in the prediction.JRC.I.3-Toxicology and chemical substance

Exploring In Vitro/In Vivo Correlation: Lessons Learned from Analyzing Phase I Results of the US EPA's ToxCast Project

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Computational Characterisation of Chemicals and Datasets in Terms of Organic Functional Groups - a New Toxtree Rulebase

Toxtree is a freely available, user-friendly and extensible software application that is designed to make structure-based predictions for a number of toxicological endpoints and mechanisms of chemical action. The platform has been developed by the Joint Research Centre in collaboration with Ideaconsult Ltd (Sofia, Bulgaria) with a range of modules developed by various contributors. One of the modules developed as an extension to Toxtree is aimed at the identification of organic functional groups in query chemicals. The rulebase consists of 204 organic functional groups recognised by the “Checkmol” program, which was developed by Dr Norbert Haider, University of Vienna. A new Functional Group Profiler, has been coded as a Toxtree module by the Istituto Superiore di Sanita’ (Rome, Italy). The Toxtree profiler, called ISSFUNC, can be used to screen and characterise chemicals as a basis for read-across, category formation and (Q)SAR analysis. It can also be used for the global comparison of datasets, such as model training and test sets and chemical inventories.JRC.DG.I.6-Systems toxicolog

Development of Structural Alerts for the In Vivo Micronucleus Assay in Rodents

In vivo mutagenicity and carcinogenicity studies are posing a high demand for test-related resources. Among these studies, the micronucleus test in rodents is the most widely used, as follow up to positive in vitro mutagenicity results. A recent survey of the (Q)SAR models for mutagenicity and carcinogenicity has indicated that no (Q)SAR models for in vivo micronucleus are available in the public domain. Therefore, the development and extensive use of estimation techniques such as (Q)SARs, read-across and grouping of chemicals, promises to have a huge animal saving potential for this endpoint. In this report, we describe the identification of structural alerts for the in vivo micronucleus assay, and provide the list of underlying chemical structures. These structural alerts provide a coarse-grain filter for the preliminary screening of potential in vivo mutagens.JRC.I.6-Systems toxicolog

The Benigni / Bossa Rulebase for Mutagenicity and Carcinogenicity - A Module of Toxtree

The Joint Resarch Centre's European Chemicals Bureau has developed a hazard estimation software called Toxtree, capable of making structure-based predictions for a number of toxicological endpoints. One of the modules developed as an extension to Toxtree is aimed at the prediction of carcinogenicity and mutagenicity. This module encodes the Benigni/Bossa rulebase for carcinogenicity and mutagenicity developed by Romualdo Benigni and Cecilia Bossa at the Istituto Superiore di Sanita¿, in Rome, Italy. The module was coded by the Toxtree programmer, Ideaconsult Ltd, Bulgaria. In the Toxtree implementation of this rulebase, the processing of a query chemical gives rise to limited number of different outcomes, namely: a) no structural alerts for carcinogenicity are recognised; b) one or more structural alerts (SAs) are recognised for genotoxic or non-genotoxic carcinogenicity; c) SAs relative to aromatic amines or aß-unsaturated aldehydes are recognised, and the chemical goes through Quantitative Structure-Activity Relationship (QSAR) analysis, which may result in a negative or positive outcome. If the query chemical belongs to the classes of aromatic amines or aß-unsaturated aldehydes, the appropriate QSAR is applied and provides a more refined assessment than the SAs, and should be given higher importance in a weight-of-evidence scheme. This report gives an introduction to currently available QSARs and SAs for carcinogenicity and mutagenicity, and provides details of the Benigni/Bossa rulebase.JRC.I.3-Consumer products safety and qualit

OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

<p>Abstract</p> <p>Background</p> <p>The OpenTox Framework, developed by the partners in the OpenTox project (<url>http://www.opentox.org</url>), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.</p> <p>Results</p> <p>The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in <it>in vivo</it> studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.</p> <p>OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.</p> <p>The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).</p> <p>Availability</p> <p>The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page <url>http://www.opentox.org/dev/ontology</url>; the OpenTox ontology is available as OWL at <url>http://opentox.org/api/1 1/opentox.owl</url>, the ToxML - OWL conversion utility is an open source resource available at <url>http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/</url></p

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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Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005

There are many national, regional and international programmes – either regulatory or voluntary – to assess the hazards or risks of chemical substances to humans and the environment. The first step in making a hazard assessment of a chemical is to ensure that there is adequate information on each of the endpoints. If adequate information is not available then additional data is needed to complete the dataset for this substance. For reasons of resources and animal welfare, it is important to limit the number of tests that have to be conducted, where this is scientifically justifiable. One approach is to consider closely related chemicals as a group, or chemical category, rather than as individual chemicals. In a category approach, data for chemicals and endpoints that have been already tested are used to estimate the hazard for untested chemicals and endpoints. Categories of chemicals are selected on the basis of similarities in biological activity which is associated with a common underlying mechanism of action. A homologous series of chemicals exhibiting a coherent trend in biological activity can be rationalised on the basis of a constant change in structure. This type of grouping is relatively straightforward. The challenge lies in identifying the relevant chemical structural and physicochemical characteristics that enable more sophisticated groupings to be made on the basis of similarity in biological activity and hence purported mechanism of action. Linking two chemicals together and rationalising their similarity with reference to one or more endpoints has been very much carried out on an ad hoc basis. Even with larger groups, the process and approach is ad hoc and based on expert judgement. There still appears to be very little guidance about the tools and approaches for grouping chemicals systematically. In November 2005, the ECB Workshop on Chemical Similarity and Thresholds of Toxicological Concern (TTC) Approaches was convened to identify the available approaches that currently exist to encode similarity and how these can be used to facilitate the grouping of chemicals. This report aims to capture the main themes that were discussed. In particular, it outlines a number of different approaches that can facilitate the formation of chemical groupings in terms of the context under consideration and the likely information that would be required. Grouping methods were divided into one of four classes – knowledge-based, analogue-based, unsupervised, and supervised. A flowchart was constructed to attempt to capture a possible work flow to highlight where and how these approaches might be best applied.JRC.I.3-Toxicology and chemical substance

Scientific opinion of Flavouring Group Evaluation 502 (FGE.502): grill flavour ‘Grillin’ 5078’

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