Substance-tailored testing strategies in toxicology : an in silico methodology based on QSAR modeling of toxicological thresholds and Monte Carlo simulations of toxicological testing

International audienceThe design of toxicological testing strategies aimed at identifying the toxic effects of chemicals without (or with a minimal) recourse to animal experimentation is an important issue for toxicological regulations and for industrial decision-making. This article describes an original approach which enables the design of substance-tailored testing strategies with a specified performance in terms of false-positive and false-negative rates. The outcome of toxicological testing is simulated in a different way than previously published articles on the topic. Indeed, toxicological outcomes are simulated not only as a function of the performance of toxicological tests but also as a function of the physico-chemical Properties of chemicals. The required inputs for Our approach are QSAR predictions for the LOAELs of the toxicological effect of interest and statistical distributions describing the relationship existing between in vivo LOAEL values and results from in vitro tests. Our methodology is able to correctly predict the performance of testing strategies designed to analyze the teratogenic effects of two chemicals: di(2-ethylhexyl)phthalate and Indomethacin. The proposed decision-support methodology can be adapted to any toxicological context as long as a Statistical Comparison between in vitro and in Vivo results is possible and QSAR models for the toxicological effect of interest can be developed

SOA formation study from limonene ozonolysis in indoor environment : gas and particulate phases chemical characterization and toxicity prediction

International audienceLimonene is widely employed in scented products used in indoor environments such as fresheners and household cleaners (Nazaroff 2004). It also displays one of the highest potential for the formation of Secondary Organic Aerosols (SOAs) following ozonolysis (Jaoui 2006; Chen 2010). Besides, indoor ozone concentration, influenced by outdoor concentration and indoor sources, can be quite important to initiate gas phase chemistry (Weschler 2000) and possibly lead to secondary products formation. This work investigates SOAs formation from the ozonolysis of limonene as emitted from a detergent, in order to gather information on aerosols that are an important source of exposure for people using household products

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

International audienceFor 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

Adverse outcome pathways:opportunities, limitations and open questions

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field

Les outils QSAR dans un contexte réglementaire : l’importance d’une validation scientifique externe

Toxicological effects and chemical structure can be related quantitatively or qualitatively through QSAR modeling (Quantitative structure-activity relationship). During the past decade, the development of QSAR tools has continued to progress especially because of the requirements of new European legislations (REACH and 7th amendment to the EU Cosmetics Directive) that encourage and support the implementation of alternative methods to animal testing including QSAR models. In order to facilitate the regulatory use of QSAR tools, the management of the environment directorate of the OECD and the former ECB (European Chemicals Bureau) have made available two free software tools (the “QSAR Toolbox” and “Toxtree”). Both tools allow the user to use several QSAR models and in particular the “Benigni/Bossa” rule-based system for predicting the mutagenic and carcinogenic potential of chemicals on the sole basis of chemical structure. Given the regulatory role of the OECD QSAR Toolbox and Toxtree, the INERIS and the CTIS jointly carried out a scientific validation of the QSAR models that form the “Benigni-Bossa” rule-based system. The main results of this study show that several conceptual and software related problems can, in some specific cases, give raise to wrong predictions.La mutagenèse et la carcinogenèse sont deux effets toxicologiques qui font partie des informations toxicologiques à fournir dans le cadre de plusieurs règlements sur les substances chimiques et notamment du règlement RE ACH. Les effets mutagènes sont habituellement caractérisés de façon relativement économique et rapide par le biais du test d’Ames qui quantifie le potentiel mutagène d’une substance grâce à une quantification du taux de révertants de souches de S. typhimurium auxotrophes pour l’histidine. En revanche, la détermination expérimentale du potentiel cancérogène des produits chimiques est particulièrement onéreuse. Le coût d’une étude de cancérogenèse chez le rongeur peut aller jusqu’à un million de dollars par substance. Ce coût élevé, ajouté à la préoccupation croissante pour le bien-être des animaux, encourage, en particulier, la mise au point de modèles QSAR pour la prédiction du potentiel cancérogène des produits chimiques. Dans ce contexte, le système « Benigni/Bossa » pour l’analyse des propriétés carcinogènes et mutagènes joue un rôle important en raison de son exhaustivité et de son intégration dans deux outils disponibles gratuitement : la boîte à outils QSAR de l’OCDE (OCDE, 2010) et Toxtree. Le travail effectué en collaboration entre l’INER IS et le CTIS (Centre de traitement de l’information scientifique) se proposait d’analyser la validité du système « Benigni/Bossa » en tant que tel et de son implémentation dans ces deux outils (boîte à outils QSAR de l’OCDE versions 1.1.01 et 1.1.02 et Toxtree version 1.6)

Évaluation de la reproductibilité des approches « read across » entre évaluateurs

The European LIFE project CALEIDOS succeeded in providing practical information on QSAR models and read-across approaches in the framework of the REACH regulation by conducting validation exercises on chemicals that were submitted to the European Chemicals Agency. This project also organized a round-robin exercise aimed at evaluating the reproducibility of toxicological predictions obtained by means of read-across approaches. Indeed, predictions obtained by read-across represent the most common alternative to animal testing reported in the dossiers submitted by registrants under the REACH regulation. Three endpoints were analysed during the exercise: mutagenicity, bioconcentration factor and fish acute toxicity. Nine chemicals were associated with each endpoint and the participants completed a questionnaire relating their conclusions. The final results suggest that the level of reproducibility changes according to the predicted endpoint and the computational tool adopted for obtaining the predictions. Overall this exercise indicates that there are several areas of uncertainty in read-across assessments and that there is a need to identify reproducible and robust arguments to substantiate read-across predictions.Les prédictions par lecture croisée (read-across, en anglais) représentent la méthode alternative à l’expérimentation animale la plus utilisée par les déclarants dans le cadre du règlement REACH. Cette approche prédictive repose sur la même hypothèse qui caractérise la modélisation QSAR : des molécules similaires induisent une toxicité similaire d’un point de vue qualitatif et quantitatif. Ces prédictions peuvent être dérivées à partir de plusieurs approches dont la pertinence relève de l’expertise de la personne qui évalue la toxicité de la substance d’intérêt. Afin de caractériser la variabilité associée aux évaluateurs lors de prédictions par lecture croisée, le projet européen CALEIDOS1 a organisé un exercice international pour la prédiction d’effets (éco)toxicologiques

La modélisation QSAR

Structure Activity Relationships are computational techniques used to predict biological activities such as toxicological effects. They are based on the “structure-function” paradigm according to which the biological effect of a molecule can be explained quantitatively (QSAR models) or qualitatively (SAR models) by considering the spatial arrangement of the atoms that constitute a molecule. These computer-based models can be used in the framework of the REACh regulation to predict toxic properties and to help in reducing the number of animal experiments. The regulatory acceptance of QSAR predictions is therefore a topic of intense interest both for industry and regulatory agencies. The expertise carried out at the INERIS highlighted the importance of validity and predictability assessment of QSAR models in order to support a correct application of their predictions within a regulatory context.La modélisation semi-empirique QSAR [(Quantitative) Structure Activity Relationship] a comme objectif la prédiction des effets d’une variation de la structure moléculaire sur l’activité biologique (exemple : relation entre structure moléculaire et propriétés mutagéniques). Cette modélisation peut être quantitative (QSAR) ou qualitative (SAR). La première stratégie de modélisation prévoit trois éléments : a) un ou plusieurs descripteurs de la structure moléculaire (figure 1) ; b) un effet à prédire ; c) une relation mathématique permettant de décrire la corrélation statistique entre les descripteurs moléculaires et l’effet biologique à modéliser. La relation mathématique est d’habitude établie grâce à des méthodes statistiques multivariées

An evaluation of the predictive ability of the (Q)SAR software packages DEREK, HAZARDEXPERT and TOPKAT to describe chemical-induced skin irritation

International audienceAccording to the REACH chemicals legislation, formally adopted by the EU in 2006, Quantitative Structure-Activity Relationships (QSARs) can be used as alternatives to animal testing, which itself poses specific ethical and economical concerns. A critical assessment of the performance of the QSAR models is therefore the first step toward the reliable use of such computational techniques. This article reports the performance of the skin irritation module of three commercially-available software packages: DEREK, HAZARDEXPERT and TOPKAT. Their performances were tested on the basis of data published in the literature, for 116 chemicals. The results of this study show that only TOPKAT was able to predict the irritative potential for the majority of chemicals, whereas DEREK and HAZARDEXPERT could correctly identify only a few irritant substances

Evaluation of the OECD (Q)SAR application toolbox for the profiling of estrogen receptor binding affinities

International audienceThe determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted

Évaluation de modèles (Q)SAR pour la prédiction de la génotoxicité d’ames : un exercice rétrospectif sur les substances chimiques enregistrées dans le cadre du règlement européen REACH

Notwithstanding the possibility of adopting (Q)SAR predictions for registration purposes, registrants providing information for the first REACH deadline (November 30, 2010) submitted (Q)SAR predictions only in an exceedingly small number of cases [3]. This observation prompted the interest of the project CALEIDOS that decided to provide a retrospective exercise on the reliability of freely available (Q)SAR models predicting Ames mutagenicity when applied to chemicals registered for this first deadline [8]. Our analysis showed that, with the only exception of one (Q)SAR tool (TEST), all the analyzed models were characterized by accuracies that were comparable to the experimental reliability of the Ames test. The best performance was displayed by the Benigni- Bossa rule-based system as implemented within the online VEGA platform (accuracy = 92 %, sensitivity = 83 %, specificity = 93 %, Matthews correlation coefficient = 0.68). The main conclusion is that our results support the accumulating evidence that the mechanistic relationship between electrophilicity and mutagenicity is properly described by existing (Q)SAR models.Certaines substances chimiques peuvent introduire des mutations dans le génome humain et provoquer des effets graves sur la santé tels que le cancer. Pour ces raisons, la détection du potentiel mutagène des substances chimiques revêt une importance fondamentale dans le cadre du règlement européen REACH sur la toxicité des substances chimiques. Une des approches les plus utilisées pour l’identification de substances mutagènes est le test d’Ames. Il permet d’évaluer les effets des substances chimiques sur l’expression des gènes pour la synthèse de l’histidine de plusieurs souches bactériennes de S. typhimurium et E. coli. Le règlement REACH prévoit la possibilité d’utiliser les prédictions élaborées par des modèles (Q)SAR1 afin de caractériser le potentiel de mutagénèse des substances chimiques. Cependant, et malgré la disponibilité de plusieurs outils (Q)SAR gratuits, les modèles (Q)SAR qui prédisent les effets de mutagénèse n’ont été utilisés que de façon exceptionnelle pour l’échéance du 30 novembre 2010 du règlement REACH. L’objectif de ce travail était donc d’évaluer la performance prédictive des outils (Q)SAR mis à disposition gratuitement sur internet, avec les substances qui ont été enregistrées pour cette échéance