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A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.

Due to the exponentially growing number of substances requiring safety evaluation, efficient prioritization strategies are needed to identify those of highest concern. To limit unnecessary animal testing, such strategies should respect the 3R principles (Replacement, Reduction, Refinement). In the present study, a strategy based on non-animal approaches was developed to prioritize non-evaluated printed paper and board food contact material (FCM) substances for further in-depth safety evaluation. Within the strategy, focus was placed on genotoxicity, a key toxicological endpoint when evaluating safety. By combining in silico predictions with existing in vitro and in vivo genotoxicity data from publicly available literature sources and results from in vitro gene mutation experiments, the 106 study substances could all be assigned to one of the four priority classes (ranging from low to very high concern). Importantly, 19 substances were considered of very high concern due to in vivo genotoxicity. Five of these are furthermore listed as substances of very high concern (SVHC) by the European Chemicals Agency (ECHA), in addition to demonstrating physicochemical properties linked to a high migration potential as well as oral bioavailability and being used in primary food packaging materials. The current animal-free strategy proved useful for the priority ranking of printed paper and board FCM substances, but it may also be considered to prioritize other substances of emerging concern.

E-cigarettes have become very popular, a trend that has been stimulated by the wide variety of available e-liquid flavours. Considering the large number of e-liquid flavours (>7000), there is an urgent need to establish a screening strategy to prioritize the flavouring substances of highest concern for human health. In the present study, a prioritization strategy combining analytical screening, in silico tools and literature data was developed to identify potentially genotoxic e-liquid flavourings. Based on the analysis of 129 e-liquids collected on the Belgian market, 60 flavourings with positive in silico predictions for genotoxicity were identified. By using literature data, genotoxicity was excluded for 33 of them whereas for 5, i.e. estragole, safrole, 2-furylmethylketon, 2,5-dimethyl-4-hydroxyl-3(2H)-furanone and transhexanal, there was a clear concern for in vivo genotoxicity. A selection of 4 out of the remaining 22 flavourings was tested in two in vitro genotoxicity assays. Three out of the four tested flavourings induced gene mutations and chromosome damage in vitro, whereas equivocal results were obtained for the fourth compound. Thus, although there is a legislative framework which excludes the use of CMR compounds in e-liquids, flavourings of genotoxic concern are present and might pose a health risk for e-cigarette users.

Prior to the downstream development of chemical substances, including pharmaceuticals and cosmetics, their influence on the genetic apparatus has to be tested. Several in vitro and in vivo assays have been developed to test for genotoxicity. In a first tier, a battery of two to three in vitro tests is recommended to cover mutagenicity, clastogenicity and aneugenicity as main endpoints. This regulatory in vitro test battery is known to have a high sensitivity, which is at the expense of the specificity. The high number of false positive in vitro results leads to excessive in vivo follow-up studies. In the case of cosmetics it may even induce the ban of the particular compound since in Europe the use of experimental animals is no longer allowed for cosmetics. In this article, an alternative approach to derisk a misleading positive Ames test is explored. Hereto we first tested the performance of five existing computational tools to predict the potential mutagenicity of a data set of 132 cosmetic compounds with a known genotoxicity profile. Furthermore, we present, as a proof-of-principle, a strategy in which a combination of computational tools and mechanistic information derived from in vitro transcriptomics analyses is used to derisk a misleading positive Ames test result. Our data shows that this strategy may represent a valuable tool in a weight-of-evidence approach to further evaluate a positive outcome in an Ames test.

In silico methodologies, such as (quantitative) structure-activity relationships ([Q]SARs), are available to predict a wide variety of toxicological properties and biological activities for structurally diverse substances. To obtain insights in the scientific value of these predictions, the capacity of the prediction models to generate (sufficiently) reliable results for a particular type of compounds needs to be evaluated. In the current study, performance parameters to predict the endpoint "bacterial mutagenicity" were calculated for a battery of common (Q)SAR tools, namely Toxtree, Derek Nexus, VEGA Consensus, and Sarah Nexus. Printed paper and board food contact material (FCM) constituents were chosen as study substances because many of these lack experimental data, making them an interesting group for in silico screening. Accuracy, sensitivity, specificity, positive predictivity, negative predictivity, and Matthews correlation coefficient for the individual models and for the combination of VEGA Consensus and Sarah Nexus were determined and compared. Our results demonstrate that performance varies among the four models, but can be increased by applying a combination strategy. Furthermore, the importance of the applicability domain is illustrated. Limited performance to predict the mutagenic potential of substances that are new to the model (ie, not included in the training set) is reported. In this context, the generally poor sensitivity for these new substances is also addressed.

Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics,...) have triggered the need for an efficient screening strategy to prioritize the substances of highest Concern. In this context, alternative methods such as in silica (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned 'high priority' as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. (C) 2017 Elsevier Ltd. All rights reserved.

Food contact materials (FCM) are estimated to be the largest source of food contamination. Apart from plastics, the most commonly used FCM are made of printed paper and board. Unlike their plastic counterparts, these are not covered by a specific European regulation. Several contamination issues have raised concerns towards potential adverse health effects caused by exposure to substances migrating from printed paper and board FCM. In the current study, an inventory combining the substances which may be used in printed paper and board FCM, was created. More than 6000 unique compounds were identified, the majority (77%) considered non-evaluated in terms of potential toxicity. Based on a preliminary study of their physicochemical properties, it is estimated that most of the non-evaluated single substances have the potential to migrate into the food and become bioavailable after oral intake. Almost all are included in the FACET tool, indicating that their use in primary food packaging has been confirmed by industry. Importantly, 19 substances are also present in one of the lists with substances of concern compiled by the European Chemicals Agency (ECHA). To ensure consumer safety, the actual use of these substances in printed paper and board FCM should be investigated urgently. (C) 2016 Elsevier Inc. All rights reserved.

Due to the worldwide concern that bisphenol A might act as an endocrine disruptor, alternative materials for polycarbonate (PC) have been introduced on the European market. However, PC -replacement products might also release substances of which the toxicological profile including their genotoxic effects has not yet been. characterized. Because a thorough characterization of the genotoxic profile of all these substances is impossible in the short term, a strategy was developed in order to prioritize those substances for which additional data are urgently needed. The strategy consisted of a decision tree using hazard information related to genotoxicity. The relevant information was obtained from the database of the European Chemicals Agency (ECHA), in silico prediction tools (ToxTree and Derek NexusTM) and the in vitro Vitotox test for detecting DNA damage. By applying the decision tree, substances could be classified into different groups, each characterized by a different probability to induce genotoxic effects. Although none of the investigated substances could be unequivocally identified as genotoxic, the presence of genotoxic effects could neither be excluded for any of them. Consequently, all substances require more data to investigate the genotoxic potential. However, the type and the urge for these data differs among the substances. (C) 2016 Elsevier Ltd. All rights reserved.

The occurrence of several recent food contamination issues due to leakage of food packaging substances into food have raised concerns towards potential adverse health effects caused by exposure to these chemicals (e.g. bisphenol A). A new EU list of authorized chemicals to manufacture plastics intended to come into contact with food was adopted in 2011 by means of Commission Regulation 10/2011. On the contrary, no such EU regulation exists on chemicals used in other types of food contact materials (FCM). Therefore, non-plastic FCM are only covered by national legislation or by the general recommendations of the Council of Europe, containing ‘inventory lists’ of additives, monomers, solvents and other compounds that might be present in non-plastic FCM. Since no (recent) safety evaluation has been carried out for thousands of substances present in these inventory lists, identification of substances requiring further safety data is urgently needed. This study attempts to identify substances of highest concern used in printed paper and board FCM. After plastics, these FCM are most widespread. First, information on substances present in the inventory lists of the Council of Europe or from national legislation was assembled in a database. Among the 6068 unique compounds, 4699 or 77% have not been officially evaluated. Next, the non-evaluated single substances were selected for further prioritisation. Over 75% of them have physicochemical characteristics in favour of both migration to food and bioavailability (required to exert toxic effects). The genotoxic potential of the non-evaluated single substances was then analysed in silico using multiple (Q)SAR models (ToxTree, VEGA Consensus, Derek NexusTM, Sarah NexusTM). The initial focus was on mutagenicity and the applicability domain tools available within the in silico tools were applied to ensure fitting of the test compounds in the models. As a result, 106 substances were found positive in all 4 tools (substances of highest concern), whereas 1107 substances were negative in all 4 tools (lowest concern). Another 515 substances were either positive in at least one tool, or could not be analysed in one or several tools (medium concern). The above findings indicate that several hundreds of non-evaluated single substances used in printed paper and board are predicted genotoxicants by multiple in silico tools. Moreover, inspection of their physicochemical properties shows considerable migration and toxicity potential. The substances containing a structural alert for genotoxicity were selected for further more in depth study. To this extent, both literature review and in vitro genotoxicity testing are being performed.