Search

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.

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.

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.