Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010.

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Use of computational tools in the field of food safety.

In this article we give an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. Our results show that currently the majority of stakeholders in the field of food safety do not apply computational methods on a routine basis, mainly because of a lack of in-house expertise. Some organisations, however, are very experienced in their use and have developed specialised in-house approaches. Despite this variable situation, computational tools are widely perceived to be a useful tool to support regulatory assessments and decision making in the field of food safety. Recognized, however, is a widespread need to develop guidance documents and software tools that will promote and harmonise the use of computational methods, together with appropriate training.

Using urinary solubility data to estimate the level of safety concern of low levels of melamine (MEL) and cyanuric acid (CYA) present simultaneously in infant formulas.

Melamine (MEL) and cyanuric acid (CYA) may occur simultaneously in milk products. There is no health based guidance value for the mixture of MEL+CYA. Limited toxicological data indicate that MEL+CYA toxicity occurs at levels lower than the toxic doses of the single compounds. The key adverse effect of MEL+CYA is the formation of crystals in the urinary tract, which is dependent on the solubility of the MEL+CYA complex. Urinary concentrations resulting from oral doses of MEL+CYA and MEL alone have been calculated from published data from animal studies. A human exposure scenario assuming consumption of infant formula contaminated at a level of 1 ppm of MEL and CYA each (2 ppm of MEL+CYA) was also analyzed. Margins of more than two orders or magnitude were observed between estimated urine concentrations known to be without detectable effects in rats and calculated human urine concentrations. Because the hazard is related to the physico-chemical characteristics of the mixture, there would be a negligible concern associated with crystal formation if the urinary concentration of the complex is within the solubility range. The solubility of MEL+CYA was higher in urine than in water. A strong pH-dependency was observed with the lowest solubility found at pH 5-5.5. The calculated human urinary concentration was about 30 times less than the solubility limit for MEL+CYA in adult human urine. Altogether, these data provide preliminary evidence suggesting that the presence of 1 ppm of MEL and CYA each in infant formula is unlikely to be of significant health concern.

Preliminary analysis of toxicity of benzoxazinones and their metabolites for folsomia Candida.

The overall objective of this study was to explore the toxicity of benzoxazinone allelochemicals and their metabolites to Folsomia candida (Collembola: Isotomidae) (Willem, 1902). Experimental tests showed transformation products to have more pronounced toxicity than parent compounds. The underlying relationship between the chemical structure and toxicity was then studied using three-dimensional QSAR approaches, and results highlighted the role of the steric contribution.

QSAR models for Daphnia magna toxicity prediction of benzoxazinone allelochemicals and their transformation products.

The overall objective of this study is the ecotoxicological characterization of the benzoxazinone 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA), the benzoxazolinones benzoxazolin-2-one (BOA) and 6-methoxybenzoxazolin-2-one (MBOA), and their transformation products: phenoxazinones 2-acetylamino-7-methoxy-3H-phenoxazin-3-one (AAMPO), 2-acetylamino-3H-phenoxazin-3-one (AAPO), 2-amino-7-methoxy-3H-phenoxazin-3-one (AMPO), and 2-amino-3H-phenoxazin-3-one (APO); aminophenol 2-aminophenol AP); acetamide N-(2-hydroxyphenyl)acetamide (HPAA); and malonamic acid amide N-(2-hydroxyphenyl)malonamic acid (HPMA). A comparison between empirical results and theoretical ones using rules-based prediction of toxicity was done, and it can be concluded that only the degradation metabolites exhibited significant ecotoxic effect. Using synthetic pesticides knowledge, several QSAR models were trained with various approaches and descriptors. The models generated exhibited good internal predictive ability (R(cv)2 > 0.6) and were used to predict the toxicity of the natural compounds studied.

Chemical analysis, distributed modelling and risk indices. Three fundamental pillars in Risk Assessment.

The Risk Assessment (RA) of pollutants from contaminated sites and landfills is a major environmental issue in Europe, due to the large number of sites and to the importance of groundwater protection. The high number of contaminants and the lack of knowledge of their environmental properties and their ecotoxicological and toxicological characteristics complicate the problem. Furthermore, this information about the chemicals has to be combined with the data relative to the territory and to the targets. We will describe the problems relative to each topic involved in the process, and we will propose an integrated methodology for coping with these problems, using state-of-the-art approaches in each part of the protocol. This methodology has been applied in several real cases.

Modeling oral rat chronic toxicity.

The chronic toxicity is fundamental for toxicological risk assessment, but its correlation with the chemical structures has been studied only little. This is partly due to the complexity of such an experimental test that embraces a plethora of different biological effects and mechanisms of action, making (Q)SAR studies extremely challenging. In this paper we report a predictive in silico study of more than 400 compounds based on two-dimensional chemical descriptors and multivariate analysis. The root mean squared error of the predictive model is 0.73 (in a logarithmic scale) on a leave-one-out cross-validation and is close to the estimated variability of experimental values (0.64). The analysis of the model revealed that the chronic toxicity effects are driven by the bioavailability of the compound that constitutes a baseline effect plus excess toxicity possible described by a few chemical moieties. The results obtained give confidence that this model can be useful for establishing a level of safety concern in the absence of hard toxicological data.

Integration of structure-activity relationship and artificial intelligence systems to improve in silico prediction of ames test mutagenicity.

The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.

QSAR model for predicting pesticide aquatic toxicity.

A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Finally, various linear and nonlinear regression techniques were used to obtain stable and thoroughly validated QSARs. The final model was developed by a counterpropagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors, namely HACA-2, HOMO-LUMO energy gap, Kier and Hall index, HA dependent HDSA-1, BETA polarizability, FHBCA fractional HBSA, and LogP. The model was extensively tested by the test set (R2= 0.79), the y-scrambling test, and sensitivity/stability tests.

ANVAS: artificial neural variables adaptation system for descriptor selection.

A new algorithm model-oriented for variable selection is presented in this study. It is based on the combination of genetic algorithms (GA) for hyperspace exploration, and counterpropagation artificial neural network (CP ANN) for deriving the fitness score. The proposed method performed very well on both well defined synthetic data sets and real academic data sets.

QSAR in ecotoxicity: an overview of modern classification techniques.

This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms: nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.

The importance of scaling in data mining for toxicity prediction.

While mining a data set of 554 chemicals in order to extract information on their toxicity value, we faced the problem of scaling all the data. There are numerous different approaches to this procedure, and in most cases the choice greatly influences the results. The aim of this paper is 2-fold. First, we propose a universal scaling procedure for acute toxicity in fish according to the Directive 92/32/EEC. Second, we look at how expert preprocessing of the data effects the performance of qualitative structure-activity relationship (QSAR) approach to toxicity prediction.

Modeling toxicity by using supervised kohonen neural networks.

Counterprogation neural network is shown to be a powerful and suitable tool for the investigation of toxicity. This study mined a data set of 568 chemicals. Two hundred eighty-two objects were used as the training set and 286 as the test set. The final model developed presents high performances on the data set R(2) = 0.83 (R(2) = 0.97 on the training set, R(2) = 0.59 on the test set). This technique distinguishes itself also for the ability to give to the expert two-dimensional maps suitable for the study of the distribution/clustering of the data and the identification of outliers.