Emilio Benfenati

CAESAR models for developmental toxicity

BackgroundThe new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (in silico) methods provided in the REACH itself, the CAESAR project has developed several models.ResultsTwo QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models.ConclusionsThe CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints).

Determination of aromatic amines by solid-phase microextraction and gas chromatography-mass spectrometry in water samples

Solid-phase microextraction (SPME) in gas chromatography–mass spectrometry (GC–MS) has been introduced as a rapid and sensitive quantitative method for the detection of some aniline derivatives (o-toluidine, p-chloroaniline, 2,4-dichloroaniline, 2,5-dichloroaniline, 3,4-dichloroaniline and 3,5-dichloroaniline) in environmental water samples. Many parameters for optimisation of the extractive method, such as linearity, sensitivity, equilibration time, precision, and different operating conditions (pH, salt) have been evaluated. After a comparison of the commercially available SPME fibers, a carbowax–divinylbenzene 65 μm polymeric phase was chosen. Linearity was excellent in the concentration range 0.05–5 μg/l, and the method showed good reproducibility (coefficient of variation of around 5%). The detection limits differ substantially for the various compounds analysed, but all were below any other limit of detection for these compounds in the literature. The addition of salt (sodium chloride) at pH 7.6 significantly improved the amount of analytes extracted by the fiber. Operating under basic conditions (pH 11), we did not observe a better sensitivity of the method. To evaluate its applicability on a real aqueous matrix, various groundwater samples collected in an industrially polluted area north of Milan, Italy, were analysed.

Computational predictive programs (expert systems) in toxicology

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

CORAL: Quantitative structure–activity relationship models for estimating toxicity of organic compounds in rats

For six random splits, one‐variable models of rat toxicity (minus decimal logarithm of the 50% lethal dose [pLD50], oral exposure) have been calculated with CORAL software (http://www.insilico.eu/coral/). The total number of considered compounds is 689. New additional global attributes of the simplified molecular input line entry system (SMILES) have been examined for improvement of the optimal SMILES‐based descriptors. These global SMILES attributes are representing the presence of some chemical elements and different kinds of chemical bonds (double, triple, and stereochemical). The “classic” scheme of building up quantitative structure–property/activity relationships and the balance of correlations (BC) with the ideal slopes were compared. For all six random splits, best prediction takes place if the aforementioned BC along with the global SMILES attributes are included in the modeling process. The average statistical characteristics for the external test set are the following: n = 119 ± 6.4, R2 = 0.7371 ± 0.013, and root mean square error = 0.360 ± 0.037.

Predicting logP of pesticides using different software

We compared experimental and calculated logP values using a data set of 235 pesticides and experimental values from four different sources: The Pesticide Manual, Hansch Manual, ANPA and KowWin databases. LogP were calculated with four softwares: HyperChem, Pallas, KowWin and TOPKAT. Crossed comparison of the experimental and calculated values proved useful, especially for pesticides. These are harder to study than simpler organic compounds. Structurally they are complex, heterogeneous and similar to drugs from a chemical point of view. They offer an interesting way to verify the goodness of the different methods. Other studies compared several logP predictors using a single set of experimental values taken as a reference. Here we discuss the utility of the different logP predictors, with reference to experimental data found in different databases. This offers three advantages: (1) it avoids bias due to the assumption that one single data set is correct; (2) a given predictor can be developed on the same data set used for evaluation; (3) it takes account of experimental variability and can compare it with the predictors variability. In our study Pallas and KowWin gave the best results for prediction, followed by TOPKAT.

A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment

Tests with vertebrates are an integral part of environmental hazard identification and risk assessment of chemicals, plant protection products, pharmaceuticals, biocides, feed additives and effluents. These tests raise ethical and economic concerns and are considered as inappropriate for assessing all of the substances and effluents that require regulatory testing. Hence, there is a strong demand for replacement, reduction and refinement strategies and methods. However, until now alternative approaches have only rarely been used in regulatory settings. This review provides an overview on current regulations of chemicals and the requirements for animal tests in environmental hazard and risk assessment. It aims to highlight the potential areas for alternative approaches in environmental hazard identification and risk assessment. Perspectives and limitations of alternative approaches to animal tests using vertebrates in environmental toxicology, i.e. mainly fish and amphibians, are discussed. Free access to existing (proprietary) animal test data, availability of validated alternative methods and a practical implementation of conceptual approaches such as the Adverse Outcome Pathways and Integrated Testing Strategies were identified as major requirements towards the successful development and implementation of alternative approaches. Although this article focusses on European regulations, its considerations and conclusions are of global relevance.

New public QSAR model for carcinogenicity

BackgroundOne of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.ResultsModels for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESARs models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.ConclusionCarcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives.

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox™, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast™. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.

Estrogenicity profile and estrogenic compounds determined in river sediments by chemical analysis, ELISA and yeast assays

An effects-directed strategy was applied to bed sediments of a polluted tributary in order to isolate and identify the major estrogenic chemicals it discharges into the River Po, the principal Italian watercourse. Sediment extract was concentrated by solid phase extraction and then fractioned into 10 fractions by reversed phase high performance liquid chromatography (RP-HPLC). Estrogenic activity of whole extract and fractions were determined using a recombinant yeast assay containing the human estrogen receptor (YES). The 10 fractions and whole extract were analysed for target compounds, e.g. estrone (E1), 17beta-estradiol (E2), estriol (E3), 4-nonylphenol (NP), 4-tert-octylphenol (t-OP), bisphenol A (BPA), using both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and non-competitive enzyme-linked immunosorbent assays (ELISA). The YES assay determined high estrogenic activity in whole sediment (15.6 ng/g EE2 equivalents), and positive results for fractions nr 1, 2, 6, 7 and 8. E1, E3 and NP were the main estrogenic chemicals, however, other unidentified compounds contributed to sediment estrogenicity, particularly for polar fractions nr 1 and 2. A GC-MS screening performed in scan mode identified other potential contributors such as phthalates (DBP, BBP), and OP isomers. A next sampling campaign extended to other tributaries and receiving stretches of the River Po confirmed E1, E3 and NP as major estrogenic chemicals potentially threatening other sites of the main river. In general, target compound ELISAs have been shown to be suitable tools for a rapid screening of wide areas or large numbers of environmental samples for estrogenic risk. The potential for interferences suggests however to use cautiously the concentration values obtained from some of the immunoassays.

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.