Modeling the migration of chemicals from food contact materials to food: The MERLIN-expo/VERMEER toolbox.

Evaluating the migration of chemicals from food contact materials (FCM) into food is a key step in the safety assessment of such materials. In this paper, a simple mechanistic model describing the migration of chemicals from FCM to food was combined with quantitative property-property relationships (QPPRs) for the prediction of diffusion coefficients and FCM-Food partition coefficients. The aim of the present study was to evaluate the performance of these operational models in the prediction of a chemical's concentration in food in contact with a plastic monolayer FCM. A comparison to experimental migration values reported in literature was conducted. Deterministic simulations showed a good match between predicted and experimental values. The tested models can be used to provide insights in the amount and the type of toxicological data that are needed for the safety evaluation of the FCM substance. Uncertainty in QPPRs used for describing the processes of both diffusion in FCM and partition at the FCM-Food interface was included in the analysis. Combining uncertainty in QPPR predictions, it was shown that the third quartile (75th percentile) derived from probabilistic calculations can be used as a conservative value in the prediction of chemical concentration in food, with reasonable safety factors.

Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach.

Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). The aim of the present study was the building and estimation of models for inhalation toxicity as No Observed Adverse Effect Concentration (NOAEC) based on the OECD guidelines 413. Three random distributions into the training set and validation set were examined. In practice, a structured training set that contains active training set, passive training set and calibration set is used as the training set. The statistical characteristics of the best model for negative logarithm of NOAEC (pNOAEC) are for training set n = 108, average r 2 = 0.52 + 0.62 + 0.76/3 = 0.63 and for validation set n = 35, r 2 = 0.73.

QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity.

Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC50) of a set of biocides collected from different sources on the freshwater crustacean Daphnia magna, one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient r2 = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance (r2 = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and r2 = 0.74 and 0.75 for the CORAL model).

A combined approach to investigate the toxicity of an industrial landfill's leachate: chemical analyses, risk assessment and in vitro assays.

Solid wastes constitute an important and emerging problem. Landfills are still one of the most common ways to manage waste disposal. The risk assessment of pollutants from landfills is becoming a major environmental issue in Europe, due to the large number of sites and to the importance of groundwater protection. Furthermore, there is lack of knowledge for the environmental, ecotoxicological and toxicological characteristics of most contaminants contained into landfill leacheates. Understanding leachate composition and creating an integrated strategy for risk assessment are currently needed to correctly face the landfill issues and to make projections on the long-term impacts of a landfill, with particular attention to the estimation of possible adverse effects on human health and ecosystem. In the present study, we propose an integrated strategy to evaluate the toxicity of the leachate using chemical analyses, risk assessment guidelines and in vitro assays using the hepatoma HepG2 cells as a model. The approach was applied on a real case study: an industrial waste landfill in northern Italy for which data on the presence of leachate contaminants are available from the last 11 years. Results from our ecological risk models suggest important toxic effects on freshwater fish and small rodents, mainly due to ammonia and inorganic constituents. Our results from in vitro data show an inhibition of cell proliferation by leachate at low doses and cytotoxic effect at high doses after 48 h of exposure.

Assessing the environmental risks associated with contaminated sites: Definition of an Ecotoxicological Classification index for landfill areas (ECRIS).

Assessing ecological risk in quantitative terms is a site-specific complex procedure requiring evaluation of all possible pathways taken by the chemicals from the contamination source to the targets to be protected. Unfortunately, too many cases lack of physico-chemical and ecotoxicological data makes impossible to quantify the ecological risk. We present the Ecotoxicological Classification Risk Index for Soil (ECRIS), a new classification system specific for soil risk assessment, which gives a comparative indication of the risk linked to environmental contamination by any chemical. The tool we propose is based on the integration of a data set characterizing the ecotoxicological and exposure profile of chemicals. ECRIS is a simple approach specifically set up for the landfill scenario. This index draws on the huge amount of data from our many years of leachate analysis. ECRIS is useful for a first screening of probably contaminated soil. A case study based on some Italian landfills is proposed.