Elena Boriani

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.

Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish

BackgroundBioconcentration factor (BCF) describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish.ResultsTo validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development). The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R2 and Q2 were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors) dropped considerably (less than 0.6% of chemicals).ConclusionsThe CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

A protocol to select high quality datasets of ecotoxicity values for pesticides.

Abstract The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.

ERICA: a multiparametric toxicological risk index for the assessment of environmental healthiness.

A risk assessment strategy considering the impact of chemicals on the whole ecosystem has been developed in order to create a sound and useful method for quantifying and comparing the global risk posed by the main different hazardous chemicals found in the environment. This index, called Environmental Risk Index for Chemical Assessment (ERICA), merges in a single number the environmental assessment, the human health risk assessment and the uncertainty due to missing or uncertain data. ERICA uses a dedicated scoring system with parameters for the main characteristics of the pollutants. The main advantage is that it preserves a simple approach by condensing in this single value an analysis of the risk for the area under observation. ERICA quantifies and compares the global risk posed by hazardous chemicals found in the environment and can be considered a diagnostic and prognostic method for environmental contaminants in critical and potentially dangerous sites, such as incinerators, landfills and industrial areas or in broader geographical areas. The application of the proposed integrated index provides a preliminary quantitative analysis of possible environmental alert due to the presence of one or some pollutants in the investigated site. This paper presents the method and the equations behind the index and a first case study based on the Italian legislation and a pilot study located on the Italian seacoast.

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.

Structural features of diverse ligands influencing binding affinities to estrogen α and estrogen β receptors. Part II. Molecular descriptors calculated from conformation of the ligands in the complex resulting from previous docking study

A QSAR study is reported, in which the relationship between chemical structure of a set of compounds and the binding affinity to human estrogen receptor α and β (ER-α and ER-β) is modelled. Counterpropagation neural networks are used to predict experimental binding affinity of a range of substances. Several compounds as estrogenic chemicals, phytoestrogens, and natural and synthetic estrogens are treated with a structure-based approach that involves the protein structure. The conformations obtained with a docking methodology are used to calculate molecular descriptors. The models are built up with the neural network training procedure, which encodes the information present in molecular descriptors and related binding affinities of the pre-selected training set of compounds. In order to reach the best possible models, a selection of the descriptors using genetic algorithm was conducted. The selection was directed by the error in the prediction of binding affinities of compounds from the test set. The final models obtained for estrogen receptor α and β were tested with an external validation set and were compared with the models obtained from a receptor-independent approach reported in the accompanying paper.

Chapter 7 – Results of DEMETRA models

The overall process in the context of DEMETRA models involves a careful selection of the data, a check of the chemical structures, and the calculation of thousands of descriptors and fragments, and on that basis a development of hundreds of models. Current computer techniques allow the exploration of a huge space of possibilities in a short time, facilitating the task. This chapter explores a full battery of models. Many of the models are not valid, and the performances are poor. However, a certain number of models give interesting results. Good results are obtained with the use of different models and different chemical descriptors. The heterogeneity of the methodologies increases the robustness of the results, once comparable results are obtained. Indeed, one model can support the other, especially when the starting point and methodology are different.

Chapter 2 – Databases for pesticide ecotoxicity

This chapter describes the databases of ecotoxicological data scrutinized for selecting appropriate sources of data. To develop successful quantitative structure–activity relationship (QSAR) models, the availability of biological data for a large number of compounds is essential. It is very important that the quality of data be high. This is a general requirement for any QSAR model, especially for maintaining a reasonable variability within the data to be modeled, avoiding an introduction of noisy data. However, in the case of models designed for their use within a regulatory perspective, it becomes important to strictly apply the same rules adopted for the production of experimental in vivo data. Thus, if the in vivo data have to be produced according to a given guideline, this rule should be adopted for the choice of data. A second requirement, which is more typical of QSAR models, is that the body of data should be as large and representative as possible to have a good basis for models to be built up.

Lubricants and Additives: A Point of View

Lubricants are used to reduce the friction between two surfaces, improving efficiency and reducing wear. The most important application of lubricants is in the automotive sector where lubes are used to avoid damages to the engines. Additives are essential components of lubricants: they are added to lube to improve its physical and chemical properties. Additives also play a key role in the reduction of environmental burden by saving important resources, avoiding leakage losses and reducing exhaust gas emissions. Some concerns have been expressed regarding the reuse and disposal of used lubricating oils due to their possible toxicity for human and ecosystems. Most ecological effects are induced by the mineral base oils because additives are normally insoluble in water. Focusing on used oils, it is possible to say that the toxicity of these products derives from their improper disposal. Heavy metals, PCBs, dioxins, and PAHs can be released into the environment as a result of lube combustion. Modern lubricants do not contain halogenated additives; hence, very small amount of halogens may be present as contaminants from chemical processing. Moreover, modern re-refining techniques, like thermal deasphaltation, reduce the environmental impact and the toxicity of the used oils that otherwise could be disposed using incorrect ways with higher environmental load.

Toxicological and Ecotoxicological Studies for Additives

As the world has become ever more industrialized, an alarmingly large number of chemicals have entered as contaminant mixtures in waste, air, water, and soil.