Androgenic and antiandrogenic activities in water and sediment samples from the river Lambro, Italy, detected by yeast androgen screen and chemical analyses

The river Lambro is the most polluted tributary of the river Po in North Italy and was chosen as a representative water course discharging industrialized areas. Water and sediment samples of the river Lambro were investigated regarding the presence of endocrine disrupting compounds. A combined procedure was used consisting of solid-phase extraction and HPLC based fractionation of samples, of screening for (anti)androgenic activity using the yeast androgen screen (YAS) and of chemical analysis using HPLC-MS/MS and GC-MS. Androgenic and antiandrogenic activities were found in specific fractions of the water and sediment while the total extracts showed antiandrogenic activity only. The chemical analysis of the fractions and total extracts with antiandrogenic activities revealed the presence of compounds with suspected antiandrogenic potency such as bisphenol A, iprodione, nonylphenol, p,p'-DDE and tert-octylphenol but other unknown compounds contributed mainly to the observed antiandrogenic activities. The antiandrogenic load of the river Lambro ranged between 1.34 and 17.1 microM flutamide-equivalents and may pose a risk to aquatic environments. Future screenings for EDC in the environment that have the potential to interfere with reproduction of aquatic organisms should be extended to different modes of actions including (anti)androgenic ones.     

Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli

Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50). In this study six random splits of the data into the training and test set were examined. It has been shown that the CORAL provides a reliable tool that could be used to build up a QSAR of the pEC50.     

Concentrations of PCDD/PCDF in soil close to a secondary aluminum smelter

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDDs/Fs) were analyzed in samples of the emissions from a secondary aluminum smelter (ALS) and soil samples around the plant. The purpose was to estimate the impact of the emissions on the surrounding environment.PCDD/F soil concentrations were higher in the proximity of the plant, exceeding the limit adopted in Italy in soils for green areas and residential uses and the upper limit of several reference concentrations. The most contaminated sites were less than 500 m from the plant and the dioxin concentration with the distance from the ALS.Principal component analysis (PCA) showed that emissions from the ALS were the source of PCDD/F contamination in the soils closest to the plant. Multivariate data analyses such as PCA are therefore useful to identify sources of emission causing contamination.     

QSAR trout toxicity models on aromatic pesticides

The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC₅₀ concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.     

Polychlorinated dibenzo-p-dioxins and dibenzofurans in River Po sediments

The River Po is the main Italian river draining one of the most populated and industrialised regions in Italy. As part of a monitoring project to assess environmental quality in the River Po, we measured the concentrations of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) in bottom sediments collected along the whole course of the river, from the spring to the delta, downstream from the confluence of its main tributaries. The aim was to investigate the level of contamination in the main Italian river and the contribution of contaminant loads from the subbasins. Composite sediment samples were collected in summer and winter surveys in low-flow water conditions and analysed by HRGC-HRMS for PCDD and PCDF homologue groups and for the 2,3,7,8-substituted congeners. The spatial trend observed in the Po River for PCDD and PCDF concentrations varied depending on the load of contaminants received from the tributaries and the processes of sedimentation. The sum of PCDD and PCDF concentrations, and the toxic equivalent content, ranged from 121 to 814 and from 1.3 to 13 ng/kg dry weight sediment, respectively. These levels of contamination seem lower than in the sediments of rivers draining highly industrialised areas. The PCDD and PCDF homologue profiles in all the samples were very similar, suggesting a common source of this contamination in the River Po. Principal component analysis suggested that widespread sources, such as urban runoff and domestic wastewaters, are probably the main cause of these levels of PCDDs and PCDFs.     

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 predictor's variability. In our study Pallas and KowWin gave the best results for prediction, followed by TOPKAT.     

Physiologically based pharmacokinetic modeling of perfluoroalkyl substances in the human body

Currently, there are limited data on the levels of perfluoroalkyl substances other than perfluorooctane sulfonic acid and perfluorooctanoic acid in the human body. Most of this information has been extracted from biological monitoring of plasma while the occurrence of perfluoroalkyl substances in other human tissues is rarely studied. The objective of the present study was to develop a physiologically based pharmacokinetic model to assess the concentration of perfluoroalkyl substances in human tissues, based on an existing model previously validated for perfluorooctane sulfonic acid and perfluorooctanoic acid. Experimental data on concentrations of perfluoroalkyl substances in human tissues from individuals in Tarragona County (Catalonia, Spain) were used to estimate the values of some distribution and elimination parameters needed for the simulation. No significant correlations were found between these parameters and the chain lengths. The model was finally validated for five perfluoroalkyl substances.     

Levels of PCDD/F and dioxin-like PCB in Baltic fish of different age and gender

Concentrations of polychlorinated dibenzo-p-dioxins (PCDD), polychlorinated dibenzofurans (PCDF) and polychlorinated biphenyls (PCB) in Baltic Sea fish like herring (Clupea harengus membras), sprat (Sprattus sprattus balticus), perch (Perca fluviatilis), pikeperch (Sander lucioperca) and flounder (Platichthys flesus trachurus) collected from four areas of the Estonian coastal waters are reported. All samples are studied for their relationship between the length (cm) and wet weight (g); length (cm) and age (years); lipid content and dry matter. The level of PCDD/F and PCB concentrations in younger 1-5 years old Baltic herring and sprat collected in 2002-2005 from the eastern and central parts of the Gulf of Finland, Gulf of Riga and Open Baltic Sea (Central Baltic) is related to the fish age and compared with those found in the 1990s. In addition, PCDD/F and PCB concentrations of different age groups herring, sprat, perch, pikeperch and flounder collected in 2003-2004 from the Lake Peipsi, Gulf of Finland, Gulf of Riga and Open Baltic Sea are related also to their age. Consequently, it was manifested that in older Baltic fish the concentrations of PCDD/F and PCB were higher than in the younger age groups. By the help of principle component analysis (PCA) the effect of gender on the concentrations of PCDD/F for the juvenile Baltic herring and sprat collected in 2004-2005 is investigated for the first time. It was summarized that the biological factor age plays a large role for the contamination of the fish with important toxic organohalogenated compounds such as PCDD/F.     

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.