Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and K OW for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

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

Background

Bioconcentration 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.

Results

To 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).

Conclusions

The 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.

     

Modelling bioconcentration of pesticides in fish using biopartitioning micellar chromatography

Ecotoxicity assessment is essential before placing new chemical substances on the market. An investigation of the use of the chromatographic retention (log k) in biopartitioning micellar chromatography (BMC) as an in vitro approach to evaluate the bioconcentration factor (BCF) of pesticides in fish is proposed. A heterogeneous set of 85 pesticides from six chemical families was used. For pesticides exhibiting bioconcentration in fish (experimental log BCF > 2), a quantitative retention-activity relationships (QRAR) model is able to perform precise log BCF estimations of new pesticides. Considering the present data, the results based on log k seem to be more reliable than those from available software (BCFWIN and KOWWIN) and from log P (quantitative structure-activity relationships (QSAR)). It is also possible to perform risk assessment tasks fixing a threshold value for log k, which substitute two common threshold values, log P and experimental log BCF, avoiding the experimental problems related with these two parameters.     

Three-phase microemulsion/sol-gel system for aqueous catalysis with hydrophobic chemicals

A facile three-phase transport process is described that allows to carry out catalytic reactions in water, whereby all components are hydrophobic. According to this process a hydrophobic substrate is microemulsified in water and subjected to an organometallic catalyst, which is entrapped within a partially hydrophobized sol-gel matrix. The surfactant molecules, which carry the hydrophobic substrate, adsorb/desorb reversibly on the surface of the sol-gel matrix breaking the micellar structure, spilling their substrate load into the porous medium that contains the catalyst. A catalytic reaction then takes place within the ceramic material to form the desired products that are extracted by the desorbing surfactant, carrying the emulsified product back into the solution. The method is general and versatile and has been demonstrated with the catalytic hydrogenations of alkenes, alkynes, aromatic C=C bonds, and nitro and cyano groups.     

Improved detection of toxic chemicals using bioluminescent bacteria

A sensitive, rapid and simple bioluminescent (BL) assay using bioluminescent bacteria to detect the toxic activity of several chemicals is described. This assay is based on the measurement of inhibition of light production of a bioluminescent bacterial strain, isolated from seawater, in the presence of different toxins like heavy metals, organic chemicals, such as benzene, toluene, ethylbenzene, xylene (BTEX) and a wide range of pesticides in environmental samples. The improvement with respect to other commercial and non-commercial bioluminescent assays consists of the possibility to work at room temperature without the need to thermostat, thus allowing the use of simpler and low cost instruments, or to improve the assay using a microplate format, which makes it possible to analyse several samples also continuously for several hours. Using lyophilised bacteria, the assay is performed in less than an hour, without any bacterial cultivation, which makes the test suitable for rapid and sensitive evaluation of chemical pollutants in environmental samples.     

Passive sampling techniques for sensing freely dissolved hydrophobic organic chemicals in sediment porewater

With compiled and analyzed information about recent advances in passive sampling techniques for sediment porewater, we discuss common quantitation methods (equilibrium and kinetic diffusion-controlled sampling), effects of temperature and salinity on passive sampling, and benefits and drawbacks of currently available passive samplers based on the principles of solid-phase microextraction.The results show that the in-fiber standardization technique, which is kinetic diffusion-controlled, could shorten sampling time and obtain accurate results using isotopically-labeled reference compounds. Another quantitative method, time-weighted average sampling, may be viable for simultaneously measuring all analytes in sediment porewater, as it is more effective with respect to cost and time. In addition, the effects of temperature and salinity on passive sampling should be quantified in field applications.Currently available passive samplers (e.g., employing polymer-coated fibers and low-density polyethylene sheets) can sense hydrophobic organic chemicals (HOCs) in sediment porewater, but the small capacity and the inflexibility of polymer-coated fibers need to be further improved, while better physical protection of polyethylene devices, particularly when they are deployed under rough conditions, should be carefully considered.In conclusion, passive samplers for in-situ measurement of dissolved HOCs in sediment porewater should be combined with a suitable quantitative method and calibration for the effects of temperature and salinity.     

Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification

Carcinogenicity prediction is an important process that can be performed to cut down experimental costs and save animal lives. The current reliability of the results is however disputed. Here, a blind exercise in carcinogenicity category assessment is performed using augmented top priority fragment classification. The procedure analyses the applicability domain of the dataset, allocates in clusters the compounds using a leading molecular fragment, and a similarity measure. The exercise is applied to three compound datasets derived from the Lois Gold Carcinogenic Database. The results, showing good agreement with experimental data, are compared with published ones. A final discussion on our viewpoint on the possibilities that the carcinogenicity modelling of chemical compounds offers is presented.     

In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects

In silico methods are a valid tool for analysing the properties of chemical compounds and interest in computational modelling techniques to predict the activity of chemicals is constantly growing. Many computational methods can be used to analyse the toxicity or biological activity of chemicals, particularly as regards their interactions with biological macromolecules (e.g. receptors) and other physico-chemical properties. An overview of these methods is provided in this tutorial review, with some examples of their application to predict oestrogen receptor (ER)-mediated effects. Nuclear receptors, particularly ER, have been studied with in silico tools since concern is growing about substances, called endocrine disrupters, that can interfere with hormone regulation. Molecular modelling techniques such as Quantitative Structure-Activity Relationships (QSAR), related methods like 3D-QSAR, and virtual docking have been used to investigate these phenomena and are described here. Implications about regulatory acceptance and use of these methods and the resulting models for identifying hazards and setting priorities are also addressed.