Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF?) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r ²?=?0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(log?BSF? ^Obs???log?BCF? ^Calc)?≤?0.75)) for 59 chemicals included within the model applicability domain was 80%.     

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.     

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.     

Comparison of three fish bioaccumulation models for ecological and human risk assessment and validation with field data

This article compares two bioconcentration Quantitative Structure Activity Relationships (QSARs) for fish applied in human risk assessments with the mechanistic bioaccumulation model OMEGA and field data. It was found that all models are virtually similar up to a Kow of 10(6). For substances with a Kow higher than 10(6), the fish bioconcentration curve in the risk assessment model EUSES decreases parabolically. In contrast, OMEGA bioaccumulation outcomes approximately show a linear increase, based on mechanistic bioconcentration and biomagnification properties of chemicals. The OMEGA-outcomes are close to the fish bioconcentration outcomes of the risk assessment model CalTOX. For very hydrophobic substances, field accumulation data in freshwater and marine fish species are closer to OMEGA- and CalTOX-outcomes compared to EUSES. The results also show that it is important to include biomagnification in fish and lipid content of fish in human exposure models.     

Nonlinear Dependence of Fish Bioconcentration on n-Octanol/Water Partition Coefficient

Abstract

The log-log relationship between the bioconcentration tendency of organic chemicals in fish and the n?octanol/water partition coefficients breaks down for very hydrophobic compounds. The use of parabolic and bilinear models allows this problem to be overcome. The QSAR equation log BCF = 0.910 log P - 1.975 log (6.8 10^?7 P + 1) - 0.786 (n = 154; r = 0.950; s = 0.347; F = 463.51) was found to be a good predictor of bioconcentration in fish.     

QSARs for identifying and prioritizing substances with persistence and bioconcentration potential

From the 8511 chemicals with 1998 production volumes reported to the U.S. Environmental Protection Agency (U.S. EPA), the TSCA Interagency Testing Committee's (ITC's) Degradation Effects Bioconcentration Information Testing Strategies (DEBITS) was used to identify 56 chemicals. The DEBITS Quantitative Structure-Activity Relationships (QSARs) and the U.S. EPA's PBT profiler QSARs were used to predict the persistence and bioconcentration factors of these 56 chemicals. Partial order ranking was used to prioritise the chemicals based on persistence and bioconcentration potential.     

QSARs for identifying and prioritizing substances with persistence and bioconcentration potential

From the 8511 chemicals with 1998 production volumes reported to the U.S. Environmental Protection Agency (U.S. EPA), the TSCA Interagency Testing Committee's (ITC's) Degradation Effects Bioconcentration Information Testing Strategies (DEBITS) was used to identify 56 chemicals. The DEBITS Quantitative Structure-Activity Relationships (QSARs) and the U.S. EPA's PBT profiler QSARs were used to predict the persistence and bioconcentration factors of these 56 chemicals. Partial order ranking was used to prioritise the chemicals based on persistence and bioconcentration potential.     

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.

     

Estimation and Prediction of Bioconcentration Factors of Nonionic Organic Chemicals in Fish by Electrotopological State Indices and Structural Parameter

Based on the characteristics of atom types, Hall＇s electrotopological state indices （En） are calculated for 165 nonionic organic compounds. On the basis of the characteristics of substituent and conjugated matrix, a novel molecular structure parameter （G） is defined and calculated for 165 molecules in this paper. En and G show good structural selectivity for organic molecules. G, a satisfactory relationship between bioconcentration factor （BCF） and En, is expressed as： 1gBCF = -0.283 ＋ 1.246G ＋ 0.079E42 ＋ 0.351E9- 0.063E17 （n＇ = 122, R = 0.967, F = 425.636, s = 0.394）, which could provide estimation and prediction for the lgBCF of nonionic organic chemicals. Furthermore, the model is examined to validate overall robustness with Jackknife tests, and the independent variables in model do not exist cross correlation with VIF. All these regression results show that the new parameter G and electrotopological state index have good rationality and efficiency. It is concluded that the En and G will be used widely in quantitative structure-property/activity relationship （QSPR/QSAR） research.     

Results of a round-robin exercise on read-across

A round-robin exercise was conducted within the CALEIDOS LIFE project. The participants were invited to assess the hazard posed by a substance, applying in silico methods and read-across approaches. The exercise was based on three endpoints: mutagenicity, bioconcentration factor and fish acute toxicity. Nine chemicals were assigned for each endpoint and the participants were invited to complete a specific questionnaire communicating their conclusions. The interesting aspect of this exercise is the justification behind the answers more than the final prediction in itself. Which tools were used? How did the approach selected affect the final answer?