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

First insights into the bioaccumulation, biotransformation and trophic transfer of typical tetrabromobisphenol A (TBBPA) analogues along a simulated aquatic food chain

Tetrabromobisphenol A (TBBPA) analogues have been investigated for their prevalent occurrence in environments and potential hazardous effects to humans and wildlife; however, there is still limited knowledge regarding their toxicokinetics and trophic transfer in aquatic food chains. Using a developed toxicokinetic model framework, we quantified the bioaccumulation, biotransformation and trophic transfer of tetrabromobisphenol S (TBBPS) and tetrabromobisphenol A di(allyl ether) (TBBPA-DAE) during trophic transfer from brine shrimp (Artemia salina) to zebrafish (Danio rerio). The results showed that the two TBBPA analogues could be readily accumulated by brine shrimp, and the estimated bioconcentration factor (BCF) value of TBBPS (5.68 L kg-1 ww) was higher than that of TBBPA-DAE (1.04 L kg-1 ww). The assimilation efficiency (AE) of TBBPA-DAE in zebrafish fed brine shrimp was calculated to be 16.3%, resulting in a low whole-body biomagnification factor (BMF) in fish (0.684 g g-1 ww). Based on the transformation products screened using ultra-high-performance liquid chromatograph-high resolution mass spectrometry (UPLC-HRMS), oxidative debromination and hydrolysis were identified as the major transformation pathways of TBBPS, while the biotransformation of TBBPA-DAE mainly took place through ether bond breaking and phase-II metabolism. Lower accumulation of TBBPA as a metabolite than its parent chemical was observed in both brine shrimp and zebrafish, with metabolite parent concentration factors (MPCFs) < 1. The investigated BCFs for shrimp of the two TBBPA analogues were only 3.77 × 10-10 - 5.59 × 10-3 times of the theoretical Kshrimp-water based on the polyparameter linear free energy relationships (pp-LFERs) model, and the BMF of TBBPA-DAE for fish was 0.299 times of the predicted Kshrimp-fish. Overall, these results indicated the potential of the trophic transfer in bioaccumulation of specific TBBPA analogues in higher trophic-level aquatic organisms and pointed out biotransformation as an important mechanism in regulating their bioaccumulation processes. ENVIRONMENTAL IMPLICATION: The internal concentration of a pollutant in the body determines its toxicity to organisms, while bioaccumulation and trophic transfer play important roles in elucidating its risks to ecosystems. Tetrabromobisphenol A (TBBPA) analogues have been extensively investigated for their adverse effects on humans and wildlife; however, there is still limited knowledge regarding their toxicokinetics and trophic transfer in aquatic food chains. This study investigated the bioaccumulation, biotransformation and trophic transfer of TBBPS and TBBPA-DAE in a simulated di-trophic food chain. This state-of-art study will provide a reference for further research on this kind of emerging pollutant in aquatic environments.

Predicting the Bioconcentration Factor in Fish from Molecular Structures

The bioconcentration factor (BCF) is one of the metrics used to evaluate the potential of a substance to bioaccumulate into aquatic organisms. In this work, linear and non-linear regression QSARs were developed for the prediction of log BCF using different computational approaches, and starting from a large and structurally heterogeneous dataset. The new MLR-OLS and ANN regression models have good fitting with R2 values of 0.62 and 0.70, respectively, and comparable external predictivity with R2ext 0.64 and 0.65 (RMSEext of 0.78 and 0.76), respectively. Furthermore, linear and non-linear classification models were developed using the regulatory threshold BCF >2000. A class balanced subset was used to develop classification models which were applied to chemicals not used to create the QSARs. These classification models are characterized by external and internal accuracy up to 84% and 90%, respectively, and sensitivity and specificity up to 90% and 80%, respectively. QSARs presented in this work are validated according to regulatory requirements and their quality is in line with other tools available for the same endpoint and dataset, with the advantage of low complexity and easy application through the software QSAR-ME Profiler. These QSARs can be used as alternatives for, or in combination with, existing models to support bioaccumulation assessment procedures.

Comparative analysis of freshwater species sensitivity distributions and ecotoxicity for priority pesticides: Implications for water quality criteria

Organochlorine pesticides (OCPs) and organophosphate pesticides (OPPs) posed severe threats to the aquatic environment in China. The toxicity data of 18 priority OCPs and OPPs for Chinese and American species were collected, and their species sensitivity distributions (SSDs) and ecotoxicity were compared. Physicochemical characteristics and chemical structures were used to analyze the difference in the pesticide toxicity. Results suggested that there is no significant difference between the HC₅ values (hazardous concentration protecting 95% of species) of China and America as a whole. However, the HC₅ values of nearly half of these pesticides (8/18) differ dramatically between Chinese and American due to the different resident species distribution. It indicated that it is necessary to develop local water quality criteria in China. The regression analysis of pesticides toxicity differences indicated a decrease in LC₅₀/EC₅₀ values (for Oncorhynchus mykiss and Carassius auratu) with the increase in molecular weights of pesticides. Pesticides with larger MWs might be more toxic to aquatic species. Similar trend was also observed in the regression analysis of a decrease in both American and Chinese HC₅ values for with increase of the number of containing halogen atoms. It indicated the potency of these parameters to be used to predict the ecotoxicity of pesticide.

Integrated Decision-tree Testing Strategies for Environmental Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for environmental (aquatic) toxicity testing. The manuscript reviews tests based on fish cells and cell lines, fish embryos, lower organisms, and the many expert systems and QSARs for aquatic toxicity testing. Ways in which reduction and refinement measures can be used are also discussed, including the Upper Threshold Concentration -- Step Down (UTC) approach, which has recently been retrospectively validated by ECVAM and subsequently endorsed by the ECVAM Scientific Advisory Committee (ESAC). It is hoped that the application of this approach could reduce the number of fish used in acute toxicity studies by around 65-70%. Decision-tree style integrated testing strategies are also proposed for acute aquatic toxicity and chronic toxicity (including bioaccumulation), followed by a number of recommendations for the future facilitation of aquatic toxicity testing with respect to environmental risk assessment.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation.

Investigating the mechanisms of bioconcentration through QSAR classification trees

This paper proposes a scheme to predict whether a compound (1) is mainly stored within lipid tissues, (2) has additional storage sites (e.g., proteins), or (3) is metabolized/eliminated with a reduced bioconcentration. The approach is based on two validated QSAR (Quantitative Structure-Activity Relationship) trees, whose salient features are: (a) descriptor interpretability and (b) simplicity. Trees were developed for 779 organic compounds, the TGD approach was used to quantify the lipid-driven bioconcentration, and a refined machine-learning optimization procedure was applied. We focused on molecular descriptor interpretation, which allowed us to gather new mechanistic insights into the bioconcentration mechanisms.

Investigation on the relationship between bioconcentration factor and distribution coefficient based on class-based compounds: The factors that affect bioconcentration

Bioconcentration factor (BCF) is one of the most important parameters in the assessment of the potential hazard of new compounds in aquatic ecosystems. However, the factors that influence the estimation of BCFs for a large variety of chemicals have not been systemically investigated in the literature. In this paper, a large BCF data set containing 1088 nonionic and ionic organic compounds was used to study the relationship between BCF and molecular descriptors and influencing factors. Step-by-step analysis on the class-based compounds showed that nonlinear Gaussian and Sigmoid equations could well describe relationships between logBCF and distribution coefficient for the compounds over a wide range of structures and chloro or/and bromo substituted aromatics, respectively. The quality of fit from the nonlinear models is better than the BCFBAF method from the Epi Suite program for the class-based compounds. Systemic prediction deviations have been observed for some types of compounds. The reasons for systemic deviations for these compounds can be attributed to the difference in bioconcentration mechanism for hydrophilic compounds, transformation for hydroxyphenols and three-membered rings, physical barrier for long chain and large polycyclic compounds, difference in determining methods of BCF (kinetic and steady-state), bioavailability for highly hydrophobic compounds and accuracy of BCF measurements for compounds with extremely high or low BCFs. These factors are important and should be considered in any reliable bioconcentration prediction.

Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling

Polybrominated diphenyl ethers (PBDEs) are used as flame retardants in textiles, foams and plastics. Highly bioaccumulative with toxic effects including developmental neurotoxicity estrogen and thyroid hormones disruption, they are considered as persistent organic pollutants (POPs) and have been found in human tissues, wildlife and biota worldwide. But only some of them are banned from EU market. For the environmental fate studies of these compounds the bioconcentration factor (BCF) is one of the most important endpoints to start with. We applied quantitative structure-activity relationships techniques to overcome the limited experimental data and avoid more animal testing. The aim of this work was to assess the bioaccumulation of PBDEs by means of QSAR. First, a BCF dataset of specifically conducted experiments was modeled. Then the study was extended by predicting the bioaccumulation and biomagnification factors using some experimental values from the literature. Molecular descriptors were calculated using DRAGON 6. The most relevant ones were selected and resulting models were compared paying attention to the applicability domain.

Linear and non-linear relationships between bioconcentration and hydrophobicity: theoretical consideration

A non-linear relationship (e.g. Gaussian-type) between measured bioconcentration factor (BCF) and octanol/water partition coefficient (K(OW)) was noted many years ago. Many studies have focused on the cause of the breakdown in the log BCF/log K(OW) curve for highly hydrophobic chemicals with log K(OW)>6. However, there has been little investigation on the theoretical background of this feature for highly hydrophilic chemicals. In this paper, the cause of linear and non-linear relationships between log BCF and log K(OW) has been investigated on the basis of the partitioning-based mechanism for classified non-ionic and ionisable compounds. For highly hydrophilic compounds, lipid tissue in fish is not the major storage site of chemicals. Uptake from other tissues/organs plays a much more important role than the lipid content, leading to a variation of measured log BCF around 0.5. For hydrophobic chemicals with 0.5<log K(OW)<6, hydrophobicity is the principal driving force of bioconcentration and log BCF increases with increasing log K(OW). The log BCF/log K(OW) curve breaks down for highly hydrophobic chemicals with log K(OW)>6. The main reason for this is attributed to the reduced bioavailability of chemicals in water. A linear solvation energy relationship shows that the bioconcentration increases with increasing molecular size by increasing the dispersion interactions between the chemical and lipid content. Bioconcentration decreases with increasing the basicity of hydrophobic compounds by increasing the H-bonding of chemicals with water. Principal component analysis shows that the octanol/water system is the closest system, but not an ideal surrogate, to describe the bioconcentration for hydrophobic compounds as compared with other solvent/water partition systems.

Linear and non-linear relationships between soil sorption and hydrophobicity

The relationship between log K (oc) and log P was examined by use of a large dataset. For most of the hydrophobic compounds (e.g. 0.5 < log P < 7.5), the organic carbon content plays a dominant role in soil sorption and the sorption coefficient is linearly related to the octanol/water partition coefficient. For hydrophilic compounds (e.g. log P < 0.5), hydrophobic sorption becomes less significant. The hydrophilic contribution to sorption is equal to, or higher than, the hydrophobic contribution to sorption, resulting in the observed K (oc) values being higher than those predicted from their log P values. For highly hydrophobic compounds (e.g. log P > 7.5), log K (oc) decreases with increasing hydrophobicity because of a lack of chemical availability due to low solubility. A linear solvation energy relationship shows that the sorption potential increases with increasing molecular size by increasing the dispersion interactions between the chemical and soil organic phase. The sorption potential decreases with increase in the basicity of hydrophobic compounds by increasing the H-bonding of chemicals with water. Principal component analysis shows that the octanol/water system is the closest system, but not an ideal surrogate, to describe the soil sorption for hydrophobic compounds as compared with other solvent/water partition systems.

Simulation of chemical metabolism for fate and hazard assessment. III. New developments of the bioconcentration factor base-line model

The new development of the bioconcentration factor (BCF) base-line model of Dimitrov et al. [SAR QSAR Environ. Res. 6 (2005), pp. 531-554] is presented. The model applicability domain was expanded by enlarging the training set of the model up to 705 chemicals. The list of chemical-dependent mitigating factors was expanded by including water solubility of chemicals. The original empirical term for estimating ionization of chemicals was mechanistically analysed using two different approaches. In the first one, the ionization potential of chemicals was estimated based on the acid dissociation constant (pK(a) ). This term was found to be less adequate for inclusion in the ultimate BCF model, due to overestimating ionization of chemicals. The second approach, estimating the ionization as a ratio between distribution and partition coefficients (log P and log D), was found to be more successful. The new ionization term allows modelling of chemicals with both acidic and basic functionalities and chemicals undergoing different degrees of ionization. The significance of the different mitigating factors which can reduce the maximum bioconcentration potential of the chemicals was re-formulated and model parameters re-evaluated.

CORAL: building up the model for bioconcentration factor and defining it's applicability domain

CORAL (CORrelation And Logic) software can be used to build up the quantitative structure--property/activity relationships (QSPR/QSAR) with optimal descriptors calculated with the simplified molecular input line entry system (SMILES). We used CORAL to evaluate the applicability domain of the QSAR models, taking a model of bioconcentration factor (logBCF) as example. This model's based on a large training set of more than 1000 chemicals. To improve the model is predictivity and reliability on new compounds, we introduced a new function, which uses the Delta(obs) = logBCF(expr)--logBCF(calc) of the predictions on the chemicals in the training set. With this approach, outliers are eliminated from the phase of training. This proved useful and increased the model's predictivity.

QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors

Bioconcentration factor (BCF) is an important step in the uptake of environmental pollutants in the food chain. It is expensive and time-consuming to measure, so predictive methods are of value. We have used an artificial neural network QSAR approach involving descriptors for hydrophobicity, hydrogen bonding and molecular topology, obtained from commercially available software, to predict the fish BCF values of a diverse data set of 624 chemicals. The training set statistics were: r²= 0.765, q²= 0.763, s = 0.610, and those of the external test set were: r²= 0.739, s = 0.627. The model complies with the OECD Principles for the Validation of (Q)SARs.

QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects

The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds.

Integrated decision-tree testing strategies for environmental toxicity with respect to the requirements of the EU REACH legislation

Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for environmental (aquatic) toxicity testing. The manuscript reviews tests based on fish cells and cell lines, fish embryos, lower organisms, and the many expert systems and QSARs for aquatic toxicity testing. Ways in which reduction and refinement measures can be used are also discussed, including the Upper Threshold Concentration--Step Down (UTC) approach, which has recently been retrospectively validated by ECVAM and subsequently endorsed by the ECVAM Scientific Advisory Committee (ESAC). It is hoped that the application of this approach could reduce the number of fish used in acute toxicity studies by around 65-70%. Decision-tree style integrated testing strategies are also proposed for acute aquatic toxicity and chronic toxicity (including bioaccumulation), followed by a number of recommendations for the future facilitation of aquatic toxicity testing with respect to environmental risk assessment.

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.

Bioaccumulation of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in wild aquatic species from an electronic waste (e-waste) recycling site in South China

Water and several wild aquatic species including Chinese mysterysnail, prawn, fish, and water snake were collected from a reservoir surrounded by several e-waste recycling workshops in South China. The samples were examined to investigate the levels and bioaccumulation extent of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) released from electronic waste (e-waste) which was processed by crude recycling method. Elevated levels of PBDEs [52.7 to 1702 ng/g wet weight (ww)] and PCBs (20.2-25958 ng/g ww) were found in the collected biota species compared to that in the reference samples (13.0-20.5 ng/g ww for PBDEs and 75.4-82.8 ng/g ww for PCBs). log BAF (bioaccumulation factor) ranged from 2.9 to 5.3 for PBDEs and from 1.2 to 8.4 for PCBs, depending on congeners and species. The relationship between log BAFs and log K(OW) (octanol-water partition coefficient) can be adequately described by species-specific parabolic models wherein log BAFs generally increased at log K(OW)<7 then decreased with further increasing log K(OW) both for PBDEs and PCBs. The exceptions were for Chinese mysterysnail and prawn, in which the log BAFs showed a positive linear correlation with log K(OW) for PBDEs. Some PBDE and PCB congeners showed BAF values declining from the general trend predicted by K(OW), largely attributing to metabolism of these congeners in species sampled.

Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches

The present work aimed at developing in silico models allowing for a reliable prediction of bioaccumulative compounds and non-bioaccumulative compounds based on the definition of Bioconcentration Factor (BCF) using a diverse data set of 238 organic molecules. The partial least squares analysis (PLS), C4.5, support vector machine (SVM), and random forest (RF) algorithms were applied, and their performance classifying these compounds in terms of their quantitative structure-activity relationships (QSAR) was evaluated and verified with 5-fold cross-validation and an independent evaluation data set. The obtained results show that the overall prediction accuracies (Q) of the optimal PLS, C4.5, SVM and RF models are 84.5-87.7% for the internal cross-validation, with prediction accuracy (CO) of 86.3-91.1% in the external test sets, and C4.5 is slightly better than the three other methods which presents a Q of 87.7%, and a CO of 91.1% for the test sets. All these results prove the reliabilities of the in silico models, which should be valuable for the environmental risk assessment of the substances.

Estimation of bioconcentration factors using molecular electro-topological state and flexibility

Bioconcentration assessment is important in the scientific evaluation of risks that chemicals may pose to humans and environment and is a current focus of regulatory effort. In this work, a new QSAR model by adopting electronic topological properties and flexibility of chemicals to predict the bioconcentration factor (BCF) in fish was established based on a large number of diverse compounds. Multiple linear regression (MLR) and partial least squares (PLS) were used to build reliable QSARs, which were evaluated with internal five cross-validations (Qcv2) and an external validation (Qex2). The proposed MLR model showed reasonable predictivity of BCF (Qcv2 = 0.79,Qex2 = 0.79) and included seven molecular descriptors, namely SsCl, SaasC, SaaaC, SsNH2, Hmin, SssO, and Phia. The PLS model (Qcv2 = 0.83, Qex2 = 0.80) was shown to be slightly better than the MLR one in prediction accuracy, using six PLS latent components. In addition, the relationship between the log BCF and the theoretical calculated log Kow was extensively investigated. These studies may help to understand the factors influencing the bioconcentration process of chemicals and to develop alternative methods for prescreening of environmental toxic compounds.

A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV)

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient (r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient (q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133-145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87-97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups >C= and -CH=. 58 PCBs were divided into an "odd set" and "even set" in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for "odd set", and ME for "even set", can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.

Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors

The development of QSAR models useful for the prediction of fish bioconcentration factor (BCF) for a wide range of different chemical classes is crucial for the assessment and prioritisation of potentially persistent bioaccumulative and toxic substances. In this study we present QSAR models for BCF developed on a wide range of chemical structural classes of environmental and toxicological interest (such as dyes and various chlorinated and brominated compounds). The aim is to provide valid QSAR models, statistically validated for predictivity, for the prediction of BCF in general, but also for problematical chemical classes such as highly hydrophobic chemicals. Several descriptors, calculated by different commercially available software packages, have been employed in order to take into account relevant information provided by physicochemical properties (octanol/water partition coefficient and water solubility) and molecular features (structural and quantum-chemical molecular descriptors). The best descriptor subsets for the models were selected using the Genetic Algorithm-Variable Subset Selection strategy (GA-VSS) and calculations were performed by ordinary least squares regression. Starting from a data set of 640 compounds (logK(ow) range from -2.34 to 12.66), we developed linear QSARs, firstly for a data set of 620 compounds (logK(ow) range from -2.34 to 10.35) and secondly specifically for 87 highly hydrophobic chemicals (logK(ow) range from 6.00 to 10.35). All these models have been statistically validated (both internally by cross-validation and bootstrap and externally, by "a priori" splitting of available data by Kohonen Map-ANN in training and prediction sets) and their structural chemical domain has been verified by the leverage approach.

Animal use replacement, reduction, and refinement: development of an integrated testing strategy for bioconcentration of chemicals in fish

When addressing the use of fish for the environmental safety of chemicals and effluents, there are many opportunities for applying the principles of the 3Rs: Reduce, Refine, and Replace. The current environmental regulatory testing strategy for bioconcentration and secondary poisoning has been reviewed, and alternative approaches that provide useful information are described. Several approaches can be used to reduce the number of fish used in the Organization for Economic Cooperation and Development (OECD) Test Guideline 305, including alternative in vivo test methods such as the dietary accumulation test and the static exposure approach. The best replacement approach would seem to use read-across, chemical grouping, and quantitative structure-activity relationships with an assessment of the key processes in bioconcentration: Adsorption, distribution, metabolism, and excretion. Biomimetic extraction has particular usefulness in addressing bioavailable chemicals and is in some circumstances capable of predicting uptake. Use of alternative organisms such as invertebrates should also be considered. A single cut-off value for molecular weight and size beyond which no absorption will take place cannot be identified. Recommendations for their use in bioaccumulative (B) categorization schemes are provided. Assessment of biotransformation with in vitro assays and in silico approaches holds significant promise. Further research is needed to identify their variability and confidence limits and the ways to use this as a basis to estimate bioconcentration factors. A tiered bioconcentration testing strategy has been developed taking account of the alternatives discussed.

Aquatic ecotoxicity of a pheromonal antagonist in Daphnia magna and Desmodesmus subspicatus

Evaluation of the ecotoxicological potential of (Z)-11-hexadecenyl trifluoromethyl ketone (Z11-16:TFMK), a new biorational agent with high prospective features to control the Mediterranean corn borer Sesamia nonagrioides in infested maize fields, in comparison to the parent pheromone compound (Z)-11-hexadecenyl acetate (Z11-16:Ac) is described. Acute and sublethal toxicity tests of both compounds against the cladoceran Daphnia magna and the chlorophyte Desmodesmus subspicatus were conducted, the endpoints being immobilisation and feeding inhibition for Daphnia and growth rate inhibition for Desmodesmus. In addition, effects on B esterases including cholinesterase and carboxylesterase activities in Daphnia were also assessed to evaluate the mode of action of both chemicals. Toxicities of both compounds were moderate with EC(50) values ranging from 3.11 to 103.74mgl(-1) in algae growth, from 0.07 to 1.20mgl(-1) in Daphnia survival, and from 0.10 to 0.53mgl(-1) in Daphnia feeding rate. In all cases Z11-16:TFMK was more toxic than the naturally occurring pheromone component. Serine esterase assays showed a strong inhibition of the carboxylesterase activities in Daphnia at concentrations with apparently no effects on survival or feeding, suggesting that inhibition of other key esterases may be the possible mechanism of toxicity of this compound. The results obtained have been related with some physico-chemical properties of the compounds, such as water solubility and octanol-water partition coefficient, suggesting that Z11-16:TFMK may affect aquatic organisms at lower concentrations than expected from non-polar narcosis.

A stepwise approach for defining the applicability domain of SAR and QSAR models

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

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(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.

POPs: a QSAR system for developing categories for persistent, bioaccumulative and toxic chemicals and their metabolites

This paper presents the framework of a QSAR-based decision support system which provides a rapid screening of potential hazards, classification of chemicals with respect to risk management thresholds, and estimation of missing data for the early stages of risk assessment. At the simplest level, the framework is designed to rank hundreds of chemicals according to their profile of persistence, bioaccumulation potential and toxicity often called the persistent organic pollutant (POP) profile or the PBT (persistent bioaccumulative toxicant) profile. The only input data are the chemical structure. The POPs framework enables decision makers to introduce the risk management thresholds used in the classification of chemicals under various authorities. Finally, the POPs framework advances hazard identification by integrating a metabolic simulator that generates metabolic map for each parent chemical. Both the parent chemicals and plausible metabolites are systematically evaluated for metabolic activation and POPs profile.

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.

Predicting the biodegradation products of perfluorinated chemicals using CATABOL

Perfluorinated chemicals (PFCs) form a special category of organofluorine compounds with particularly useful and unique properties. Their large use over the past decades increased the interest in the study of their environmental fate. Fluorocarbons may have direct or indirect environmental impact through the products of their decomposition in the environment. It is a common knowledge that biodegradation is restricted within non-perfluorinated part of molecules: however, a number of studies showed that defluorination can readily occur during biotransformation. To evaluate the fate of PFCs in the environment a set of principal transformations was developed and implemented in the simulator of microbial degradation using the catabolite software engine (CATABOL). The simulator was used to generate metabolic pathways for 171 perfluorinated substances on Canada's domestic substances list. It was found that although the extent of biodegradation of parent compounds could reach 60%, persistent metabolites could be formed in significant quantities. During the microbial degradation a trend was observed where PFCs are transformed to more bioaccumulative and more toxic products. Perfluorooctanoic acid and perfluorooctanesulfonate were predicted to be the persistent biodegradation products of 17 and 27% of the perfluorinated sulphonic acid and carboxylic acid containing compounds, respectively.

Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa, or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.