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.     

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

Both the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.     

Qsar Models for Predicting the Acute Toxicity of Selected Organic Chemicals with Diverse Structures to Aquatic Non-Vertebrates and Humans

Abstract

The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n?octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the ¹³C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.     

Identification of Phototoxic Polycyclic Aromatic Hydrocarbons in Sediments Through Sample Fractionation and QSAR Analysis

Abstract

The toxicity of certain polycyclic aromatic hydrocarbons (PAHs) can be greatly increased by simultaneous exposure of test organisms to ultraviolet (UV) wavelengths present in sunlight. This phenomenon, commonly termed photoinduced toxicity, had been evaluated extensively in laboratory settings where only one chemical of concern was present. However, more recent studies have demonstrated that complex mixtures of PAHs present, for example in sediments, also can cause phototoxicity to a variety of aquatic species when the samples are tested in simulated sunlight. Unfortunately, because these types of samples can contain thousands of substituted and unsubstituted PAHs it is difficult, if not impossible, to use conventional analytical techniques to identify those responsible for photoinduced toxicity. The objective of the present study was to link two powerful ecotoxicology tools, toxicity-based fractionation techniques and QSAR models, to identify phototoxic chemicals in a sediment contaminated with PAHs emanating from an oil refinery. Extensive chromatographic fractionation of pore water from the sediment, in conjunction with toxicity testing, yielded a simplified set of sample fractions containing 12 PAHs that were identified via mass spectroscopy. Evaluation of these compounds using a recently developed QSAR model revealed that, based upon their HOMO-LUMO gap energies, about half were capable of producing photoinduced toxicity. We further evaluated the phototoxic potential of the reduced set of PAHs by determining their propensity to bioaccumulate in test organisms, through calculation of octanol-water partition coefficients for the chemicals. These studies represent a novel linkage of sample fractionation methods with QSAR models for conducting an ecological risk assessment.     

QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity

ABSTRACT

Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC₅₀) of a set of biocides collected from different sources on the freshwater crustacean Daphnia magna, one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient r² = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance (r² = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and r² = 0.74 and 0.75 for the CORAL model).     

Evaluation of Critical Body Residue QSARS for Predicting Organic Chemical Toxicity to Aquatic Organisms

Abstract

The critical body residue (CBR) is the concentration of chemical bioaccumulated in an aquatic organism that corresponds to a defined measure of toxicity (e.g., mortality). The CBR can provide an alternative measure of toxicity to traditional waterborne concentration measurements (e.g., concentration in water causing 50% mortality). The CBR has been suggested as a better estimator of dose than the external water concentration and has been postulated to be constant for chemicals with the same mode of action. CBR QSARs have both theoretical and experimental support, developed primarily from studies on the acute toxicity of narcotic chemicals to small fish. CBR QSARs are less well developed for the aquatic toxicity of non-narcotic chemicals. CBRs vary substantially with the mode of action and toxicity endpoint, and may be affected by genetic, hormonal or environmental variation. CBR QSARs may not be applicable to very hydrophobic chemicals, chemicals with specific modes of action, or those with toxicity controlled by kinetic processes such as biotransformation. CBRs models have not been developed or evaluated for sediment and dietary exposure routes. Application of CBR QSARs to contaminated site assessments will require further research and development.     

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.

     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

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%.     

The Application of Structure-Activity Relationships (SARs) in the Aquatic Toxicity Evaluation of Discrete Organic Chemicals

Abstract

The Office of Pollution Prevention and Toxics (OPPT), United States Environmental Protection Agency (USEPA) routinely uses structure-activity relationships (SAR) for the aquatic hazard assessment of new chemicals submitted under Section 5 of the Toxic Substances Control Act (TSCA). With 15 years of experience and the general acceptance of toxicity predictions based on SARs, OPPT has expanded the use and application of the methodology to include existing chemicals used in printing, dry cleaning, and paint stripping. SAR analysis has also been used in the hazard evaluation of the U.S. and EU/OECD high production volume (HPV) chemicals. This paper describes the assumptions, limitations, and methodology for the use of SARs to evaluate large sets of discrete organic chemicals.     

A baseline inhalation toxicity model for narcosis in mammals

This paper presents the results of an analysis of the rodent inhalation literature and the development of a quantitative structure–activity relationships (QSAR) model for 4-hour LC50 as baseline toxicity to complement the baseline toxicity model for aquatic animals. We used the same literature review criteria developed for the ECOTOX database which selects only primary references with explicit experimental methods to form a high-quality database. Our literature review focused on the primary references reporting a 4-hour exposure for a single species of rodent in which the chemical had been clearly tested as a vapour and for which the exposure concentrations were not ambiguous. An expert system was used to remove reactive chemicals, receptor-mediated toxicants, and any test that produced symptoms inconsistent with non-polar narcosis. The QSAR model derived for narcosis in rodents was log LC50 = 0.69 × log VP + 1.54 which had an r ² of 0.91, which is significantly better than the baseline toxicity model for aquatic animals. This simple model suggests that there is no intrinsic barrier to estimating baseline toxicity for in vivo endpoints in mammalian or terrestrial toxicology.     

Toxicity assays applied to wastewater treatment

The utility and validity of toxicity tests for monitoring of wastewater treatment have been assessed. The evaluated acute toxicity tests have been Vibrio fischeri, Selenastrum capricornotum and Daphnia magna tests. The validation studies indicated that the acute toxicity tests can be considered as high sensitivity analytical tools to detect common environmental concentrations of the pollutants at concentration levels as low as ng l⁻¹. The toxicity tests showed to have discriminatory ability to distinguish between different degrees of toxicity, and the toxic specificity of the compounds on target organisms. Synergistic, additive or antagonistic effects were evaluated indicating the capacity of the toxicity test to assess the combined effects of chemicals in wastewaters. The reproducibility of these tests, calculated as relative standard deviation, is acceptable in the range of 5-22.3%. The application of multivariate date analysis proved that toxicity and chemical measures are complementary analytical tools for monitoring of wastewaters quality. The toxicity tests are useful analytical tools for screening of chemical analysis and as an early warning system to monitor the treatment of WWTPs. The use of single toxicity test or battery of tests is the best approach to evaluate the risk because they are reliable indices of the toxic impact of effluents in the aquatic environment. The toxicity tests were applied in the quality control of different European WWTPs.     

U.S. EPA Regulatory Perspectives on the Use of QSAR for New and Existing Chemical Evaluations

Abstract

As testing is not required, ecotoxicity or fate data are available for ≈ 5% of the approximately 2,300 new chemicals/year (26,000 + total) submitted to the US-EPA. The EPA's Office of Pollution Prevention and Toxics (OPPT) regulatory program was forced to develop and rely upon QSARs to estimate the ecotoxicity and fate of most of the new chemicals evaluated for hazard and risk assessment. QSAR methods routinely result in ecotoxicity estimations of acute and chronic toxicity to fish, aquatic invertebrates, and algae, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. The EPA's Toxic Substances Control Act (TSCA) Inventory of existing chemicals currently lists over 72,000 chemicals. Most existing chemicals also appear to have little or no ecotoxicity or fate data available and the OPPT new chemical QSAR methods now provide predictions and cross-checks of test data for the regulation of existing chemicals. Examples include the Toxics Release Inventory (TRI), the Design for the Environment (DfE), and the OECD/SIDS/HPV Programs. QSAR screening of the TSCA Inventory has prioritized thousands of existing chemicals for possible regulatory testing of: 1) persistent bioaccumulative chemicals, and 2) the high ecotoxicity of specific discrete organic chemicals.     

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.     

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system,March 2009 version

The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure–activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r ² > 0.7, RMSE ≤ 0.5, and n > 5, give acceptable q ² values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.