Assessment of in silico models for acute aquatic toxicity towards fish under REACH regulation

We evaluated the performance of eight QSAR in silico modelling packages (ACD/ToxSuite?, ADMET Predictor?, DEMETRA, ECOSAR, TerraQSAR?, Toxicity Estimation Software Tool, TOPKAT? and VEGA) for acute aquatic toxicity towards two species of fish: Fathead Minnow and Rainbow Trout. For the Fathead Minnow, we compared model predictions for 567 substances with the corresponding experimental values for 96-h median lethal concentrations (LC50). Some models gave good results, with r² up to 0.85. We also classified the predictions of all the models into four toxicity classes defined by CLP. This permitted us to assess other parameters, such as the percentage of correct predictions for each class. Then we used a set of 351 substances with toxicity data towards Rainbow Trout (96-h LC50). In this case the predictability was unacceptable for all the in silico models. The calculated r² gave poor correlations (≤0.53). Another analysis was performed according to chemical classes and for mode of action. In the first case, all the classes show a high percentage of correct predictions, in the second case only narcotics and polar narcotics were predicted with good confidence. The results indicate the possibility of using in silico methods to estimate aquatic toxicity within REACH regulation, after careful evaluation.     

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.     

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible “molecular initiating events” based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.     

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.     

Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna

Quantitative structure–activity relationship (QSAR) models for predicting acute toxicity to Daphnia magna are often associated with poor performances, urging the need for improvement to meet REACH requirements. The aim of this study was to evaluate the accuracy, stability and reliability of a previously published QSAR model by means of further external validation and to optimize its performance by means of extension to new data as well as a consensus approach. The previously published model was validated with a large set of new molecules and then compared with ChemProp model, from which most of the validation data were taken. Results showed better performance of the proposed model in terms of accuracy and percentage of molecules outside the applicability domain. The model was re-calibrated on all the available data to confirm the efficacy of the similarity-based approach. The extended dataset was also used to develop a novel model based on the same similarity approach but using binary fingerprints to describe the chemical structures. The fingerprint-based model gave lower regression statistics, but also less unpredicted compounds. Eventually, consensus modelling was successfully used to enhance the accuracy of the predictions and to halve the percentage of molecules outside the applicability domain.     

The physicochemical basis of QSARs for baseline toxicity

The physico-chemical properties relevant to the equilibrium partitioning (bioconcentration) of chemicals between organisms and their respired media of water and air are reviewed and illustrated for chemicals that range in hydrophobicity. Relationships are then explored between freely dissolved external concentrations such as LC50s and chemical properties for one important toxicity mechanism, namely baseline toxicity or narcosis. The ‘activity hypothesis’ proposed by Ferguson in 1939 provides a coherent and compelling explanation for baseline toxicity of chemicals in both water- and air-respiring organisms, as well as a reference point for identifying more specific toxicity pathways. From inhalation studies with fish and rodents, narcosis is shown to occur at a chemical activity exceeding approximately 0.01 and there is no evidence of narcosis at activities less than 0.001. The activity hypothesis provides a framework for directly comparing the toxic potency of chemicals in both air- and water-breathing animals. The activity hypothesis is shown to be consistent with the critical body residue concept, but it has the advantage of avoiding the confounding effect of lipid content of the test organism. It also provides a theoretically sound basis for assessing the baseline toxicity of mixtures. It is suggested that since activity is readily calculated from fugacity, observed or predicted environmental abiotic and biotic fugacities can be used to evaluate the potential for baseline toxicity. Further, models employing fugacity or activity can be used to improve the experimental design of bioassays, thus possibly reducing unnecessary animal testing.     

The index of ideality of correlation: improvement of models for toxicity to algae

Toxicity to algae is important characteristic of substances from ecologic point of view. The CORAL software (http://www.insilico.eu/coral) gives possibility to build up model of toxicity to algae using data on the molecular architecture and experimental toxicity, without additional data on physicochemical and/or biochemical parameters. Considerable improvement of the model is observed in the case of using the index of ideality of correlation (IIC) in the role of additional criterion of predictive potential. The IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The best model calculated with use the IIC is characterized (the validation set) by n?=?50, r²?=?0.947, RMSE?=?0.401 whereas, model calculated without use the IIC is characterized by n?=?50, r²?=?0.805, and RMSE?=?0.539. The suggested models are built up in accordance to five OECD principles.

     

Comparison of in silico models for prediction of Daphnia magna acute toxicity

Eight in silico modelling packages were evaluated and compared for the prediction of Daphnia magna acute toxicity from the viewpoint of the European legislation on chemicals, REACH. We tested the following models: Discovery Studio (DS) TOPKAT, ACD/Tox Suite, ADMET Predictor?, ECOSAR (Ecological Structure Activity Relationships), TerraQSAR?, T.E.S.T. (Toxicity Estimation Software Tool) and two models implemented in VEGA on 480 industrial compounds for 48-h median lethal concentrations (LC₅₀) to D. magna, matching them with experimental values. The quality of the estimates was compared using a standard statistical review and an additional classification approach in which the hazard predictions were grouped using well-defined regulatory criteria. The regression parameters, correlation coefficient being the most influential, showed that four models (ADMET Predictor?, DS TOPKAT, TerraQSAR? and VEGA DEMETRA) had similar reliability. These performed better than the others, but the coefficient of determination was still low (r² around 0.6), considering that at least half the predicted compounds were inside the training sets. Additionally, we grouped the results in four defined toxicity classes. TerraQSAR? gave 60% of correct classifications, followed by DS TOPKAT, ADMET Predictor? and VEGA DEMETRA, with 56%, 54% and 48%, respectively. These results highlight the challenges associated with developing reliable and easily applied acceptability criteria for the regulatory use of QSAR models to D. magna acute toxicity.     

人工神经网络预测非环状亚硝胺急性毒性的QSAR研究

在DFT-B3LYP/6-311+G(d,p)水平对60种非环状亚硝胺分子结构进行几何全优化,通过多元逐步线性回归(MSR)分析筛选出9个量子化学描述符作为自变量,log LD50(lethal dose 50％,LD50:大鼠口服急性毒性)作为因变量,采用人工神经网络(ANN)方法构建QSAR模型.经Levenber...     

Development of a mediated whole cell-based electrochemical biosensor for joint toxicity assessment of multi-pollutants using a mixed microbial consortium

Since most risk assessment for toxicants is based on individual single-species test, the deduction of such results to ecosystem evaluation is afflicted with uncertainties. Herein, we successfully developed a p-benzoquinone mediated whole-cell electrochemical biosensor for multi-pollutants toxicological analysis by co-immobilizing mixed strains of microorganism, including Escherichia coli (gram-negative bacteria), Bacillus subtilis (gram-positive bacteria) and Saccharomyces cerevisiae (fungus). The individual and combined toxicities of heavy metal ions (Cu²⁺, Cd²⁺), phenol (3,5-dichlorophenol) and pesticides (Ametryn, Acephate) were examined. The experimental results showed that the order of toxicity for individual toxicant was ranked as Cu²⁺ > 3,5-dichlorophenol (DCP) > Ametryn > Cd²⁺ > Acephate. Then the toxic unit (TU) model was applied to determine the nature of toxicological interaction of the toxicants which can be classified as concentration additive (IC50_mix = 1TU), synergistic (IC50_mix < 1TU) and antagonistic (IC50_mix > 1TU) responses. The binary combination of Cu²⁺ + Cd²⁺, Cu²⁺ + DCP, Cu²⁺ + Acephate, DCP + Acephate, Acephate + Ametryn were analyzed and the three kind of joint toxicity effects (i.e. additive, synergistic and antagonistic) mentioned above were observed according to the dose-response relationship. The results indicate that the whole-cell electrochemical biosensor based on mixed microbial consortium is more reasonable to reflect the joint biotoxicity of multi-pollutants existing in real wastewater, and combined effects of toxicants is extremely necessary to be taken into account in ecological risk assessment. Thus, present study has provided a promising approach to the quality assessment of wastewater and a reliable way for early risk warning of acute biotoxicity.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure–activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure–activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log?P data from a widely used steroid benchmark data set.     

Structure-Toxicity Relationships for Aliphatic Compounds Encompassing a Variety of Mechanisms of Toxic Action to Vibrio fischeri

Abstract

QSARs based upon the logarithm of the octanol-water partition coefficient, logP, and energy of the lowest unoccupied molecular orbital, E_LUMO were developed to model the toxicity of aliphatic compounds to the marine bacterium Vibrio fischeri. Statistically robust, hydrophobic-dependent QSARs were found for chloroalcohols and haloacetonitriles. Modelling of the toxicity of the haloesters and the diones required the use of terms to describe both hydrophobicity and electrophilicity. The differences in intercepts, slopes, and fit of these models suggest different electrophilic mechanisms occur between classes, as well as within the diones and haloesters. In order to model globally the toxicity of aliphatic compounds to V. fischeri, all the data determined in this study were combined with those determined previously for alkanones, alkanals, and alkenals. A highly predictive two-parameter QSAR [pT₁₅ = 0.760(log P) ?0.625(E _LUMO) ?0.466; n = 63, s = 0.462, r ² = 0.846, F = 171, Pr > F = 0.0001] was developed for the combined data that models across classes and is independent of mechanisms of action. The toxicity of these compounds to V. fischeri compares well to the toxicity (50% population growth inhibition) to the ciliate Tetrahymena pyriformis (r ² = 0.850).