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

Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods

There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.

Degradation of difenoconazole in water and soil: Kinetics, degradation pathways, transformation products identification and ecotoxicity assessment

Difenoconazole is a widely used triazole fungicide that has been frequently detected in the environment, but comprehensive study about its environmental fate and toxicity of potential transformation products (TPs) is still lacking. Here, laboratory experiments were conducted to investigate the degradation kinetics, pathways, and toxicity of transformation products of difenoconazole. 12, 4 and 4 TPs generated by photolysis, hydrolysis and soil degradation were identified via UHPLC-QTOF/MS and the UNIFI software. Four intermediates TP295, TP295A, TP354A and TP387A reported for the first time were confirmed by purchase or synthesis of their standards, and they were further quantified using UHPLC-MS/MS in all tested samples. The main transformation reactions observed for difenoconazole were oxidation, dechlorination and hydroxylation in the environment. ECOSAR prediction and laboratory tests showed that the acute toxicities of four novel TPs on Brachydanio rerio, Daphnia magna and Selenastrum capricornutum are substantially lower than that of difenoconazole, while all the TPs except for TP277C were predicted chronically very toxic to fish, which may pose a potential threat to aquatic ecosystems. The results are important for elucidating the environmental fate of difenoconazole and assessing the environmental risks, and further provide guidance for scientific and reasonable use.

Pharmaceuticals and personal care products in the seawater around a typical subtropical tourist city of China and associated ecological risk

Pharmaceuticals and personal care products (PPCPs) in the sea area surrounding a densely populated tourist city in southeastern China were investigated. In total, 32 PPCP pollutants classified into 23 categories were detected. Different spatial distribution patterns of PPCPs indicated possible contamination from runoff and multiple local sources. The labile-to-conservative ratios of PPCPs showed the influence of untreated domestic sewage. In addition, increased concentrations of ciprofloxacin, enrofloxacin, and erythromycin around aquaculture farms imply that aquaculture cannot be neglected as a source. The concentrations of oxytetracycline, ranitidine, ciprofloxacin, miconazole, and sulfamethizole were higher in the wet season than those in the dry season, and the difference in pharmaceutical consumption was suspected to be the main driving factor of this seasonal variation. The risk quotients calculated with the maximum concentrations of miconazole, triclosan, dehydronifedipine, and triclocarban exceeded 0.1, indicating potential moderate or high risks. Antibacterial agents in daily chemicals and azole broad-spectrum antifungals were associated with the highest risks in this study; this might be another significant pollution characteristic in the sea area around this subtropical tourist city.

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

BACKGROUND

A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset.

RESULTS

In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools.

CONCLUSION

QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

Estimating species sensitivity distributions on the basis of readily obtainable descriptors and toxicity data for three species of algae, crustaceans, and fish

Estimation of species sensitivity distributions (SSDs) is a crucial approach to predicting ecological risks and water quality benchmarks, but the amount of data required to implement this approach is a serious constraint on the application of SSDs to chemicals for which there are few or no toxicity data. The development of statistical models to directly estimate the mean and standard deviation (SD) of the logarithms of log-normally distributed SSDs has recently been proposed to overcome this problem. To predict these two parameters, we developed multiple linear regression models that included, in addition to readily obtainable descriptors, the mean and SD of the logarithms of the concentrations that are acutely toxic to one algal, one crustacean, and one fish species, as predictors. We hypothesized that use of the three species' mean and SD would improve the accuracy of the predicted means and SDs of the logarithms of the SSDs. We derived SSDs for 60 chemicals based on quality-assured acute toxicity data. Forty-five of the chemicals were used for model fitting, and 15 for external validation. Our results supported previous findings that models developed on the basis of only descriptors such as log K OW had limited ability to predict the mean and SD of SSD (e.g., r 2 = 0.62 and 0.49, respectively). Inclusion of the three species' mean and SD, in addition to the descriptors, in the models markedly improved the predictions of the means and SDs of SSDs (e.g., r 2 = 0.96 and 0.75, respectively). We conclude that use of the three species' mean and SD is promising for more accurately estimating an SSD and thus the hazardous concentration for 5% of species in cases where limited ecotoxicity data are available.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

Development of a rapid and comprehensive method for identifying organic micropollutants with high ecological risk to the aquatic environment

Currently, tens-of-thousands of chemicals are used in Japan, and their presence in and impact on aquatic ecosystems are poorly understood. Because conventional risk evaluation processes using target analysis and biological tests are time-consuming and costly, it is challenging to investigate all substances. Therefore, we aimed to develop a rapid and highly efficient screening scheme for identifying hazardous organic micropollutants (OMPs) in aquatic ecosystems. The scheme is divided into two steps: chemical analysis and risk evaluation. First, a comprehensive screening method (CSM) using gas chromatography (GC)-mass spectrometry (MS) and a database containing nearly 1000 compounds is used to identify known compounds, and nontargeted analysis is carried out using a GC × GC-time-of-flight (TOF)MS to detect compounds not registered in the database. Secondly, the predicted toxicity values obtained by quantitative structure-activity relationship (QSAR) are used to evaluate and rank the ecological risk of each detected OMPs and to identify priority compounds for detailed survey. To assess the proposed scheme, we surveyed representative urban rivers in Japan and ranked the potential toxicity of the identified compounds. The total number of compounds detected in water from each river ranged from 29 to 87, and the total concentrations ranged from 2.3 to 63 μg L^-1. Pharmaceuticals and personal care products, such as crotamiton and galaxolide, were identified in the urban rivers and found to have high ecotoxicity rankings. Thus, the scheme combining CSM and risk evaluation using QSAR is a novel screening that can identify candidates with high ecological risk in aquatic environment rapidly and efficiently.

[AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP]

Toxicity testing is critical for new drug and chemical development process. A clinical study, experimental animal models, and in vitro study are performed to evaluate the safety of a new drug. The limitations of these methods include extensive time for toxicity testing, an ethical problem, and high costs of experimentation. Therefore computational methods are considered useful for estimating chemical toxicity. In silico toxicity prediction is one of the toxicity assessments that uses computational methods to predict and stimulate the toxicity of chemicals. In silico study aims to contribute to effective development of new drug and chemical design. In this study, quantitative structure-activity relationship (QSAR) models will be used to predict toxicities based on chemical structural parameters. Because toxicities are complicated physiological phenomena, a similar toxicity expression might cause a different pathway. Also, since many drugs with unknown mechanisms of actions are available, the application of artificial intelligence (AI)-which uses sophisticated algorithms- is increasingly used to predict toxicities. Recently, the QSAR model was applied to determine complex relations between chemical structures and toxicities. However, accuracy of QSAR for toxicity prediction remains an important issue. International competitions funded by public institutions can address this issue. Two important toxicity challenges were organized in the past decade; this article presents issues of toxicity based on these challenges.

Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

In life cycle assessment, characterization factors are used to convert the amount of the chemicals and other pollutants generated in a product's life cycle to the standard unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chemicals due to the lack of laboratory experiment data. Here we develop machine learning models to estimate ecotoxicity hazardous concentrations 50% (HC₅₀) in USEtox to calculate characterization factors for chemicals based on their physical-chemical properties in EPA's CompTox Chemical Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The average root mean squared error of the estimated HC₅₀ on the test sets is 0.761. The average coefficient of determination (R²) on the test set is 0.630, meaning 63% of the variability of HC₅₀ in USEtox can be explained by the predicted HC₅₀ from the random forest model. Our model outperforms a traditional quantitative structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide estimates of missing ecotoxicity characterization factors for 552 chemicals in USEtox using the validated random forest model.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

Interspecies quantitative structure-activity relationships (QSARs) for eco-toxicity screening of chemicals: the role of physicochemical properties

In addition to molecular structure profiles, descriptors based on physicochemical properties are useful for explaining the eco-toxicities of chemicals. In a previous study we reported that a criterion based on the difference between the partition coefficient (log POW) and distribution coefficient (log D) values of chemicals enabled us to identify aromatic amines and phenols for which interspecies relationships with strong correlations could be developed for fish-daphnid and algal-daphnid toxicities. The chemicals that met the log D-based criterion were expected to have similar toxicity mechanisms (related to membrane penetration). Here, we investigated the applicability of log D-based criteria to the eco-toxicity of other kinds of chemicals, including aliphatic compounds. At pH 10, use of a log POW - log D > 0 criterion and omission of outliers resulted in the selection of more than 100 chemicals whose acute fish toxicities or algal growth inhibition toxicities were almost equal to their acute daphnid toxicities. The advantage of log D-based criteria is that they allow for simple, rapid screening and prioritizing of chemicals. However, inorganic molecules and chemicals containing certain structural elements cannot be evaluated, because calculated log D values are unavailable.

Development of ecotoxicity QSAR models based on partial charge descriptors for acrylate and related compounds

Using Gasteiger's partial equalization of orbital electronegativity (PEOE) method, we constructed ecotoxicity prediction equations based on two-dimensional descriptors for α,β-unsaturated carbonyl compounds. After examining electrostatic effects on the calculated ecotoxicities of 10 α,β-unsaturated ketones and aldehydes (A-group compounds) by using the Mulliken atomic charges on the carbonyl oxygen atoms, we investigated the efficacy of the PEOE descriptors for the same 10 compounds and the correlation between the PEOE descriptors and the Mulliken charge. We then constructed QSAR models for acute fish and Daphnia toxicities by using the PEOE descriptors for acrylic acids and compounds with acrylate-like substructures (CH-group compounds). In the constructed models, the adjusted squared correlation coefficients between measured and calculated toxicities with the lowest Akaike information criterion were 0.77 and 0.79, respectively. The applicability of the constructed models was then evaluated for various methacrylates and similar compounds (CH(3)-group compounds). Both the fish and the Daphnia toxicities of some of the CH(3)-group compounds were underestimated by these models. Nevertheless, we concluded that the QSAR models based on the PEOE descriptors were practical for predicting acute toxicity, especially for α,β-unsaturated carbonyl compounds with an α-hydrogen. Combining hydrophobicity and PEOE descriptors led to accurate predictions for fish toxicity.

Consideration of reactivity to acute fish toxicity of α,β-unsaturated carbonyl ketones and aldehydes

To understand the key factor for fish toxicity of 11 α,β-unsaturated carbonyl aldehydes and ketones, we used quantum chemical calculations to investigate their Michael reactions with methanethiol or glutathione. We used two reaction schemes, with and without an explicit water molecule (Scheme-1wat and Scheme-0wat, respectively), to account for the effects of a catalytic water molecule on the reaction pathway. We determined the energies of the reactants, transition states (TS), and products, as well as the activation energies of the reactions. The acute fish toxicities of nine of the carbonyl compounds were evaluated to correlate with their hydrophobicities; no correlation was observed for acrolein and crotonaldehyde. The most toxic compound, acrolein, had the lowest activation energy. The activation energy of the reaction could be estimated with Scheme-1wat but not with Scheme-0wat. The complexity of the reaction pathways of the compounds was reflected in the difficulty of the TS structure searches when Scheme-1wat was used with the polarizable continuum model. The theoretical estimations of activation energies of α,β-unsaturated carbonyl compounds with catalytic molecules or groups including hydrogen-bond networks may complement traditional tools for predicting the acute aquatic toxicities of compounds that cannot be easily obtained experimentally.

Receptor-based QSAR study for a series of 3,3-disubstituted-5-aryl oxindoles and 6-aryl benzimidazol-2-ones derivatives as progesterone receptor inhibitors

Receptor-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 54 progesterone receptor (PR) inhibitors. The established CoMFA model from the training set gives statistically significant results with the cross-validated q (2) of 0.534 and non-cross-validated [Formula: see text] of 0.947. The best CoMSIA model was derived by the combination of steric field and hydrophobic field with a q (2) of 0.615 and [Formula: see text] of 0.954. A test set of 18 compounds was used to validate the predictive ability of the two models. The predicted correlation coefficients [Formula: see text] are 0.681 and 0.677 for CoMFA and CoMSIA models, respectively. Based on the CoMFA maps, the key structural characters of progesterone receptor inhibitors are identified. Moreover, the binding modes of oxindoles and benzimidazol-2-ones are also given by the quantum mechanical/molecular mechanical (QM/MM) calculations. This may provide useful information for drug design.

Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish

The European regulation on chemicals (REACh) places emphasis on reduction of systematic toxicity testing, thus fostering development of alternative methods. Consequently, we analysed acute toxicity data gathered by the Japanese Ministry of Environment for three species belonging to three different trophic levels (i.e., Pseudokirchneriella subcapitata 72-hour EC50, Daphnia magna 48-hour EC50 and Oryzias latipes 96-hour LC50). This paper investigates the relationships between the chemical structure and both the toxicity of the chemicals and the cross-species differences in sensitivity. The physicochemical properties of the chemicals were represented by the categories they belonged to in several widely-used categorisation schemes implemented by the freely available OECD (Q)SAR Toolbox and by quantitative molecular descriptors using DRAGON software. The outputs of these software products were analysed and compared in terms of quality of prediction and biological interpretation. Amongst the categorisations implemented by the OECD Toolbox, those focussing on bioaccumulation or biotransformation appeared to be the most interesting in terms of environmental prediction on a whole set of chemicals, in particular as the predicted biotransformation half-life is strongly dependent on hydrophobicity. In predicting toxicity towards each species, simple linear regression on logP performed better than PLS regression of toxicity on a very large set of molecular descriptors. However, the predictions based on the interspecies correlations performed better than the QSAR predictions. The results in terms of cross-species comparisons encourage the use of test strategies focussing on reducing the number of tests on fish.

Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE)

The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure-Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructures under identical C- or log P-judgements in KATE. However, in the case of the Daphnia end-point this was not so, and the group of chemicals with reactive substructures did not always have higher RMSEs: the Schiff base mechanism did not function as a high error detector. In addition to the RMSE findings, the presence of outliers suggested that the KATE classification rules needs to be reconsidered, particularly for the amine group. Examination of the dependency of the organism on the toxic action of chemicals in fish and Daphnia revealed that some of the reactive substructures could be applied to the improvement of the KATE system. It was concluded that the reaction mechanistic domains of toxic action for skin sensitisation could provide useful complementary information in predicting acute aquatic ecotoxicity, especially at the fish end-point.