Discriminating toxicant classes by mode of action: 4. Baseline and excess toxicity

Comparison of modes of toxic action between Rana chensinensis tadpoles and Limnodrilus hoffmeisteri worms based on interspecies correlation, excess toxicity and QSAR for class-based compounds

Insecticides, fungicides, dinitrobenzenes, resorcinols, phenols and anilines are widely used in agricultural and industrial productions. However, their modes of toxic action are unclear in some nontarget organisms, such as worms and tadpoles. In this study, acute toxicity data was experimentally collected for Limnodrilus hoffmeisteri worms and Rana chensinensis tadpoles, respectively. Interspecies correlation and excess toxicity were calculated to determine modes of action (MOAs) between the two species for class-based compounds. The result showed that, although the interspecies correlation of toxicity between the tadpoles and worms is significant with a coefficient of determination (R²) of 0.83, tadpoles are more sensitive than the worms and toxicity values between these two species are not identical with an overall 0.43 log unit difference. Regression analysis revealed that the toxicity of nonpolar narcotics or baseline compounds is linearly related to hydrophobicity for both the tadpoles and worms and the two baseline models are parallel, suggesting that these nonpolar narcotics share the same MOA between the two species. The difference of baseline toxicities between the two species is attributed to differences in bioconcentration factors. Analysis of the excess toxicity calculated from the toxicity ratio (TR) suggested that phenols and anilines can be classified as polar narcotics, not only to fish, but also to the tadpoles and worms. These compounds are more toxic than the baseline compounds and quantitative structure-activity relationship (QSAR) models show that their toxicity is linearly related to chemical hydrophobicity and polarity. Analysis of the excess toxicity reveals that aminophenols and resorcinols can be classified as reactive compounds, and insecticides and fungicides can be classified as specifically-acting compounds for both species. These compounds exhibited significantly greater toxic effect to both the tadpoles and worms. QSAR models have been developed to describe the toxic mechanisms for nonpolar narcotics, polar narcotics, reactive chemicals and specifically-acting compounds, and a theoretical equation has been derived to explain the effect of bio-uptake and interaction of the chemical with target receptors for both tadpole and worm toxicity. Our study reveals that tadpole toxicity can be estimated from worm toxicity data and the two species can serve as surrogates for each other in the safety evaluation of organic pollutants.

Environmental Metabolomics Promises and Achievements in the Field of Aquatic Ecotoxicology: Viewed through the Pharmaceutical Lens

Scientists often set ambitious targets using environmental metabolomics to address challenging ecotoxicological issues. This promising approach has a high potential to elucidate the mechanisms of action (MeOAs) of contaminants (in hazard assessments) and to develop biomarkers (in environmental biomonitoring). However, metabolomics fingerprints often involve a complex mixture of molecular effects that are hard to link to a specific MeOA (if detected in the analytical conditions used). Given these promises and limitations, here we propose an updated review on the achievements of this approach. Metabolomics-based studies conducted on the effects of pharmaceutical active compounds in aquatic organisms provide a relevant means to review the achievements of this approach, as prior knowledge about the MeOA of these molecules could help overcome some shortcomings. This review highlighted that current metabolomics advances have enabled more accurate MeOA assessment, especially when combined with other omics approaches. The combination of metabolomics with other measured biological endpoints has also turned out to be an efficient way to link molecular effects to (sub)-individual adverse outcomes, thereby paving the way to the construction of adverse outcome pathways (AOPs). Here, we also discuss the importance of determining MeOA as a key strategy in the identification of MeOA-specific biomarkers for biomonitoring. We have put forward some recommendations to take full advantage of environmental metabolomics and thus help fulfil these promises.

A QSAR-ICE-SSD Model Prediction of the PNECs for Per- and Polyfluoroalkyl Substances and Their Ecological Risks in an Area of Electroplating Factories

Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated aliphatic compounds that are persistent and bioaccumulate, posing a potential threat to the aquatic environment. The electroplating industry is considered to be an important source of PFASs. Due to emerging PFASs and many alternatives, the acute toxicity data for PFASs and their alternatives are relatively limited. In this study, a QSAR–ICE–SSD composite model was constructed by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the predicted no-effect concentrations (PNECs) of selected PFASs. The PNECs for the selected PFASs ranged from 0.254 to 6.27 mg/L. The ΣPFAS concentrations ranged from 177 to 983 ng/L in a river close to an electroplating industry in Shenzhen. The ecological risks associated with PFASs in the river were below 2.97 × 10⁻⁴.

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.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Toxicity of some prevalent organic chemicals to tadpoles and comparison with toxicity to fish based on mode of toxic action

Although mode of action (MOA) plays a key role in the understanding of the toxic mechanism of chemicals, the MOAs of class-based compounds to tadpoles have not been investigated. To explore the MOAs, acute toxicity (expressed as log 1/LC₅₀) to Rana chensinensis tadpoles were determined and molecular descriptors were calculated. Quantitative structure-activity relationship (QSAR) showed that toxicity to tadpoles is closely related to the chemical octanol/water partition coefficient (log K_OW), energy of the lowest unoccupied molecular orbital (E_LUMO), and number of hydrogen bond donors and acceptors (NH_DA), representing the bio-uptake potential in tadpoles, the electrophilicity and hydrogen bonding capacity with target site(s), respectively. Comparison of the toxicity values between tadpoles and fish revealed that there were no significant differences for the overlapping compounds (average residual = 0.29 between tadpole and fish toxicity) with P values of interspecies correlation substantially less than 0.001. Classification of MOAs for the class-based compounds based on the excess toxicity calculated from toxicity ratio suggested that baseline, less inert compounds and some reactive or specifically-acting compounds share same MOAs between tadpoles and fish. Fish and tadpoles can serve as surrogates for each other in the safety evaluation of organic pollutants.

Environmental hazard and risk assessment of thiochemicals. Application of integrated testing and intelligent assessment strategies (ITS) to fulfil the REACH requirements for aquatic toxicity

REACH requires information on hazardous properties of substances to be generated avoiding animal testing where possible. It is the objective of the present case study with thiochemicals to extract as much information as possible from available experimental data with fish, daphnia and algae and to fill data gaps for analogues to be registered under REACH in 2018. Based on considerations of chemical similarity and common mode of action (MOA) the data gaps regarding the aquatic toxicity of the thiochemicals were largely closed by trend analysis ("category approach") and read-across within the same group, for example, thioglycolates or mercaptopropionates. Among 16 thiochemicals to be registered by 2018 there are only 2 substances with sufficient data. 36 data gaps for 14 thiochemicals were identified. Most of the required data (>60%) could be estimated by in silico methods. Only 14 tests (6 algae, 6 daphnia, 1 limit fish test and 1 acute fish test) were proposed. When the results of these tests are available it has to be discussed whether 2 further fish (limit) tests are required. For two substances (exposure-based) waiving was suggested. The relatively high toxicity of the thiochemicals is manifested in low predicted no-effect concentrations (PNECs). Only preliminary predicted environmental concentrations (PECs) could be derived for the thiochemicals for which a risk assessment has to be performed (production rate >10 t/y). The preliminary PEC/PNEC ratios indicate no risk for the aquatic compartment at the production site. PECs due to down-stream use must not exceed the estimated PNECs.

Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches

Persistent organic products are compounds used for various purposes, such as personal care products, surfactants, colorants, industrial additives, food, pesticides and pharmaceuticals. These substances are constantly introduced into the environment and many of these pollutants are difficult to degrade. Toxic compounds classified as MoA 1 (Mode of Action 1) are low toxicity compounds that comprise nonreactive chemicals. In silico methods such as Quantitative Structure-Activity Relationships (QSARs) have been used to develop important models for prediction in several areas of science, as well as aquatic toxicity studies. The aim of the present study was to build a QSAR model-based set of theoretical Volsurf molecular descriptors using the fish acute toxicity values of compounds defined as MoA 1 to identify the molecular properties related to this mechanism. The selected Partial Least Squares (PLS) results based on the values of cross-validation coefficients of determination (Q_cv²) show the following values: Q_cv² = 0.793, coefficient of determination (R²) = 0.823, explained variance in external prediction (Q_ext²) = 0.87. From the selected descriptors, not only the hydrophobicity is related to the toxicity as already mentioned in previously published studies but other physicochemical properties combined contribute to the activity of these compounds. The symmetric distribution of the hydrophobic moieties in the structure of the compounds as well as the shape, as branched chains, are important features that are related to the toxicity. This information from the model can be useful in predicting so as to minimize the toxicity of organic compounds.

Functional genomics assessment of narcotic and specific acting chemical pollutants using E. coli

The knowledge of gene-chemical interaction can be used to derive toxicological mechanism of chemical pollutants, therefore, it might be useful to discriminate chemicals with different mechanisms. In this study, three narcotic chemicals (4-chlorophenol (4-CP), 3, 4-dichloroaniline (DCA) and 2, 2, 2-trichloroethanol (TCE)) and three specific acting chemicals (triclosan (TCS), clarithromycin (CLARY), sulfamethoxazole (SMX)) were assessed by Escherichia coli (E. coli) genome-wide knockout screening. 66, 97, 88, 144, 198 and 180 initial robust hits were identified by exposure to 4-CP, DCA, TCE, TCS, CLARY and SMX with two replicates at the concentration of IC50, respectively. The average fold change values of responsive mutants to the three narcotic chemicals were smaller than the three specific acting chemicals. The common gene ontology (GO) term of biological process enriched by the three narcotic chemicals was "response to external stimulus" (GO: 0009605). Other GO terms like "lipopolysaccharide biosynthetic process" (induced by 4-CP) and "purine nucleotide biosynthetic process" (induced by DCA) were also influenced by the narcotic chemicals. The toxic target of three known specific acting chemicals could be validated by GSEA of responsive genes. Four genes (flhC, fliN, fliH and flhD) might serve as potential biomarkers to distinguish narcotic chemicals and specific acting chemicals. The E. coli functional genomic approach presented here has shown great potential not only for the molecular mechanistic screening of chemicals, rather it can discriminate chemicals based on their mode-of-action.

Aquatic ecotoxicological models and their applicability in Arctic regions

Dose-response modeling is one of the most important steps of ecological risk assessment. It requires concentration-effects relationships for the species under consideration. There are very limited studies and experimental data available for the Arctic aquatic species. Lack of toxicity data hinders obtaining dose-response relationships for lethal (LC50 values), sub-lethal and carcinogenic effects. Gaps in toxicity data could be filled using a variety of in-silico ecotoxicological methods. This paper reviews the suitability of such methods for the Arctic scenario. Mechanistic approaches like toxicokinetic and toxicodynamic analysis are found to be better suited for interspecies extrapolation than statistical methods, such as Quantitative Structure-Activity Relationships/Quantitative Structure Activity-Activity Relationship, ICE, and other empirical models, such as Haber's law and Ostwald's equation. A novel approach is proposed where the effects of the toxicant exposure are quantified based on the probability of cellular damage and metabolites interactions. This approach recommends modeling cellular damage using a toxicodynamic model and physiology or metabolites interactions using a toxicokinetic model. Together, these models provide more reliable estimates of toxicity in the Arctic aquatic species, which will assist in conducting ecological risk assessment of Arctic environment.

General baseline toxicity QSAR for nonpolar, polar and ionisable chemicals and their mixtures in the bioluminescence inhibition assay with Aliivibrio fischeri

The Microtox assay, a bioluminescence inhibition assay with the marine bacterium Aliivibrio fischeri, is one of the most popular bioassays for assessing the cytotoxicity of organic chemicals, mixtures and environmental samples. Most environmental chemicals act as baseline toxicants in this short-term screening assay, which is typically run with only 30 min of exposure duration. Numerous Quantitative Structure-Activity Relationships (QSARs) exist for the Microtox assay for nonpolar and polar narcosis. However, typical water pollutants, which have highly diverse structures covering a wide range of hydrophobicity and speciation from neutral to anionic and cationic, are often outside the applicability domain of these QSARs. To include all types of environmentally relevant organic pollutants we developed a general baseline toxicity QSAR using liposome-water distribution ratios as descriptors. Previous limitations in availability of experimental liposome-water partition constants were overcome by reliable prediction models based on polyparameter linear free energy relationships for neutral chemicals and the COSMOmic model for charged chemicals. With this QSAR and targeted mixture experiments we could demonstrate that ionisable chemicals fall in the applicability domain. Most investigated water pollutants acted as baseline toxicants in this bioassay, with the few outliers identified as uncouplers or reactive toxicants. The main limitation of the Microtox assay is that chemicals with a high melting point and/or high hydrophobicity were outside of the applicability domain because of their low water solubility. We quantitatively derived a solubility cut-off but also demonstrated with mixture experiments that chemicals inactive on their own can contribute to mixture toxicity, which is highly relevant for complex environmental mixtures, where these chemicals may be present at concentrations below the solubility cut-off.

Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies

Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K_ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR predictions.

Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action

Alternative approaches have been promoted to reduce the number of vertebrate and invertebrate animals required for the assessment of the potential of compounds to cause harm to the aquatic environment. A key philosophy in the development of alternatives is a greater understanding of the relevant adverse outcome pathway (AOP). One alternative method is the fish embryo toxicity (FET) assay. Although the trends in potency have been shown to be equivalent in embryo and adult assays, a detailed mechanistic analysis of the toxicity data has yet to be performed; such analysis is vital for a full understanding of the AOP. The research presented herein used an updated implementation of the Verhaar scheme to categorize compounds into AOP-informed categories. These were then used in mechanistic (quantitative) structure-activity relationship ((Q)SAR) analysis to show that the descriptors governing the distinct mechanisms of acute fish toxicity are capable of modeling data from the FET assay. The results show that compounds do appear to exhibit the same mechanisms of toxicity across life stages. Thus, this mechanistic analysis supports the argument that the FET assay is a suitable alternative testing strategy for the specified mechanisms and that understanding the AOPs is useful for toxicity prediction across test systems.

Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from Toxtree ver. 2.6

Assessment of the potential of compounds to cause harm to the aquatic environment is an integral part of the REACH legislation. To reduce the number of vertebrate and invertebrate animals required for this analysis alternative approaches have been promoted. Category formation and read-across have been applied widely to predict toxicity. A key approach to grouping for environmental toxicity is the Verhaar scheme which uses rules to classify compounds into one of four mechanistic categories. These categories provide a mechanistic basis for grouping and any further predictive modelling. A computational implementation of the Verhaar scheme is available in Toxtree v2.6. The work presented herein demonstrates how modifications to the implementation of Verhaar between version 1.5 and 2.6 of Toxtree have improved performance by reducing the number of incorrectly classified compounds. However, for the datasets used in this analysis, version 2.6 classifies more compounds as outside of the domain of the model. Further amendments to the classification rules have been implemented here using a post-processing filter encoded as a KNIME workflow. This results in fewer compounds being classified as outside of the model domain, further improving the predictivity of the scheme. The utility of the modification described herein is demonstrated through building quality, mechanism-specific Quantitative Structure Activity Relationship (QSAR) models for the compounds within specific mechanistic categories.

A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas)

REACH regulation demands information about acute toxicity of chemicals towards fish and supports the use of QSAR models, provided compliance with OECD principles. Existing models present some drawbacks that may limit their regulatory application. In this study, a dataset of 908 chemicals was used to develop a QSAR model to predict the LC50 96 hours for the fathead minnow. Genetic algorithms combined with k nearest neighbour method were applied on the training set (726 chemicals) and resulted in a model based on six molecular descriptors. An automated assessment of the applicability domain (AD) was carried out by comparing the average distance of each molecule from the nearest neighbours with a fixed threshold. The model had good and balanced performance in internal and external validation (182 test molecules), at the expense of a percentage of molecules outside the AD. Principal Component Analysis showed apparent correlations between model descriptors and toxicity.

Optimizing the aquatic toxicity assessment under REACH through an integrated testing strategy (ITS)

To satisfy REACH requirements a high number of data on chemical of interest should be supplied to the European Chemicals Agency. To organize the various kinds of information and help the registrants to choose the best strategy to obtain the needed information limiting at the minimum the use of animal testing, integrated testing strategies (ITSs) schemes can be used. The present work deals with regulatory data requirements for assessing the hazards of chemicals to the aquatic pelagic environment. We present an ITS scheme for organizing and using the complex existing data available for aquatic toxicity assessment. An ITS to optimize the choice of the correct prediction strategy for aquatic pelagic toxicity is described. All existing information (like physico-chemical information), and all the alternative methods (like in silico, in vitro or the acute-to-chronic ratio) are considered. Moreover the weight of evidence approach to combine the available data is included.