Classification of chemicals according to mechanism of aquatic toxicity: an evaluation of the implementation of the Verhaar scheme in Toxtree

To Split or Not to Split: Characterizing Chemical Pollution Impacts in Aquatic Ecosystems with Species Sensitivity Distributions for Specific Taxonomic Groups

Bridging applied ecology and ecotoxicology is key to protect ecosystems. These disciplines show a mismatch, especially when evaluating pressures. Contrasting to applied ecology, ecotoxicological impacts are often characterized for whole species assemblages based on Species Sensitivity Distributions (SSDs). SSDs are statistical models describing per chemical across-species sensitivity variation based on laboratory toxicity tests. To assist in the aligning of the disciplines and improve decision-support uses of SSDs, we investigate taxonomic-group-specific SSDs for algae/cyanobacteria/aquatic plants, invertebrates, and vertebrates for 180 chemicals with sufficient test data. We show that splitting improves pollution impact assessments for chemicals with a specific mode of action and, surprisingly, for narcotic chemicals. We provide a framework for splitting SSDs that can be applied to serve in environmental protection, life cycle assessment, and management of freshwater ecosystems. We illustrate that using split SSDs has potentially large implications for the decision-support of SSD-based outputs around the globe.

Evaluation of Complex Mixture Toxicity in the Milwaukee Estuary (WI, USA) Using Whole-Mixture and Component-Based Evaluation Methods

Anthropogenic activities introduce complex mixtures into aquatic environments, necessitating mixture toxicity evaluation during risk assessment. There are many alternative approaches that can be used to complement traditional techniques for mixture assessment. Our study aimed to demonstrate how these approaches could be employed for mixture evaluation in a target watershed. Evaluations were carried out over 2 years (2017-2018) across 8-11 study sites in the Milwaukee Estuary (WI, USA). Whole mixtures were evaluated on a site-specific basis by deploying caged fathead minnows (Pimephales promelas) alongside composite samplers for 96 h and characterizing chemical composition, in vitro bioactivity of collected water samples, and in vivo effects in whole organisms. Chemicals were grouped based on structure/mode of action, bioactivity, and pharmacological activity. Priority chemicals and mixtures were identified based on their relative contributions to estimated mixture pressure (based on cumulative toxic units) and via predictive assessments (random forest regression). Whole mixture assessments identified target sites for further evaluation including two sites targeted for industrial/urban chemical mixture effects assessment; three target sites for pharmaceutical mixture effects assessment; three target sites for further mixture characterization; and three low-priority sites. Analyses identified 14 mixtures and 16 chemicals that significantly contributed to cumulative effects, representing high or medium priority targets for further ecotoxicological evaluation, monitoring, or regulatory assessment. Overall, our study represents an important complement to single-chemical prioritizations, providing a comprehensive evaluation of the cumulative effects of mixtures detected in a target watershed. Furthermore, it demonstrates how different tools and techniques can be used to identify diverse facets of mixture risk and highlights strategies that can be considered in future complex mixture assessments. Environ Toxicol Chem 2023;42:1229-1256. © 2023 SETAC.

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods

Glutamic acid is a non-essential amino acid involved in multiple metabolic pathways. Of high importance is its relationship with glutamine, an essential fuel for cancer cell development. Compounds that can modify glutamine or glutamic acid behaviour in cancer cells have resulted in attractive anticancer therapeutic alternatives. Based on this idea, we theoretically formulated 123 glutamic acid derivatives using Biovia Draw. Suitable candidates for our research were selected among them. For this, online platforms and programs were used to describe specific properties and their behaviour in the human organism. Nine compounds proved to have suitable or easy to optimise properties. The selected compounds showed cytotoxicity against breast adenocarcinoma, lung cancer cell lines, colon carcinoma, and T cells from acute leukaemia. Compound 2Ba5 exhibited the lowest toxicity, and derivative 4Db6 exhibited the most intense bioactivity. Molecular docking studies were also performed. The binding site of the 4Db6 compound in the glutamine synthetase structure was determined, with the D subunit and cluster 1 being the most promising. In conclusion, glutamic acid is an amino acid that can be manipulated very easily. Therefore, molecules derived from its structure have great potential to become innovative drugs, and further research on these will be conducted.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation

Lignin-modifying enzymes (LMEs) are impactful biocatalysts in environmental remediation applications. However, LMEs-assisted experimental degradation neglects the molecular basis of pollutant degradation. Furthermore, throughout the remediation process, the inherent hazards of environmental pollutants remain untapped for in-depth toxicological endpoints. In this investigation, a predictive toxicological framework and a computational framework adopting LMEs were employed to assess the hazards of Priority Pollutants (PP) and its possible LMEs-assisted catalytic screening. The potential hazardous outcomes of PP were assessed using Quantitative structure-activity relationship (QSARs)-based techniques including Toxtree, ECOSAR, and T.E.S.T. tools. Toxicological findings revealed positive outcomes in a multitude of endpoints for all PP. The PP compound 2,3,7,8-TCDD (dioxin) was found to exhibit the lowest concentration of aquatic toxicity implementing aquatic model systems; LC₅₀ as 0.01, 0.01, 0.04 (mg L^-1) for Fish (96 H), Daphnid (48 H), Green algae (96 H) respectively. T.E.S.T. results revealed that chloroform, and 2-chlorophenol both seem to be developmental toxicants. Subsequently, LMEs-assisted docking procedure was employed in predictive mitigation of PP. The docking approach as predicted degradation revealed the far lowest docking energy score for Versatile peroxidase (VP)- 2,3,7,8-TCDD docked complex with a binding energy of -9.2 (kcal mol^-1), involved PHE-46, PRO-139, PRO-141, ILE-148, LEU-165, HIS-169, LEU-228, MET-262, and MET-265 as key interacting amino acid residues. Second most ranked but lesser than VP, Lignin peroxidase (LiP)- 2,3,7,8-TCDD docked complex exhibited a rather lower binding affinity score (-8.8 kcal mol^-1). Predictive degradation screening employing comparative docking revealed varying binding affinities, portraying that each LMEs member has independent feasibility to bind PP as substrate. Predictive findings endorsed the hazardous nature of associated PP in a multitude of endpoints, which could be attenuated by undertaking LMEs as a predictive approach to protect the environment and implement it in regulatory considerations.

Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library

Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.

In silico analytical toolset for predictive degradation and toxicity of hazardous pollutants in water sources

Different phenolic compounds, including multimeric lignin derivatives in the β-O-4 form, are among the most prevalent compounds in wastewater, often generated from paper industries. Relatively small concentrations of lignin are hazardous to aquatic organisms and can trigger severe environmental hazards. Herein, we present a predictive toolset to insight the induced toxic hazards prediction, and their Lignin peroxidase (LiP)-assisted degradation mechanism of selected multimeric lignin model compounds. T.E.ST and Toxtree toolset were deployed for toxic hazards estimation in different endpoints. To minimize the concerning hazards, we screened multimeric compounds for binding affinity with LiP. The binding affinity was found to be significantly lower than the reference compound. An Extra precision (XP) Glide score of -6.796 kcal/mol was found for dimer (guaiacyl 4-O-5 guaiacyl) complex as lowest compared to reference compound (-4.007 kcal/mol). The active site residues ASP-153, HIP-226, VAL-227, ARG-244, GLU-215, 239, PHE-261 were identified as site-specific key binding AA residues actively involved with corresponding ligands, forming Hydrophobic, H-Bond, π-Stacking, π-π type interactions. The DESMOND-assisted molecular dynamics simulation's (MDS) trajectories of protein-ligand revealed the considerable binding behavior and attained stability and system equilibrium state. Such theoretical and predictive conclusions indicted the feasibility of LiP assisted sustainable mitigation of lignin-based compounds, and such could be used to protect the environment from the potential hazards posed by recognized similar pollutants.

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.

Computational prediction of photosensitizers’ toxicity

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.

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.

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.

In silico study of garlic (Allium sativum L.)-derived compounds molecular interactions with α-glucosidase

Diabetes mellitus is a metabolic syndrome characterized by elevated blood glucose. The α-glucosidase enzyme is responsible for the hydrolysis of carbohydrates. This in silico study aimed to evaluate the inhibitory effects of the isolated compounds from Allium sativum L. on α-glucosidase. At first, sulfur and phenolic compounds of A. sativum L. were obtained from PubChem database, and α-glucosidase enzyme structure was obtained from Protein Data Bank. Toxicity class of compounds and the Lipinski parameter were predicted by Toxtree and Protox II and the Swiss ADME tools, respectively. Finally, the molecular interaction analysis between α-glucosidase and compounds from A. sativum L. was performed by AutoDock 4.2.6. Molecular interactions were investigated using Discovery Studio Visulizer and Ligplot 2.1 program. All of the selected sulfur and phenolic compounds from A. sativum L. followed the Lipinski's rules, had an acceptable binding energy, and lacked toxicity; therefore, they were appropriate candidates for α-glucosidase inhibition. Among these compounds, methionol and caffeic acid showed the lowest binding energy, and the highest inhibitory effect on α-glucosidase enzyme with - 3.9 and - 4.8 kcal/mol, respectively. These compounds also indicated the lower binding energy than the standard inhibitor (miglitol). Among the sulfur and phenolic compounds in A. sativum L., methionol and caffeic acid were predicted to be the powerful inhibitors, due to having more hydrogen binds and hydrophobic interactions with the active site of α-glucosidase.

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.

Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation

Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD₅₀) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R² = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD_50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.

Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish

We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSE_ext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC₅₀) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

MOA-based linear and nonlinear QSAR models for predicting the toxicity of organic chemicals to Vibrio fischeri

Risk assessment of pollutants to humans and ecosystems requires much toxicological data. However, experimental testing of compounds expends a large number of animals and is criticized for ethical reasons. The in silico method is playing an important role in filling the data gap. In this paper, the acute toxicity data of 1221 chemicals to Vibrio fischeri were collected. The global models obtained showed that there was a poor relationship between the toxicity data and the descriptors calculated based on linear and nonlinear regression analysis. This is due to the fact that the studied compounds contain not only non-reactive compounds but also reactive and specifically acting compounds with different modes of action (MOAs). MOAs are fundamental for the development of mechanistically based QSAR models and toxicity prediction. To investigate MOAs and develop MOA-based prediction models, the compounds were classified into baseline, less inert, reactive, and specifically acting compounds based on the modified Verhaar's classification scheme. Satisfactory models were established by multivariate linear regression (MLR) and support vector machine (SVM) analysis not only for baseline and less inert chemicals, but also for reactive and specifically acting compounds. Compared with linear models obtained by the MLR method, the nonlinear models obtained by the SVM method had better performance. The cross validation proved that all of the models were robust except for those for reactive chemicals with nN (number of nitrogen atoms) = 0 and n(C=O) (number of carbonyl groups) > 0 (Q²_ext < 0.5). The application domains and outliers are discussed for those MOA-based models. The models developed in this paper are significantly helpful not only because the application domains for baseline and less inert compounds have been expended, but also the toxicity of reactive and specifically acting compounds can be successfully predicted. This work will promote understanding of toxic mechanisms and toxicity prediction for the chemicals with structural diversity, especially for reactive and specifically acting compounds.

Glycine-betaine-derived ionic liquids: Synthesis, characterization and ecotoxicological evaluation

Ionic Liquids (ILs) are generically regarded as environmentally "harmless" and thus, assumed as "non-toxic". However, due to the endless design possibilities, their ecotoxicological profile is still poorly known. An accurate knowledge on the toxicity of a substance is required, under the scope of environmental regulation worldwide, before their application and commercialization. Knowledge on the relationship between the chemical structure and toxic effects is essential for the future design of more biocompatible solvents. Focusing on the use of ILs as base lubricants, lubricant additives, or even as potential working fluids for absorption heat pumps, the knowledge on its environmental impact is of great importance, due to the possibility of spills. In this specific context, four analogues of glycine-betaine-based ILs (AGB-ILs) and four glycine-betaine based ILs (GB-ILs) were synthesized and characterized. Their ecotoxicity was assessed using representatives of two trophic levels in aquatic ecosystems, the bacteria Allivibrio fischeri (commonly used as a screening test organism) and the microalgae Raphidocelis subcapitata (as an alternative test organism that has been proven very sensitive to several IL families). The microalgae were more sensitive than the bacteria, hence, following a precautionary principle, we recommend considering the toxicity towards microalgae as an indicator in future studies regarding the focused ILs. Although four of the studied ILs were derived from a natural amino acid, all were considered hazardous for the aquatic environment, disproving the primary theory that all ILs derived from natural compounds are benign. Furthermore, the modification in the structure of anion and the cation can lead to the increase of toxicity.

Mode of Action Classifications in the EnviroTox Database: Development and Implementation of a Consensus MOA Classification

Multiple mode of action (MOA) frameworks have been developed in aquatic ecotoxicology, mainly based on fish toxicity. These frameworks provide information on a key determinant of chemical toxicity, but the MOA categories and level of specificity remain unique to each of the classification schemes. The present study aimed to develop a consensus MOA assignment within EnviroTox, a curated in vivo aquatic toxicity database, based on the following MOA classification schemes: Verhaar (modified) framework, Assessment Tool for Evaluating Risk, Toxicity Estimation Software Tool, and OASIS. The MOA classifications from each scheme were first collapsed into one of 3 categories: non-specifically acting (i.e., narcosis), specifically acting, or nonclassifiable. Consensus rules were developed based on the degree of concordance among the 4 individual MOA classifications to attribute a consensus MOA to each chemical. A confidence rank was also assigned to the consensus MOA classification based on the degree of consensus. Overall, 40% of the chemicals were classified as narcotics, 17% as specifically acting, and 43% as unclassified. Sixty percent of chemicals had a medium to high consensus MOA assignment. When compared to empirical acute toxicity data, the general trend of specifically acting chemicals being more toxic is clearly observed for both fish and invertebrates but not for algae. EnviroTox is the first approach to establishing a high-level consensus across 4 computationally and structurally distinct MOA classification schemes. This consensus MOA classification provides both a transparent understanding of the variation between MOA classification schemes and an added certainty of the MOA assignment. In terms of regulatory relevance, a reliable understanding of MOA can provide information that can be useful for the prioritization (ranking) and risk assessment of chemicals. Environ Toxicol Chem 2019;38:2294-2304. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods

With industrial development and eventual commercial use, environmental chemicals through accidental spills and effluents appear more frequently in aquatic ecosystems and may produce an enormous effect on water, soil, wildlife and human health. Therefore, aquatic toxicity becomes an increasingly important endpoint in the evaluation of the environmental impact of chemicals. In this study, based on ECOTOX database, a large data set containing 824 diverse compounds with experimental 48 h EC50 values on crustaceans was compiled. A series of in silico models were then developed using six machine learning methods combined with seven types of molecular fingerprints. Performance of these models was measured by an external validation set, involving 246 molecules. The best model proposed is MACCS fingerprint and SVM algorithm with high accuracy of 0.87 for external validation set. Additionally, we proposed five structural alerts identified by information gain and substructure frequency analysis for mechanistic interpretation. The models and structural alerts can provide critical information and useful tools for a priori evaluation of chemical aquatic toxicity in environmental hazard assessment.

In vitro or not in vitro: a short journey through a long history

The aim of ecotoxicology is to study toxic effects on constituents of ecosystems, with the protection goal being populations and communities rather than individual organisms. In this ecosystem perspective, the use of in vitro methodologies measuring cellular and subcellular endpoints at a first glance appears to be odd. Nevertheless, more recently in vitro approaches gained momentum in ecotoxicology. In this article, we will discuss important application domains of in vitro methods in ecotoxicology. One area is the use of in vitro assays to replace, reduce, and refine (3R) in vivo tests. Research in this field has focused mainly on the use of in vitro cytotoxicity assays with fish cells as non-animal alternative to the in vivo lethality test with fish and on in vitro biotransformation assays as part of an alternative testing strategy for bioaccumulation testing with fish. Lessons learned from this research include the importance of a critical evaluation of the sensitivity, specificity and exposure conditions of in vitro assays, as well as the availability of appropriate in vitro-in vivo extrapolation models. In addition to this classical 3R application, other application domains of in vitro assays in ecotoxicology include the screening and prioritization of chemical hazards, the categorization of chemicals according to their modes of action and the provision of mechanistic information for the pathway-based prediction of adverse outcomes. The applications discussed in this essay may highlight the potential of in vitro technologies to enhance the environmental hazard assessment of single chemicals and complex mixtures at a reduced need of animal testing.

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.

Developing predictive models for toxicity of organic chemicals to green algae based on mode of action

Organic chemicals in the aquatic ecosystem may inhibit algae growth and subsequently lead to the decline of primary productivity. Growth inhibition tests are required for ecotoxicological assessments for regulatory purposes. In silico study is playing an important role in replacing or reducing animal tests and decreasing experimental expense due to its efficiency. In this work, a series of theoretical models was developed for predicting algal growth inhibition (log EC₅₀) after 72 h exposure to diverse chemicals. In total 348 organic compounds were classified into five modes of toxic action using the Verhaar Scheme. Each model was established by using molecular descriptors that characterize electronic and structural properties. The external validation and leave-one-out cross validation proved the statistical robustness of the derived models. Thus they can be used to predict log EC₅₀ values of chemicals that lack authorized algal growth inhibition values (72 h). This work systematically studied algal growth inhibition according to toxic modes and the developed model suite covers all five toxic modes. The outcome of this research will promote toxic mechanism analysis and be made applicable to structural diversity.

Acute toxicity of emerging atmospheric pollutants from wood lignin due to biomass burning

Guaiacol (2-methoxyphenol) is an important atmospheric pollutant. It is the major component of wood lignin and is essentially emitted to the atmosphere during biomass burning. Its aging in the tropospheric aqueous phase leads to the generation of the following ring-retaining transformation products, also during nighttime: 4-nitroguaiacol, 6-nitroguaiacol, and dinitroguaiacol. This study presents the first toxicological data of guaiacol and its nitro derivatives and reveals their harmful potential for the ecosystem. Applying V. fischeri bioluminescence acute toxicity test, EC50 values range from 16.7 to 103mgL^-1 after a 30-min incubation period, which classifies all investigated compounds as 'harmful' according to the European legislation. The investigation of environmentally relevant mixtures did not show significant joint actions between the four studied compounds. Therefore, their concentration addition can be considered for ecotoxicological purposes. However, a synergistic effect between guaiacol and a minor unidentified first-generation product of its aqueous-phase aging was observed and should be taken into account when assessing the reaction mixture toxicity. These results stress the need for further toxicological testing, including organisms of different trophic levels, to better evaluate the environmental hazard of guaiacol and especially its nitro derivatives.

Mode of Action (MOA) Assignment Classifications for Ecotoxicology: An Evaluation of Approaches

The mode of toxic action (MOA) is recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, MOA classification has never been standardized in ecotoxicology, and a comprehensive comparison of classification tools and approaches has never been reported. Here we critically evaluate three MOA classification methodologies using an aquatic toxicity data set of 3448 chemicals, compare the approaches, and assess utility and limitations in screening and early tier assessments. The comparisons focused on three commonly used tools: Verhaar prediction of toxicity MOA, the U.S. Environmental Protection Agency (EPA) ASsessment Tool for Evaluating Risk (ASTER) QSAR (quantitative structure activity relationship) application, and the EPA Mode of Action and Toxicity (MOAtox) database. Of the 3448 MOAs predicted using the Verhaar scheme, 1165 were classified by ASTER, and 802 were available in MOAtox. Of the subset of 432 chemicals with MOA assignments for each of the three schemes, 42% had complete concordance in MOA classification, and there was no agreement for 7% of the chemicals. The research shows the potential for large differences in MOA classification between the five broad groups of the Verhaar scheme and the more mechanism-based assignments of ASTER and MOAtox. Harmonization of classification schemes is needed to use MOA classification in chemical hazard and risk assessment more broadly.

Toxicity of Quaternary Ammonium Compounds (QACs) as single compounds and mixtures to aquatic non-target microorganisms: Experimental data and predictive models

The toxic effects of five Quaternary Ammonium Compounds (QACs) that are widely used as active ingredients in personal care products were assessed using the bioluminescent bacterium Aliivibrio fischeri (formerly Vibrio fischeri) (Microtox® test system). The experimental results showed a relevant toxicity for almost all of the single QACs, with IC₅₀ values lower than 1mgL^-1. Analysis of the mode of action through the application of the Quantitative Structure-Activity Relationship (QSAR) models indicated an a-specific reactivity for most of the QACs toward A. fischeri. Only hexadecyl trimethyl ammonium chloride (ATMAC-16) behaved as a polar-narcotic, with a low reactivity toward the bacterial cell membrane. The concentration response curves of the different binary and multicomponent mixtures of QACs were also evaluated with respect to the predictions from the Concentration Addition (CA) and Independent Action (IA) models. For almost all of the binary and multicomponent mixtures (7 out of 11 mixtures tested), an agreement between the experimental and predicted IC_x was observed and confirmed via application of the Model Deviation Ratio (MDR). In four cases, some deviations from the expected behaviour were observed (potential antagonistic and synergistic interactions) at concentrations on the order of hundreds of µgL^-1, which could be of environmental concern, especially in the case of synergistic effects. The analysis of aquatic ecotoxicity data and the few available values of the measured environmental concentrations (MECs) from the literature for wastewaters and receiving waterbodies suggest that a potential risk toward aquatic life cannot be excluded.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework.

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.

(Q)SARs to predict environmental toxicities: current status and future needs

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test

Fish embryos have become a popular model in ecotoxicology and toxicology. The fish embryo acute toxicity test (FET) with the zebrafish embryo was recently adopted by the OECD as technical guideline TG 236 and a large database of concentrations causing 50% lethality (LC₅₀) is available in the literature. Quantitative Structure-Activity Relationships (QSARs) of baseline toxicity (also called narcosis) are helpful to estimate the minimum toxicity of chemicals to be tested and to identify excess toxicity in existing data sets. Here, we analyzed an existing fish embryo toxicity database and established a QSAR for fish embryo LC₅₀ using chemicals that were independently classified to act according to the non-specific mode of action of baseline toxicity. The octanol-water partition coefficient K_ow is commonly applied to discriminate between non-polar and polar narcotics. Replacing the K_ow by the liposome-water partition coefficient K_lipw yielded a common QSAR for polar and non-polar baseline toxicants. This developed baseline toxicity QSAR was applied to compare the final mode of action (MOA) assignment of 132 chemicals. Further, we included the analysis of internal lethal concentration (ILC₅₀) and chemical activity (La₅₀) as complementary approaches to evaluate the robustness of the FET baseline toxicity. The analysis of the FET dataset revealed that specifically acting and reactive chemicals converged towards the baseline toxicity QSAR with increasing hydrophobicity. The developed FET baseline toxicity QSAR can be used to identify specifically acting or reactive compounds by determination of the toxic ratio and in combination with appropriate endpoints to infer the MOA for chemicals.

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by identifying a subset of the key chemical descriptors associated with broad aquatic toxicity MoAs, and by providing a computational chemistry-based network classification model with reasonable prediction accuracy.

Use of acute and chronic ecotoxicity data in environmental risk assessment of pharmaceuticals

For many older pharmaceuticals, chronic aquatic toxicity data are limited. To assess risk during development, scale-up, and manufacturing processes, acute data and physicochemical properties need to be leveraged to reduce potential long-term impacts to the environment. Aquatic toxicity data were pooled from daphnid, fish, and algae studies for 102 active pharmaceutical ingredients (APIs) to evaluate the relationship between predicted no-effect concentrations (PNECs) derived from acute and chronic tests. The relationships between acute and chronic aquatic toxicity and the n-octanol/water distribution coefficient (D(OW)) were also characterized. Statistically significant but weak correlations were observed between toxicity and log D(OW), indicating that D(OW) is not the only contributor to toxicity. Both acute and chronic PNEC values could be calculated for 60 of the 102 APIs. For most compounds, PNECs derived from acute data were lower than PNECs derived from chronic data, with the exception of steroid estrogens. Seven percent of the PNECs derived from acute data were below the European Union action limit of 0.01 μg/L and all were anti-infectives affecting algal species. Eight percent of available PNECs derived from chronic data were below the European Union action limit, and fish were the most sensitive species for all but 1 API. These analyses suggest that the use of acute data may be acceptable if chronic data are unavailable, unless specific mode of action concerns suggest otherwise.

Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties

We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities against Pseudokirchneriella subcapitata. In Step 1, using a log D-based criterion and structural alerts, we produced an interspecies QSAR between algal and acute daphnid toxicities for initial screening of chemicals. In Step 2, we categorized chemicals according to the Verhaar scheme for aquatic toxicity, and we developed QSARs for toxicities of Class 1 (non-polar narcotic) and Class 2 (polar narcotic) chemicals by means of simple regression with a hydrophobicity descriptor and multiple regression with a hydrophobicity descriptor and a quantum chemical descriptor. Using the algal toxicities of the Class 1 chemicals, we proposed a baseline QSAR for calculating their excess toxicities. In Step 3, we used structural profiles to predict toxicity either quantitatively or qualitatively and to assign chemicals to the following categories: Pesticide, Reactive, Toxic, Toxic low and Uncategorized. Although this three-step strategy cannot be used to estimate the algal toxicities of all chemicals, it is useful for chemicals within its domain. The strategy is also applicable as a component of Integrated Approaches to Testing and Assessment.

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.

Application of the Activity Framework for Assessing Aquatic Ecotoxicology Data for Organic Chemicals

Toxicological research in the 1930s gave the first indications of the link between narcotic toxicity and the chemical activity of organic chemicals. More recently, chemical activity has been proposed as a novel exposure parameter that describes the fraction of saturation and that quantifies the potential for partitioning and diffusive uptake. In the present study, more than 2000 acute and chronic algal, aquatic invertebrates and fish toxicity data, as well as water solubility and melting point values, were collected from a series of sources. The data were critically reviewed and grouped by mode of action (MoA). We considered 660 toxicity data to be of acceptable quality. The 328 data which applied to the 72 substances identified as MoA 1 were then evaluated within the activity-toxicity framework: EC50 and LC50 values for all three taxa correlated generally well with (subcooled) liquid solubilities. Acute toxicity was typically exerted within the chemical activity range of 0.01-0.1, whereas chronic toxicity was exerted in the range of 0.001-0.01. These results confirm that chemical activity has the potential to contribute to the determination, interpretation and prediction of toxicity to aquatic organisms. It also has the potential to enhance regulation of organic chemicals by linking results from laboratory tests, monitoring and modeling programs. The framework can provide an additional line of evidence for assessing aquatic toxicity, for improving the design of toxicity tests, reducing animal usage and addressing chemical mixtures.

European Chemicals Agency dossier submissions as an experimental data source: refinement of a fish toxicity model for predicting acute LC50 values

As a result of the stringent data requirements of the Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) regulation, a vast amount of ecotoxicological data has become available through the dissemination portal of the European Chemicals Agency (ECHA). As of April 2014, the database contained 12,439 unique substances from 47,909 dossiers. This vast database could be used to refine existing, or to create new, non-testing methods, such as quantitative structure-activity relationships (QSARs). Acute fish toxicity data were mined from the ECHA database using the eChemPortal; after filtering for single organic substances, 1159 experimental data points remained, representing 564 compounds. To evaluate the quality and accessibility of this data, the authors used the data to refine and improve an existing QSAR. The reliability of the data submitted to the ECHA database, as well as the effectiveness of the Klimisch scoring system, were assessed by comparing the refined QSAR with established QSAR benchmarks. The model developed meets all Organisation for Economic Co-operation and Development principles, has strong internal (leave-one-out internally cross-validated correlation coefficient [Q(2)(LOO)] = 0.91) and external (external coefficient of determination (predicted vs experimental [test set])) validation statistics, and can provide reliable fish median lethal concentration (LC50) predictions for non-polar narcotics. Although some issues with dossier misinformation were discovered, it was found that the ECHA dissemination portal is a valuable and reliable data source. When queried using the eChemPortal, chemical dossiers containing reliable data could be found quickly. The ECHA dissemination portal holds great potential for future QSAR development and improvement, such as updating QSARs within the Ecological Structure-Activity Relationships (ECOSAR) program.

Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across

Creating suitable chemical categories and developing read-across methods, supported by quantum mechanical calculations, can be an effective solution to solving key problems related to current scarcity of data on the toxicity of various nanoparticles. This study has demonstrated that by applying a nano-read-across, the cytotoxicity of nano-sized metal oxides could be estimated with a similar level of accuracy as provided by quantitative structure-activity relationship for nanomaterials (nano-QSAR model(s)). The method presented is a suitable computational tool for the preliminary hazard assessment of nanomaterials. It also could be used for the identification of nanomaterials that may pose potential negative impact to human health and the environment. Such approaches are especially necessary when there is paucity of relevant and reliable data points to develop and validate nano-QSAR models.

The discrimination of excess toxicity from baseline effect: effect of bioconcentration

Toxic ratio TR is a valuable tool in the discrimination of excess toxicity from baseline effect. Although some authors realized that internal effect concentration or critical body residual (CBR) calculated from bioconcentration factor (BCF) should be used in the TR, the effect of BCF on the discrimination of excess toxicity from baseline effect has not been investigated. In this paper, 951 acute toxicity data to fish (LC50) and 1088 BCFs were used to investigate the relationship between TR and BCF. The results showed that some compounds identified as reactive compounds exhibit excess toxicity, but some do not. BCF is closely related to TR and can significantly affect the TR value. The real excess toxicity which is used to identify reactive chemicals from baseline should be based on the toxic ratio of internal effect concentrations, rather than on the ratio of external effect concentrations, TR. The use of LC50 alone to determine TR can result in errors in TR because toxicokinetics (as estimated by the BCF) are ignored. The foundation in the discrimination of excess toxicity from baseline effect is based on the linear relationship between log BCF and hydrophobicity expressed as log KOW. However, log BCF is not linearly related with log KOW for all the compounds. The BCFs with log KOW >7 or <0 are either overestimated or underestimated by the linear baseline BCF model. Parallel lines are observed from calculated log CBR values for baseline and less inert compounds. The log BCF values overestimated or underestimated by log KOW from the baseline BCF model can result in mis-prediction and mis-classification among baseline, less inert and reactive compounds.

Methods for assigning confidence to toxicity data with multiple values--Identifying experimental outliers

The assessment of data quality is a crucial element in many disciplines such as predictive toxicology and risk assessment. Currently, the reliability of toxicity data is assessed on the basis of testing information alone (adherence to Good Laboratory Practice (GLP), detailed testing protocols, etc.). Common practice is to take one toxicity data point per compound - usually the one with the apparently highest reliability. All other toxicity data points (for the same experiment and compound) from other sources are neglected. To show the benefits of incorporating the "less reliable" data, a simple, independent, statistical approach to assess data quality and reliability on a mathematical basis was developed. A large data set of toxicity values to Aliivibrio fischeri was assessed. The data set contained 1813 data points for 1227 different compounds, including 203 identified as non-polar narcotic. Log KOW values were calculated and non-polar narcosis quantitative structure-activity relationship (QSAR) models were built. A statistical approach to data quality assessment, which is based on data outlier omission and confidence scoring, improved the linear QSARs. The results indicate that a beneficial method for using large data sets containing multiple data values per compound and highly variable study data has been developed. Furthermore this statistical approach can help to develop novel QSARs and support risk assessment by obtaining more reliable values for biological endpoints.

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

Functional similarity of chemicals combines toxicological knowledge (which toxicity pathways can happen in which species under which exposure conditions) with chemical expertise (which parts of the chemical structures and physico-chemical properties are involved in which interactions) to discriminate between baseline and excess toxicants. The objective is to identify as many baseline toxicants as possible because their acute fish toxicities can be predicted with sufficient accuracy from their log Kow. Established tools like structural alerts are used to indicate modes of action (MOAs) that are typical causes of excess toxicity. Verhaar classifications are supplemented with additional chemical attributes and physico-chemical property thresholds to cover a larger range of compounds within the baseline toxicity domain. Our approach is precautionary to avoid false negatives with a sensitivity of 96.3%. It classifies 57.1% of the compounds of the EPA Fathead Minnow Acute Toxicity Database (EPAFHM) as baseline toxicants and suggests that more than 50% of acute fish toxicity testing could be replaced by reliable QSAR predictions. Furthermore, functional similarity can support the MOA classification of chemicals in different species. Toxicity profiles with fish, Daphnia and algae reveal specific targets for the compounds and, particularly for chemicals with multiple MOA, identify the most sensitive species.

QSARs for aquatic toxicity: celebrating, extending and displaying the pioneering contributions of Ferguson, Konemann and Veith

Significant advances were made in the development of quantitative structure-activity relationships (QSARs) relating molecular structure to aquatic toxicity by three studies over 30 years ago by Ferguson in 1939, Konemann in 1981, and Veith and colleagues in 1983. We revisit the original concepts and data from these studies and review these contributions from the bases of current perspectives on the hypothesized mechanism of baseline narcotic toxicity and the underlying thermodynamic and kinetic aspects. The relationships between LC50, octanol-water partition coefficient, aqueous solubility, chemical activity and chemical volume fraction in lipid phases are outlined including kinetic influences on measured toxicities. These relationships provide a compelling and plausible explanation of the success of these and other QSARs for aquatic toxicity. Suggestions are made for further advances in these QSARs to improve assessments of toxicity by baseline narcotic toxicity and selective modes of action, especially using emerging quantum chemical computational capabilities.

In Silico Prediction of Tyrosinase and Adenylyl Cyclase Inhibitors from Natural Compounds

Although many herbal medicines are effective in the treatment of hyperpigmentation, the potency of different constituents remains unknown. In this work, more than 20,000 herbal ingredients from 453 herbs were docked into the crystal structures of adenylyl cyclase and a human homology tyrosinase model using Surflex-Dock. These two enzymes are responsible for melanin production and inhibition of them may attain a skin-whitening effect superior to currently available agents. The essential drug properties for topical formulation of the herbal ingredients, including skin permeability, sensitization, irritation, corrosive and carcinogenic properties were predicted by Dermwin, Skin Sensitization Alerts (SSA), Skin Irritation Corrosion Rules Estimation Tool (SICRET) and Benigni/Bossa rulebase module of Toxtree. Moreover, similarity ensemble and pharmacophore mapping approaches were used to forecast other potential targets for these herbal compounds by the software, SEArch and PharmMapper. Overall, this study predicted seven compounds to have advanced drug-like properties over the well-known effective tyrosinase inhibitors, arbutin and kojic acid. These seven compounds have the highest potential for further in vitro and in vivo investigation with the aim of developing safe and high-efficacy skin-whitening agents.

Development of a chronic fish toxicity model for predicting sub-lethal NOEC values for non-polar narcotics

To comply with the REACH (Registration, Evaluation, Authorisation and restriction of Chemicals) regulations, the generation of chronic fish toxicity data is required for chemicals produced or imported within or into the EU in quantities greater than 100 tonnes per year. This comes at a great cost to industry and consumers alike and requires the sacrifice of many vertebrates. In acknowledgment of these issues the REACH regulations encourage the use of non-testing methods (NTM). These include read-across, weight-of-evidence and QSAR (quantitative structure-activity relationship) techniques. There are many QSAR tools available to generate predictive values for a number of physico-chemical properties, as well as human and environmental health end points; however, close analysis of the currently available chronic fish models identified room for improvement in both the selection of data used and in its application in model creation. In light of this a model was developed using only sub-lethal no-observed-effect concentration (NOEC) end-point data according to best practice QSAR development. Only the lowest value was taken for each compound, in line with the conservative approach taken by the European Chemicals Agency (ECHA). The model developed meets the Organisation for Economic Co-operation and Development (OECD) principles, has strong internal and external validation statistics, and can reliably predict sub-lethal chronic NOEC values for fish within its defined applicability domain.

Development and validation of a quantitative structure-activity relationship for chronic narcosis to fish

Vertebrate testing under the European Union's regulation on Registration, Evaluation, Authorisation and Restriction of Chemical substances (REACH) is discouraged, and the use of alternative nontesting approaches such as quantitative structure-activity relationships (QSARs) is encouraged. However, robust QSARs predicting chronic ecotoxicity of organic compounds to fish are not available. The Ecological Structure Activity Relationships (ECOSAR) Class Program is a computerized predictive system that estimates the acute and chronic toxicity of organic compounds for several chemical classes based on their log octanol-water partition coefficient (K(OW)). For those chemical classes for which chronic training data sets are lacking, acute to chronic ratios are used to predict chronic toxicity to aquatic organisms. Although ECOSAR reaches a high score against the Organisation for Economic Co-operation and Development (OECD) principles for QSAR validation, the chronic QSARs in ECOSAR are not fully compliant with OECD criteria in the framework of REACH or CLP (classification, labeling, and packaging) regulation. The objective of the present study was to develop a chronic ecotoxicity QSAR for fish for compounds acting via nonpolar and polar narcosis. These QSARs were built using a database of quality screened toxicity values, considering only chronic exposure durations and relevant end points. After statistical multivariate diagnostic analysis, literature-based, mechanistically relevant descriptors were selected to develop a multivariate regression model. Finally, these QSARs were tested for their acceptance for regulatory purposes and were found to be compliant with the OECD principles for the validation of a QSAR.