Discrimination of excess toxicity from narcotic effect: influence of species sensitivity and bioconcentration on the classification of modes of action

Aquatic toxicity, bioaccumulation potential, and human estrogen/androgen activity of three oxo-Liquid Organic Hydrogen Carrier (oxo-LOHC) systems

The Liquid Organic Hydrogen Carrier (LOHC) technology offers a technically attractive way for hydrogen storage. If LOHC systems were to fully replace liquid fossil fuels, they would need to be handled at the multi-million tonne scale. To date, LOHC systems on the market based on toluene or benzyltoluene still offer potential for improvements. Thus, it is of great interest to investigate potential LOHCs that promise better performance and environmental/human hazard profiles. In this context, we investigated the acute aquatic toxicity of oxygen-containing LOHC (oxo-LOHC) systems. Toxic Ratio (TR) values of oxo-LOHC compounds classify them baseline toxicants (0.1 < TR < 10). Additionally, the mixture toxicity test conducted with D. magna suggests that the overall toxicity of a benzophenone-based system can be accurately predicted using a concentration addition model. The estimation of bioconcentration factors (BCF) through the use of the membrane-water partition coefficient indicates that oxo-LOHCs are unlikely to be bioaccumulative (BCF < 2000). None of the oxo-LOHC compounds exhibited hormonal disrupting activities at the tested concentration of 2 mg/L in yeast-based reporter gene assays. Therefore, the oxo-LOHC systems seem to pose a low level of hazard and deserve more attention in ongoing studies searching for the best hydrogen storage technologies.

A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction

With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this study, five graph models (message-passing neural network, graph convolution network, graph attention network, path-augmented graph transformer network, and Attentive FP) were applied on four toxicity tasks (fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri). With the lowest prediction error, Attentive FP was reported to have the best performance in all four tasks. Moreover, the attention weights of the Attentive FP model helped to construct atomic heatmaps and provide good explainability.

Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri

For the first time, a global regression quantitative structure-toxicity/activity relationship (QSTR/QSAR) model was developed for the toxicity of a large data set including 1236 chemicals towards Vibrio fischeri, by using random forest (RF) regression algorithm. The optimal RF model with RF parameters of mtry = 3, ntree = 150 and nodesize = 5 was based on 13 molecular descriptors. It can achieve accurate prediction for the toxicity of 99.1% of 1236 chemicals, and yield coefficients of determination R² of 0.893 for 930 log(Mw/IBC50) in the training set, 0.723 for 306 log(Mw/IBC50) in the test se, and 0.865 for 1236 toxicity log(Mw/IBC50) in the total set. The optimal RF global model proposed in this work is comparable to other published local QSTR models on small datasets of the toxicity to Vibrio fischeri.

Assessment of toxic mechanisms and mode of action to three different levels of species for 14 antibiotics based on interspecies correlation, excess toxicity, and QSAR

Antibiotics have received much attention owing to their ecotoxicity toward nontarget aquatic creatures. However, the mode of action (MOA) of toxicity against nontarget organisms is unclear in some aquatic organisms. In this study, the comparison of toxicities through interspecies correlations, excess toxicity calculated from toxicity ratio, and quantitative structure-activity relationship (QSAR) was carried out to investigate the MOAs for 14 antibiotics among Daphnia magna, Vibrio fischeri, and Pseudokirchneriella subcapitata. The results showed that interspecies toxicity correlations were very poor between any two of the three species for the 14 antibiotics. The toxicity ratio revealed that most antibiotics exhibited excess toxicity to algae and Daphnia magna but not to V. fischeri, demonstrating that some antibiotics share the same MOA, but some antibiotics share different MOAs among the three different levels of species. P. subcapitata was the most sensitive species, and V. fischeri was the least sensitive species. This is because of the differences in the biouptake and interactions of antibiotics with the target receptors between the three different trophic levels of the species. Molecular docking simulations suggested that the toxicity of antibiotics depends highly on their interactions with target receptors through hydrogen bonds, electrostatic or polar interactions, π bond interactions, and van der Waals forces. QSAR models demonstrated that hydrogen bonding and electrophilicity/nucleophilicity play key roles in the interaction of antibiotics with different receptors in the three species. The toxic mechanisms of antibiotics are attributed to the interactions between electrophilic antibiotics and biological nucleophiles, and hydrogen-bond interactions. These results are valuable for understanding the toxic mechanisms and MOA of the three different levels of species.

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance.

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.

Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach

For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (R_train² = 0.64, RMSE_train = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (R_test²,R_m², Q_F1², Q_F2², Q_F3² and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.

Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action

In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.

Identification of active and inactive agonists/antagonists of estrogen receptor based on Tox21 10K compound library: Binomial analysis and structure alert

Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.

Evaluation of modes of action of pesticides to Daphnia magna based on QSAR, excess toxicity and critical body residues

Agricultural pesticides serve as effective controls of unwanted weeds and pests. However, these same chemicals can exert toxic effects in non-target organisms. To determine chemical modes of action, the toxicity ratio (TR) and critical body residues (CBRs) of 57 pesticides were calculated for Daphnia magna. Results showed that the CBR values of inert compounds were close to a constant while the CBR values of pesticides varied over a wider range. Although herbicides are categorized as specifically-acting compounds to plants, herbicides did not exhibit excess toxicity to Daphnia magna and were categorized as inert compounds with an average logTR = 0.41, which was less than a threshold of one. Conversely, fungicides and insecticides exhibited strong potential for toxic effects to Daphnia magna with an average logTR >2. Many of these chemicals act via disruption of the nervous, respiratory, or reproductive system, with high ligand-receptor binding activity which leads to higher toxicity for Daphnia magna. Molecular docking using acetylcholinesterase revealed that fungicides and insecticides bind more easily with the biological macromolecule when compared with inert compounds. Quantitative structure-activity relationship (QSAR) analysis revealed that the toxicity of fungicides was mainly dependent upon the heat of formation and polar surface area, while the toxicity of insecticides was more related to hydrogen-bond properties. This comprehensive analysis reveals that there are specific differences in toxic mechanisms between fungicides and insecticides. These results are useful for determining relative risk associated with pesticide exposure to aquatic crustaceans, such as Daphnia magna.

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

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.

Comparison of modes of action among different trophic levels of aquatic organisms for pesticides and medications based on interspecies correlations and excess toxicity: Theoretical consideration

Pesticides and medications have adverse effects in non-target organisms that can lead to different modes of action (MOAs). However, no study has been performed to compare the MOAs between different levels of aquatic species. In this study, theoretical equations of interspecies relationship and excess toxicity have been developed and used to investigate the MOAs among fish, Daphnia magna, Tetrahymena pyriformis and Vibrio fischeri for pesticides and medications. The analysis on the interspecies correlation and excess toxicity suggested that fungicides, herbicides and medications share the similar MOAs among the four species. On the other hand, insecticides share different MOAs among the four species. Exclusion of insecticides from the interspecies correlation can significantly improve regression coefficient. Interspecies relationship is dependent not only on the difference in interaction of chemicals with the target receptor(s), but also on the difference in bio-uptake between two species. The difference in physiological structures will result in the difference in bioconcentration potential between two different trophic levels of organisms. Increasing of molecular size or hydrophobicity will increase the toxicity to higher level of aquatic organisms; on the other hand, chemical ionization will decrease the toxicity to higher level organisms. Hydrophilic compounds can more easily pass through cell membrane than skin or gill, leading to greater excess toxicity to Vibrio fischeri, but not to fish and Daphnia magna.

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.

How in silico and QSAR approaches can increase confidence in environmental hazard and risk assessment

In silico methods are typically underrated in the current risk assessment paradigm, as evidenced by the recent document from the European Chemicals Agency (ECHA) on animal alternatives, in which quantitative structure-activity relationships (QSARs) were practically used only as a last resort. Their primary use is still to provide supporting evidence for read-across strategies or to add credence to experimental results of unknown or limited validity (old studies, studies without good laboratory practices [GLPs], limited information reported, etc.) in hazard assessment, but under the pressure of increasing burdens of testing, industry and regulators alike are at last warming to them. Nevertheless, their true potential for data-gap filling and for resolving sticking points in risk assessment methodology and beyond has yet to be recognized. We postulate that it is possible to go beyond the level of simply increasing confidence to the point of using in silico approaches to accurately predict results that cannot be resolved analytically. For example, under certain conditions it is possible to obtain meaningful results by in silico extrapolation for tests that would be technically impossible to conduct in the laboratory or at least extremely challenging to obtain reliable results. The following and other concepts are explored in this article: the mechanism of action (MechoA) of the substance should be determined, as an aid verifying that the QSAR model is applicable to the substance under review; accurate QSARs should be built with high-quality data that were not only curated but also validated with expert judgment; although a rule of thumb for acute to chronic ratios appears applicable for nonpolar narcotics, it seems unlikely that a "one-value-fits-all" answer exists for other MechoAs; a holistic approach to QSARs can be employed (via reverse engineering) to help validate or invalidate an experimental endpoint value on the basis of multiple experimental studies. Integr Environ Assess Manag 2019;15:40-50. © 2018 SETAC.

Comparison of modes of action between fish and zebrafish embryo toxicity for baseline, less inert, reactive and specifically-acting compounds

The mode of action (MOA) plays a key role in the risk assessment of pollutants in water. Although fish is a key model organism used in the risk assessment of pollutants in water, the MOAs have not been compared between fish and embryo toxicity for classified compounds. In this paper, regression analysis was carried out for fish and embryo toxicities against the calculated molecular descriptors and MOAs were evaluated from toxicity ratio. The toxicity significantly related with the chemical hydrophobicity for baseline and less inert compounds, respectively, indicates that these two classes of compounds share the same MOAs between fish and embryos. Comparison of the toxicity ratios shows that reactive compounds exhibit excess toxicity to both fish and embryos. These compounds can react covalently with biologically target molecules through nucleophilic addition reactions, Michael addition oxidation, or amination. Comparing with baseline, less inert and reactive compounds, many specifically-acting compounds have strong docking capacity with protein molecules. Some specifically-acting compounds, such as fungicides, have very similar toxic effect to both fish and embryos. However, insecticides are more toxic to fish than embryos; herbicides and medications are more toxic to embryos than fish. Differences in the interactions of chemicals with target molecules or bioconcentration potentials between fish and embryos may result in the differences in toxic effects. There are some factors that influence the identification of MOAs, such as quality of toxicity data, bioavailability and ionization. These factors should be considered in the identification of MOAs in the risk assessment of organic pollutants.

Development of thresholds of excess toxicity for environmental species and their application to identification of modes of acute toxic action

The acute toxicity of organic pollutants to fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri was investigated. The results indicated that the Toxicity Ratio (TR) threshold of log TR =1, which has been based on the distribution of toxicity data to fish, can also be used to discriminate reactive or specifically acting compounds from baseline narcotics for Daphnia magna and Vibrio fischeri. A log TR=0.84 is proposed for Tetrahymena pyriformis following investigation of the relationships between the species sensitivity and the absolute averaged residuals (AAR) between the predicted baseline toxicity and the experimental toxicity. Less inert compounds exhibit relatively higher toxicity to the lower species (Tetrahymena pyriformis and Vibrio fischeri) than the higher species (fish and Daphnia magna). A greater number of less inert compounds with log TR greater than the thresholds was observed for Tetrahymena pyriformis and Vibrio fischeri. This may be attributed to the hydrophilic compounds which may pass more easily through cell membranes than the skin or exoskeleton of organisms and have higher bioconcentration factors in the lower species, leading to higher toxicity. Most of classes of chemical associated with excess toxicity to one species also exhibited excess toxicity to other species, however, a few classes with excess toxicity to one species exhibiting narcotic toxicity to other species and thus may have different MOAs between species. Some ionizable compounds have log TR much lower than one because of the over-estimated log K_OW. The factors that influence the toxicity ratio calculated from baseline level are discussed in this paper.

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.

Relationship between acute and chronic toxicity for prevalent organic pollutants in Vibrio fischeri based upon chemical mode of action

Chemicals show diverse modes of action (MOAs) in aquatic organisms depending upon acute and chronic toxicity evaluations. Here, toxicity data for Vibrio fischeri involving 52 compounds for acute and chronic toxicity were used to determine the congruence of acute and chronic toxicity for assessing MOAs. Using toxic ratios, most of the compounds categorized into MOAs that included baseline, less inert or reactive compounds with acute toxicity were also categorized as baseline, less inert or reactive compounds with chronic toxicity. However, significantly different toxic effects were observed with acute and chronic toxicity for the reactive and specific-acting compounds. The acute-chronic toxic ratios were smaller and less variable for the baseline and less inert compounds, but were greater and more variable for the reactive and specific-acting compounds. Baseline and less inert compounds share same MOAs, but reactive and specific-acting compounds have different MOAs between acute and chronic toxicity. Bioconcentration processes cannot reach an equilibrium for highly hydrophilic and ionized compounds with short-term exposure, resulting in lower toxicity compared to long-term exposure. Pronounced differences for the antibiotics were not only due to the difference in bioconcentration, but also due to a predicted difference in MOAs during acute and chronic exposures.

Baseline toxicity and ion-trapping models to describe the pH-dependence of bacterial toxicity of pharmaceuticals

In numerous studies on the toxicity of ionisable organic chemicals, it has been shown that the toxicity was typically higher, when larger fractions of the neutral species were present. This observation was explained in some cases by slower uptake of charged species. In other cases it was suggested that the neutral species has intrinsically higher toxicity than the charged species or is alone responsible for the toxicity. However, even permanently charged and organic chemicals with multiple acid and base functional groups and zwitterions are toxic. We set out to reconcile the divergent views and to compare the various existing models for describing the pH-dependence of toxicity with the goal to derive one model that is valid independent of the type and number of charges on the molecule. To achieve this goal we measured the cytotoxicity of 18 acidic, 15 basic and 9 multiprotic/zwitterionic pharmaceuticals at pH 5.5 to pH 9 with the bioluminescence inhibition test using Aliivibrio fischeri (Microtox assay). This assay is useful for an evaluation of various models to describe pH-dependent toxicity because the majority of chemicals act as baseline toxicants in this 30 min cytotoxicity assay. Therefore baseline toxicity with constant membrane concentrations of the sum of all chemical species of approximately 200 mmol kg_lip^-1 served for the validation of the suitability of the various tested models. We confirmed that most tested pharmaceuticals acted as baseline toxicants in this assay at all examined pH values, when toxicity was modeled with a mixture model of concentration addition between the neutral species and all charged species. An ion trapping model, that assumes that the membrane permeability of charged species is kinetically limited, improved model predictions for some pharmaceuticals and pH values. However, neither unhindered uptake nor no uptake of the charged species were ideal models; the reality lies presumably between the two limiting cases with a slower uptake of the charged species than the neutral species. For practical applications a previously developed QSAR model with the ionisation-corrected liposome-water distribution ratio as the sole physicochemical descriptor proved to be generally applicable for all ionisable organic chemicals including those with multiple charges and zwitterions.

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.

Comparison of Toxicities to Vibrio fischeri and Fish Based on Discrimination of Excess Toxicity from Baseline Level

Investigations on the relationship of toxicities between species play an important role in the understanding of toxic mechanisms to environmental organisms. In this paper, the toxicity data of 949 chemicals to fish and 1470 chemicals to V. fischeri were used to investigate the modes of action (MOAs) between species. The results show that although there is a positive interspecies correlation, the relationship is poor. Analysis on the excess toxicity calculated from toxic ratios (TR) shows that many chemicals have close toxicities and share the same MOAs between the two species. Linear relationships between the toxicities and octanol/water partition coefficient (log KOW) for baseline and less inert compounds indicate that the internal critical concentrations (CBRs) approach a constant both to fish and V. fischeri for neutral hydrophobic compounds. These compounds share the same toxic mechanisms and bio-uptake processes between species. On the other hand, some hydrophilic compounds exhibit different toxic effects with greatly different log TR values between V. fischeri and fish species. These hydrophilic compounds were identified as reactive MOAs to V. fischeri, but not to fish. The interspecies correlation is improved by adding a hydrophobic descriptor into the correlation equation. This indicates that the differences in the toxic ratios between fish and V. fischeri for these hydrophilic compounds can be partly attributed to the differences of bioconcentration between the two species, rather than the differences of reactivity with the target macromolecules. These hydrophilic compounds may more easily pass through the cell membrane of V. fischeri than the gill and skin of fish, react with the target macromolecules and exhibit excess toxicity. The compounds with log KOW > 7 exhibiting very low toxicity (log TR < -1) to both species indicate that the bioconcentration potential of a chemical plays a very important role in the identification of excess toxicity and MOAs.

Comparison of toxicity of class-based organic chemicals to algae and fish based on discrimination of excess toxicity from baseline level

Toxicity data to fish and algae were used to investigate excess toxicity between species. Results show that chemicals exhibiting excess toxicity to fish also show excess toxicity to algae for most of the compounds. This indicates that they share the same mode of action between species. Similar relationships between logKOW and toxicities to fish and algae for baseline and less inert compounds suggest that they have similar critical body residues in the two species. Differences in excess toxicity for some compounds suggest that there is a difference of physiological structure and metabolism between fish and algae. Some reactive compounds (e.g. polyamines) exhibit greater toxic effects for algae than those for fish because of relatively low bio-uptake potential of these hydrophilic compounds in fish as compared with that in algae. Esters exhibiting greater toxicity in fish than that in algae indicate that metabolism can affect the discrimination of excess toxicity from baseline level. Algae growth inhibition is a very good surrogate for fish lethality. This is not only because overall toxicity sensitivity to algae is greater than that to fish, but also the excess toxicity calculated from algal toxicity can better reflect reactivity of compounds with target molecules than fish toxicity.