Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds

Investigation on toxicity mechanism of halogenated aromatic disinfection by-products to zebrafish based on molecular docking and QSAR model

Halogenated aromatic disinfection by-products (DBPs) are a new type of DBPs that have been detected in various water bodies. Previous studies have shown that most of them can induce in vivo toxicity in aquatic organisms. In this study, in order to further investigate the toxic effects and mechanisms of aromatic DBPs, the toxicity and ecological risks of 10 halogenated aromatic DBPs were assessed using the model organism zebrafish. It was found that the toxicity of DBPs was related to the number, type, and position of halogen and the type of substituent, and the 24 h-toxicity value of DBPs in this experiment could replace their 96 h-toxicity value to reduce the test time and save the test cost. Halogenated phenol and halogenated nitrophenol were more toxic, but the current ecological risks of DBPs were relatively low. In addition, the toxicity mechanism of DBPs was analyzed based on molecular docking and quantitative structure-activity relationship (QSAR) models. The molecular docking results showed that all 10 DBPs could bind to zebrafish's catalase (CAT), cytochrome P450 (CYP450), p53, and acetylcholinesterase (AChE), thereby affecting their normal life activities. QSAR models indicated that the toxicity of halogenated aromatic DBPs to zebrafish mainly depended on their hydrophobicity (log D), the interaction with CAT (ECAT), and hydrogen bonding acidity (A).

Assessment of the Effects of Chitosan, Chitooligosaccharides and Their Derivatives on Lemna minor

Chitosan, chitooligosaccharides and their derivatives’ production and use in many fields may result in their release to the environment, possibly affecting aquatic organisms. Both an experimental and a computational approach were considered for evaluating the effects of these compounds on Lemna minor. Based on the determined EC50 values against L. minor, only D-glucosamine hydrochloride (EC50 = 11.55 mg/L) was considered as “slightly toxic” for aquatic environments, while all the other investigated compounds, having EC50 > 100 mg/L, were considered as “practically non-toxic”. The results obtained in the experimental approach were in good agreement with the predictions obtained using the admetSAR2.0 computational tool, revealing that the investigated compounds were not considered toxic for crustacean, fish and Tetrahymena pyriformis aquatic microorganisms. The ADMETLab2.0 computational tool predicted the values of IGC50 for Tetrahymena pyriformis and the LC50 for fathead minnow and Daphnia magna, with the lowest values of these parameters being revealed by totally acetylated chitooligosaccharides in correlation with their lowest solubility. The effects of the chitooligosaccharides and chitosan on L. minor decreased with increased molecular weight, increased with the degree of deacetylation and were reliant on acetylation patterns. Furthermore, the solubility mainly influenced the effects on the aqueous environment, with a higher solubility conducted to lower toxicity.

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.

Aquatic toxicity and aquatic ecological risk assessment of wastewater-derived halogenated phenolic disinfection byproducts

Increasing number of wastewater-derived aliphatic and phenolic disinfection byproducts (DBPs) were discharged into aquatic environment with the discharge of disinfected wastewater. However, the currently available aquatic toxicity data and the aquatic ecological risk information of them are limited, especially for wastewater-derived phenolic DBPs. In this study, we investigated the acute toxicity of 7 phenolic DBPs that selected from the typical five groups of phenolic DBPs (2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids and halo-salicylic acids) and 4 aliphatic DBPs to Gobiocypris rarus and also assessed their potential aquatic ecological risk. Experimental results indicated that the half lethal concentration (LC₅₀) values of 2,4,6-trihalo-phenols and 2,6-dihalo-4-nitrophenols ranged from 1 to 10 mg/L; While that of 3,5-dihalo-4-hydroxybenzaldehydes was between 10 and 100 mg/L, and 3,5-dihalo-4-hydroxybenzoic acids and halo-salicylic acids was >100 mg/L. The toxicity mode of action (MOA) identification results from three methods suggested that no clear and consistent MOA were obtained for those 11 DBPs currently. The species-specific aquatic toxicity analysis results highlighted that no aquatic species would be considered as the most sensitive species for all 11 DBPs. However, crustacean and fish were more sensitive than that of algae for most of tested compounds. Lastly, the aquatic ecological risk assessment results of those 11 DBPs revealed that all 7 phenolic and 2 aliphatic DBPs (2-bromoacetamide and bromodichloromethane) had low aquatic ecological risk, while dichloroacetic acid and dibromoacetonitrile had high aquatic ecological risk. The low environmental concentration was the main reason why high toxic phenolic DBPs (2,4,6-trihalo-phenols and 2,6-dihalo-4-nitrophenols) exhibited low ecological risk. Their ecological risk may increase with the increases of corresponding environmental concentration. Thus, more efforts should be made to determine other potential harmful effects of those high toxic phenolic DBPs and to minimize their potential ecological risk by taking appropriate measures.

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.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R_training² and R_test², respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R_training² and R_test², respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R_training² and R_test² were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.

In silico models for predicting vector control chemicals targeting Aedes aegypti

Human arboviral diseases have emerged or re-emerged in numerous countries worldwide due to a number of factors including the lack of progress in vaccine development, lack of drugs, insecticide resistance in mosquitoes, climate changes, societal behaviours, and economical constraints. Thus, Aedes aegypti is the main vector of the yellow fever and dengue fever flaviviruses and is also responsible for several recent outbreaks of the chikungunya alphavirus. As for the other mosquito species, the A. aegypti control relies heavily on the use of insecticides. However, because of increasing resistance to the different families of insecticides, reduction of Aedes populations is becoming increasingly difficult. Despite the unquestionable utility of insecticides in fighting mosquito populations, there are very few new insecticides developed and commercialized for vector control. This is because the high cost of the discovery of an insecticide is not counterbalanced by the 'low profitability' of the vector control market. Fortunately, the use of quantitative structure-activity relationship (QSAR) modelling allows the reduction of time and cost in the discovery of new chemical structures potentially active against mosquitoes. In this context, the goal of the present study was to review all the existing QSAR models on A. aegypti. The homology and pharmacophore models were also reviewed. Specific attention was paid to show the variety of targets investigated in Aedes in relation to the physiology and ecology of the mosquito as well as the diversity of the chemical structures which have been proposed, encompassing man-made and natural substances.

Discrimination of excess toxicity from narcotic effect: comparison of toxicity of class-based organic chemicals to Daphnia magna and Tetrahymena pyriformis

The discrimination of excess toxicity from narcotic effect plays a crucial role in the study of modes of toxic action for organic compounds. In this paper, the toxicity data of 758 chemicals to Daphnia magna and 993 chemicals to Tetrahymena pyriformis were used to investigate the excess toxicity. The result showed that mode of toxic action of chemicals is species dependent. The toxic ratio (TR) calculated from baseline model over the experimentally determined values showed that some classes (e.g. alkanes, alcohols, ethers, aldehydes, esters and benzenes) shared same modes of toxic action to both D. magna and T. pyriformis. However, some classes may share different modes of toxic action to T. pyriformis and D. magna (e.g. anilines and their derivatives). For the interspecies comparison, same reference threshold need to be used between species toxicity. The excess toxicity indicates that toxicity enhancement is driven by reactive or specific toxicity. However, not all the reactive compounds exhibit excess toxicity. In theory, the TR threshold should not be related with the experimental uncertainty. The experimental uncertainty only brings the difficulty for discriminating the toxic category of chemicals. The real threshold of excess toxicity which is used to identify baseline from reactive chemicals should be based on the critical concentration difference inside body, rather than critical concentration outside body (i.e. EC50 or IGC50). The experimental bioconcentration factors can be greatly different from predicted bioconcentration factors, resulting in different toxic ratios and leading to mis-classification of toxic category and outliers.

Structure-activity relationship study of trifluoromethylketones: inhibitors of insect juvenile hormone esterase

The juvenile hormone esterase (JHE) regulates juvenile hormone titre in insect hemolymph during its larval development. It has been suggested that JHE could be targeted for use in insect control. This enzyme can also be considered as involved in the phenomenon of endocrine disruption by xenobiotics in beneficial insects. Consequently, there is a need to know the characteristics of the molecules able to act on the JHE. Trifluoromethylketones (TFKs) are the most potent JHE inhibitors found to date and different quantitative structure-activity relationships (QSARs) have been derived for this group of chemicals. In this context, a set of 181 TFKs (118 active and 63 inactive compounds), tested on Trichoplusia ni for their JHE inhibition activity and described by physico-chemical descriptors, was split into different training and test sets to derive structure-activity relationship (SAR) models from support vector classification (SVC). A SVC model including 88 descriptors and derived from a Gaussian kernel was selected for its predictive performances. Another model computed only with 13 descriptors was also selected due to its mechanistic interpretability. This study clearly illustrates the difficulty in capturing the essential structural characteristics of the TFKs explaining their JHE inhibitory activity.

Straightforward MIA-QSTR evaluation of environmental toxicities of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are toxic environmental contaminants which cause severe health hazards. There is a growing need by industries and regulatory agencies for the development of tools able to assess the potential hazardous effects of chemicals on living organisms. In this background, multivariate image analysis combined with quantitative structure-toxicity relationships (MIA-QSTR) was used to evaluate the toxicity of aromatic aldehydes to Tetrahymena pyriformis. The techniques of genetic algorithm-partial least squares (GA-PLS) were applied effectively as MIA descriptor selection and mapping tools. In MIA-QSTR evaluation, pixels of 2D images of chemical structures could be used to recognize physicochemical information and predict changes in the toxicities. The resulting MIA-QSTR explains 90.3% leave-one-out predicted variance and 93.1% external predicted variance. The MIA-QSTR/GA-PLS performances were validated using various evaluation techniques such as cross-validation, applicability domain and Y-scrambling procedures, suggesting that the present methodology together with mechanistic interpretation may be useful to evaluate toxicity, safety and risk assessment of toxic environmental contaminants.