A DFT-based toxicity QSAR study of aromatic hydrocarbons to Vibrio fischeri: Consideration of aqueous freely dissolved concentration

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.

Support Vector Machine-Based Global Classification Model of the Toxicity of Organic Compounds to Vibrio fischeri

Vibrio fischeri is widely used as the model species in toxicity and risk assessment. For the first time, a global classification model was proposed in this paper for a two-class problem (Class − 1 with log1/IBC50 ≤ 4.2 and Class + 1 with log1/IBC50 > 4.2, the unit of IBC50: mol/L) by utilizing a large data set of 601 toxicity log1/IBC50 of organic compounds to Vibrio fischeri. Dragon software was used to calculate 4885 molecular descriptors for each compound. Stepwise multiple linear regression (MLR) analysis was used to select the descriptor subset for the models. The ten molecular descriptors used in the classification model reflect the structural information on the Michael-type addition of nucleophiles, molecular branching, molecular size, polarizability, hydrophobic, and so on. Furthermore, these descriptors were interpreted from the point of view of toxicity mechanisms. The optimal support vector machine (SVM) model (C = 253.8 and γ = 0.009) was obtained with the genetic algorithm. The SVM classification model produced a prediction accuracy of 89.1% for the training set (451 log1/IBC50), of 80.0% for the test set (150 log1/IBC50), and of 86.9% for the total data set (601 log1/IBC50), which are higher than that (80.5%, 76%, and 79.4%, respectively) from the binary logistic regression (BLR) model. The global SVM classification model is successful, although it deals with a large data set in relation to the toxicity of organics to Vibrio fischeri.

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

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

Lethal, behavioral, growth and developmental toxicities of alkyl-PAHs and non-alkyl PAHs to early-life stage of brine shrimp, Artemia parthenogenetica

Alkyl-PAHs are the predominant form of PAHs in crude oils which are supposed to demonstrate different toxicities compared to non-alkyl PAHs. Little information is available about the toxicity of alkyl-PAHs on marine Artemia. This study addressed and compared the lethal, behavioral, growth and developmental toxicities of three alkyl-PAHs, namely 3-methyl phenanthrene (3-mPhe), retene (Ret) and 2-methyl anthracene (2-mAnt), to their non-alkyl forms, phenanthrene (Phe) and anthracene (Ant) using Artemia parthenogenetica (nauplii, <24 h) as test organism following a 48 h and a 7 d of exposure, respectively. Benzo-a-pyrene (Bap) was selected as a reference toxicant for the comparison with the above alkyl-PAHs and non-alkyl PAHs. Results showed that for all tested endpoints, A. parthenogenetica nauplii had the highest sensitivity to Bap while Ant had no significant effect on nauplii survival or development within given concentrations. Considering the aqueous freely dissolved PAH concentrations, the 48 h-LC₅₀ (survival), 48 h-EC₅₀ (immobility) and 7 d-LC₁₀ (survival) of Bap were calculated as 0.321, 0.285 and 0.027 μg/L, respectively, which were twofold to fivefold lower than those of Phe, 3-mPhe, Ret, Ant and 2-mAnt. A higher acute toxicity of alkyl-PAHs (3-mPhe and 2-mAnt) than their non-alkyl forms (Phe and Ant) was observed. Not limited to Phe, the common non-polar narcotic mode of action was also observed for Bap, 3-mPhe, Ret and 2-mAnt, which was evident by the inhibited mobility of nauplii. The decreased body lengths were found for all PAH treatments compared to the solvent control, whereas instar retardations were only found in nauplii exposed to Bap, Phe and Ret. Our findings emphasized the sensitivity differences of A. parthenogenetica nauplii to selected alkyl PAHs and non-alkyl PAHs and confirmed the application of lethal, behavioral and growth indicators in the toxicity evaluation of selected PAHs other than Ant. However, the distinct toxicities of these PAHs suggested other toxic modes of action may play more important roles apart from narcotic mode of action and need to be elucidated in future studies. In addition, a strong correlation between the body length and the instar of A. parthenogenetica nauplii was observed for each PAH exposure, suggesting that body length can be representative for both growth and developmental indicators during biological monitoring of PAH pollution in marine environment.

Effects of crude oil and aromatic compounds on growth and bioluminescence of Vibrio campbellii FS5

Fifteen native luminescent bacteria were isolated from the Gulf of Thailand, and their sensitivity for the detection of toxicity of crude oil and its aromatic components was investigated. Of these isolates, Vibrio campbellii strain FS5 was one of the two most highly inhibited bacteria at all crude oil concentrations. This bacterium showed a decrease in luminescence intensity of between 10.7 and 80.2% after a 15-min exposure to 0.0001-10 mg/L of crude oil. The degree of bioluminescence inhibition increased with increasing concentrations of crude oil. The presence of crude oil at all concentrations had negative effects on the log bioluminescence per log number of viable cells after 15- to 105-min exposure. About 10 to 100 times, lower half maximal effective concentration (EC₅₀) values were observed for polycyclic aromatic hydrocarbons (PAHs) than those for benzene, toluene, ethylbenzene, and xylene (BTEX). In the presence of each individual BTEX and PAH, the bioluminescence inhibition increased with increasing exposure time (1-32 h). This indigenous bacterium can be used as a simple and general indicator of oil contamination and its impact on coastal waters as well as for assessing potential toxicity during oil bioremediation.

Polarizability and aromaticity index govern AhR-mediated potencies of PAHs: A QSAR with consideration of freely dissolved concentrations

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants associated with adverse human effects including cancer, and the aryl hydrocarbon receptor (AhR) is a key ligand-activated transcription factor mediating their toxicity. However, there is presently a lack of data on AhR potencies of PAHs. Simple, transparent, interpretable and predictive quantitative structure-activity relationship (QSAR) models are helpful, especially with the consideration of freely dissolved concentrations linked to bioavailability. Here, QSAR models on AhR-mediated luciferase activity of PAHs were developed with nominal median effect concentrations (EC₅₀, _nom) and freely dissolved concentration (EC_{50, free}) as endpoints, and quantum chemical and Dragon descriptors as predictor variables. Results indicated that only the EC_{50, free} model met the acceptable criteria of QSAR model (determination coefficient (R²) > 0.600, leave-one-out cross validation (Q_LOO²) > 0.500, and external validation coefficient (Q_EXT²) > 0.500), implying that it has good goodness-of-fit, robustness and external predictive power. Molecular polarizability and aromaticity index reflecting the partition behavior and intermolecular interactions can effectively predict AhR-mediated potencies of PAHs. The results highlight the necessity of adoption of the freely dissolved concentration in the QSAR modeling and more in silico models need to be further developed for different animal models (in vivo or in vitro).

Polarizability: a promising descriptor to study chemical-biological interactions

Recently, we have defined atomic polarizability, a Conceptual Density Functional Theory (CDFT)-based reactivity descriptor, through an empirical method. Though the method is empirical, it is competent enough to meet the criteria of periodic descriptors and exhibit relativistic effect. Since the atomic data are very accurate, we have applied them to determine molecular polarizability. Molecular polarizability is an electronic parameter and has an impact on chemical-biological interactions. Thus, it plays a pivotal role in explaining such interactions through Structure Activity Relationships (SAR). In the present work, we have explored the application of polarizability in the real field through investigation of chemical-biological interactions in terms of molecular polarizability. A Quantitative Structure-Activity Relationship (QSAR) model is constructed to account for electronic effects owing to polarizability in ligand-substrate interactions. The study involves the prediction of various biological activities in terms of minimum block concentration, relative biological response, inhibitory growth concentration or binding affinity. Superior results are presented for the predicted and observed activities which support the accuracy of the proposed polarizability-QSAR model. Further, the results are considered from a biological viewpoint in order to understand the mechanism of interactions. The study is performed to explore the efficacy of the computational model based on newly proposed polarizability and not to establish the finest QSAR. For future studies, it is suggested that the descriptor polarizability should be contrasted with the use of other drug-like descriptors.

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.

Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure

With the extensive applications and ongoing world demand, more and more amine surfactants are discharged into natural environment. However, the database about toxicity of amine surfactants is incomplete, which is not beneficial to environmental protection process. In this paper, the toxicity of 20 amine surfactants on Daphnia magna were tested to extend the toxicity data of amine surfactants. Besides, 35 molecular structure descriptors including quantum parameters, physicochemical parameters and topological indices were chosen and calculated as independent variables to develop the quantitative structure-activity relationship (QSAR) model between the toxicity of amine surfactants and their molecular structure by genetic function approximation (GFA) algorithm. According to statistical analysis, a robust model was built with the determination coefficient of (R²) was 0.962 and coefficient determinations of cross-validation (R_cv²) was 0.794. Meanwhile, external validation was implemented to evaluate the QSAR model. The result of coefficient determinations of cross-validation (R_ext²) for external validation was calculated as 0.942, illustrating the model has great goodness-of-fit and good prediction ability.

A model of the physiological and biochemical characteristics of earthworms (Eisenia fetida) in petroleum-contaminated soil

Current researches found some terrestrial animals absorb petroleum hydrocarbons (PHCs) in oil-polluted soil. However, the absorption behaviour between various biological tissues remains unclear. The aim of our study is to determine the toxic effects and enrichment behaviours of earthworms (Eisenia fetida) in petroleum-contaminated soils and to provide a reasonable dynamics model to explain the migration of PHCs within earthworm tissues. The PHCs are divided into three fractions by equivalent carbon number. An experimental analysis of the PHC concentrations in 3 different earthworm organ systems (body-wall tissue, body fluid and gut tissue) from a contamination exposure experiment at different time intervals was implemented. A dynamics model was built to simulate the absorption mechanism. The model results perform well. The PHC concentrations in the earthworm tissues were gut > body fluid > body wall. The PHCs in the gut reached equilibrium first, and those in the body-wall tissues reached equilibrium last. In the gut tissue, the PHC concentration was different from those in the body-wall tissue and body fluid due to the influence of the feeding rule. In addition, as the length of the carbon chain increases, the molecular size increases, which makes it more difficult for petroleum hydrocarbon fractions to enter an organ system. As a result, the concentration of PHCs in each type of tissue decreases with increasing carbon chain length. This study can provide a theoretical foundation for chemical monitoring in soil.