Prediction of nitrobenzene toxicity to the algae (Scenedesmus obliguus) by quantitative structure-toxicity relationship (QSTR) models with quantum chemical descriptors

Phytotoxicity of nitrobenzene bioaccumulation in rice seedlings: Nitrobenzene inhibits growth, induces oxidative stress, and reduces photosynthetic pigment synthesis

Nitrobenzene (NB) has been used in numerous industrial and agricultural fields as an organic compound intermediate. NB has mutagenicity and acute toxicity, and is typically a toxic pollutant in industrial wastewater worldwide. To evaluate its phytotoxicity, we treated rice (Oryza sativa) with different concentrations of NB (0, 5, 25, 50, 75, and 100 mg L-1). NB inhibited growth indices of rice (shoot and root length, fresh shoot and root weight, and dry shoot and root weight) as NB treatment concentrations increased. High concentrations (>25 mg L-1) of NB significantly inhibited rice root and shoot growth; root growth was more susceptible to NB. NB treatment could damage the structure and reduce the activity of rice seedling roots. The result of high performance liquid chromatography (HPLC) indicated that the bioaccumulation of NB in rice seedlings had a dose-dependent effect on the growth inhibition. NB reduced the photosynthetic pigment content and the expression levels of chlorophyll synthesis genes. NB treatment increased active oxygen radicals, electrical conductivity, malondialdehyde (MDA), proline, and soluble sugar contents. The expressions of antioxidant enzyme genes were induced by NB stress, and exhibited a phenomenon of initial increase followed by decrease. When the NB concentration was higher than 50 mg L-1, the gene expression levels decreased rapidly. This study provides insight into the association between exposure to NB and its phytotoxic effects on rice seedlings, and assesses the potential risk of NB bioaccumulation for crops that require a large amount of irrigation water.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q²_LOO = 0.620-0.663) and externally (Q²F_n = 0.693-0.868, CCC_ext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO₂, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

Quantitative Structure-Toxicity Relationship analysis of combined toxic effects of lignocellulose-derived inhibitors on bioethanol production

This study performed a Quantitative Structure-Toxicity Relationship (QSTR) model to evaluate the combined toxicity of lignocellulose-derived inhibitors on bioethanol production. Compared with all the control groups, the combined systems exhibited lower conductivity values, higher oxidation-reduction potential values, as well as maximum inhibition rates. These results indicated that the presence of combined inhibitors had a negative effect on the bioethanol fermentation process. Meanwhile, QSTR model was excellent for evaluating the combined toxic effects at lower ferulic acid concentration (([1:4] × IC50)) and (([1:1] × IC50)), due to higher R² values (0.994 and 0.762), lower P values (0.000 and 0.023) and relative error values (less than 30%). The obtained results also showed that the combined toxic effects of ferulic acid and representative lignocellulose-derived inhibitors were relevant to different molecular descriptors. Meanwhile, the interactions of combined inhibitors were weaker when ferulic acid was at low concentration ([1:4] × IC50).

Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-Based Reactivity Descriptors

Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.

The Effects of Molecular Properties on Ready Biodegradation of Aromatic Compounds in the OECD 301B CO2 Evolution Test

Ready biodegradation is the primary biodegradability of a compound, which is used for discriminating whether a compound could be rapidly and readily biodegraded in the natural ecosystems in a short period and has been applied extensively in the environmental risk assessment of many chemicals. In this study, the effects of 24 molecular properties (including 2 physicochemical parameters, 10 geometrical parameters, 6 topological parameters, and 6 electronic parameters) on the ready biodegradation of 24 kinds of synthetic aromatic compounds were investigated using the OECD 301B CO2 Evolution test. The relationship between molecular properties and ready biodegradation of these aromatic compounds varied with molecular properties. A significant inverse correlation was found for the topological parameter TD, five geometrical parameters (Rad, CAA, CMA, CSEV, and N c), and the physicochemical parameter K ow, and a positive correlation for two topological parameters TC and TVC, whereas no significant correlation was observed for any of the electronic parameters. Based on the correlations between molecular properties and ready biodegradation of these aromatic compounds, the importance of molecular properties was demonstrated as follows: geometrical properties > topological properties > physicochemical properties > electronic properties. Our study first demonstrated the effects of molecular properties on ready biodegradation by a number of experiment data under the same experimental conditions, which should be taken into account to better guide the ready biodegradation tests and understand the mechanisms of the ready biodegradation of aromatic compounds.

Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

In this study, we established nonlinear quantitative-structure toxicity relationship (QSTR) models for predicting the toxicities of chemical pesticides in multiple aquatic test species following the OECD (Organization for Economic Cooperation and Development) guidelines. The decision tree forest (DTF) and decision tree boost (DTB) based QSTR models were constructed using a pesticides toxicity dataset in Selenastrum capricornutum and a set of six descriptors. Other six toxicity data sets were used for external validation of the constructed QSTRs. Global QSTR models were also constructed using the combined dataset of all the seven species. The diversity in chemical structures and nonlinearity in the data were evaluated. Model validation was performed deriving several statistical coefficients for the test data and the prediction and generalization abilities of the QSTRs were evaluated. Both the QSTR models identified WPSA1 (weighted charged partial positive surface area) as the most influential descriptor. The DTF and DTB QSTRs performed relatively better than the single decision tree (SDT) and support vector machines (SVM) models used as a benchmark here and yielded R(2) of 0.886 and 0.964 between the measured and predicted toxicity values in the complete dataset (S. capricornutum). The QSTR models applied to six other aquatic species toxicity data yielded R(2) of >0.92 (DTF) and >0.97 (DTB), respectively. The prediction accuracies of the global models were comparable with those of the S. capricornutum models. The results suggest for the appropriateness of the developed QSTR models to reliably predict the aquatic toxicity of chemicals and can be used for regulatory purpose.