QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R² = 0.8053, 0.7591, and 0.8283) and robustness (Q²_LOO = 0.7507, 0.7227, and 0.8194; Q²_LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R²_EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

Quantum parameter analysis of the adsorption mechanism by freshly formed ferric hydroxide for synthetic dye and antibiotic wastewaters

In this study, the adsorption effect by freshly formed ferric hydroxide (FFFH) for the removal of 47 synthetic dye and antibiotic wastewaters under different pH conditions (i.e., pH = 4, 7, and 10) was investigated. The average total organic carbon (TOC) removal rates (R_exp) of pollutants under acidic, neutral, and alkaline conditions were 27.10 ± 3.21%, 15.16 ± 2.48%, and 9.72 ± 2.81%, respectively. By analyzing the characteristics of FFFH measured by SEM, XRD, FT-IR, TGA and BET, the properties of pollutants, and the values of R_exp, one can conclude that the large specific surface area and rich hydroxyl groups on the surface of FFFH were the reasons for its adsorption capacity for organic pollutants, and the electrostatic adsorption was the main reason for higher removal rate in acidic condition. Subsequently, to better elucidate the intrinsic factors influencing the removal rates at the molecular structure level, three optimal quantitative structure-activity relationship (QSAR) models were established by using multiple linear regression (MLR) analysis. Results of model validations (e.g., regression coefficient, internal and external verifications, and Y-randomization) showed that the established models exhibited excellent stability, reliability, and robustness with the values of R² = 0.7544, 0.7764, 0.7528, Q²_INT = 0.6451, 0.6836, 0.6228, and Q²_EXT = 0.5890, 0.6029, 0.7298 under acidic, neutral, and alkaline conditions, respectively. The results of quantum parameter analysis revealed that the adsorption mechanism of FFFH for dyes and antibiotics mainly includes the activity of adsorption site, the behavior of electron transfer and the strength of molecular polarity.

Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice

Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure-toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.

Molecular insights into the mechanism and the efficiency-structure relationship of phosphorus removal by coagulation

Types and structures of phosphorus compounds influence the removal of phosphorus by coagulation. Until now, the molecular-level interaction between coagulants and phosphorus (especially organophosphates) and the relationship between removal efficiency and phosphorus structure have not been clear. This work investigated the removal of phosphorus with different structures using conventional coagulants (poly aluminum chloride (PACl) and polymerized ferric sulfate (PFS)) and a novel covalently-bound inorganic-organic hybrid coagulant (CBHyC). CBHyC removed more than 98% of phosphate and most of organophosphates, had more stable performance than PACl and PFS, and was less affected by pH, initial phosphorus concentration, and co-occurring materials. Molecular dynamics simulation demonstrated that CBHyC removed phosphorus mainly through electrostatic attraction and hydrophobic interaction. Furthermore, this work established QSAR (quantitative structure activity relationship) models for removal efficiency and organophosphate structure for the first time. The model showed that atomic charges of phosphorus atoms (Q_P) and hydrogen atoms (Q_H⁺) in the system and the energy gap (ΔE_MO) affected electronegativity and hydrophobicity, thus influencing organophosphate removal efficiency. The model had high fitting precision and good predictive ability and has the potential to greatly reduce the cost of optimizing processes and conditions for phosphorus removal.

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.

Ecotoxicity of two organophosphate pesticides chlorpyrifos and dichlorvos on non-targeting cyanobacteria Microcystis wesenbergii

Organophosphate pesticides (OPs), as a replacement for the organochlorine pesticides, are generally considered non-toxic to plants and algae. Chlorpyrifos and dichlorvos are two OPs used for pest control all over the world. In this study, the dose-response of cyanobacteria Microcystis wesenbergii on OPs exposure and the stimulating effect of OPs with and without phosphorus source were investigated. The results showed that high concentrations of chlorpyrifos and dichlorvos caused significant decrease of chlorophyll a content. The median inhibitory concentrations (EC50) of chlorpyrifos and dichlorvos at 96 h were 15.40 and 261.16 μmol L(-1), respectively. Growth of M. wesenbergii under low concentration of OPs (ranged from 1/10,000 to 1/20 EC50), was increased by 35.85 % (chlorpyrifos) and 41.83 % (dichlorvos) at 120 h, respectively. Correspondingly, the highest enhancement on the maximum quantum yield (F v/F m) was 4.20 % (24 h) and 9.70 % (48 h), respectively. Chlorophyll fluorescence kinetics, known as O-J-I-P transients, showed significant enhancements in the O-J, J-I, and I-P transients under low concentrations of dichlorvos at 144 h, while enhancements of chlorophyll fluorescence kinetics induced by low concentrations of chlorpyrifos were only observed in the J-I transient at 144 h. Significant decreases of chlorophyll content, F v/F m and O-J-I-P transients with OPs as sole phosphorus source were found when they were compared with inorganic phosphate treatments. The results demonstrated an evidently hormetic dose-response of M. wesenbergii to both chlorpyrifos and dichlorvos, where high dose (far beyond environmental concentrations) exposure caused growth inhibition and low dose exposure induced enhancement on physiological processes. The stimulating effect of two OPs on growth of M. wesenbergii was negligible under phosphate limitation.

Quantitative structure-activity relationship of organophosphate compounds based on molecular interaction fields descriptors

By using multi-block partial least-squares (MBPLS) method, quantitative structure-activity relationship (QSAR) between 35 organophosphate compounds (OP) and their 24h acute toxicities towards the housefly (Musca nebulo L.) was built on the molecular interaction fields (MIF) descriptors, which were obtained with O, N and DRY as probes, and then normalised with block unscaled weights (BUW) technique. The best QSAR model had 8 principal components, with the coefficient of determination R(2)=0.995 and that of leave-one-out cross-validation Q(2)=0.865, and the corresponding standard deviation of error 0.076 and 0.361, respectively. Block importance in the prediction (BIP) for O, N and DRY probe were 1.030, 0.962 and 1.007, respectively. Contour map of variable coefficients showed that hydrogen bonding between the O atom in PO and the NH groups in acetylcholinesterase (AChE) played an important role in the interaction between OP and AChE. Meanwhile, the hydrophobicity of OP also had significant contribution. QSAR based on the MIF descriptors could be a potential means to interpret the mechanisms of ligand-receptor interaction when the receptor was well known.

Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach

Agriculture is needed to deal with crop losses caused by biotic stresses like pests. The use of pesticides has played a vital role, contributing to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health. An important group of these pesticides is fungicides. However, the use of these agrochemical fungicides is an important source of contamination, damaging the ecosystems. Several studies have been realized for the assessment of the toxicity in agrochemical fungicides, but the principal limitation is the use of structurally related compounds against usually one indicator species. In order to overcome this problem, we explore the quantitative structure-toxicity relationships (QSTR) in agrochemical fungicides. Here, we developed the first multi-species (ms) chemoinformatic approach for the prediction multiple ecotoxicological profiles of fungicides against 20 indicators species and their classifications in toxic or nontoxic. The ms-QSTR discriminant model was based on substructural descriptors and a heterogeneous database of compounds. The percentages of correct classification were higher than 90% for both, training and prediction series. Also, substructural alerts responsible for the toxicity/no toxicity in fungicides respect all ecotoxicological profiles, were extracted and analyzed.