Quantitative structure-activity relationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs) dissipation in rhizosphere based on molecular structure and effect size

Heterogeneous Photodegradation Behavior of Liquid Crystal Monomers in Dust: Quantitative Structure-Activity Relationship and Product Identification

The heterogeneous photodegradation behavior of liquid crystal monomers (LCMs) in standard dust (standard reference material, SRM 2583) and environmental dust was investigated. The measured photodegradation ratios for 23 LCMs in SRM and environmental dust in 12 h were 11.1 ± 1.8 to 23.2 ± 1.1% and 8.7 ± 0.5 to 24.0 ± 2.8%, respectively. The degradation behavior of different LCM compounds varied depending on their structural properties. A quantitative structure-activity relationship model for predicting the degradation ratio of LCMs in SRM dust was established, which revealed that the molecular descriptors related to molecular polarizability, electronegativity, and molecular mass were closely associated with LCMs' photodegradation. The photodegradation products of the LCM compound 4'-propoxy-4-biphenylcarbonitrile (PBIPHCN) in dust, including •OH oxidation, C-O bond cleavage, and ring-opening products, were identified by nontarget analysis, and the corresponding degradation pathways were suggested. Some of the identified products, such as 4'-hydroxyethoxy-4-biphenylcarbonitrile, showed predicted toxicity (with an oral rat lethal dose of 50%) comparable to that of PBIPHCN. The half-lives of the studied LCMs in SRM dust were estimated at 32.2-82.5 h by fitting an exponential decay curve to the observed photodegradation data. The photodegradation mechanisms of LCMs in dust were revealed for the first time, enhancing the understanding of LCMs' environmental behavior and risks.

Exploring the Gallic and Cinnamic Acids Chimeric Derivatives as Anticancer Agents over HeLa Cell Line: An in silico and in vitro Study

Cervical cancer is one of the most aggressive and important cancer types in the female population, due to its low survival rate. Actually, the search for new bioactive compounds, like gallic and cinnamic acid, is one of the most employed options to finding a treatment. In the present study, 134 phenolic compounds with cytotoxic activity over HeLa cell line were used to generate a descriptive ( R 2 ${{R}^{2}}$ =0.76) and predictive ( Q 2 ${{Q}^{2}}$ =0.69 and Q e x t 2 ${{Q}_{{\rm e}{\rm x}{\rm t}}^{2}}$ =0.62) QSAR model. Structural, electronic, steric, and hydrophobic features are represented as different molecular descriptors in our QSAR model. From this model, nine gallate-cinnamate ester derivatives (N1-N9) were designed and synthesized. Furthermore, in vitro cytotoxic activity was evaluated against HeLa and non-tumorigenic cells. Derivatives N6, N5, N1, and N9 were the most active molecules with IC_50ExpHeLa values from 7.26 to 11.95 μM. Finally, the binding of the synthesized compounds to the colchicine binding site on tubulin was evaluated by molecular docking as a possible action mechanism. N1, N5 and N6 can be considered as templates for the design of new cervical anticancer compounds.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Quantitative structure-activity relationship in the photodegradation of azo dyes

The photolysis characteristics of azo dyes are critically important in environmental pollution control, dye-sensitized solar cells, and dyeing-related industries. However, there is still lack of quantitative relationship between the structures of azo dyes and their photolysis characteristics. To address this issue, the photolysis of 22 azo dyes were conducted side by side at three pH (4.0, 6.0, 9.0). The obtained pseudo-first order photodegradation rate constants (k₁) were processed with meta-analysis. Statistically, the hydrazone tautomer had a smaller excitation energy and was easier to undergo photolysis than the azo tautomer. The ortho-substituted sulfonate groups had an obvious protective effect on the photostability of azo dyes. The softness (s), the most positive and negative partial charge on a carbon atom (qC⁺, qC^-) were found to be crucial descriptors in the establishment of QSAR models for the photostability of azo dyes. The QSAR model at pH 9.0 was robust for predicting the photostability of azo dyes under UV irradiation. N₂-purging experiments and quantum chemical computation verified that the cleavage of azo bond was not a result of direct photolysis but was caused by the attack of photoinduced reactive oxygen species. The results here are helpful for the design of more stable azo dyes or the selection of suitable approaches for the treatment of dye-contaminated water bodies.

In silico toxicity evaluation of dioxins using structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR)

Prediction of pEC₅₀ values of dioxins binding with the aryl hydrocarbon receptor (AhR) is of great significance for exploring how dioxins induce toxicity in human body and evaluating their environmental behaviors and risks. To reveal the factors that influence the toxicity of dioxins, provide more accurate mathematical models for predicting the pEC₅₀ values of dioxins, and supplement the toxicity database of persistent organic pollutants, qualitative structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR) were used in this study. The research objects in this study were 60 organic compounds with pEC₅₀ values and 162 compounds without pEC₅₀ values, which included polychlorinated dibenzofurans (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs). The qualitative structure-activity relationship (SAR) was performed first and concluded that halogen substitutions at any of the 2, 3, 7, and 8 sites increased the pEC₅₀ value of the compound. Moreover, two-dimensional quantitative structure-activity relationship (2D-QSAR) models were established by employing multiple linear regression (MLR) method and artificial neural network (ANN) algorithm to investigate the factors affecting the pEC₅₀ values of dioxins molecules. MLR was used to establish the well-understood linear model and ANN was used to establish a more accurate non-linear model. Both models have good fitting, robustness, and predictive ability. Importantly, the ability of dioxins binding to AhR is mainly determined by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl-Cl], and Neoplastic-80. In addition, the pEC₅₀ values of the 162 dioxins without toxicity data were predicted by MLR and ANN models, respectively.

A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEP_ext), fitting correlation coefficient (R²), and leave-one-out cross-validation (Q²_LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R² and Q²_LOO of 4 kinds of nonlinear models are more than 0.6 and SDEP_ext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R² = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

Assessing adsorption of polycyclic aromatic hydrocarbons on Rhizopus oryzae cell wall components with water-methanol cosolvent model

The contribution of different fungal cell wall components in adsorption of polycyclic aromatic hydrocarbons (PAHs) is still unclear. We isolated Rhizopus oryzae cell walls components with sequential extraction, characterized functional groups with NEXAFS spectra, and determined partition coefficients of PAHs on cell walls and cell wall components with cosolvent model. Spectra of NEXAFS indicated that isolated cell walls components were featured with peaks at ~532.7 and ~534.5eV energy. The lipid cosolvent partition coefficients were approximately one order of magnitude higher than the corresponding carbohydrate cosolvent partition coefficients. The partition coefficients for four tested carbohydrates varied at approximate 0.5 logarithmic units. Partition coefficients between biosorbents and water calculated based cosolvent models ranged from 0.8 to 4.2. The present study proved the importance of fungal cell wall components in adsorption of PAHs, and consequently the role of fungi in PAHs bioremediation.

QSAR models for removal rates of organic pollutants adsorbed by in situ formed manganese dioxide under acid condition

Manganese dioxide formed in oxidation process by potassium permanganate exhibits promising adsorptive capacity which can be utilized to remove organic pollutants in wastewater. However, the structure variances of organic molecules lead to wide difference of adsorption efficiency. Therefore, it is of great significance to find a general relationship between removal rate of organic compounds and their quantum parameters. This study focused on building up quantitative structure activity relationship (QSAR) models based on experimental removal rate (r(exp)) of 25 organic compounds and 17 quantum parameters of each organic compounds computed by Gaussian 09 and Material Studio 6.1. The recommended model is rpre = -0.502-7.742 f(+)x + 0.107 E HOMO + 0.959 q(H(+)) + 1.388 BOx. Both internal and external validations of the recommended model are satisfied, suggesting optimum stability and predictive ability. The definition of applicability domain and the Y-randomization test indicate all the prediction is reliable and no possibility of chance correlation. The recommended model contains four variables, which are closely related to adsorption mechanism. f(+)x reveals the degree of affinity for nucleophilic attack. E HOMO represents the difficulty of electron loss. q(H(+)) reflect the distribution of partial charge between carbon and hydrogen atom. BO x shows the stability of a molecule.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

Dissipation of phenanthrene and pyrene at the aerobic-anaerobic soil interface: differentiation induced by the rhizosphere of PAH-tolerant and PAH-sensitive rice (Oryza sativa L.) cultivars

A pot experiment was conducted to reveal the removal of two polycyclic aromatic hydrocarbons (PAHs) (phenanthrene, PHE, and pyrene, PYR) during rice cultivation in a paddy field. The rhizosphere effect on facilitating dissipation of PAHs varied simultaneously as a function of soil properties, PAH types, cultivation time, and genotypes within rice cultivars, with differences performed for PYR but not PHE. Changes in soil PLFA profiles evidenced that the growth of rice roots modified the dominant species within rhizosphere microbial communities and induced a selective enrichment of Gram-negative aerobic bacteria capable of degrading, thereby resulting in the differentiated dissipation of PYR. While the insignificant differences in PHE dissipation might be attributed to its higher solubility and availability under flooded condition that concealed the differences in improvement of bioavailability for microorganisms between rhizosphere and non-rhizosphere, and between both soils and both rice cultivars. Our findings illustrate that the removal of PAHs in paddy soils was more complex relative to those in dryland soils. This was possibly due to the specialty of rice roots for oxygen secretion that provides development of redox heterogeneous microbial habitats at root-soil interface under flooded condition.

QSAR models for degradation of organic pollutants in ozonation process under acidic condition

Although some researches about the degradation of organic pollutants have been carried out during recent years, reaction rate constants are available only for homologue compounds with similar structures or components. Therefore, it is of great significance to find a universal relationship between reaction rate and certain parameters of several diverse organic pollutants. In this study, removal ratio and kinetics of 33 kinds of organic substances were investigated by ozonation process, including azo dyes, heterocyclic compounds, ionic compounds and so on. Most quantum chemical parameters were conducted by using Gaussian 09 at the DFT B3LYP/6-311G level, including μ, q H(+), q(C)minq(C)max, ELUMO and EHOMO. Other descriptors, bond order (BO) as well as Fukui indices (f(+), f(-) and f(0)), were calculated by Material Studio 6.1 at Dmol(3)/GGA-BLYP/DNP(3.5) basis for each organic compound. The recommended model for predicting rate constants was lnk'=1.978-95.484f(0)x-3.350q(C)min+38.221f(+)x, which had the squared regression coefficient R(2)=0.763 and standard deviation SD=0.716. The results of t test and the Fisher test suggested that the model exhibited optimum stability. Also, the model was validated by internal and external validations. Recommended QSAR model showed that the highest f(0) value of main-chain carbons (f(0)x) is more closely related to lnk' than other quantum descriptors.

A multimedia fate model to evaluate the fate of PAHs in Songhua River, China

A multimedia fate model coupling dynamic water flow with a level IV fugacity model has been developed and applied to simulate the temporal and spatial fate of Polycyclic Aromatic Hydrocarbons (PAHs) in the Songhua River, China. The model has two components: in the first, the one-dimensional network kinematic wave equation is used to calculate varying water flow and depth. In the second, Fugacity IV equations are implemented to predict contaminant distributions in four environmental media. The estimated concentrations of eight PAHs in Songhua River are obtained, and all simulated results are in acceptable agreement with monitoring data, as verified with the Theil's inequality coefficient test. The sensitivity of PAH concentration in each environmental phase to input parameters are also evaluated. Our results show the model predicts reasonably accurate contaminant concentrations in natural rivers, and that it can be used to supply necessary information for control and management of water pollution.

Adsorption of polycyclic aromatic hydrocarbons (PAHs) on Rhizopus oryzae cell walls: application of cosolvent models for validating the cell wall-water partition coefficient

The cell wall-cosolvent partition coefficients (Km) of polycyclic aromatic hydrocarbons (PAHs) were determined for Rhizopus oryzae cell walls by controlling the volume fraction of methanol (f) ranging from 0.1 to 0.5. Five cosolvent models were employed for extrapolating the cell wall-water partition coefficients (Kw) in pure water. The extrapolated Kw values of four PAHs on R. oryzae cell walls were ranged from 2.9 to 5.1. Comparison of various Kw values of pyrene generated from extrapolation and the QSPR model, together with predicted different (PD), mean percentage deviations (MPD), and root mean square errors (RSE), revealed that the performance of the LL and Bayesian models were the best among all five tested cosolvent models. This study suggests that R. oryzae cell walls play an important role in the partitioning of PAHs during bioremediation because of the high Kw of fungal cell walls.