Two-dimensional and Three-dimensional quantitative structure-activity relationship models for the degradation of organophosphate flame retardants during supercritical Water oxidation

A review of the present methods used to remediate soil and water contaminated with organophosphate esters and developmental directions

Organophosphate esters (OPEs) are widely used commercial additives, but their environmental persistence and toxicity raise serious concerns necessitating associated remediation strategies. Although there are various existing technologies for OPE removal, comprehensive screening for them is urgently needed to guide further research. This review provides a comprehensive overview of the techniques used to remove OPEs from soil and water, including their related influencing factors, removal mechanisms/degradation pathways, and practical applications. Based on an analysis of the latest literature, we concluded that (1) methods used to decontaminate OPEs include adsorption, hydrolysis, photolysis, advanced oxidation processes (AOPs), activated sludge processes, and microbial degradation; (2) factors such as the quantity/characteristics of the catalysts/additives, pH value, inorganic ion concentration, and natural organic matter (NOM) affect OPE removal; (3) primary degradation mechanisms involve oxidation induced by reactive oxygen species (ROS) (including •OH and SO4•-) and degradation pathways include hydrolysis, hydroxylation, oxidation, dechlorination, and dealkylation; (5) interference from the pH value, inorganic ion and the presence of NOM may limit complete mineralization during the treatment, impacting practical application of OPE removal techniques. This review provides guidance on existing and potential OPE removal methods, providing a theoretical basis and innovative ideas for developing more efficient and environmentally friendly techniques to treat OPEs in soil and water.

Discovery of novel bromodomain-containing protein 4 (BRD4-BD1) inhibitors combined with 3d-QSAR, molecular docking and molecular dynamics in silico

Bromine-containing domain protein 4 (BRD4) plays a crucial role in regulating transcription and genome stability. Selective inhibitors of BRD4-BD1 can specifically target specific bromine domains to affect cell proliferation, apoptosis, and differentiation. In this work, 43 selective benzoazepinone BRD4-BD1 inhibitors were studied using molecular simulations and three-dimensional quantitative conformation relationships (3D-QSAR). A reliable 3D-QSAR model was established based on COMFA (Q2 = 0.532, R2 = 0.981) and COMSIA (S + E + H (Q2 = 0.536, R2 = 0.979) two different analysis methods. Through 3D-QSAR model prediction and quantum chemical analysis, 15 small molecules with stronger inhibitory activity than the template compounds were constructed, and 5 new compounds with higher predictive activity and binding affinity were screened by molecular docking and ADMET methods. According to the molecular dynamics simulation, the key residues that can interact with BRD4-BD1 protein and molecular docking results are consistent, including ASN140, MET132, GLN85, MET105, ASN135 and TYR97. From the MD trajectory, we calculated and analyzed RMSD, RMSF, free binding energy, FECM, DCCM and PCA, the loop region formed by amino acids VAL45∼PRO62 showed α-helix, β-folding and clustering towards the active center with the extension of simulation time. Further optimization of the structure of active candidate compounds A6, A11, A14, and A15 will provide the necessary theoretical basis for the synthesis and activity evaluation of novel BRD4-BD1 inhibitors.Communicated by Ramaswamy H. Sarma.

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

Quantitative structure-activity relationship(QSAR) models for color and COD removal for some dyes subjected to electrochemical oxidation

Electrochemical oxidation is an efficient method for the destruction of dyes in wastewater streams. The experimental conditions during electrochemical oxidation (EO) and molecular structure of a dye greatly influence the extent of degradation. The extent of degradation for a variety of dyes by EO can be predicted conveniently by the use of Quantitative structure-activity Relationship (QSAR) models. An abundant amount of published data on dye degradation by EO using highly variable experimental conditions lies unutilized to prepare QSAR models. In this study, an effort is made to use published experimental data on EO of aqueous dyes after applying an easy method of normalization, to prepare QSAR models for percent color and COD removal. Normalized color and COD removal were obtained by multiplying the reported removal by volume of reactor and concentration of dye; and divided by total current passed and the time of electrolysis. More than 15 molecular descriptors were computed using Schrodinger-suit 2018-3. The multiple linear regression (MLR) approach was used to develop normalized color and COD removal models. The quantum chemical descriptors: highest occupied molecular orbital energy (HOMO) and lowest unoccupied molecular orbital energy (LUMO), polar surface area (PSA), hydrogen bond donor count (HBD), and number of atoms were found significant. The statistical indices: goodness-of-fit, R² > 0.75, and internal and external validations, Q²_LOOCV and Q²_ext, > 0.5, satisfied the criteria for predictive models and indicated that the method of normalization used in this study is adequate. Developed QSAR models are quite simple, interpretable, and transparent.

A multi-framework for bisphenols based on their high performance and environmental friendliness: Design, screening, and recommendations

Bisphenols (BPs) have gained significant attention due to their extensive use in the production of medical equipment, packaging materials, and everyday commodities. Urgent attention is required for assessing and identifying the risks associated with BP exposure to the environment and human health, as well as developing regulatory strategies. In this paper, 29 common BPs were selected as the research object, high-performance BP substitutes with environmental and human health friendliness characteristics were designed and screened. The above eight BP substitutes were considered as examples, and the first-level evaluation indicators of BPs and their substitutes were predicted using a random forest classification/regression model. Subsequently, the key indicators affecting the first-level evaluation indicators were ranked. The ranking results were environmental friendliness (64.30%) > human health risk (18.00%) > functionality (17.69%), indicating that environmental friendliness was the main influencing factor for the first-level evaluation indicators of BPs and their substitutes. Therefore, the study employed density functional theory (DFT) to simulate the biodegradation pathways of BPs and their substitutes in contaminated soil and landfill leachate, using Derivative-50 as an example. Furthermore, the environmental risk associated with the degradation products was evaluated, and regulatory recommendations based on risk identification were proposed.

Organophosphate Esters (OPEs) Flame Retardants in Water: A Review of Photocatalysis, Adsorption, and Biological Degradation

As a substitute for banned brominated flame retardants (BFRs), the use of organophosphate esters (OPEs) increased year by year with the increase in industrial production and living demand. It was inevitable that OPEs would be discharged into wastewater in excess, which posed a great threat to the health of human beings and aquatic organisms. In the past few decades, people used various methods to remove refractory OPEs. This paper reviewed the photocatalysis method, the adsorption method with wide applicability, and the biological method mainly relying on enzymolysis and hydrolysis to degrade OPEs in water. All three of these methods had the advantages of high removal efficiency and environmental protection for various organic pollutants. The degradation efficiency of OPEs, degradation mechanisms, and conversion products of OPEs by three methods were discussed and summarized. Finally, the development prospects and challenges of OPEs’ degradation technology were discussed.

Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds

Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.

Identifying molecular structural features by pattern recognition methods

Identification of molecular structural features is a central part of computational chemistry. It would be beneficial if pattern recognition techniques could be incorporated to facilitate the identification. Currently, the quantification of the structural dissimilarity is mainly carried out by root-mean-square-deviation (RMSD) calculations such as in molecular dynamics simulations. However, the RMSD calculation underperforms for large molecules, showing the so-called "curse of dimensionality" problem. Also, it requires consistent ordering of atoms in two comparing structures, which needs nontrivial effort to fulfill. In this work, we propose to take advantage of the point cloud recognition using convex hulls as the basis to recognize molecular structural features. Two advantages of the method can be highlighted. First, the dimension of the input data structure is largely reduced from the number of atoms of molecules to the number of atoms of convex hulls. Therefore, the dimensionality curse problem is avoided, and the atom ordering process is saved. Second, the construction of convex hulls can be used to define new molecular descriptors, such as the contact area of molecular interactions. These new molecular descriptors have different properties from existing ones, therefore they are expected to exhibit different behaviors for certain machine learning studies. Several illustrative applications have been carried out, which provide promising results for structure-activity studies.

The investigation of influencing factors on the degradation of sulfonamide antibiotics in iron-impregnated biochar-activated urea-hydrogen peroxide system: A QSAR study

Iron-impregnated biochar-activated urea-hydrogen peroxide (FB-activated UHP) is a potential in-situ technology for simultaneously reducing soil sulfonamide antibiotic contaminants and improving soil fertility. To better understand the degradation of sulfonamide antibiotics by FB-activated UHP, a two-dimensional quantitative structure-activity relationship (2D-QSAR) model based on quantum chemical parameters and a three-dimensional QSAR (3D-QSAR) model based on molecular force field were developed to investigate the factors influencing the removal efficiencies (Re%). The optimal 2D-QSAR model was Re%= 0.858-8.930 E-5 E_B3LYP-0.175 f(+)_x with the evaluation indices of R²= 0.732, q²= 0.571, and Q_ext²= 0.673. The given 2D-QSAR model indicated that the molecular size (E_B3LYP) and Fukui index with respect to nucleophilic attack (f(+)) were intrinsic factors influencing Re%. Three degradation pathways were subsequently proposed based on the f(+) distribution. Compared to the 2D-QSAR model, the developed 3D-QSAR model exhibited a better predictive ability, with the evaluation indices of R²= 0.989, q²= 0.696, and SEE= 0.001. The analysis of field contribution rates suggested that electrostatic field (48.2%), hydrophobic field (25.3%), and hydrogen-bond acceptor field (12.7%) were the main factors influencing Re%. These findings generated critical information for evaluating the degradation mechanisms/rules and provided theoretical bases for initially estimating the Re% of sulfonamide antibiotics undergoing FB-activated UHP process.

Degradation of tris(1-chloro-2-propanyl) phosphate by the synergistic effect of persulfate and zero-valent iron during a mechanochemical process

This study revealed a dual pathway for the degradation of tris(1-chloro-2-propanyl) phosphate (TCPP) by zero-valent iron (ZVI) and persulfate as co-milling agents in a mechanochemical (MC) process. Persulfate was activated with ZVI to degrade TCPP in a planetary ball mill. After milling for 2 h, 96.5% of the TCPP was degraded with the release of 63.16, 50.39, and 42.01% of the Cl^-, SO₄^2-, and PO₄^3-, respectively. In the first degradation pathway, persulfate was activated with ZVI to produce hydroxyl (·OH) radicals, and ZVI is oxidized to Fe(II) and Fe(III). A substitution reaction occurred as a result of the attack of ·OH on the P-O-C bonds, leading to the successive breakage of the three P-O-C bonds in TCPP to produce PO₄^3-. In the second pathway, a C-Cl bond in part of the TCPP molecule was oxidized by SO₄·^- to carbonyl and carboxyl groups. The P-O-C bonds continued to react with ·OH to produce PO₄^3-. Finally, the intermediate organochloride products were further reductively dechlorinated by ZVI. However, the synergistic effect of the oxidation (·OH and SO₄·^-) and the reduction reaction (ZVI) did not completely degrade TCPP to CO₂, resulting in a low mineralization rate (35.87%). Moreover, the intermediate products still showed the toxicities in LD₅₀ and developmental toxicant. In addition, the method was applied for the degradation of TCPP in soil, and high degradations (> 83.83%) were achieved in different types of soils.

Prediction of phthalate acid esters degradation in soil using QSAR model: A combined consideration of soil properties and quantum chemical parameters

Phthalic acid esters (PAEs) are predominant hazardous substances and endocrine-disrupting compounds to be controlled in soil. The degradation behaviors of PAEs in soil had been long term concerned. Thus, the degradation rate (K) is important for assessing theexposure risk and is of great significance in evaluating the ecological risk of PAEs in soil environment. But by far, quantitative structure activity relationship (QSAR) models for PAEs degradation have rarely been considered in soil environment. In this study, quantum chemical parameters were considered along with soil properties as two kinds of descriptors in QSAR model. A total of 32 logk of PAEs were collected from reference and experiment. Degradation kinetics in soils were determined by pseudo-first order kinetic models. The residual concentration of PAEs in Udic ferrosols and Aquic cambisols suggesting a potential expose risks of PAEs to ecosystem in soil. The QSAR model between logk and quantum chemical parameters revealed that E_HOMO and qC- are two predominant factors in determining logk value. Furthermore,our study further indicated that soil organic matter (SOM) as new predictor contributes more to predict logk values of PAEs during degradation process than pH. Results from this study make a new contribution for methods to predict the degradation of PAEs in soil environment and highlight the potential to evaluate the environmental risks of degradation of PAEs.

Rapid electrochemical reduction of a typical chlorinated organophosphorus flame retardant on copper foam: degradation kinetics and mechanisms

With the widespread use, chlorinated organophosphorus flame retardants (Cl-OPFRs) as a new emerging contaminant have been widely detected in water environments over the last few years. In this study, the degradation of a typical Cl-OPFR, TCEP (tris (2-chloroethyl) phosphate), by electrochemical reduction was investigated. It was found that copper (Cu) foam as the cathode showed more rapid and effective degradation for TCEP, compared to other cathodes. When TCEP was at the low concentrations (0.1 and 1 mg L^-1), its degradation by Cu foam could reach above 95% within 20 min, and the maximum rate constant was 0.127 min-1. TCEP reduction was little influenced by the co-existing humic substance and anions, except Cl^-. Compared with the reported oxidation methods, electrochemical reduction showed fast and stable degradation for TCEP. For other types of Cl-OPFRs, electrochemical reduction displayed a fast and effective removal for tris (1,3-dichloro-2-propyl) phosphate but lower removal for tris (2-cholroisopropyl) phosphate who possessed methyl units in the branched chains, influencing its reducibility. Based on the product analysis and Fukui function calculation, the bonds of TCEP molecule were found to be gradually broken, and the three oxygen-ethyl-chlorine arms were cleaved one by one. The products including C₆H₁₃Cl₂O₄P (MW = 249.99278 Da), C₄H₉Cl₂O₄P (MW = 221.96105 Da) and C₄H₁₀ClO₄P (MW = 188.0002 Da) were detected at 60 min reaction, and those intermediates showed much lower toxicities than TCEP according to the previous report. The findings may provide a promising treatment for Cl-OPFRs removal from aqueous environments and help understand their reductive fate.