Biochemical interpretation of quantitative structure-activity relationships (QSAR) for biodegradation of N-heterocycles: a complementary approach to predict biodegradability

New insights into the typical nitrogen-containing heterocyclic compound-quinoline degradation and detoxification by microbial consortium: Integrated pathways, meta-transcriptomic analysis and toxicological evaluation

As the primary source of COD in industrial wastewater, quinoline has aroused increasing attention because of its potential teratogenic, carcinogenic, and mutagenic effects in the environment. The activated sludge isolate quinoline-degrading microbial consortium (QDMC) efficiently metabolizes quinoline. However, the molecular underpinnings of the degradation mechanism of quinoline by QDMC have not been elucidated. High-throughput sequencing revealed that the dominant genera included Diaphorobacter, Bacteroidia, Moheibacter and Comamonas. Furthermore, a positive strong correlation was observed between the key bacterial communities (Diaphorobact and Bacteroidia) and quinoline degradation. According to metatranscriptomics, genes associated with quorum sensing, ABC transporters, component systems, carbohydrate, aromatic compound degradation, energy metabolism and amino metabolism showed high expression, thus improving adaptability of microbial community to quinoline stress. In addition, the mechanism of QDMC in adapting and resisting to extreme environmental conditions in line with the corresponding internal functional properties and promoting biogegradation efficiency was illustrated. Based on the identified products, QDMC effectively mineralized quinoline into low-toxicity metabolites through three major metabolic pathways, including hydroxyquinoline, 1,2,3,4-H-quinoline, 5,6,7,8-tetrahydroquinoline and 1-oxoquinoline pathways. Finally, toxicological, genotoxicity and phytotoxicity studies supported the detoxification of quinoline by the QDMC. This study provided a promising approach for the stable, environmental-friendly and efficient bioremediation applications for quinoline-containing wastewater.

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water

Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R² = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH₃ > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pK_a and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

An adjusted 3D-QSAR model for the combined activity of fluoroquinolones photodegradation and microbial degradation assisted by dynamic simulation and its application in molecular modification

This study developed a comprehensive characterization method for the combined degradation effect of modified fluoroquinolones (FQs) photodegradation and microbial degradation. A combination of revised 3D-QSAR model, molecular docking, path simulation inference, pharmacokinetics, molecular dynamics (MD) simulation and toxicokinetics simulation was used to construct a systematic environment-friendly drug screening system. Five derivatives were screened with significantly improved combined degradation effect (over 20%) and functional characteristics and human health parameters through combined model verification, functional and human health risk assessment. The simulation path of photo- and microbial-degradation of gatifloxacin and new gatifloxacin molecules was derived, and the reaction energy barrier was also calculated. The ratio of the total rate-determining steps change rate of the decreased energy barrier (14.10%:26.30%) was consistent with the ratio of the increased degradation performance predicted by the model (22.87%:19.77%), demonstrating the reliability of revised 3D-QSAR model and it could be applied in molecular modification. MD and toxicokinetics simulation were used to predict the binding energy and aquatic toxicity between photo- and microbial-degradation products and the degradation enzymes, which further to screen the degradation pathways with low potential environmental risks. The findings will be helpful to screen environment-friendly drug and develop appropriate strategies for its risk management.

Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms

Biodegradation is a significant process for removing organic chemicals from water, soil and sediment environments, and therefore biodegradability is critical to evaluate the environmental persistence of organic chemicals. In this study, based on a dataset with 171 compounds, four quantitative structure-activity relationship (QSAR) models were developed for predicting primary and ultimate biodegradation rate rating with multiple linear regression (MLR) and support vector machine (SVM) algorithms. Two MLR models were built with a dataset with carbon atom number ≤9, and two SVM models were built with a dataset with carbon atom number >9. In the MLR models, n_ArX (number of X on aromatic ring) is the most important descriptor governing primary and ultimate biodegradation of organic chemicals. For the SVM models, determination coefficient (R²) values, cross-validated coefficients (Q²_LOO) and external validation coefficient (Q²_ext) values are over 0.9, indicating the SVM models have satisfactory goodness-of-fit, robustness and external predictive abilities. The applicability domains of these models were visualized by the Williams plot. The developed models can be used as effective tools to predict biodegradability of organic chemicals.

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.

Synthesis, characterization, ecotoxicity and biodegradability evaluations of novel biocompatible surface active lauroyl sarcosinate ionic liquids

Ionic liquids (ILs) based surfactants have been emerged as attractive alternatives to the conventional surfactants owing to their tailor-made and eco-friendly properties. Therefore, present study described the synthesis of nine new fatty amino acids based IL surfactants utilizing lauroyl sarcosinate anion and pyrrolidinium, imidazolium, pyridinium, piperidinium, morpholinium and cholinium cations for the first time. The synthesized surface active lauroyl sarcosinate ionic liquids (SALSILs) were characterized by ¹H NMR, ¹³C NMR and TGA. Next, the surface tension and critical micellar concentrations were determined and compared with the surface properties of ILs based surfactants. Further, the toxicity and biodegradability of the synthesized SALSIILs were evaluated to confirm their safe and efficient process applications. The studies revealed that three out of nine synthesized SALSILs containing pyridinium cation have showed strong activity towards the tested microbial growth. The remaining six SALSILs met the biocompatible measures demonstrating moderate to low activity depends on the tested microbes. The alicyclic SALSILs containing morpholinium and piperidinium cations have demonstrated 100% biodegradation after 28 days of the test period. Overall, it is believed that the synthesized SALSILs could effectively replace the conventional surfactants in a wide variety of applications.

Comparative analysis of freshwater species sensitivity distributions and ecotoxicity for priority pesticides: Implications for water quality criteria

Organochlorine pesticides (OCPs) and organophosphate pesticides (OPPs) posed severe threats to the aquatic environment in China. The toxicity data of 18 priority OCPs and OPPs for Chinese and American species were collected, and their species sensitivity distributions (SSDs) and ecotoxicity were compared. Physicochemical characteristics and chemical structures were used to analyze the difference in the pesticide toxicity. Results suggested that there is no significant difference between the HC₅ values (hazardous concentration protecting 95% of species) of China and America as a whole. However, the HC₅ values of nearly half of these pesticides (8/18) differ dramatically between Chinese and American due to the different resident species distribution. It indicated that it is necessary to develop local water quality criteria in China. The regression analysis of pesticides toxicity differences indicated a decrease in LC₅₀/EC₅₀ values (for Oncorhynchus mykiss and Carassius auratu) with the increase in molecular weights of pesticides. Pesticides with larger MWs might be more toxic to aquatic species. Similar trend was also observed in the regression analysis of a decrease in both American and Chinese HC₅ values for with increase of the number of containing halogen atoms. It indicated the potency of these parameters to be used to predict the ecotoxicity of pesticide.

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.

Quantitative structure-biodegradability relationships for biokinetic parameter of polycyclic aromatic hydrocarbons

Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper, stepwise multiple linear regression analysis method was applied to establish quantitative structure biodegradability relationship (QSBR) between the chemical structure and a novel biodegradation activity index (qmax) of 20 polycyclic aromatic hydrocarbons (PAHs). The frequency B3LYP/6-311+G(2df,p) calculations showed no imaginary values, implying that all the structures are minima on the potential energy surface. After eliminating the parameters which had low related coefficient with qmax, the major descriptors influencing the biodegradation activity were screened to be Freq, D, MR, EHOMO and ToIE. The evaluation of the developed QSBR mode, using a leave-one-out cross-validation procedure, showed that the relationships are significant and the model had good robustness and predictive ability. The results would be helpful for understanding the mechanisms governing biodegradation at the molecular level.

Linear and nonlinear relationships between biodegradation potential and molecular descriptors/fragments for organic pollutants and a theoretical interpretation

Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper, linear and nonlinear relationships between biological oxygen demand (BOD) and molecular descriptors/fragments have been investigated for 1130 organic chemicals. Significant relationships have been observed between the simple molecular descriptors and %BOD for some homologous compounds, but not for the whole set of compounds. Electronic parameters, such as E(HOMO) and E(LUMO), are the dominant factors affecting the biodegradability for some homologous chemicals. However, other descriptors, such as molecular weight, acid dissociation constant and polarity still have a significant impact on the biodegradation. The best global model for %BOD prediction is that developed from a chain-based fragmentation scheme. At the same time, the theoretical relationship between %BOD and molecular descriptors/fragments has been investigated, based on a first-order kinetic process. The %BOD is nonlinearly, rather than linearly, related to the descriptors. The coefficients of determination can be significantly improved by using nonlinear models for the homologous compounds and the whole data set. After analysing 1130 ready and not ready biodegradable compounds using 23 simple descriptors and various fragmentation schemes, it was revealed that biodegradation could be well predicted from a chain-based fragmentation scheme, a decision tree and a %BOD model. The models were capable of separating NRB and RB with an overall accuracy of 87.2%, 83.0% and 82.5%, respectively. The best classification model developed was a chain-based model but it used 155 fragments. The simplest model was a decision tree which only used 10 structural fragments. The effect of structures on the biodegradation has been analysed and the biodegradation pathway and mechanisms have been discussed based on activating and inactivating fragments.

In silico assessment of chemical biodegradability

Biodegradation is the principal environmental dissipation process. Due to a lack of comprehensive experimental data, high study cost and time-consuming, in silico approaches for assessing the biodegradable profiles of chemicals are encouraged and is an active current research topic. Here we developed in silico methods to estimate chemical biodegradability in the environment. At first 1440 diverse compounds tested under the Japanese Ministry of International Trade and Industry (MITI) protocol were used. Four different methods, namely support vector machine, k-nearest neighbor, naïve Bayes, and C4.5 decision tree, were used to build the combinatorial classification probability models of ready versus not ready biodegradability using physicochemical descriptors and fingerprints separately. The overall predictive accuracies of the best models were more than 80% for the external test set of 164 diverse compounds. Some privileged substructures were further identified for ready or not ready biodegradable chemicals by combining information gain and substructure fragment analysis. Moreover, 27 new predicted chemicals were selected for experimental assay through the Japanese MITI test protocols, which validated that all 27 compounds were predicted correctly. The predictive accuracies of our models outperform the commonly used software of the EPI Suite. Our study provided critical tools for early assessment of biodegradability of new organic chemicals in environmental hazard assessment.

Importance of heterocylic aromatic compounds in monitored natural attenuation for coal tar contaminated aquifers: A review

NSO heterocycles (HET) are typical constituents of coal tars. However, HET are not yet routinely monitored, although HET are relatively toxic coal tar constituents. The main objectives of the study is therefore to review previous studies and to analyse HET at coal tar polluted sites in order to assess the relevance of HET as part of monitored natural attenuation (MNA) or any other long-term monitoring programme. Hence, natural attenuation of typical HET (indole, quinoline, carbazole, acridine, methylquinolines, thiophene, benzothiophene, dibenzothiophene, benzofuran, dibenzofuran, methylbenzofurans, dimethylbenzofurans and xanthene) were studied at three different field sites in Germany. Compound-specific plume lengths were determined for all main contaminant groups (BTEX, PAH and HET). The results show that the observed plume lengths are site-specific and are above 250m, but less than 1000m. The latter, i.e. the upper limit, however mainly depends on the level of investigation, the considered compound, the lowest measured concentration and/or the achieved compound-specific detection limit and therefore cannot be unequivocally defined. All downstream contaminant plumes exhibited HET concentrations above typical PAH concentrations indicating that some HET are generally persistent towards biodegradation compared to other coal tar constituents, which results in comparatively increased field-derived half-lives of HET. Additionally, this study provides a review on physicochemical and toxicological parameters of HET. For three well investigated sites in Germany, the biodegradation of HET is quantified using the centre line method (CLM) for the evaluation of bulk attenuation rate constants. The results of the present and previous studies suggest that implementation of a comprehensive monitoring programme for heterocyclic aromatic compounds is relevant at sites, if MNA is considered in risk assessment and for remediation.

Removal of trace organics by MBR treatment: the role of molecular properties

This study examined the relationship between specific molecular features of trace organic contaminants and their removal efficiencies by a laboratory scale membrane bioreactor (MBR). Removal efficiencies of 40 trace organic compounds were assessed under stable operating conditions. The reported results demonstrate an apparent correlation between chemical structures and the removal of trace organic contaminants by the laboratory scale MBR system. The removal of all 14 very hydrophobic (Log D > 3.2) trace organic compounds selected in this study was consistently high and was above 85%. The occurrence and types of electron withdrawing or donating functional groups appear to be important factors governing their removal by MBR treatment. In this study, all hydrophilic and moderately hydrophobic (Log D<3.2) compounds possessing strong electron withdrawing functional groups showed removal efficiency of less than 20%. In contrast, high removal efficiencies were observed with most compounds bearing electron donating functional groups such as hydroxyl and primary amine groups. A qualitative framework for the assessment of trace organic removal by MBR treatment was proposed to provide further insights into the removal mechanisms.

Identification of ionic liquid breakdown products in an advanced oxidation system

Commonly used alkylimidazolium ionic liquids are poorly to negligibly biodegradable, and some are toxic, with the potential to poison typical biological test systems. Therefore, when ionic liquids are present in technological wastewaters they could break through classical wastewater treatment systems into natural waters and become potentially persistent pollutants. A recent study investigating different advanced oxidation processes found that the H(2)O(2)/UV system degraded dissolved imidazolium ionic liquids with the greatest efficiency. In the present study, high performance liquid chromatography was coupled with electrospray mass spectrometry to separate, analyse and identify degradation products following the treatment of ionic liquid solutions with H(2)O(2) in the presence of UV irradiation. It was found that hydroxylation in short-chain entities occurred mainly within the ring moiety, whereas in the case of longer alkylated cations, oxidation of the alkyl chain yielded several products. The potential transformation products were identified structurally by MS/MS analysis and are discussed in the light of their putative toxicity and biodegradability.