Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study

Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.

Biosensing of multiple aromatic xenobiotics in water by in-house fabricated prototype device

Portable, low-cost, and accurate monitoring of hazardous mono-aromatic pollutants, such as phenol or benzene group of compounds in water is a challenging task due to the lack of suitable detectable functional groups and complex matrix of environmental samples. Here, we use a series of protein-based biosensing recognition scaffolds to enable specific detection of several mono-aromatic classes of xenobiotics. The biosensor is tuned to perform in intricate environmental conditions and is interfaced with an in-house manufactured, multi-channel device (AroTrack) capable of direct and sensitive detection of several of these aromatic contaminants, such as phenol, benzene, and 2,3-dimethylphenol (2,3-DMP) in the low ppb range (10-200 ppb). The efficiency of the prototype device was benchmarked in both simulated wastewater and real environmental samples comprising 10 times higher isostructural aromatic pollutants or ions. It was established that AroTrack is reliable for environmental sample testing with a high degree of reproducibility and efficiency comparable to that of modern spectrophotometers (<5 % error). The battery-operated device costs less than $50 to fabricate and this low cost makes it effective to be implemented in rural and low-income settings which suggests immense field deployable potential.

Systematic computational toxicity analysis of the ozonolytic degraded compounds of azo dyes: Quantitative structure-activity relationship (QSAR) and adverse outcome pathway (AOP) based approach

The present study identifies and analyses the degraded products of three azo dyes (Reactive Orange 16, Reactive Red 120, and Direct Red 80) and proffers their in silico toxicity predictions. In our previously published work, the synthetic dye effluents were degraded using an ozonolysis-based Advanced Oxidation Process. In the present study, the degraded products of the three dyes were analysed using GC-MS at endpoint strategy and further subjected to in silico toxicity analysis using Toxicity Estimation Software Tool (TEST), Prediction Of TOXicity of chemicals (ProTox-II), and Estimation Programs Interface Suite (EPI Suite). Several physiological toxicity endpoints, such as hepatotoxicity, carcinogenicity, mutagenicity, cellular and molecular interactions, were considered to assess the Quantitative Structure-Activity Relationships (QSAR) and adverse outcome pathways. The environmental fate of the by-products in terms of their biodegradability and possible bioaccumulation was also assessed. Results of ProTox-II suggested that the azo dye degradation products are carcinogenic, immunotoxic, and cytotoxic and displayed toxicity towards Androgen Receptor and Mitochondrial Membrane Potential. TEST results predicted LC50 and IGC50 values for three organisms Tetrahymena pyriformis, Daphnia magna, and Pimephales promelas. EPISUITE software via the BCFBAF module surmises that the degradation products' bioaccumulation (BAF) and bioconcentration factors (BCF) are high. The cumulative inference of the results suggests that most degradation by-products are toxic and need further remediation strategies. The study aims to complement existing tests to predict toxicity and prioritise the elimination/reduction of harmful degradation products of primary treatment procedures. The novelty of this study is that it streamlines in silico approaches to predict the nature of toxicity of degradation by-products of toxic industrial affluents like azo dyes. These approaches can assist the first phase of toxicology assessments for any pollutant for regulatory decision-making bodies to chalk out appropriate action plans for their remediation.

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Toxicity prediction of 1,2,4-triazoles compounds by QSTR and interspecies QSTTR models

1,2,4-triazole derivatives exhibit various biological activities, including antibacterial and antifungal properties. On the other hand, these chemicals may have unique cumulative and harmful effects on living organisms. The goal of this work is to use quantitative structure-toxicity relationship (QSTR) and interspecies quantitative toxicity-toxicity relationship (iQSTTR) models to predict the acute toxicity of 1,2,4-triazole derivatives. The QSTR models were generated by multiple linear regression (MLR) following the OECD recommendations for QSAR model development and validation. The iQSTTR models were constructed using data on acute oral toxicity in rats and mice, as well as the 2D descriptor. The application domain (AD) analysis was used to identify model outliers and determine if the forecast was credible. Six QSTR models were successfully constructed in rats and mice using various delivery methods, and the scatter plots demonstrated excellent consistency across training and test sets. According to external and internal validation criteria, all six QSTR models may be broadly accepted; however, the orally administered mice model was the optimum one among the six species. Several chemicals with leverage values above the requirements were identified as response or structural outliers in the training sets for six QSTR and two iQSTTR models. All outliers, however, fell slightly outside the threshold or had low prediction errors, which may have had little impact on the capacity to forecast and were therefore preserved in the final models. In fact, neither the QSTR nor the iQSTTR test sets contained any response outliers. Additionally, all external and internal validation results for the iQSTTR models were approved, with the iQSTTR models outperforming the comparable QSTR models, which are deemed more dependable. The QSTR and iQSTTR models performed well in predicting toxicity using test sets, which would be beneficial in evaluating and synthesizing newly discovered 1,2,4-triazoles derivatives with low toxicity and environmental hazard.

An improved opposition-based crow search algorithm for biodegradable material classification

The development of a reliable quantitative structure-activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure-biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.

Synthetically engineered microbial scavengers for enhanced bioremediation

Microbial bioremediation has gained attention as a cheap, efficient, and sustainable technology to manage the increasing environmental pollution. Since microorganisms in nature are not evolved to degrade pollutants, there is an increasing demand for developing safer and more efficient pollutant-scavengers for enhanced bioremediation. In this review, we introduce the strategies and technologies developed in the field of synthetic biology and their applications to the construction of microbial scavengers with improved efficiency of biodegradation while minimizing the impact of genetically engineered microbial scavengers on ecosystems. In addition, we discuss recent achievements in the biodegradation of fastidious pollutants, greenhouse gases, and microplastics using engineered microbial scavengers. Using synthetic microbial scavengers and multidisciplinary technologies, toxic pollutants could be more easily eliminated, and the environment could be more efficiently recovered.

Predicting anionic surfactant toxicity to Daphnia magna in aquatic environment: a green approach for evaluation of EC50 values

The median effective concentration (EC₅₀) is the concentration of a substance expected to produce a specific effect in 50% of the populations with a certain density under defined conditions. This parameter is expressed as an acute toxicity and is obtained via chemical toxicity testing. But, the laboratory work is time-consuming, expensive, and not eco-friendly. Therefore, to predict EC₅₀ for new anionic surfactants, a quantitative structure-activity relationship (QSAR) tool was studied for modeling the EC₅₀ of anionic surfactants on Daphnia magna based on the molecular descriptors. The best model (R² = 0.901 and F = 118.077, p<0.01) included 3 variables of the number of carbons, hydrogens, and the octanol-water partition coefficient logarithm. The main contribution to the toxicity was the octanol-water partition coefficient logarithm descriptor that had a negative effect on the toxicity of surfactants. The QSAR approach exhibited good results in predicting anionic surfactants EC₅₀, which allows the building of a simple, valid, and interpretable model that can be utilized as potential tools for rapidly predicting the lnEC50 of new or untested anionic surfactants to Daphnia magna.

In vivo and in silico evaluations of survival and cardiac developmental toxicity of quinolone antibiotics in zebrafish embryos (Danio rerio)

Quinolones are ranked as the second most commonly used class of antibiotics in China, despite their adverse clinical and environmental effects. However, information on their cardiac developmental toxicity to zebrafish is limited. This study investigates the relationships between different quinolone structures and toxicity in zebrafish embryos using in vivo and in silico methods. All of the experimentally tested quinolones show cardiac developmental toxicity potential and present mortality and teratogenic effects in a dose-dependent manner. Theoretically, the acute toxicity values predicted using quantitative structure-toxicity relationship (QSTR) modeling based on previously reported LC₅₀ values are in good agreement with the in vivo results. Further investigation demonstrates that the hormetic concentration response of some quinolones may be related to methylation on the piperazine ring at the C-7 position. The amino group at the C-5 position, the methylated or ethylated piperazine group at the C-7 position, halogens at the C-8 position and a cyclopropyl ring at N1 position may be responsible for cardiac developmental toxicity. In terms of survival (key ecological endpoint), the naridine ring is more toxic than the quinoline ring. This combined approach can predict the acute and cardiac developmental toxicity of other quinolones and impurities.

Environmental aspects of UV-C-based processes for the treatment of oxytetracycline in water

This study is focused on oxytetracycline (OTC) degradation by direct photolysis (UV-C) and photobased advanced oxidation processes (AOPs) (UV-C/H₂O₂ and UV-C/S₂O₈^2-). OTC degradation pathways were revealed by LC-MS/MS and GC-MS/MS analyses. The evolution/degradation profiles of 12 detected byproducts were correlated with changes in biodegradability and toxicity toward Vibrio fischeri recorded during the treatment. Both photobased AOPs yielded higher OTC degradation and mineralization rates than direct photolysis. The OTC degradation pathway was found to be rather specific regarding the main reactive species (HO• or SO₄•^-)/mechanism, yielding different patterns in toxicity changes, while biodegradability profiles were less affected. Biodegradability was correlated with the observed degradation and mineralization kinetics. The recorded toxicity changes indicate that byproducts formed by initial OTC degradation are more toxic than the parent pollutant. The prolonged treatment resulted in the formation of byproducts that contributed to a decrease in toxicity and an increase in biodegradability, as particularly emphasized in the case of UV-C/S₂O₈^2-.

Toxicity of binary mixtures of pesticides and pharmaceuticals toward Vibrio fischeri: Assessment by quantitative structure-activity relationships

Pollutants in real aquatic systems commonly occur as chemical mixtures. Yet, the corresponding risk assessment is still mostly based on information on single-pollutant toxicity, accepting the assumption that pollutant mixtures exhibit additive toxicity effect which is often not the case. Therefore, it is still better to use the experimental approach. Unfortunately, experimental determination of toxicity for each mixture is practically unfeasible. In this study, quantitative structure-activity relationship (QSAR) models for the prediction of toxicity of binary mixtures towards bioluminescent bacteria Vibrio fischeri were developed at three toxicity levels (EC₁₀, EC₃₀ and EC₅₀). For model development, experimentally determined toxicity values of 14 pollutants (pharmaceuticals and pesticides) were correlated with their structural features, applying multiple linear regression together with genetic algorithm. Statistical analysis, internal validation and external validation of the models were carried out. The toxicity is accurately predicted by all three models. EC₃₀ and EC₅₀ values are mostly influenced by geometrical distances between nitrogen and sulfur atoms. Furthermore, the simultaneous presence of oxygen and chlorine atoms in mixture can induce the increase in toxicity. At lower effect levels (EC₁₀), nitrogen atom bonded to different groups has the highest impact on mixture toxicity. Thus, the analysis of the descriptors involved in the developed models can give insight into toxic mechanisms of the binary systems.

Garbage in garbage out: the contribution of our industrial advancement to wastewater degeneration

Natural water sources are habitually marred by insidious anthropogenic practices and municipal wastewater discharges that contain either of xenobiotic pollutants and their sometimes more toxic degradation products, or both. Although wastewater is considered as both a resource and a problem, as explained in this review, it is however daunting that, while the global village is still struggling to decipher the mode of proper handling, subsequent discharge and regulation of already established aromatic contaminants in wastewater, there emanates some more aggressive, stealth and sinister groups of compounds. It is quite ironic that majority of these compounds are the 'go through' consumables in our present society and have been suspected to pose several health risks to the aquatic ecosystem, eliciting unfavourable clinical manifestations in aquatic animals and humans, which has heightened the uncertainties conferred on freshwater use and consumption of some aquatic foods. This review therefore serves to give a brief account on the metamorphosis of approach in detection of aromatic pollutants and ultimately their implications along the trophic chains in the community.

Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics

The n-octanol/water partition coefficient (logK_ow) is widely used in the environmental, agricultural and pharmaceutical fields for the risk evaluation and application of organic chemicals. In this work, grounded on atomic distribution matrices, a norm index-based QSPR model was built for organic chemicals with 18 kinds of diverse structures. The statistical results (R² = 0.9037, RMSE = 0.4515) showed that the QSPR model for describing the logK_ow of organics was fitted well. Various validation results showed that the model had good robustness, good predictability and wide applicability. These satisfactory results indicated that the model was applicable for the logK_ow description of organic chemicals and that norm descriptors were reliable and general for the description of organic structures. The model was relatively better at describing logK_ow for aromatics, alcohols, nitriles, esters, amides, halogenated compounds, acids and amine compounds. The intensity of spatial branching and the space charge distribution intensity descriptors could have a greater impact on the logK_ow value of a compound.

Analyzing a broader spectrum of endocrine active organic contaminants in sewage sludge with high resolution LC-QTOF-MS suspect screening and QSAR toxicity prediction

Endocrine active contaminants (EACs) in environmental samples can pose a range of toxicological threats to ecosystems, especially through their impacts on reproductive pathways mediated by the estrogen receptor. The physicochemical properties of known organic EACs vary greatly and typically require different sample preparation techniques to identify different classes of compounds. EAC sources are similarly diverse, including both endogenous compounds and anthropogenic chemicals found in personal care products, pharmaceuticals, and their transformation products, which are often disposed of to sewers at their end of use. Looking for EACs in sewage sludge proposes a bottom-up, or end-of-use and treatment approach to discover environmentally relevant EACs, since many EACs accumulate in sludges even after application of robust wastewater treatment processes. This study demonstrates an extraction and analytical method capable of detecting a broad spectrum of known and suspected EACs via High Resolution Liquid Chromatography Quadropole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) suspect screening of fourteen California sewage sludge samples. Spike-recovery experiments were performed using twelve carefully selected surrogates to assess different extraction solvents, sample weights, extraction pH values, procedures for combining extracts with different extraction pH's, and solid phase extraction cartridges. Using LC-QTOF-MS, identifications of several other organic compounds in the samples were made, a goal unachievable with unit resolution mass spectrometry. Suspect screening of California sludge samples discovered 118 compounds including hormones, pharmaceuticals, phosphate flame retardants, recreational drugs, antimicrobials, and pesticides. Additionally, 22 of these identified compounds are predicted to interfere with estrogen receptors or other reproductive/developmental pathways based on the VEGA QSAR toxicity prediction model.