Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources

Haloketones: A class of unregulated priority DBPs with high contribution to drinking water cytotoxicity

Although unregulated aliphatic disinfection byproducts (DBPs) had a much higher concentration and cytotoxicity than known aromatic DBPs, a recent study indicated that seven classes of regulated and unregulated priority DBPs (one and two-carbon-atom DBPs) just accounted for 16.2% of disinfected water cytotoxicity in the U.S., meaning some of the highly toxic aliphatic DBPs may be overlooked. Haloketones (HKs) are an essential class of priority DBPs with a 1-100 µg/L concentration in drinking water but lack cytotoxicity data. This study investigated the cytotoxicity of seven HKs using Chinese hamster ovary (CHO) cells. The order for cytotoxicity of HKs from most to least toxic was: 1,3-dichloroacetone (LC50: 1.0 ± 0.20 μM) ≈ 1,3-dibromoacetone (1.5 ± 0.19 μM) ≈ bromoacetone (1.9 ± 0.49 μM) > chloroacetone (4.3 ± 0.22 μM) > 1,1,3-trichloropropanone (6.6 ± 0.46 μM) > 1,1,1-trichloroacetone (222 ± 7.7 μM) > hexachloroacetone (3269 ± 344 μM). The cytotoxicity of HKs was higher than most regulated and priority aliphatic DBPs in mono-halogenated, di-halogenated, and tri-halogenated categories. A prediction model of HK cytotoxicity was developed based on the quantitative structure-activity relationship (QSAR), optimizing structures and computing descriptors with Gaussian 09 W. The average concentrations of HKs in representative drinking water samples from South Carolina (U.S.) and Suzhou (China) were 12.4 and 0.9 μg/L, respectively, accounting for 18.8% and 1.7% of their specific total DBPs measured (i.e. not TOX). For South Carolina drinking water, their contributions to total calculated additive cytotoxicity of aliphatic DBPs and overall drinking water cytotoxicity were 86.7% and 14.0%, respectively, demonstrating that HKs are an essential class of overlooked DBPs with a high contribution to drinking water cytotoxicity. Our study can help to explain the conflict that why regulated and priority DBPs (except HKs) just accounted for 16% of chlorinated drinking water cytotoxicity even enough they had much higher concentration and cytotoxicity than known aromatic DBPs.

Cytotoxicity of emerging halophenylacetamide disinfection byproducts in drinking water: Mechanism and prediction

Halophenylacetamides (HPAcAms) have been identified as a new group of nitrogenous aromatic disinfection byproducts (DBPs) in drinking water, but the toxicity mechanisms associated with HPAcAms remain almost completely unknown. In this work, the cytotoxicity of HPAcAms in human hepatoma (HepG2) cells was evaluated, intracellular oxidative stress/damage levels were analyzed, their binding interactions with antioxidative enzyme were explored, and a quantitative structure-activity relationship (QSAR) model was established. Results indicated that the EC50 values of HPAcAms ranged from 2353 μM to 9780 μM, and the isomeric structure as well as the type and number of halogen substitutions could obviously induce the change in the cytotoxicity of HPAcAms. Upon exposure to 2-(3,4-dichlorophenyl)acetamide (3,4-DCPAcAm), various important biomarkers linked to oxidative stress and damage, such as reactive oxygen species, 8‑hydroxy-2-deoxyguanosine, and cell apoptosis, exhibited a significant increase in a dose-dependent manner. Moreover, 3,4-DCPAcAm could directly bind with Cu/Zn-superoxide dismutase and induce the alterations in the structure and activity, and the formation of complexes was predominantly influenced by the van der Waals force and hydrogen bonding. The QSAR model supported that the nucleophilic reactivity as well as the molecular compactness might be highly important in their cytotoxicity mechanisms in HepG2 cells, and 2-(2,4-dibromophenyl)acetamide and 2-(3,4-dibromophenyl)acetamide deserved particular attention in future studies due to the relatively higher predicted cytotoxicity. This study provided the first comprehensive investigation on the cytotoxicity mechanisms of HPAcAm DBPs.

Degradation products and transformation pathways of sulfamethoxazole chlorination disinfection by-products in constructed wetlands

Antibiotics and available chlorine coexist in multiple aquatic environments, and thus antibiotics and their chlorinated disinfection by-products (Cl-DBPs) have been a great concern for the nature and human health. Herein, the degradation intermediates and transformation pathways of sulfamethoxazole (SMX) Cl-DBPs in constructed wetlands (CWs) were investigated. A total of five SMX Cl-DBPs and their twenty degradation products in CWs was identified in this study. SMX and its Cl-DBPs influenced the biodegradation rather than the adsorption process in CWs. S1 atom on sulfonyl group of SMX had the strongest nucleophilicity, and was most vulnerable for nucleophilic attack. N5 and N7 on amino groups, and C17 on the methyl group had great electronegativity, and were susceptible to electrophilic reactions. S1-N5 and S1-C8 bonds of SMX are the most prone to cleavage, followed by C11-N5, C16-C17, and C12-N7. The chlorination of SMX mainly occurred at S1, N5, and N7 sites, and went through S-C cleavage, S-N hydrolysis, and desulfonation. The biodegradation of SMX Cl-DBPs in CWs mainly occurred at S1, N5, N7, C8, and C17 sites, and went through processes including oxidation of methyl, hydroxyl and amino groups, desulfonation, decarboxylation, azo bond cleavage, benzene ring cleavage, β-oxidation of fatty acids under the action of coenzymes. Over half of the SMX Cl-DBPs had greater bioaccumulation potential than their parent SMX, but the environmental risk of SMX Cl-DBPs was effectively reduced through the degradation by CWs.

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At least 91 peer-reviewed articles published since 1997 reported the application of AI techniques to coagulation/flocculation (41), membrane filtration (21), disinfection byproducts (DBPs) formation (13), adsorption (16) and other operational management in WTPs. In this paper, these publications were reviewed with the goal of assessing the development and applications of AI techniques in WTPs and determining their limitations and areas for improvement. The applications of the AI techniques have improved the predictive capabilities of coagulant dosages, membrane flux, rejection and fouling, disinfection byproducts (DBPs) formation and pollutants' removal for the WTPs. The deep learning (DL) technology showed excellent extraction capabilities for features and data mining ability, which can develop an image recognition-based DL framework to establish the relationship among the shapes of flocs and dosages of coagulant. Further, the hybrid techniques (e.g., combination of regression and AI; physical/kinetics and AI) have shown better predictive performances. The future research directions to achieve better control for WTPs through improving these techniques were also emphasized.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Target analysis, occurrence and cytotoxicity of halogenated polyhydroxyphenols as emerging disinfection byproducts in drinking water

Halogenated aromatic disinfection byproducts (DBPs) in drinking water, such as halogenated phenols, have received widespread attention due to their high toxicity and ubiquitous occurrence in recent years. This study identified a group of emerging halogenated aromatic DBPs, known as halogenated polyhydroxyphenols (HPPs), and investigated their occurrence and cytotoxicity. We developed a highly sensitive solid-phase extraction ultra-performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) method under multiple reaction monitoring (MRM) mode, with recoveries ranging from 86 to 115% and method detection limits (MDLs) ranging from 0.10 to 1.87 ng/L for the analysis of 15 HPPs. Eleven of these HPP DBPs were detected in collected drinking water samples using this method with detection frequencies ranging from 14 to 100% and a maximum concentration of 24 ng/L. The IC50 of the 15 HPPs in Chinese hamster ovary (CHO-K1) cells were ranged from 15.13 µM to 6.08×103 µM. The tested HPPs with -CHO substitution exhibited higher cytotoxicity compared to those with -COOH substitution. The TIC-Tox values of HPPs were calculated to be higher than those of HPs, indicating a potential necessity to pay attention to HPP DBPs. A quantitative structure-activity relationship (QSAR) model was developed for the cytotoxicity of HPPs, which was shown to be significantly associated with acid dissociation constant (pKa) and total valence connectivity (TVCon). To the best of our knowledge, this study reported the analysis, occurrence, and cytotoxicity of HPP DBPs in drinking water for the first time.

Analyzing disinfection by-products yield and mechanisms in UV/Cl2 using response surface methodology and quantitative structure-activity relationship models

The study aimed to investigate the formation of halogenated disinfection byproducts (DBPs) during applying UV/chlorine (UV/Cl2) and unravel the interactive impacts of critical operational parameters and the mechanisms behind DBPs formation. Response surface methodology and quantitative structure-activity relationship models were developed to evaluate the contribution of electrophilic, nucleophilic, and free radical reactions to the formation of DBPs in UV/Cl2. The study found that Cl2 and its interactions dominated the total DBPs and non-Br-DBPs formation, while Br- and the Cl2-Br- interaction played a decisive role in the Br-DBPs formation. The study also observed significant interactions of Br, Cl2, and pH on chloroform, bromodichloromethane, dichloroacetonitrile, 1,1-dichloro-2-propanone, trichloroactic acid, and chlorodibromoacetic acid formations, while no evident interaction on chloral hydrate, dibromochloromethane, trichloroacetone, dibromoacetic acid, and bromodichloroacetic acid formations. The electrophilic substitution of HOBr mainly controlled the formation of trihalomethanes, and the contribution of nucleophilic, electrophilic, and free radical (•OH, Cl•, Cl2•- and ClO•) reactions depended on the molar ratio of Cl2 to Br, and pH-determined hydrolysis rate constants of DBPs and the types of free radicals. Overall, the response surface methodology and quantitative structure-activity relationship models provided a reference for revealing DBPs formation mechanisms in other disinfection processes.

Investigation on toxicity mechanism of halogenated aromatic disinfection by-products to zebrafish based on molecular docking and QSAR model

Halogenated aromatic disinfection by-products (DBPs) are a new type of DBPs that have been detected in various water bodies. Previous studies have shown that most of them can induce in vivo toxicity in aquatic organisms. In this study, in order to further investigate the toxic effects and mechanisms of aromatic DBPs, the toxicity and ecological risks of 10 halogenated aromatic DBPs were assessed using the model organism zebrafish. It was found that the toxicity of DBPs was related to the number, type, and position of halogen and the type of substituent, and the 24 h-toxicity value of DBPs in this experiment could replace their 96 h-toxicity value to reduce the test time and save the test cost. Halogenated phenol and halogenated nitrophenol were more toxic, but the current ecological risks of DBPs were relatively low. In addition, the toxicity mechanism of DBPs was analyzed based on molecular docking and quantitative structure-activity relationship (QSAR) models. The molecular docking results showed that all 10 DBPs could bind to zebrafish's catalase (CAT), cytochrome P450 (CYP450), p53, and acetylcholinesterase (AChE), thereby affecting their normal life activities. QSAR models indicated that the toxicity of halogenated aromatic DBPs to zebrafish mainly depended on their hydrophobicity (log D), the interaction with CAT (ECAT), and hydrogen bonding acidity (A).

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Zerovalent Iron/Cu Combined Degradation of Halogenated Disinfection Byproducts and Quantitative Structure-Activity Relationship Modeling

Previous studies have reported that zerovalent iron (ZVI) can reduce several aliphatic groups of disinfection byproducts (DBPs) (e.g., haloacetic acids and haloacetamides) effectively, and the removal efficiency can be significantly improved by metallic copper. Information regarding ZVI/Cu combined degradation of different types of halogenated DBPs can help understand the fate of overall DBPs in drinking water distribution and storage systems consisting of unlined cast iron/copper pipes and related potential control strategies. In this study, we found that, besides aliphatic DBPs, many groups of new emerging aromatic DBPs formed in chlorinated and chloraminated drinking water can be effectively degraded by ZVI/Cu; meanwhile, total organic halogen and total ion intensity were reduced significantly after treatment. Moreover, a robust quantitative structure-activity relationship model was developed and validated based on the ZVI/Cu combined degradation rate constants of 14 typical aromatic DBPs; it can predict the degradation rate constants of other aromatic DBPs for screening and comparative purposes, and the optimized descriptors indicate that DBPs possessing a lower value of the lowest unoccupied molecular orbital energy and a higher value of dipole moment tend to present higher degradation rate constants. In addition, toxicity data of 47 DBPs (belonging to 18 groups) were predicted by two previously established toxicity models, demonstrating that, although most DBPs exhibit higher toxicity than their dehalogenated products, some DBPs show lower toxicity than their lowly halogenated analogs.

Comparative cytotoxicity analyses of disinfection byproducts in drinking water using dimensionless parameter scaling method: Effect of halogen substitution type and number

Up to date, over 700 disinfection byproducts (DBPs) have been detected and identified in drinking water. It has been recognized that cytotoxicity of DBPs varied significantly among groups. Even within the same group, cytotoxicity of different DBP species was also different due to different halogen substitution types and numbers. However, it is still difficult to quantitatively determine the inter-group cytotoxicity relationships of DBPs under the effect of halogen substitution in different cell lines, especially when a large number of DBP groups and multiple cytotoxicity cell lines are involved. In this study, a powerful dimensionless parameter scaling method was adopted to quantitatively determine the relationship of halogen substitution and the cytotoxicity of various DBP groups in three cell lines (i.e., the human breast carcinoma (MVLN), Chinese hamster ovary (CHO), and human hepatoma (Hep G2) cell cytotoxicity) with no need to consider their absolute values and other influences. By introducing the dimensionless parameters D_{x-orn-species}^cellline and D¯_{x-orn-species}^cellline, as well as their corresponding linear regression equation coefficients k_typeornumber^cellline and k¯_typeornumber^cellline, the strength and trend of halogen substitution influences on the relative cytotoxic potency could be determined. It was found that the effect of halogen substitution type and number on the cytotoxicity of DBPs followed the same patterns in the three cell lines. The CHO cell cytotoxicity was the most sensitive cell line to evaluate the effect of halogen substitution on the aliphatic DBPs, whereas the MVLN cell cytotoxicity was the most sensitive cell line to evaluate the effect of halogen substitution on the cyclic DBPs. Notably, seven quantitative structure activity relationship (QSAR) models were established, which could not only predict the cytotoxicity data of DBPs, but also help to explain and verify the patterns of halogen substitution effect on cytotoxicity of DBPs.

In vitro toxicity assessment of haloacetamides via a toxicogenomics assay

It is important to study the stress effects and mechanisms of haloacetamide (HAcAm) disinfection byproducts to reveal their health hazards. In this context, toxicological g was applied to evaluate the effects of four HAcAms, revealing the status of gene expression on Escherichia coli in different stress response types (oxidative, protein, membrane, general, DNA). This study revealed that the main toxic action modes of these HAcAms were general and membrane stresses by high-resolution, real-time gene expression profiling combined with clustering analysis. The results of time-gene evaluation showed that the presence of chloroacetamide (CAcAm) and bromoacetamide (BAcAm) generated more reactive oxygen species, thus activating oxidative stress. Trichloroacetamide (tCAcAm) induced altered expression of glutathione marker genes and membrane stress-related genes, and iodoacetamide (IAcAm) caused severe DNA damage by damaging DNA strands and individual nucleotides mainly through damage to nucleic acids and bases. Furthermore, quantitative structure-activity relationship (QSAR) modelling results indicated that the biological activities of HAcAms were related to their quantum chemical and topological properties.

Antibiotics degradation by advanced oxidation process (AOPs): Recent advances in ecotoxicity and antibiotic-resistance genes induction of degradation products

Antibiotic contamination could cause serious risks of ecotoxicity and resistance gene induction. Advanced oxidation processes (AOPs) such as Fenton, photocatalysis, activated persulfate, electrochemistry and other AOPs technologies have been proven effective in the degradation of high-risk, refractory organic pollutants such as antibiotics. However, due to the limited mineralization ability, a large number of degradation intermediates will be produced in the oxidation process. The residual or undiscovered ecological risks of degradation products are potential safety hazards and problems necessitating comprehensive studies. In-depth investigations especially on the full assessments of ecotoxicity and resistance genes induction capability of antibiotic degradation products are important issues in reducing the environmental problems of antibiotics. Therefore, this review presents an overview of the current knowledge on the efficiency of different AOPs systems in reducing antibiotics toxicity and antibiotic resistance.

A novel procedure for predicting chronic toxicities and ecological risks of perfluorinated compounds in aquatic environment

Perfluorinated compounds (PFCs) can pose adverse effect on aquatic species and community structure. However, little is known about how the characteristics of molecules of PFCs affect their chronic toxic potencies to aquatic species, and the species sensitivity distributions (SSDs) and ecological risk assessments of PFCs are hampered by limited available data of chronic toxicity. In the present study, a novel procedure is proposed to obtain the ecological risk of PFCs using existing exposure concentrations of PFCs and SSDs integrated with the chronic toxicity prediction through robust QSAR models. The results showed that the energy of the lowest unoccupied molecular orbital (E_LUMO) exhibited the strongest correlation with the chronic toxicities of 15 PFCs (R² > 0.844, F > 16.206, p < 0.05). SSDs of 15 PFCs on eight species were first constructed, and the SSD fitting parameters were significantly correlated with E_LUMO (R² > 0.610, F > 19.471, p < 0.05). The QSAR-SSDs support the evaluation of hazardous criteria of PFCs for which data are lacking. Given environmental exposure distributions (EEDs) of the national presence of PFCs in aquatic systems in China, the QSAR-SSDs models allow the development of the ecological risk assessment for PFCs. This way, it was concluded that negligible environmental risk (defined as 5% of the species being potentially exposed to concentrations able to cause effects in < 5% of the case) could be expected from exposure to PFCs in surface waters in China. This method may be helpful for providing an evidence-based approach to guide the risk management for PFCs in aquatic environment.

Phthalates - A family of plasticizers, their health risks, phytotoxic effects, and microbial bioaugmentation approaches

Phthalates are a family of reprotoxicant compounds, predominantly used as a plasticizer to improve the flexibility and longevity of consumable plastic goods. After their use these plastic products find their way to the waste disposal sites where they leach out the hazardous phthalates present within them, into the surrounding environment, contaminating soil, groundwater resources, and the nearby water bodies. Subsequently, phthalates move into the living system through the food chain and exhibit the well-known phenomenon of biological magnification. Phthalates as a primary pollutant have been classified as 1B reprotoxicants and teratogens by different government authorities and they have thus imposed restrictions on their use. Nevertheless, the release of these compounds in the environment is unabated. Bioremediation has been suggested as one of the ways of mitigating this menace, but studies regarding the field applications of phthalate utilizing microbes for this purpose are limited. Through this review, we endeavor to make a deeper understanding of the cause and concern of the problem and to find out a possible solution to it. The review critically emphasizes the various aspects of phthalates toxicity, including their chemical nature, human health risks, phytoaccumulation and entry into the food chain, microbial role in phthalate degradation processes, and future challenges.

Inputs of disinfection by-products to the marine environment from various industrial activities: Comparison to natural production

Oxidative treatment of seawater in coastal and shipboard installations is applied to control biofouling and/or minimize the input of noxious or invasive species into the marine environment. This treatment allows a safe and efficient operation of industrial installations and helps to protect human health from infectious diseases and to maintain the biodiversity in the marine environment. On the downside, the application of chemical oxidants generates undesired organic compounds, so-called disinfection by-products (DBPs), which are discharged into the marine environment. This article provides an overview on sources and quantities of DBP inputs, which could serve as basis for hazard analysis for the marine environment, human health and the atmosphere. During oxidation of marine water, mainly brominated DBPs are generated with bromoform (CHBr₃) being the major DBP. CHBr₃ has been used as an indicator to compare inputs from different sources. Total global annual volumes of treated seawater inputs resulting from cooling processes of coastal power stations, from desalination plants and from ballast water treatment in ships are estimated to be 470-800 × 10⁹ m³, 46 × 10⁹ m³ and 3.5 × 10⁹ m³, respectively. Overall, the total estimated anthropogenic bromoform production and discharge adds up to 13.5-21.8 × 10⁶ kg/a (kg per year) with contributions of 11.8-20.1 × 10⁶ kg/a from cooling water treatment, 0.89 × 10⁶ kg/a from desalination and 0.86 × 10⁶ kg/a from ballast water treatment. This equals approximately 2-6% of the natural bromoform emissions from marine water, which is estimated to be 385-870 × 10⁶ kg/a.

The structure-activity relationship of aromatic compounds in advanced oxidation processes：a review

Advanced oxidation processes (AOPs) are widely used as efficient technologies to treat highly toxic and harmful substances in wastewater. Taking the most representative aromatic compounds (monosubstituted benzenes, substituted phenols and heterocyclic compounds) as examples, this paper firstly introduces their structures and the structural descriptors studied in AOPs before, and the influence of structural differences in AOPs with different reactive oxygen species (ROS) on the degradation rate was discussed in detail. The structure-activity relationship of pollutants has been previously analyzed through quantitative structure-activity relationship (QSAR) model, in which ROS is a very important influencing factor. When electrophilic oxidative species attacks pollutants, aromatic compounds with electron donating groups are more favorable for degradation than aromatic compounds with electron donating groups. While nucleophilic oxidative species comes to the opposite conclusion. The choice of advanced oxidation processes, the synergistic effect of various active oxygen species and the used catalysts will also change the degradation mechanism. This makes the structure-dependent activity relationship uncertain, and different conclusions are obtained under the influence of various experimental factors.

Formation and Cytotoxicity of Halophenylacetamides: A New Group of Nitrogenous Aromatic Halogenated Disinfection Byproducts in Drinking Water

Nitrogenous aromatic halogenated disinfection byproducts (DBPs) in drinking water have received considerable attention recently owing to their relatively high toxicity. In this study, a new group of nitrogenous aromatic halogenated disinfection byproducts, halophenylacetamides (HPAcAms), were successfully identified for the first time in both the laboratory experiments and realistic drinking water. The formation mechanism of HPAcAms during chlorination of phenylalanine in the presence of Br^- and I^-, occurrence frequencies, and concentrations in authentic drinking water were investigated, and a quantitative structure-activity relationship (QSAR) model was developed based on the acquired cytotoxicity data. The results demonstrated that HPAcAms could be formed from phenylalanine in chlorination via electrophilic substitution, decarboxylation, hydrochloric acid elimination, and hydrolysis. The HPAcAm yields from phenylalanine were significantly affected by contact time, pH, chlorine dose, and temperature. Nine HPAcAms with concentrations in the range of 0.02-1.54 ng/L were detected in authentic drinking water samples. Most tested HPAcAms showed significantly higher cytotoxicity compared with dichloroacetamide, which is the most abundant aliphatic haloacetamide DBP. The QSAR model demonstrated that the cellular uptake efficiency and the polarized distributions of electrons of HPAcAms play essential roles in their cytotoxicity mechanisms.

Surface functional groups determine adsorption of pharmaceuticals and personal care products on polypropylene microplastics

The pervasiveness of microplastics (MPs), which can absorb pharmaceuticals and personal care products (PPCPs), has a certain impact on pollutant migration in natural waters. The adsorption behaviors of PPCPs on the aged polypropylene (PP) followed the pseudo-second-order kinetics and Langmuir isotherm, and the adsorption capacity (q_e) on the aged PP was much higher than that on the fresh PP. The Weber-Morris and Boyd models confirmed that the liquid-film and intra-particle diffusion affected the adsorption of PPCPs on the aged PP while the surface diffusion was a rate-limiting step for the fresh PP. The analysis of SEM-EDS, BET, FT-IR, and XPS further showed that changes in the type and content of the surface functional groups of PP led to differences in adsorption capacity and adsorption interactions. The Dragon-descriptor-based LFER and the quantum-chemical-descriptor-based QSAR models reflected the difference in adsorption interaction mechanisms. The examined models showed that the adsorption of the fresh PP toward PPCPs relied on hydrophobic and hydrogen bonding interaction, while for the aged PP electrostatic interaction and hydrogen bonding controlled the adsorption. The findings clarified interactions between PPCPs and MPs and provided a theoretical basis for the assessment of environmental behavior and ecological risk when MPs and PPCPs coexist.

Comparative Toxicity Analyses from Different Endpoints: Are New Cyclic Disinfection Byproducts (DBPs) More Toxic than Common Aliphatic DBPs?

In recent years, dozens of halogenated disinfection byproducts (DBPs) with cyclic structures were identified and detected in drinking water globally. Previous in vivo toxicity studies have shown that a few new cyclic DBPs possessed higher developmental toxicity and growth inhibition rate than common aliphatic DBPs; however, in vitro toxicity studies have proved that the latter exhibited higher cytotoxicity and genotoxicity than the former. Thus, to provide a more comprehensive toxicity comparison of DBPs from different endpoints, 11 groups of cyclic DBPs and nine groups of aliphatic DBPs were evaluated for their comparative in vitro and in vivo toxicity using human hepatoma cells (Hep G2) and zebrafish embryos. Notably, results showed that the in vitro Hep G2 cytotoxicity index of the aliphatic DBPs was nearly eight times higher than that of the cyclic DBPs, whereas the in vivo zebrafish embryo developmental/acute toxicity indexes of the cyclic DBPs were roughly 48-50 times higher than those of the aliphatic DBPs, indicating that the toxicity rank order differed when different endpoints were applied. For a broader comparison, a Pearson correlation analysis of DBP toxicity data from nine different endpoints was conducted. It was found that the observed Hep G2 cytotoxicity and zebrafish embryo developmental/acute toxicity in this study were highly correlated with the previously reported in vitro CHO cytotoxicity and in vivo toxicity in aquatic organisms (P < 0.01), respectively. However, the observed in vitro toxicity had no correlation with the in vivo toxicity (P > 0.05), suggesting that the toxicity rank orders obtained from in vitro and in vivo bioassays had large discrepancies. According to the observed toxicity data in this study and the candidate descriptors, two quantitative structure-activity relationship (QSAR) models were established, which help to further interpret the toxicity mechanisms of DBPs from different endpoints.

Prediction of degradability of micropollutants by sonolysis in water with QSPR - a case study on phenol derivates

The increasing quantity and variety of organic contaminants discharged into surface and groundwater increase the necessity of additional and suitable water treatment methods, which can be incorporated into existing wastewater treatment plants. The huge variety of micropollutants and local variability of the composition of the organic load or matrix effects paired with multiple possible degradation processes lead to the requirement of a recommendation tool for the best possible water treatment method under given local conditions. Due to the diversity of physicochemical properties of micropollutants, such predictions are challenging. In this study, a quantitative correlation between the structural properties of certain micropollutants and their degradability using high-frequency sonolysis has been investigated. Therefore, Quantitative Structure-Property Relationship (QSPR) has been applied on a set of phenol derivates. To obtain the kinetic data, all experiments have been conducted in standardized, constant conditions for all 32 investigated phenol derivates. QSPR modelling was then executed using the software PaDEL for descriptor calculation and the software QSARINS for the overall modelling process including genetic algorithm (GA) and multiple linear regression (MLR). The final model consisting of 5 molecular descriptors was selected using a multi-criteria decision-making method based on extensive statistical parameters. The predictive power and robustness of the model was evaluated by means of internal cross validation and external validation using an independent validation set. The final selected model showed very good values for regression abilities, predictive power as well as stability (R²_adj = 0.9455, CCC_tr = 0.9777, Q²_loo = 0.9285, CCC_ext = 0.9797 and Q²_ext-F1 = 0.9711). The applicability domain of the QSPR model was defined based on the Williams plot and Insubria plot. The five OECD principles for the application of QSPR/QSAR modelling in industry and regulation were fulfilled in the whole process to the best of our knowledge, including the collection of the underlying experimental data as well as the entire modelling process. The final QSPR model included the molecular polarity and occurrence of hydrogen bonds as major influences on the reaction rate constants in accordance with previous studies. Nevertheless, potential biases in the selection of these descriptors due to the small size of the dataset were highlighted.

Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach

For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (R_train² = 0.64, RMSE_train = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (R_test²,R_m², Q_F1², Q_F2², Q_F3² and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.

Toxicity of disinfection byproducts formed during the chlorination of sulfamethoxazole, norfloxacin, and 17β-estradiol in the presence of bromide

Brominated disinfection byproducts (Br-DBPs) are formed during the disinfection process of water containing bromine ions, such as marine aquaculture water. Little attention has been paid to Br-DBPs with anthropogenic chemicals as precursors. This study summarized the sodium hypochlorite (NaClO) oxidation of three frequently used pharmaceuticals, including two antibiotics, norfloxacin (NOR) and sulfamethoxazole (SMX), and the growth hormone estrogen 17β-estradiol (E2). Transformations of the pharmaceuticals were found to be faster in marine aquaculture water than in distilled water. Several Br-DBPs and Cl-DBPs were identified for NOR, SMX, and E2. It was shown that the carboxyl group, piperazine ring, C3, and C8 atoms were the primary reaction sites on NOR. The aniline moiety and S-N bond were identified to be the reaction sites on SMX. The C2, C4, C9, and C16 atoms were the potential reaction centers on E2. Preliminary calculation by QSAR model indicated that the value of logKow significantly increased with an increase in the number of bromine atoms in the Br-DBPs. The results of the bioconcentration factors (BCF) analysis suggested that the bioaccumulation of Br-DBPs were greater than that chlorinated DBPs (Cl-DBPs) in distilled water.

Nontargeted identification of chlorinated disinfection byproducts formed from natural organic matter using Orbitrap mass spectrometry and a halogen extraction code

Natural organic matter is a major source of precursors of hazardous chlorinated disinfection byproducts (Cl-DBPs) formed during water treatment, but the majority of Cl-DBPs are still unidentified. In this study, we used a self-written halogen extraction code to identify halogen isotopic patterns in combination with the R package MFAssignR, to identify Cl-DBPs from Orbitrap mass spectra. One hundred and eighty-nine Cl-DBPs were detected during chlorination of a Suwannee River natural organic matter solution, and the structures of 20 of these compounds are reported for the first time. Kendrick mass defect analysis and structural identification confirmed that chlorinated carboxylic acids are common and likely to form during chlorination. A toxicity prediction using quantitative structure-activity relationship models indicated that most of the chlorinated carboxylic acids may be highly toxic. Our analytical strategy can identify Cl-DBPs accurately from complex mixtures and may also be applicable to the identification of other halogenated disinfection byproducts formed during water treatment.

Roles and Knowledge Gaps of Point-of-Use Technologies for Mitigating Health Risks from Disinfection Byproducts in Tap Water: A Critical Review

Due to rising concerns about water pollution and affordability, there is a rapidly-growing public acceptance and global market for a variety of point-of-use (POU) devices for domestic uses. However, the efficiencies and mechanisms of POU technologies for removing regulated and emerging disinfection byproducts (DBPs) are still not systematically known. To facilitate the development of this field, we summarized performance trends of four common technologies (i.e., boiling, adsorption, membrane filtration, and advanced oxidation) on mitigating preformed DBPs and identified knowledge gaps. The following highest priority knowledge gaps include: 1) data on DBP levels at the tap or cup in domestic applications; 2) certainty regarding the controls of DBPs by heating processes as DBPs may form and transform simultaneously; 3) standards to evaluate the performance of carbon-based materials on varying types of DBPs; 4) long-term information on the membrane performance in removing DBPs; 5) knowledge of DBPs' susceptibility toward advanced redox processes; 6) tools to monitor/predict the toxicity and diversity of DBPs formed in waters with varying precursors and when implementing different treatment technologies; and 7) social acceptance and regulatory frameworks of incorporating POU as a potential supplement to current centralized-treatment focused DBP control strategies. We conclude by identifying research needs necessary to assure POU systems protect the public against regulated and emerging DBPs.

Concentration Addition, Independent Action, and Quantitative Structure-Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure-activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol-water partition coefficient and the acid dissociation constant (pK_a ). The toxicities of synergetic mixtures can be interpreted with the total energy (E_T ) and pK_a of the mixtures. Dipole moment and E_T are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by-product mixture toxicities. Environ Toxicol Chem 2021;40:1431-1442. © 2021 SETAC.

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.

Predicting formation of haloacetic acids by chlorination of organic compounds using machine-learning-assisted quantitative structure-activity relationships

The presence of disinfection byproducts (DBPs) in drinking water is a major public health concern, and an effective strategy to limit the formation of these DBPs is to prevent their precursors. In silico prediction from chemical structure would allow rapid identification of precursors and could be used as a prescreening tool to prioritize testing. We present models using machine learning algorithms (i.e., support vector regressor, random forest regressor, and multilayer perceptron regressor) and chemical descriptors as features to predict the formation of haloacetic acids (HAAs). A robust model with good predictivity (i.e., leave-one-out cross-validated Q² > 0.5) to predict the formation of trichloroacetic acid (TCAA) was developed using a random forest regressor. The number of aromatic bonds, hydrophilicity, and electrotopological descriptors related to electrostatic interactions and the atomic distribution of electronegativity were identified as important predictors of TCAA formation potentials (FPs). However, the prediction of dichloroacetic acid was less accurate, which is congruent with the presence of different types of precursors exhibiting distinct mechanisms. This study demonstrates that nonlinear combinations of general chemical descriptors can adequately estimate HAAFPs, and we hope that our study can be used to predict precursors of other disinfection byproducts based on chemical structures using a similar workflow.

Removal of disinfection byproducts in drinking water by flexible reverse osmosis: Efficiency comparison, fates, influencing factors, and mechanisms

Flexible reverse osmosis (FLERO) is a newly proposed technology for purifying and saving water simultaneously by recycling brine to inlet water. However, it is unknown if and how much FLERO may compromise micropollutant treatment efficiency. Hence, this study examined FLERO in removing twenty disinfection byproducts (DBPs) from simulated water under a constant 80% water recovery condition. The results achieved ≥ 80.8% removals for most of DBPs while varying ionic strength, methanol content, and water matrix affected only small DBPs. From chemical structure perspective, the removals of DBPs were ranked as tetra- ≥ tri- ≥ di- ≥ mono- DBPs for compounds containing identical functional groups (FGs) and halogen types, iodinated ≥ bromated ≥ chlorinated DBPs for compounds with identical FG and halogenation degrees, and HAAs ≥ HALs ≈ HMs for compounds with identical halogenation types and degrees. From chemical property viewpoint, the rejections of DBPs were modeled well (R² = 0.76) by a quantitative structure-activity relationship model that incorporates four parameters (i.e., molecular volume, octanol-water partitioning coefficient, steric and electronic effects). From membrane-pollutant interaction standpoint, we for the first time revealed membrane trapping/sorption as another important mechanism for DBPs removal besides previously-known mechanisms like size exclusion and electrostatic repulsion.

The occurrence, characteristics, transformation and control of aromatic disinfection by-products: A review

With the development of analytical technology, more emerging disinfection by-products (DBPs) have been identified and detected. Among them, aromatic DBPs, especially heterocyclic DBPs, possess relatively high toxicity compared with regulated DBPs, which has been proved by bioassays. Thus, the occurrence of aromatic DBPs is of great concern. This article provides a comprehensive review and summary of the characteristics, occurrence, transformation pathways and control of aromatic DBPs. Aromatic DBPs are frequently detected in drinking water, wastewater and swimming pool water, among which swimming pool water illustrates highest concentration. Considering the relatively high concentration and toxicity, halophenylacetonitriles (HPANs) and halonitrophenols (HNPs) are more likely to be toxicity driver among frequently detected phenyl DBPs. Aromatic DBPs can be viewed as important intermediate products of dissolved organic matter (DOM) during chlor(am)ination. High molecular weight DOM could convert to aromatic DBPs via direct or indirect pathways, and they can further decompose into regulated aliphatic DBPs such as trihalomethanes (THMs) and haloacetic acids (HAAs) by ring opening and side chain cleavage. Even though no single DBPs control strategy is efficient to all aromatic DBPs, the decrease of overall toxicity may be achieved by several methods including absorption, solar radiation and boiling. By systematically considering aromatic DBPs and aliphatic DBPs, a better trade-off can be made to reduce health risk induced by DBPs.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (K_POM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log K_POM-w with R²_adj of 0.825. The values of statistical parameters including R²_adj, Q²_ext, RMSE_tra and RMSE_ext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.

A comprehensive review of mathematical models developed for the estimation of organic disinfection byproducts

In this review, we present comparative and comprehensive views on the foundations, potentials and limitations of the previously reported mathematical models for the estimation of the concentration of disinfection byproducts (DBPs) generated during the chlor(am)ination of water. To this end, DBPs models were divided into two major categories: static variable (SV) and dynamic variable (DV) or differential models. In SV models, variables remain in their original form throughout a chlor(am)ination modelling period while DV models consider the changes driven by a chlor(am)ination treatment as the variables. This classification and the comparative study of the two types of models led to a better understanding of the assumptions, potentials, and limitations of the existing DBP models. In opposition to several claims in the literature, certain DV models based on UV absorbance/fluorescence failed to selectively track the chromophores responsible for DBP formation. In this critical review, a conceptual model for the photophysics of dissolved organic matter (DOM) based on the theory of electron delocalization was proposed to explain some inconsistent spectroscopic properties of DOM following chlor(am)ination and several unique photophysical properties of DOM. New insights for the development and deployment of mathematical models were also provided to estimate DBPs in various settings.

Comparative cytotoxicity of halogenated aromatic DBPs and implications of the corresponding developed QSAR model to toxicity mechanisms of those DBPs: Binding interactions between aromatic DBPs and catalase play an important role

Halogenated aromatic disinfection byproducts (DBPs) are a new group of emerging DBPs identified recently. They have been detected in disinfected drinking water, wastewater effluents, recreational water and oil/gas produced water, at concentrations of ng/L to μg/L in general. Previously studies have demonstrated that most of them can induce developmental toxicity and growth inhibition in aquatic organisms based on in vivo bioassays. In this study, to further understand the adverse effects of aromatic DBPs to human health, the comparative cytotoxicity of 15 halogenated aromatic DBPs belonging to four subgroups (i.e., halophenols, halonitrophenols, halohydroxybenzaldehydes and halohydroxybenzoic acids) was evaluated with mammalian Chinese Hamster Ovary cells. The results indicated that the selected aromatic DBPs exhibited an in vitro toxicity rank order of halonitrophenols > halophenols > halohydroxybenzaldehydes > halohydroxybenzoic acids. The potential toxicity mechanisms involved with the antioxidant system were investigated by using molecular docking analysis between key antioxidant enzymes (i.e., catalase, superoxide dismutase, and glutathione S-transferase) and aromatic DBPs. Based on the observed cytotoxicity data and screening the candidate descriptors (including binding energies between the aromatic DBPs and key antioxidant enzymes as well as physical-chemical/quantum-chemical/topological descriptors), a QSAR model was developed as log (LC₅₀) ^-1 = - 1.050E_CAT + 0.300E_HOMO - 0.238E_LUMO- 0.164, indicating the importance of the interactions of aromatic DBPs towards catalase and the electrophilic/nucleophilic reactivity of aromatic DBPs in the toxicity mechanisms. In addition, the occurrence of the aromatic DBPs in tap water and finished water was studied in a mega city Shenzhen located in South China. Results showed that halogenated aromatic DBPs commonly existed in Shenzhen drinking water at ng/L levels, and three nitrogenous aromatic DBPs were detected in real drinking water for the first time. The major toxicity drivers among the target aromatic DBPs were identified through the integration of the measured concentrations and observed cytotoxicity; notably, DBPs with the highest concentrations may not contribute the highest proportions of overall toxicity.

Conlecs: A novel procedure for deriving the concentration limits of chemicals outside the criteria of human drinking water using existing criteria and species sensitivity distribution based on quantitative structure-activity relationship prediction

Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.

Two new predictors combined with quantum chemical parameters for the selection of oxidants and degradation of organic contaminants: A QSAR modeling study

Oxidation is an attractive treatment method to effectively remove organic contaminants in water. In this study, degradation of 30 organic compounds in different oxidation systems was evaluated, including oxygen (O₂), hydrogen peroxide (H₂O₂), ozone (O₃) and hydroxyl radical (HO). First, a quantitative structure-activity relationship (QSAR) model for oxidation-reduction potentials (ORPs) of organics was developed and exhibited a good performance to predict ORP values of organics with evaluation indices of squared correlation coefficient (R²) = 0.866, internal validation (q²) = 0.811 and external validation (Q_ext²) = 0.669. Four quantum parameters, including f(+)_n, f(-)_n, E_HOMO and E_B3LYP dominate the ORP values. Subsequently, a relationship between reaction rates (k) and the difference of ORP for oxidants and organics (ΔE_oxi-org) was established, however, which was limited (R²= 0.697). Therefore, two new predictors (slopes and intercepts) are proposed based on the linear relationships between k values and ORPs of oxidants. These new predictors can be applied to estimate the reaction rates and minimum oxidation potential for organic compounds. Afterwards, to express the two predictors, QSAR models were established. The two optimal QSAR models fitted very well with experimental values and were demonstrated to be stable and accurate based on R² (0.982 and 0.965), q² (0.950 and 0.950) and Q_ext² (0.985 and 0.989). BO_x, q(H)⁺ and q(C)_x were main factors influencing the slopes and intercepts. This study developed methods to predict ORPs of organics and established two new predictors to estimate the reaction rates undergoing different oxidation processes, offering new insights into the oxidant selection.

Formation of Brominated Organic Compounds and Molecular Transformations in Dissolved Organic Matter (DOM) after Ballast Water Treatment with Sodium Dichloroisocyanurate Dihydrate (DICD)

Estuarine water treated with a ballast water management system (BWMS) using a solution of dissolved dichloroisocyanurate dihydrate (DICD) resulted in the formation of newly described brominated disinfection byproducts (Br-DBPs). Analysis of dissolved organic matter (DOM) in untreated water with ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) identified 3897 m/z ions and their exact molecular formulas. After DICD treatment, a total of 213 halogenated molecular ions with relative abundance of at least 1% were assigned and confirmed using isotope simulation. Halogenated ions were assigned in four DBP elemental groups including CHOBr (180), CHONBr (13), CHOCl (16), and CHOBrCl (4). Forty-nine of the 197 brominated formulas have not been previously reported. We also were able to tentatively assign possible structures to the formula C₃HBr₃N₂ due to very limited isomeric possibilities. The tentatively assigned compound found at 6.4% relative abundance was identified as either tribromoimidazole or tribromopyrazole. Our results show the formation of complex halogenated DBPs that are formed in the treatment of water with a novel BWMS that employs granular DICD as a biocide. The toxicological and mutagenic properties as well as the fate of these newly identified brominated DBPs are unknown.

Synergetic effects of novel aromatic brominated and chlorinated disinfection byproducts on Vibrio qinghaiensis sp.-Q67

Aromatic halogenated chemicals are an unregulated class of byproducts (DBPs) generated from disinfection processes in the water environment. Information on the toxicological interactions, such as antagonism and synergism, present in DBP mixtures remains limited. This study aimed to determine the toxicological effects of aromatic halogenated DBP mixtures on the freshwater bacterium Vibrio qinghaiensis sp.-Q67. The acute toxicities of seven DBPs and their binary mixtures toward V. qinghaiensis sp.-Q67 were determined through microplate toxicity analysis. The toxicities of single DBPs were ranked as follows: 2,5-dibromohydroquinone > 2,4-dibromophenol > 4-bromo-2-chlorophenol ≈ 2,6-dibromo-4-nitrophenol > 2,6-dichloro-4-nitrophenol > 2-bromo-4-chlorophenol > 4-bromophenol. The percentages of synergism (experimental values higher than the predicted concentration addition) on the levels of 50%, 20%, and 10% effective concentrations reached 61%, 41%, and 31%, respectively. These results indicated that the probability of synergism decreased as concentration levels decreased. The synergetic effects of the compounds were dependent on concentration levels and concentration ratios. The proposed quantitative structure-activity relationship model can be used to predict the interactive toxicities exerted by 105 binary DBP mixture rays of 21 DBP mixture systems.

Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity Human Transthyretin Disruptors: An in Vitro and in Silico Study

Phenolic disinfection byproducts (phenolic-DBPs) have been identified in recent years. However, the toxicity data for phenolic-DBPs are scarce, hampering their risk assessment and the development of regulations on the acceptable concentration of phenolic-DBPs in water. In this study, the binding potency and underlying interaction mechanism between human transthyretin (hTTR) and five groups of representative phenolic-DBPs (2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids, halo-salicylic acids) were determined and probed by competitive fluorescence displacement assay integrated with in silico methods. Experimental results implied that 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, and 3,5-dihalo-4-hydroxybenzaldehydes have a high binding affinity with hTTR. The hTTR binding potency of the chemicals with electron-withdrawing groups on their molecular structures were higher than that with electron-donor groups. Molecular modeling methods were used to decipher the binding mechanism between model compounds and hTTR. The results documented that ionic pair, hydrogen bonding and hydrophobic interactions were dominant interactions. Finally, a mechanism-based model for predicting the hTTR binding affinity was developed. The determination coefficient ( R²), leave-one-out cross validation Q² ( Q_LOO²), bootstrapping coefficient ( Q_BOOT²), external validation coefficient ( Q_EXT²) and concordance correlation coefficient ( CCC) of the developed model met the acceptable criteria ( Q² > 0.600, R² > 0.700, CCC > 0.850), implying that the model had good goodness-of-fit, robustness, and external prediction performances. All the results indicated that the phenolic-DBPs have the hTTR disrupting effects, and further studies are needed to investigate their other mechanism of endocrine disruption.

Predicting the cytotoxicity of disinfection by-products to Chinese hamster ovary by using linear quantitative structure-activity relationship models

A suitable model to predict the toxicity of current and continuously emerging disinfection by-products (DBPs) is needed. This study aims to establish a reliable model for predicting the cytotoxicity of DBPs to Chinese hamster ovary (CHO) cells. We collected the CHO cytotoxicity data of 74 DBPs as the endpoint to build linear quantitative structure-activity relationship (QSAR) models. The linear models were developed by using multiple linear regression (MLR). The MLR models showed high performance in both internal (leave-one-out cross-validation, leave-many-out cross-validation, and bootstrapping) and external validation, indicating their satisfactory goodness of fit (R² = 0.763-0.799), robustness (Q²_LOO = 0.718-0.745), and predictive ability (CCC = 0.806-0.848). The generated QSAR models showed comparable quality on both the training and validation levels. Williams plot verified that the obtained models had wide application domains and covered the 74 structurally diverse DBPs. The molecular descriptors used in the models provided comparable information that influences the CHO cytotoxicity of DBPs. In conclusion, the linear QSAR models can be used to predict the CHO cytotoxicity of DBPs.

Construction of a Quantitative Structure Activity Relationship (QSAR) Model to Predict the Absorption of Cephalosporins in Zebrafish for Toxicity Study

Cephalosporins are beta-lactam antibiotics that are widely used in China. Five generations of cephalosporins have been introduced in clinical practice to date; moreover, some new candidates are also undergoing clinical evaluations. To improve the success rates of new drug development, we need to have a comprehensive understanding about the relationship between the structure of cephalosporins and the toxicity that it induces at an early stage. In the cephalosporins toxicity study using zebrafish, the drug absorption is a key point. In this study, we determined the absorption of cephalosporins in zebrafish during toxicity test. The internal concentrations of 19 cephalosporins in zebrafish were determined using a developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Furthermore, a quantitative structure-activity relationship (QSAR) model was established by multilinear regression; moreover, it was used to predict the absorption of cephalosporins in zebrafish. During leave-one-out cross-validation, a satisfactory performance was obtained with a predictive ability (q ²) of 0.839. The prediction ability of the model was further confirmed when the predictive ability (q ²) was 0.859 in external prediction. The best QSAR model, which was based on five molecular descriptors, exhibited a promising predictive performance and robustness. In experiments involving drug toxicity, the developed QSAR model was used to estimate internal concentrations of cephalosporins. Thus, the toxicity results were correlated with the internal concentration of the drug within the larvae. The developed model served as a new powerful tool in zebrafish toxicity tests.

The Antecedents of Electronic Banking Adoption in Saudi Arabia: Using Diffusion of Innovation Theory

Despite the rapid growth in technology and the research effort given on investigating the adoption of electronic banking services, both banks and academic researchers in developing countries perceive the problem of low-level adoption of electronic banking. Most studies which have investigated the adoption, acceptance, or intention to use electronic banking, have agreed that customers «trust is one of the most important impediments that have frustrated the success of the adoption process. Therefore, in developed countries,customers» trust became the pivot of research studies that investigated the electronic dealings between customers and new IT/IS innovations. Recently, a few researchers have started investigating online trust in developing countries. Although electronic banking services have been widely adopted in developed countries, there is still low usage in developing countries such as Saudi Arabia. This quantitative research aims to empirically determine the significant antecedents of electronic Banking Service Adoption, as well as to explain how the Diffusion of Innovation Theory (DOIT) is being used in determining the antecedent of electronic banking in Saudi Arabia. Using an adopted survey instrument, (5-point-Likert scale) is used. Were eventually used to further analyse the data using mainly The Partial Least Square (PLS). This study contribute to the body of knowledge by extending existing literature to identifying the factors influencing the acceptance of electronic banking among banks customers, developing a comprehensive model which contributes to online customer literature by extending the Diffusion of Innovation Theory (DOIT) to include trust and system reliability applying them to the context of electronic banking, extending existing literature by applying the developed model to Saudi customers, contributing to technology acceptance theories by showing the role of system reliability in the acceptance of electronic banking, Contributing to the electronic trust literature by examining the role of customer trust in the context of electronic banking and how it might be increased. Keywords: electronic banking services, developing countries, Diffusion of Innovation Theory, trust, partial least square.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

Rejection of haloacetic acids in water by multi-stage reverse osmosis: Efficiency, mechanisms, and influencing factors

Among available technologies to ensure drinking water security, reverse osmosis (RO) has become the gold-standard for purification due to its maturity and reliability. However, high energy consumption and low water recovery are the major impediments for extensive adoption of RO. Multi-stage RO process is an innovative system design that can offer a more effective way to improve energy efficiency and water recovery, but it is rarely employed for disinfection by-product (DBP) treatment in drinking water. Thus, this study applied multi-stage RO to treat water containing haloacetic acids (HAAs), a prevalent class of DBPs with widespread occurrence and high toxicity, under a variety of environmental and operational conditions. Overall, we found that >75% HAAs were rejected and 87% of water was recovered with a five-stage RO process. For compounds with identical number of halogen substitutions, iodinated, brominated, and chlorinated HAAs were almost equally removed; however, highly halogenated species were easier to be rejected than lowly halogenated HAAs. By developing quantitative structure-activity relationship models, the importance of size exclusion, charge repulsion, and hydrophobic interaction effects on multi-stage RO removal was revealed. Environmental and operational variables like pH, operating pressure, water matrix, and membrane age also played important roles in this process. Increasing pH from 6.5 to 8.5 and membrane age apparently enhanced HAA rejections. In contrast, HAA rejection increased only slightly from an operating pressure of 4-6 bars but decreased markedly from 6 to 8 bars. Compared to ultrapure water, equal or higher removal efficiency was observed for HAAs spiked to tap water. Considering the need to balance water quality and quantity, a four-stage RO was preferred under this study's condition.