Biological-Activity-Based Prioritization of Antidepressants in Wastewater in England and Japan

A novel framework for industrial pesticide effluent assessment: Integrating chemical screening, multi-endpoint responses and literature-based validation

Industrial pesticide effluents pose substantial risks to aquatic ecosystems, yet comprehensive understanding of their toxicological impacts remains limited. This study presents an integrated approach to evaluate the ecological risks of pesticide manufacturing effluents through chemical screening and multi-endpoints biological responses. Using zebrafish embryos as a model organism, we demonstrated that effluent discharge point (EDP) sample induced 100 % mortality, while diluted samples exhibited significant developmental toxicity, cardiovascular injury, immunosuppression, and behavioral alterations. Non-targeted metabolomics analysis revealed the molecular mechanisms underlying these toxic responses. Through chemical screening and targeted quantification, we identified three predominant azole fungicides - propiconazole (2.11 μg/L), hexaconazole (13.3 μg/L), and tebuconazole (18.66 μg/L) - that exhibited synergistic toxicity. Notably, our innovative meta-analysis framework based on literature data validated the toxicological profiles of detected compounds, providing an efficient alternative to conventional bioassays. This study establishes a comprehensive framework for assessing industrial effluent toxicity and demonstrates the value of integrating chemical analysis with biological responses for environmental risk assessment.

A novel data-driven screening method of antidepressants stability in wastewater and the guidance of environmental regulations

Wastewater-based epidemiology (WBE) represents a powerful technique for quantifying the attenuation characteristics and consumption of pharmaceuticals. In addition to WBE, no further methods have been developed to assess the wastewater stability related to antidepressants (ADs). In this study, the biodegradability, solubility, and adsorption or partition of 66 ADs were objectively scored according to the relevant guidelines of the Organisation for Economic Cooperation and Development. An assessment framework and the MSSL-RealFormer classification model of ADs wastewater stability were constructed based on physicochemical properties to predict the ADs wastewater stability and the quantitative structure-activity relationship. The constructed MSSL-RealFormer classification model exhibited a markedly higher prediction accuracy than traditional methods. Furthermore, 15 high-stable ADs in wastewater with low biodegradability, high solubility, and low adsorption or partition were identified. SHapley Additive exPlanation method demonstrated that group hydrophobicity, electrostatic and van der Waals forces exerted a significant influence on the ADs wastewater stability. And molecular stability was found to be significantly correlated with the ADs wastewater stability. A combination of density functional theory and MSSL-RealFormer classification model was employed to identify 17 high-stable transformation products of nine medium- and low-stable ADs in wastewater. The Ecological Structure Activity Relationships model demonstrated that bupropion, tapentadol and chlorpheniramine exhibited significant acute toxicity to the aquatic food chain. In this study, a novel deep learning model was constructed to rapidly screen the correlation between the ADs wastewater stability and their molecular structures. It is anticipated to prove a favorable tool for optimizing the wastewater stability screening of pharmaceuticals.

Using Zebrafish G Protein-Coupled Receptors to Obtain a Better Appreciation of the Impact of Pharmaceuticals in Wastewater to Fish

Pharmaceutical discharge to the environment is of concern due to its potential adverse effects on aquatic species. It is estimated that around 40% of pharmaceuticals target G protein-coupled receptors (GPCRs). The in vitro transforming growth factor-α (TGFα) shedding assay was applied to measure the antagonistic activities of pharmaceuticals against human GPCRs. However, their ability to stimulate fish GPCRs remains unclear. Here, antagonistic activities of 30 pharmaceuticals against zebrafish dopamine (zD2a and zD2c), adrenergic family member (zβ1), and histamine (zH1 and zH3) receptors were measured by the TGFα shedding assay. The study found an interspecies difference in binding affinities between human and zebrafish: pharmaceuticals more strongly inhibited the zD2c and zH1 receptors than human D2 (hD2) and hH1 receptors, while zD2a and zβ1 receptors were less inhibited than hD2 and hβ1 receptors. The potential molecular explanations for the observed interspecies differences in binding affinity for hydroxyzine and bisoprolol were investigated using molecular docking. Pharmaceutical potency against zebrafish GPCRs and predicted effluent concentrations were used to predict equivalent quantities (EQs), and these EQs were used to prioritize pharmaceuticals of concern in wastewater in England and Japan. This study highlights the use of the TGFα shedding assay adopting zebrafish GPCRs to better understand the ecological effects of pharmaceuticals on fish.

Deciphering the inhibitory mechanisms of didecyldimethylammonium chloride on microalgal removal of fluoxetine: Insights from the alterations in cell surface properties and the physio-biochemical and molecular toxicity

The COVID-19 pandemic has increased the co-occurrence of quaternary ammonium compounds (QACs) and antidepressants in aquatic environments. Microalgae are sustainable and cost-effective candidates for removing emerging pollutants. QACs have a robust ability to adsorb on cell surface and alter membrane permeability, but little is known about the influence of QACs on microalgal bioremediation of co-existing pollutants like antidepressants. In this study, the influence mechanisms of didecyldimethylammonium chloride (DDAC) on the removal of fluoxetine (FLX) by C. pyrenoidosa were explored. The results showed that C. pyrenoidosa exhibited high removal efficiency of single FLX (75.23 %-88.65 %) mainly through biodegradation (57.12 %-67.19 %). However, the coexisting medium and high concentrations of DDAC considerably decreased the biodegradation amount (10.50 %-33.30 %) and removal efficiency (29.47 %-52.89 %) of FLX by C. pyrenoidosa. In contrast, the presence of DDAC increased extracellular and intracellular FLX concentrations due to the enhanced extracellular polymeric substance content, cell surface hydrophobicity, and cell membrane permeability. Meanwhile, DDAC showed synergistic effects with FLX on microalgal growth through exacerbated oxidative damage and photosynthesis inhibition. Moreover, transcriptomics revealed that the dysregulations of key genes involved in the DNA replication and repair, ribosome biogenesis, photosynthesis-antenna proteins, peroxisomes, and glutathione metabolism pathways were important molecular mechanisms underlying the synergistic toxicity. Furthermore, the principal component analysis suggested that the enhancement of extracellular and intracellular FLX concentrations induced by the coexistence of DDAC increased the mixture's toxicity, resulting in the decreased biodegradation amount and ultimately a reduction in the removal efficiency of FLX. Our results highlight the significance of recognizing the influence of QACs on microalgal remediation and ecological risks of antidepressants.

Towards sustainable water reuse: A critical review and meta-analysis of emerging chemical contaminants with risk-based evaluation, health hazard prediction and prioritization for assessment of effluent water quality

Reuse of treated wastewater is necessary to address water shortages in a changing climate. Sustainability of wastewater reuse requires reducing the environmental impacts of contaminants of emerging concern (CECs), but it is being questioned as CECs are not regulated in the assessment of effluent water quality for reuse both nationally in Sweden and at the broader European Union level. There is also a lack of details in this topic on which CECs to be addressed and methodologies to be used for assessing their environmental impacts. A better understanding of the ecological risks and health hazards of CECs associated with wastewater reuse will assist in the development of effective regulations on water reuse, (inter)nationally, as well as related treatment/monitoring guidelines. This review provides a list of specific chemical CECs that hinder sustainable wastewater reuse, and also demonstrates a holistic quantitative methodology for assessing, scoring and prioritizing their associated ecological risks and health hazards posed to the environment and humans. To achieve this, we compile information and concentrations of a wide range of CECs (∼15 000 data entries) identified in Swedish effluent wastewater from domestic (blackwater, greywater, mixture of both) and municipal settings, and further perform a meta-analysis of their potentials for 14 risk and hazard features, consisting of ecological risk, environmental hazard, and human health hazard. The features are then scored against defined criteria including guideline values, followed by score ranking for prioritization. This finally produces a unique list of chemical CECs from high to low priority based on risk- and hazard-evaluations. Out of the priority chemicals, 30, mainly pharmaceuticals, had risk quotient ≥ 1, indicating ecological risk, 16 had environmental hazard being persistent and mobile, and around 60 resulted in positive predictions for at least four human health hazards (particularly skin sensitization, developmental toxicity, hepatoxicity, and carcinogenicity). The 10 highest-priority chemicals (final score 2.3-3.0 out of 4.0) were venlafaxine, bicalutamide, desvenlafaxine, diclofenac, amoxicillin, clarithromycin, diethyltoluamide, genistein, azithromycin, and fexofenadine. Potential crop exposure to selected chemicals following one year of wastewater reuse for agricultural irrigation was also estimated, resulting in a range of 0.04 ng/kg (fluoxetine) to 1160 ng/kg (carbamazepine). Overall, our work will help focus efforts and costs on the critical chemicals in future (waste)water-related studies, such as, to evaluate removal efficiency of advanced treatment technologies and to study upstream source tracing (polluter-pays principle), and also in supporting policymakers to better regulate CECs for sustainable wastewater reuse in the future.

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model

As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occurrence, persistence, bioaccumulation and toxicity based on the integrated assignment method. A classification model for ADs was constructed using an improved mixup-transformer deep learning method, and its classification accuracy was compared with those of other models. The accuracy of the proposed model improved by up to 23.25 % compared with the random forest model, and the reliability was 80 % more than that of the TOPSIS method. A priority screening candidate list was proposed to screen 33 high-priority ADs. Finally, SHapley Additive explanation (SHAP) visualization, molecular dynamics, and amino acid analysis were performed to analyze the correlation between AD structure and toxic receptor binding characteristics and reveal the differences in AD risk priority. ADs with more intramolecular hydrogen bonds, higher hydrophobicity, and electronegativity had a more significant risk. Van der Waals and electrostatic interactions were the primary influencing factors, and significant differences in the types and proportions of the main amino acids in the interaction between ADs and receptors were observed. The results of the study provide constructive schemes and insights for AD priority screening and risk management.

Application of in vitro bioassays to monitor pharmaceuticals in water: A synthesis of chronological analysis, mode of action, and practical insights

Pharmaceutical compounds in wastewater have emerged as a significant concern for the aquatic environment. The use of in vitro bioassays represents a sustainable and cost-effective approach for assessing the potential toxicological risks of these biologically active compounds in wastewater and aligns with ethical considerations in research. It facilitates high-throughput analysis, captures mixture effects, integrates impacts of both known and unknown chemicals, and reduces reliance on animal testing. The core aim of the current review was to explore the practical application of in vitro bioassays in evaluating the environmental impacts of pharmaceuticals in wastewater. This comprehensive review strives to achieve several key objectives. First, it provides a summary categorisation of pharmaceuticals based on their mode of action, providing a structured framework for understanding their ecological significance. Second, a chronological analysis of pharmaceutical research aims to document their prevalence and trends over time, shedding light on evolving environmental challenges. Third, the review critically analyses existing bioassay applications in wastewater, while also examining bioassay coverage of representative compounds within major pharmaceutical classes. Finally, it explores the potential for developing innovative bioassays tailored for water quality monitoring of pharmaceuticals, paving the way for more robust environmental monitoring and risk assessment. Overall, adopting effect-based methods for pharmaceutical monitoring in water holds significant promise. It encompasses a broad spectrum of biological impacts, promotes standardized protocols, and supports a bioassay test battery approach indicative of different endpoints, thereby enhancing the effectiveness of environmental risk assessment.

Insight into chemically reactive metabolites of aliphatic amine pollutants: A de novo prediction strategy and case study of sertraline

The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.

Non-target analysis for water characterization: wastewater treatment impact and selection of relevant features

Non-target analyses were conducted to characterize and compare the molecular profiles (UHPLC-HRMS fingerprint) of water samples from a wastewater treatment plant (WWTP). Inlet and outlet samples were collected from three campaigns spaced 6 months apart in order to highlight common trends. A significant impact of the treatment on the sample fingerprints was shown, with a 65-70% abatement of the number of features detected in the effluent, and more polar, smaller and less intense molecules found overall compared to those in WWTP influent waters. Multivariate analysis (PCA) associated with variations of the features between inlets and outlets showed that features appearing or increasing were correlated with effluents while those disappearing or decreasing were correlated with influents. Finally, effluent features considered as relevant to a potentially adverse effect on aqueous media (i.e. those which appeared or increased or slightly varied from the influent) were highlighted. Three hundred seventy-five features common with the 3 campaigns were thus selected and further characterized. For most of them, elementary composition was found to be C, H, N, O (42%) and C, H, N, O, P (18%). Considering the MS2 spectra and several reference MS2 databases, annotations were proposed for 35 of these relevant features. They include synthetic products, pharmaceuticals and metabolites.