Multiobjective optimization of effect-directed planar chromatography as a promising tool for fast selection of polypotent natural products.

A new method for efficiently selecting polypotent natural products is proposed in this study. The method involves using effect-directed HPTLC data and multiobjective optimization algorithms to extract chromatographic signals from HPTLC bioassay images. Three different multiobjective optimization methods, namely Derringer's desirability approach, Technique for order of preference by similarity to ideal solution (TOPSIS), and Sum of ranking differences (SRD), were applied to the chromatographic signals. In combination with jackknife cross-validation, Derringer's approach and TOPSIS demonstrated high similarity in finding the best (most polypotent), next to the best, next to the worst, and worst (least polypotent) extracts, while the SRD resulted in slightly different outcomes. Furthermore, a new method for identifying the chromatographic features that characterize the most polypotent extracts was proposed. This method is based on partial least square regression (PLS) and can be used in combination with HPTLC-chemical fingerprints to predict the desirability of new extracts. The resulting PLS models demonstrated high statistical performance with determination coefficients ranging from R2 = 0.885 in the case of Derringer's desirability, to 0.986 for TOPSIS. However, the PLS modeling of SRD values was not successful.

Identification of Key Toxic Substances Considering Metabolic Activation: A Combination of Transcriptome and Nontarget Analysis.

There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.

Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges.

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC-MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17ß-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC-MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.

Screening Organic Components and Toxicogenic Structures from Regional Fine Particulate Matters Responsible for Myocardial Fibrosis in Male Mice.

Numerous studies indicate that fine particulate matters (PM2.5) and its organic components are urgent risk factors for cardiovascular diseases (CVDs). Combining toxicological experiments, effect-directed analyses, and nontarget identification, this study aims to explore whether PM2.5 exposure in coal-combustion areas induces myocardial fibrosis and how to identify the effective organic components and their toxic structures to support regional risk control. First, we constructed an animal model of real-world PM2.5 exposure during the heating season and found that the exposure impaired cardiac systolic function and caused myocardial fibrosis, with chemokine Ccl2-mediated inflammatory response being the key cause of collagen deposition. Then, using the molecular event as target coupled with two-stage chromatographic isolation and mass spectrometry analyses, we identified a total of 171 suspect organic compounds in the PM2.5 samples. Finally, using hierarchical characteristic fragment analysis, we predicted that 40 of them belonged to active compounds with 6 alert structures, including neopentane, butyldimethylamine, 4-ethylphenol, hexanal, decane, and dimethylaniline. These findings provide evidence for risk management and prevention of CVDs in polluted areas.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review.

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Concept of a six-fold multiplex planar bioassay to distinguish endocrine agonist, antagonist, cytotoxic and false-positive responses.

To analyze a complex sample for endocrine activity, different tests must be performed to clarify androgen/estrogen agonism, antagonism, cytotoxicity, anti-cytotoxicity, and corresponding false-positive reactions. This means a large amount of work. Therefore, a six-fold planar multiplex bioassay concept was developed to evaluate up to the mentioned six endpoints or mechanisms simultaneously in the same sample analysis. Separation of active constituents from interfering matrix via high-performance thin-layer chromatography and effect differentiation via four vertical stripes (of agonists and end-products of the respective enzyme-substrate reaction) applied along each separated sample track were key to success. First, duplex endocrine bioassay versions were established. For the androgen/anti-androgen bioassay applied via piezoelectric spraying, the mean limit of biological detection of bisphenol A was 14 ng/band and its mean half maximal inhibitory concentration IC50 was 116 ng/band. Applied to trace analysis of six migrate samples from food packaging materials, 19 compound zones with agonistic or antagonistic estrogen/androgen activities were detected, with up to seven active compound zones within one migrate. For the first time, the S9 metabolism of endocrine effective compounds was studied on the same surface and revealed partial deactivation. Coupled to high-resolution mass spectrometry, molecular formulas were tentatively assigned to compounds, known to be present in packaging materials or endocrine active or previously unknown. Finally, the detection of cytotoxicity/anti-cytotoxicity and false-positives was integrated into the duplex androgen/anti-androgen bioassay. The resulting six-fold multiplex planar bioassay was evaluated with positive control standards and successfully applied to one migrate sample. The streamlined stripe concept for multiplex planar bioassays made it possible to assign different mechanisms to individual active compounds in a complex sample. The concept is generic and can be transferred to other assays.

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments.

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

Identification of hot spots and co-occurrence patterns of activities on thyroid hormone receptor and transthyretin binding in passive samplers from Czech surface waters.

One of the less studied in vitro biological activities in the aquatic environment are thyroid hormone receptor beta (TRß)-mediated agonistic and antagonistic activities and transthyretin (TTR) binding activity. They were measured mostly using active sampling methods, but rarely found. It is unclear if these activities co-occur, and the drivers of the (anti-)TRß activity are mostly unknown. Therefore, the main aim of the study was to determine (anti-)TRß activities as well as transthyretin (TTR) binding activity in passive samplers from Czech surface waters in combination with the search for the effect drivers based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis by applying suspect screening. Passive samplers (polar organic chemical integrative samplers, POCIS) were deployed at twenty-one sites (all ends of watersheds and other important sites in Elbe River) in the Czech rivers. The (anti-)TRß and TTR binding activity were measured using (anti-)TRß-CALUX and TTR-TRß-CALUX bioassays. Anti-TRß activity was found at eight sites, and TTR binding activity co-occurred there at six of these sites. The co-occurrence of TRß-mediated antagonistic activity and TTR binding indicate that they may have common effect drivers. No sample exhibited TRß agonistic activity. The extract from the site Bílina River, the most burdened with anti-TRß activity, was further successfully fractionated, and this activity was revealed in the fraction, where mid-polar compounds prevailed. However, the suspect LC-HRMS analysis did not reveal the chemical effect drivers. Our results showed that anti-TRß activity can be found in surface waters by employing passive sampling and frequently co-occurs with TTR binding activity. Overall, the fractionation procedure and non-target data acquisition used in this study can serve as a basis for searching the effect drivers in future research.

Development of a rapid screening method utilizing 2D LC for effect-directed analysis in the identification of environmental toxicants.

Effect-directed analysis (EDA) is a crucial tool in environmental toxicology, effectively integrating toxicity testing with chemical analysis. The conventional EDA approach, however, presents challenges such as significant solvent consumption, extended analysis time, labor intensity, and potential contamination risks. In response, we introduce an innovative alternative to the conventional EDA. This method utilizes the MTT bioassay and online two-dimensional liquid chromatography (2D LC) coupled with high-resolution mass spectrometry (HR-MS), significantly reducing the fractionation steps and leveraging the enhanced sensitivity of the bioassay and automated chemical analysis. In the chemical analysis phase, a switching valve interface is employed for comprehensive analysis. We tested the performance of both the conventional and our online 2D LC-based methods using a household product. Both methods identified the same number of toxicants in the sample. Our alternative EDA is 22.5 times faster than the conventional method, fully automated, and substantially reduces solvent consumption. This novel approach offers ease, cost-effectiveness, and represents a paradigm shift in EDA methodologies. By integrating a sensitive bioassay with online 2D LC, it not only enhances efficiency but also addresses the challenges associated with traditional methods, marking a significant advancement in environmental toxicology research.

Rapid screening of acetylcholinesterase active contaminants in water: A solid phase microextraction-based ligand fishing approach.

Effect-directed analysis (EDA) has been increasingly used for screening toxic contaminants in the environment, but conventional EDA procedures are often time-consuming and labor-extensive. This challenges the use of EDA for toxicant identification in the scenarios when quick answers are demanded. Herein, a solid phase microextraction ligand fishing (SPME-LF) strategy has been proposed as a rapid EDA approach for identifying acetylcholinesterase (AChE) active compounds in water. The feasibility of ligand fishing techniques for screening AChE active chemicals from environmental mixtures was first verified by a membrane separation method. Then, SPME fibers were prepared through self-assembly of boronic acid groups with AChE via co-bonding and applied for SPME-LF. As AChE coated SPME fibers selectively enriched AChE-active compounds from water, comparing sorbing compounds by the SPME fibers with and without AChE coating can quickly distinguish AChE toxicants in mixtures. Compared with conventional EDA, SPME-LF does not require repeating sample separations and bioassays, endowing SPME-LF with the merits of low-cost, labor-saving, and user-friendly. It is believed that cost-efficient and easy-to-use SPME-LF strategy can potentially be a rapid EDA method for screening receptor-specific toxicants in aquatic environment, especially applicable in time-sensitive screening.

Effect-directed analysis of androgenic compounds from sewage sludges in China.

The safety of municipal sewage sludge has raised great concerns because of the accumulation of large-scale endocrine disrupting chemicals in the sludge during wastewater treatment. The presence of contaminants in sludge can cause secondary pollution owing to inappropriate disposal mechanisms, posing potential risks to the environment and human health. Effect-directed analysis (EDA), involving an androgen receptor (AR) reporter gene bioassay, fractionation, and suspect and nontarget chemical analysis, were applied to identify causal AR agonists in sludge; 20 of the 30 sludge extracts exhibited significant androgenic activity. Among these, the extracts from Yinchuan, Kunming, and Shijiazhuang, which held the most polluted AR agonistic activities were prepared for extensive EDA, with the dihydrotestosterone (DHT)-equivalency of 2.5 - 4.5 ng DHT/g of sludge. Seven androgens, namely boldione, androstenedione, testosterone, megestrol, progesterone, and testosterone isocaproate, were identified in these strongest sludges together, along with testosterone cypionate, first reported in sludge media. These identified androgens together accounted for 55 %, 87 %, and 52 % of the effects on the sludge from Yinchuan, Shijiazhuang, and Kunming, respectively. This study elucidates the causative androgenic compounds in sewage sludge and provides a valuable reference for monitoring and managing androgens in wastewater treatment.

Fast and sustainable planar yeast-based bioassay for endocrine disruptors in complex mixtures: Start of cell cultivation to result within one day.

High-performance thin-layer chromatography hyphenated with planar multiplex bioassays and high-resolution tandem mass spectrometry contributes to the non-target detection or even identification of active compounds in complex mixtures such as food, feed, cosmetics, commodities, and environmental samples. It can be used to discover previously unknown harmful or active substances in complex samples and to tentatively assign molecular formulas. This method is already faster than the commonly used in vitro assays along with liquid chromatographic separations, but overnight cell cultivation still prevents a planar bioassay from being performed within one day. There is also still potential for optimization in terms of sustainability. To achieve this, the planar bioassay protocols for the detection of androgen-like and estrogen-like compounds were harmonized. The successful minimization of the cell culture volume enabled accelerated cell cultivation, which allowed the bioassay to be performed within one day. This was considered a milestone achieved, as up to 23 samples per plate can now be analyzed from the start of cultivation to the biological endpoint on the same day. Doubling the substrate amount and increasing the pH of the silica gel layer led to a more sensitive and selective bioassay due to the enhanced fluorescence of the formed end-product. The faster and more sustainable bioassay protocol was applied to complex samples such as sunscreen and red wine to detect estrogen-like compounds. The developed method was validated by comparison with a standard method.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment.

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Bioassay-Guided Assessment of Antioxidative, Anti-Inflammatory and Antimicrobial Activities of Extracts from Medicinal Plants via High-Performance Thin-Layer Chromatography.

Natural products and their analogues have contributed significantly to treatment options, especially for anti-inflammatory and infectious diseases. Thus, the primary objective of this work was to compare the bioactivity profiles of selected medicinal plants that are historically used in folk medicine to treat inflammation and infections in the body. Chemical HPTLC fingerprinting was used to assess antioxidant, phenolic and flavonoid content, while bioassay-guided HPTLC was used to detect compounds with the highest antibacterial and anti-inflammatory activities. The results of this study showed that green tea leaf, walnut leaf, St. John's wort herb, wild thyme herb, European goldenrod herb, chamomile flower, and immortelle flower extracts were strong radical scavengers. Green tea and nettle extracts were the most active extracts against E. coli, while calendula flower extract showed significant potency against S. aureus. Furthermore, green tea, greater celandine, and fumitory extracts exhibited pronounced potential in suppressing COX-1 activity. The bioactive compounds from the green tea extract, as the most bioactive, were isolated by preparative thin-layer chromatography and characterized with their FTIR spectra. Although earlier studies have related green tea's anti-inflammatory properties to the presence of catechins, particularly epigallocatechin-3-gallate, the FTIR spectrum of the compound from the most intense bioactive zone showed the strongest anti-inflammatory activity can be attributed to amino acids and heterocyclic compounds. As expected, antibacterial activity in extracts was related to fatty acids and monoglycerides.

Bioactive profiles of edible vegetable oils determined using 10D hyphenated comprehensive high-performance thin-layer chromatography (HPTLC×HPTLC) with on-surface metabolism (nanoGIT) and planar bioassays.

Introduction: Vegetable oils rich in unsaturated fatty acids are assumed to be safe and even healthy for consumers though lipid compositions of foods vary naturally and are complex considering the wealth of minor compounds down to the trace level. Methods: The developed comprehensive high-performance thin-layer chromatography (HPTLC×HPTLC) method including the on-surface metabolization (nanoGIT) and bioassay detection combined all steps on the same planar surface. The pancreatic lipolysis (intestinal phase) experiment and the subsequent analysis of the fatty acid composition including its effect-directed detection using a planar bioassay was performed without elaborate sample preparation or fractionation to ensure sample integrity. Thus, no sample part was lost, and the whole sample was studied on a single surface regarding all aspects. This made the methodology as well as technology miniaturized, lean, all-in-one, and very sustainable. Results and discussion: To prioritize important active compounds including their metabolism products in the complex oil samples, the nanoGIT method was used to examine the pancreatic lipolysis of nine different vegetable oils commonly used in the kitchen and food industry, e.g., canola oil, flaxseed oil, hemp oil, walnut oil, soybean oil, sunflower oil, olive oil, coconut oil, and palm oil. The digested oils revealed antibacterial and genotoxic effects, which were assigned to fatty acids and oxidized species via high-resolution tandem mass spectrometry (HRMS/MS). This finding reinforces the importance of adding powerful techniques to current analytical tools. The 10D hyphenated nanoGIT-HPTLC×HPTLC-Vis/FLD-bioassay-heart cut-RP-HPLC-DAD-HESI-HRMS/MS has the potential to detect any potential hazard due to digestion/metabolism, improving food safety and understanding on the impact of complex samples.

Bioactivity profiles of six baobab fruit pulp powders via planar chromatography hyphenated with effect-directed analysis.

Baobab (Adansonia digitata) fruit pulp has a high nutrient content and has been traditionally used for medicinal purposes (e.g., as an anti-inflammatory and antioxidant agent) that may help protect against chronic diseases. Six different baobab fruit pulp powders were investigated using three different extractants and analyzed by high-performance thin-layer chromatography (HPTLC) hyphenated with antibacterial bioassays and enzyme inhibition assays. The developed non-target effect-directed screening was performed after extraction with pentyl acetate - ethanol 1:1 (V/V) on the HPTLC plate silica gel 60 using toluene - ethyl acetate - methanol 6:3:1 (V/V/V) as mobile phase system and derivatization via the anisaldehyde sulfuric acid reagent for detection. The physico-chemical profiles of the six baobab fruit pulp powder extracts were comparable, although the intensity of some zones was moderately different. The following effect-directed profiling via tyrosinase, α-glucosidase, and acetylcholinesterase inhibition assays as well as antibacterial Aliivibrio fischeri and Bacillus subtilis bioassays revealed one prominent multipotent bioactive compound zone in common, more or less active in all five studied (bio)assays. Via the recording of high-resolution mass spectra, this compound zone was tentatively assigned to coeluting saturated (palmitic acid 16:0 and stearic acid 18:0), monounsaturated (oleic acid 18:1), and polyunsaturated (linoleic acid 18:2 and linolenic acid 18:3) fatty acids. This finding was confirmed by other studies, which already proved individual activities of fatty acids. The first (bio)activity profiling of baobab fruit pulp powders via HPTLC-effect-directed analysis revealed that the baobab fruit could be considered as a functional food, however, further research is needed to study the impact on health and the influences on the bioactivity arising from different climates, years and soils or regions.

Neurotoxicity in complex environmental mixtures-a case-study at River Danube in Novi Sad (Serbia) using zebrafish embryos.

Acetylcholinesterase (AChE) inhibitors are an important class of neuroactive chemicals that are often detected in aquatic and terrestrial environments. The correct functionality of the AChE enzyme is linked to many important physiological processes such as locomotion and respiration. Consequently, it is necessary to develop new analytical strategies to identify harmful AChE inhibitors in the environment. It has been shown that mixture effects and oxidative stress may jeopardize the application of in vivo assays for the identification of AChE inhibitors in the environment. To confirm that in vivo AChE assays can be successfully applied when dealing with complex mixtures, an extract from river water impacted by non-treated wastewater was bio-tested using the acute toxicity fish embryo test (FET) and AChE inhibition assay with zebrafish. The zebrafish FET showed high sensitivity for the extract (LC10 = relative extraction factor 2.8) and we observed a significant inhibition of the AChE (40%, p < 0.01) after 4-day exposure. Furthermore, the extract was chromatographically fractionated into a total of 26 fractions to dilute the mixture effect and separate compounds according to their physico-chemical properties. As expected, non-specific acute effects (i.e., mortality) disappeared or evenly spread among the fractions, while AChE inhibition was still detected in five fractions. Chemical analysis did not detect any known AChE inhibitors in these active fractions. These results confirm that the AChE assay with Danio rerio can be applied for the detection of neuroactive effects induced in complex environmental samples, but also, they highlight the need to increase analytical and identification techniques for the detection of neurotoxic substances.

Advances in PAH mixture toxicology enabled by zebrafish.

Polycyclic aromatic hydrocarbons (PAHs) are a class of organic compounds produced by a variety of petrogenic and pyrogenic sources. PAHs inherently occur in the environment in complex mixtures. The early life-stage zebrafish model is a valuable tool for high-throughput screening (HTS) for toxicity of complex chemical mixtures due to its rapid development, high fecundity, and superb sensitivity to chemical insult. Zebrafish are amenable to exposure to surrogate mixtures as well as extracts of environmental samples and effect-directed analysis. In addition to its utility to HTS, the zebrafish has proven an excellent model for assessing chemical modes of action and identifying molecular initiating and other key events in an Adverse Outcome Pathway framework. Traditional methods of assessing PAH mixture toxicity prioritize carcinogenic potential and lack consideration of non-carcinogenic modes of action, assuming a similar molecular initiating event for all PAHs. Recent work in zebrafish has made it clear that while PAHs belong to the same chemical class, their modes of action can be divergent. Future research should use zebrafish to better classify PAHs by their bioactivity and modes of action to better understand mixture hazards.

Identification of antimicrobial and glucocorticoid compounds in wastewater effluents with effect-directed analysis.

Pharmaceuticals, such as glucocorticoids and antibiotics, are inadequately removed from wastewater and may cause unwanted toxic effects in the receiving environment. This study aimed to identify contaminants of emerging concern in wastewater effluent with antimicrobial or glucocorticoid activity by applying effect-directed analysis (EDA). Effluent samples from six wastewater treatment plants (WWTPs) in the Netherlands were collected and analyzed with unfractionated and fractionated bioassay testing. Per sample, 80 fractions were collected and in parallel high-resolution mass spectrometry (HRMS) data were recorded for suspect and nontarget screening. The antimicrobial activity of the effluents was determined with an antibiotics assay and ranged from 298 to 711 ng azithromycin equivalents·L-1. Macrolide antibiotics were identified in each effluent and found to significantly contribute to the antimicrobial activity of each sample. Agonistic glucocorticoid activity determined with the GR-CALUX assay ranged from 98.1 to 286 ng dexamethasone equivalents·L-1. Bioassay testing of several tentatively identified compounds to confirm their activity revealed inactivity in the assay or the incorrect identification of a feature. Effluent concentrations of glucocorticoid active compounds were estimated from the fractionated GR-CALUX bioassay response. Subsequently, the biological and chemical detection limits were compared and a sensitivity gap between the two monitoring approaches was identified. Overall, these results emphasize that combining sensitive effect-based testing with chemical analysis can more accurately reflect environmental exposure and risk than chemical analysis alone.

Toxicity Evaluation and Effect-Based Identification of Chlorine Disinfection Products of the Anti-COVID-19 Drug Chloroquine Phosphate.

Antiviral transformation products (TPs) generated during wastewater treatment are an environmental concern, as their discharge, in considerable amounts, into natural waters during a pandemic can pose possible risks to the aquatic environment. Identification of the hazardous TPs generated from antivirals during wastewater treatment is important. Herein, chloroquine phosphate (CQP), which was widely used during the coronavirus disease-19 (COVID-19) pandemic, was selected for research. We investigated the TPs generated from CQP during water chlorination. Zebrafish (Danio rerio) embryos were used to assess the developmental toxicity of CQP after water chlorination, and hazardous TPs were estimated using effect-directed analysis (EDA). Principal component analysis revealed that the developmental toxicity induced by chlorinated samples could be relevant to the formation of some halogenated TPs. Fractionation of the hazardous chlorinated sample, along with the bioassay and chemical analysis, identified halogenated TP387 as the main hazardous TP contributing to the developmental toxicity induced by chlorinated samples. TP387 could also be formed in real wastewater during chlorination in environmentally relevant conditions. This study provides a scientific basis for the further assessment of environmental risks of CQP after water chlorination and describes a method for identifying unknown hazardous TPs generated from pharmaceuticals during wastewater treatment.