High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget 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.

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.

Identification of Hydrocarbon Sulfonates as Previously Overlooked Transthyretin Ligands in the Environment

Incidences of thyroid disease, which has long been hypothesized to be partially caused by exposure to thyroid hormone disrupting chemicals (TDCs), have rapidly increased in recent years. However, known TDCs can only explain a small portion (∼1%) of in vitro human transthyretin (hTTR) binding activities in environmental samples, indicating the existence of unknown hTTR ligands. In this study, we aimed to identify the major environmental hTTR ligands by employing protein Affinity Purification with Nontargeted Analysis (APNA). hTTR binding activities were detected in all 11 indoor dust and 9 out of 10 sewage sludge samples by the FITC-T4 displacement assay. By using APNA, 31 putative hTTR ligands were detected including perfluorooctanesulfonate (PFOS). Two of the most abundant ligands were identified as hydrocarbon surfactants (e.g., dodecyl benzenesulfonate). Moreover, another abundant ligand was surprisingly identified as a disulfonate fluorescent brightener, 4,4'-bis(2-sulfostyryl)biphenyl sodium (CBS). CBS was validated as a nM-affinity hTTR ligand with an IC50 of 345 nM. In total, hydrocarbon surfactants and fluorescent brighteners explain 1.92-17.0 and 5.74-54.3% of hTTR binding activities in dust and sludge samples, respectively, whereas PFOS only contributed <0.0001%. Our study revealed for the first time that hydrocarbon sulfonates are previously overlooked hTTR ligands in the environment.

Anthropogenic-induced ecological risks on marine ecosystems indicated by characterizing emerging pollutants in Pearl River Estuary, China

Anthropogenic Contaminants of Emerging Concern (CECs) in marine environments have raised significant concerns. Yet, analyses detailing their origins, fate, and environmental effects are limited. This study employs an integrated non-target screening methodology to elucidate CECs existence across 46 sampling sites in the Pearl River Estuary (PRE) of the South China Sea. Assisted by advanced liquid chromatography-high resolution mass spectrometry, we discovered 208 chemicals in six usage categories, with pesticides (33 %) and pharmaceuticals (29 %) predominating. Several CECs drew attention for their consistent detections, profound abundance, and significant ecotoxicities. The wide detection of them at offshore sites further implies that anthropogenic activities may contribute to large-scale contamination. Meanwhile, distinct distribution patterns of CECs across PRE are evident in semi-quantitative results, indicating regional anthropogenic influences. Identified transformation products may establish a novel and non-negligible negative contribution to ecology through elevated environmental toxicities, exemplified by HMMM and atrazine. Based on the ecological risks, we compiled a prioritized list of 21 CECs warranting intensified scrutiny. Our findings indicate the introduction of various CECs into the South China Sea via PRE, emphasizing the urgent necessity for ongoing surveillance of discharged CECs at estuary areas and assessment of their marine ecological consequences.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

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.

Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration

Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.

Effect-Directed Analysis Based on Transthyretin Binding Activity of Per- and Polyfluoroalkyl Substances in a Contaminated Sediment Extract

Only a fraction of the total number of per- and polyfluoroalkyl substances (PFAS) are monitored on a routine basis using targeted chemical analyses. We report on an approach toward identifying bioactive substances in environmental samples using effect-directed analysis by combining toxicity testing, targeted chemical analyses, and suspect screening. PFAS compete with the thyroid hormone thyroxin (T4 ) for binding to its distributor protein transthyretin (TTR). Therefore, a TTR-binding bioassay was used to prioritize unknown features for chemical identification in a PFAS-contaminated sediment sample collected downstream of a factory producing PFAS-coated paper. First, the TTR-binding potencies of 31 analytical PFAS standards were determined. Potencies varied between PFAS depending on carbon chain length, functional group, and, for precursors to perfluoroalkyl sulfonic acids (PFSA), the size or number of atoms in the group(s) attached to the nitrogen. The most potent PFAS were the seven- and eight-carbon PFSA, perfluoroheptane sulfonic acid (PFHpS) and perfluorooctane sulfonic acid (PFOS), and the eight-carbon perfluoroalkyl carboxylic acid (PFCA), perfluorooctanoic acid (PFOA), which showed approximately four- and five-times weaker potencies, respectively, compared with the native ligand T4 . For some of the other PFAS tested, TTR-binding potencies were weak or not observed at all. For the environmental sediment sample, not all of the bioactivity observed in the TTR-binding assay could be assigned to the PFAS quantified using targeted chemical analyses. Therefore, suspect screening was applied to the retention times corresponding to observed TTR binding, and five candidates were identified. Targeted analyses showed that the sediment was dominated by the di-substituted phosphate ester of N-ethyl perfluorooctane sulfonamido ethanol (SAmPAP diester), whereas it was not bioactive in the assay. SAmPAP diester has the potential for (bio)transformation into smaller PFAS, including PFOS. Therefore, when it comes to TTR binding, the hazard associated with this substance is likely through (bio)transformation into more potent transformation products. Environ Toxicol Chem 2024;43:245-258. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The contribution of PFAS to thyroid hormone-displacing activity in Dutch waters: A comparison between two in vitro bioassays with chemical analysis

Per- and polyfluoroalkyl substances (PFAS) are a group of xenobiotics that are widely distributed throughout the aquatic environment. Many PFAS are possible thyroid hormone (TH) system disrupting compounds, because they have the capacity to -amongst other- inhibit the TH thyroxine (T4) from binding to its transport protein transthyretin (TTR). This study investigated the occurrence of TH-displacing activity in the Dutch water cycle, and more specifically, the contribution of PFAS to this effect. Over one year of monitoring data of 29 PFAS (linear and branched) showed the continuous presence of PFAS in drinking waters and their surface water sources. Secondly, the FITC-T4 and TTR-TRβ-CALUX bioassays were mutually compared using positive (HPLC-grade water spiked with PFOA) and negative control samples (HPLC-grade water), as well as relative potency factors (RPFs) of up to 20 PFAS congeners. Both assays were found to be suitable for measuring TH-displacing activity in water samples. As a third aim, a field study was performed in the Dutch water cycle that was comprised of samples from drinking water, surface water, PFAS contaminated sites, and 2 wastewater treatment plants. All samples were analyzed with 1. chemical analysis for 29 PFAS, 2. the FITC-T4 bioassay, and 3. the TTR-TRβ-CALUX bioassay. The bioassays mutually showed good correlation (R2 0.85). Bioanalytical equivalent concentrations (BEQ) based on chemically-determined concentrations and RPFs (BEQchem) revealed that analyzed PFAS only explained ≤4.1 % of their activity in water extracts measured by both bioassays (BEQbio). This indicated that as yet unknown compounds contribute to the majority of the measured TH-displacing activity. Moreover, water treatment processes (e.g. DW production from SW) showed a larger contribution of target PFAS to the BEQbio. This could be a first lead to identify unknown compounds that contribute to this activity, and as such, enable the assessment of possible risks associated by the occurrence of TH-displacing activity in water.

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.

An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis

Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method.

Identifying antimicrobials and their metabolites in wastewater and surface water with effect-directed analysis

This study aimed to identify antimicrobial contaminants in the aquatic environment with effect-directed analysis. Wastewater influent, effluent, and surface water (up- and downstream of the discharge location) were sampled at two study sites. The samples were enriched, subjected to high-resolution fractionation, and the resulting 80 fractions were tested in an antibiotics bioassay. The resulting bioactive fractions guided the suspect and nontargeted identification strategy in the high-resolution mass spectrometry data that was recorded in parallel. Chemical features were annotated with reference databases, assessed on annotation quality, and assigned identification confidence levels. To identify antibiotic metabolites, Phase I metabolites were predicted in silico for over 500 antibiotics and included as a suspect list. Predicted retention times and fragmentation patterns reduced the number of annotations to consider for confirmation testing. Overall, the bioactivity of three fractions could be explained by the identified antibiotics (clarithromycin and azithromycin) and an antibiotic metabolite (14-OH(R) clarithromycin), explaining 78% of the bioactivity measured at one study site. The applied identification strategy successfully identified antibiotic metabolites in the aquatic environment, emphasizing the need to include the toxic effects of bioactive metabolites in environmental risk assessments.

Mixture Risk Assessment of Complex Real-Life Mixtures-The PANORAMIX Project

Humans are involuntarily exposed to hundreds of chemicals that either contaminate our environment and food or are added intentionally to our daily products. These complex mixtures of chemicals may pose a risk to human health. One of the goals of the European Union's Green Deal and zero-pollution ambition for a toxic-free environment is to tackle the existent gaps in chemical mixture risk assessment by providing scientific grounds that support the implementation of adequate regulatory measures within the EU. We suggest dealing with this challenge by: (1) characterising 'real-life' chemical mixtures and determining to what extent they are transferred from the environment to humans via food and water, and from the mother to the foetus; (2) establishing a high-throughput whole-mixture-based in vitro strategy for screening of real-life complex mixtures of organic chemicals extracted from humans using integrated chemical profiling (suspect screening) together with effect-directed analysis; (3) evaluating which human blood levels of chemical mixtures might be of concern for children's development; and (4) developing a web-based, ready-to-use interface that integrates hazard and exposure data to enable component-based mixture risk estimation. These concepts form the basis of the Green Deal project PANORAMIX, whose ultimate goal is to progress mixture risk assessment of chemicals.