The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Network analysis to reveal the most commonly detected compounds in predator-prey pairs in freshwater and marine mammals and fish in Europe

Marine and freshwater mammalian predators and fish samples, retrieved from environmental specimen banks (ESBs), natural history museum (NHMs) and other scientific collections, were analysed by LIFE APEX partners for a wide range of legacy and emerging contaminants (2545 in total). Network analysis was used to visualize the chemical occurrence data and reveal the predominant chemical mixtures for the freshwater and marine environments. For this purpose, a web tool was created to explore these chemical mixtures in predator-prey pairs. Predominant chemicals, defined as the most prevalent substances detected in prey-predator pairs were identified through this innovative approach. The analysis established the most frequently co-occurring substances in chemical mixtures from AP&P in the marine and freshwater environments. Freshwater and marine environments shared 23 chemicals among their top 25 predominant chemicals. Legacy chemical, including perfluorooctanesulfonic acid (PFOS), brominated diphenyl ethers (BDEs), polychlorinated biphenyls (PCBs), hexachlorobenzene and mercury were dominant chemicals in both environments. Furthermore, N-acetylaminoantipyrine was a predominant pharmaceutical in both environments. The LIFE APEX chemical mixture application (https://norman-data.eu/LIFE_APEX_Mixtures) was proven to be useful to establish most prevalent compounds in terms of number of detected counts in prey-predator pairs. Nonetheless, further research is needed to establish food chain associations of the predominant chemicals.

An automated and high-throughput data processing workflow for PFAS identification in biota by direct infusion ultra-high resolution mass spectrometry

The increasing recognition of the health impacts from human exposure to per- and polyfluorinated alkyl substances (PFAS) has surged the need for sophisticated analytical techniques and advanced data analyses, especially for assessing exposure by food of animal origin. Despite the existence of nearly 15,000 PFAS listed in the CompTox chemicals dashboard by the US Environmental Protection Agency, conventional monitoring and suspect screening methods often fall short, covering only a fraction of these substances. This study introduces an innovative automated data processing workflow, named PFlow, for identifying PFAS in environmental samples using direct infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). PFlow's validation on a bream liver sample, representative of low-concentration biota, involves data pre-processing, annotation of PFAS based on their precursor masses, and verification through isotopologues. Notably, PFlow annotated 17 PFAS absent in the comprehensive targeted approach and tentatively identified an additional 53 compounds, thereby demonstrating its efficiency in enhancing PFAS detection coverage. From an initial dataset of 30,332 distinct m/z values, PFlow thoroughly narrowed down the candidates to 84 potential PFAS compounds, utilizing precise mass measurements and chemical logic criteria, underscoring its potential in advancing our understanding of PFAS prevalence and of human exposure.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Suspect and non-target screening of chemicals of emerging Arctic concern in air, biota and human serum

Contaminants of emerging concern receive increasing attention in the Arctic environment. The aim of this study was to screen for chemicals of emerging Arctic concern (CEACs) in different types of Arctic samples including biota, air and human serum. We used a combination of gas chromatography (GC) and liquid chromatography (LC) with high resolution mass spectrometry (HRMS) for suspect and non-target screening (NTS). Suspect screening of 25 CEACs was based on published in-silico approaches for the identification of CEACs and revealed tetrabromophthalic anhydride (TBPA) in pilot whale and air, albeit with low detection frequencies (17 and 33%, respectively). An NTS workflow detected a total of 112 contaminants, i.e. 49, 42, 31 and 30 compounds in pilot whale, ringed seal, air, and human serum respectively at confidence level 2 and 3. Although legacy POPs still dominated the samples, 64 CEACs were tentatively identified and further tentatively assessed for persistence (P), bioaccumulation (B), mobility (M), toxicity (T), and long-range transport potential (LRTP). While four PBT compounds were identified, 37 PMT substances dominated among these 64 contaminants. Our study indicated that many chemicals of potential risk might be present in Arctic samples that would benefit from confirmation and further studies of their transport to and accumulation in the Arctic environment.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC-MS experiments. GC-MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS + RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI + EI-MS data through matching to its own large library of >1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.

Grouping strategies for assessing and managing persistent and mobile substances

Background

Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050.

Main

Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples.

Conclusions

Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

Should Transformation Products Change the Way We Manage Chemicals?

When chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed.

Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatography - high resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

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.

The underappreciated diversity of bile acid modifications

The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.

Asphaltene-derived nanocomposites for the removal of emerging pollutants and its antimicrobial effects: batch and continuous column studies

Emerging contaminants are diverse ecotoxic materials requiring unique treatment for removal. Asphaltenes are environmentally hazardous carbon-rich solid waste product of the petroleum industry. In the current work, asphaltene-derived activated carbon (AC) was loaded with silver (Ag/AC) and used to remove amoxicillin (AMX) and tetracycline (TC) from aqueous phase. The prepared Ag/AC was characterised using FESEM, FTIR, XRD and surface area analysis. The FESEM micrographs confirmed the spherical silver nanoparticle-laden porous AC, and the BET surface area was found to be 213 m2/g. Batch adsorption studies were performed, and the equilibrium data were fit into adsorption isotherm and kinetic models. The Ag/AC exhibited superior monolayer adsorption capacity of 1012 mg/g and 770 mg/g for AMX and TC, respectively. The continuous column studies were also performed to evaluate the breakthrough parameters. Furthermore, the antimicrobial activity of the adsorbent was evaluated using zone of inhibition studies. Ag/AC was found to have an 8-mm-diameter zone of microbial inhibition. The obtained results showed that Ag/AC was a promising material for the removal of antibiotics and inhibition of resistance-developed mutated microbes in effluent water.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

Cyanopeptolins and Anabaenopeptins Are the Dominant Cyanopeptides from Planktothrix Strains Collected in Canadian Lakes

Common bloom-forming cyanobacteria produce complex strain-specific mixtures of secondary metabolites. The beneficial and toxic properties of these metabolite mixtures have attracted both research and public health interest. The advancement of mass spectrometry-based platforms and metabolomics data processing has accelerated the identification of new metabolites and feature dereplication from microbial sources. The objective of this study was to use metabolomics data processing to decipher the intracellular cyanopeptide diversity of six Planktothrix strains collected from Canadian lakes. Data-dependent acquisition experiments were used to collect a non-targeted high-resolution mass spectrometry dataset. Principal component analysis and factor loadings were used to visualize cyanopeptide variation between strains and identified features contributing to the observed variation. GNPS molecular networking was subsequently used to show the diversity of cyanopeptides produced by the Planktothrix strains. Each strain produced a unique mixture of cyanopeptides, and a total of 225 cyanopeptides were detected. Planktothrix sp. CPCC 735 produced the most (n = 68) cyanopeptides, and P. rubescens CPCC 732 produced the fewest (n = 27). Microcystins and anabaenopeptins were detected from all strains. Cyanopeptolins, microviridins and aeruginosins were detected from five, four and two strains, respectively. Cyanopeptolin (n = 80) and anabaenopeptin (n = 61) diversity was the greatest, whereas microcystins (n = 21) were the least diverse. Interestingly, three of the P. rubescens strains had different cyanopeptide profiles, despite being collected from the same lake at the same time. This study highlights the diversity of cyanopeptides produced by Planktothrix and further hints at the underestimated cyanopeptide diversity from subpopulations of chemotypic cyanobacteria in freshwater lakes.

Multi-target assessment of advanced oxidation processes-based strategies for indirect potable reuse of tertiary wastewater: Fate of compounds of emerging concerns, microbial and ecotoxicological parameters

Two advanced oxidation processes (AOPs), namely ozone/H2O2 and UV/H2O2, were tested at pilot scale as zero-liquid-discharge alternative treatments for the removal of microbiological (bacteria and viruses), chemical (compounds of emerging concern (CECs)) and genotoxic responses from tertiary municipal wastewater for indirect potable reuse (IPR). The AOP treated effluents were further subjected to granular activated carbon (GAC) adsorption and UV disinfection, following the concept of multiple treatment barriers. As a reference, a consolidated advanced wastewater treatment train consisting of ultrafiltration, UV disinfection, and reverse osmosis (RO) was also employed. The results showed that, for the same electrical energy applied, the ozone/H2O2 treatment was more effective than the UV/H2O2 treatment in removing CECs. Specifically, the ozone/H2O2 treatment, intensified by high pressure and high mixing, achieved an average CECs removal efficiency higher than UV/H2O2 (66.8% with respect to 18.4%). The subsequent GAC adsorption step, applied downstream the AOPs, further improved the removal efficiency of the whole treatment trains, achieving rates of 98.5% and 96.8% for the ozone/H2O2 and UV/H2O2 treatments, respectively. In contrast, the ultrafiltration step of the reference treatment train only achieved a removal percentage of 22.5%, which increased to 99% when reverse osmosis was used as the final step. Microbiological investigations showed that all three wastewater treatment lines displayed good performance in the complete removal of regulated and optional parameters according to both national and the European Directive 2020/2184. Only P. aeruginosa resulted resistant to all treatments with a higher removal by UV/H2O2 when higher UV dose was applied. In addition, E. coli STEC/VTEC and enteric viruses, were found to be completely removed in all tested treatments and no genotoxic activity was detected even after a 1000-fold concentration. The obtained results suggest that the investigated treatments are suitable for groundwater recharge to be used as a potable water source being such a procedure an IPR. The intensified ozone/H2O2 or UV/H2O2 treatments can be conveniently incorporated into a multi-barrier zero-liquid-discharge scheme, thus avoiding the management issues associated with the retentate of the conventional scheme that uses reverse osmosis. By including the chemical cost associated with using 11-12 mg/L of H2O2 in the cost calculations, the overall operational cost (energy plus chemical) required to achieve 50% average CECs removal in tertiary effluent for an hypothetical full-scale plant of 250 m3/h (or 25,000 inhabitants) was 0.183 €/m3 and 0.425 €/m3 for ozone/H2O2 and UV/H2O2 treatment train, respectively.

Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment

Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.

Identification of nonvolatile organic compounds (NVOCs) in biopharmaceuticals through non-target analysis and quantification using complexation-precipitation extraction

Single-use systems in biopharmaceutical manufacturing can potentially release chemical constituents (leachables) into drug products. Prior to conducting toxicological risk assessments, it is crucial to establish the qualitative and quantitative methods for these leachables. In this study, we conducted a comprehensive screening and structure elucidation of 23 leachables (nonvolatile organic compounds, NVOCs) in two antibody drugs using multiple (self-built and public) databases and mass spectral simulation. We identified 7 compounds that have not been previously reported in medical or medicinal extractables and leachables. The confidence levels for identified compounds were classified based on analytical standards, literature references, and fragment assignments. Most of the identified leachables were found to be plasticizers, antioxidants, slip agents or polymer degradants. Polysorbate (namely Tween) is commonly used as an excipient for protein stabilization in biopharmaceutical formulations, but its ionization in liquid chromatography-electrospray ionization mass spectrometry can interfere with compound quantification. To address this, we employed a complexation-precipitation extraction method to reduce polysorbate content and quantify the analytes. The developed quantitative method for target NVOCs demonstrated high sensitivity (limit of quantification: 20 or 50 μg/L), accuracy (recoveries: 77.2 to 109.5 %) and precision (RSD ≤ 8.2 %). Overall, this established method will facilitate the evaluation of NVOC safety in drug products.

Validating blood microsampling for per- and polyfluoroalkyl substances quantification in whole blood

Microsampling allows the collection of blood samples using a method which is inexpensive, simple and minimally-invasive, without the need for specially-trained medical staff. Analysis of whole blood provides a more holistic understanding of per- and polyfluoroalkyl substances (PFAS) body burden. Capillary action microsamplers (Trajan hemaPEN®) allow the controlled collection of whole blood as dried blood spots (DBS) (four 2.74 µL ± 5 %). The quantification of 75 PFAS from DBS was evaluated by comparing five common extraction techniques. Spiked blood (5 ng/mL PFAS) was extracted by protein precipitation (centrifuged; filtered), acid-base liquid-liquid extraction, trypsin protease digestion, and weak anion exchange (WAX) solid-phase extraction with analysis by high-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Filtered protein precipitation was the most effective extraction method, recovering 72 of the 75 PFAS within 70 to 130 % with method reporting limit (MRL) for PFOS of 0.17 ng/L and ranging between 0.05 ng/mL and 0.34 ng/mL for all other PFAS. The optimised method was applied to human blood samples to examine Inter- (n = 7) and intra-day (n = 5) PFAS blood levels in one individual. Sixteen PFAS were detected with an overall Σ16PFAS mean = 6.3 (range = 5.7-7.0) ng/mL and perfluorooctane sulfonate (branched and linear isomers, ΣPFOS) = 3.3 (2.8-3.7) ng/mL being the dominant PFAS present. To the authors knowledge, this minimally invasive self-sampling protocol is the most extensive method for PFAS in blood reported and could be a useful tool for large scale human biomonitoring studies.

Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome

In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m/z, experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation's combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.

Nontarget Identification of Novel Per- and Polyfluoroalkyl Substances (PFAS) in Soils from an Oil Refinery in Southwestern China: A Combined Approach with TOP Assay

Oil refinery activity can be an emission source of perfluoroalkyl and polyfluoroalkyl substances (PFAS) to the environment, while the contamination profiles in soils remain unknown. This study investigated 44 target PFAS in soil samples collected from an oil refinery in Southeastern China, identified novel PFAS, and characterized their behaviors by assessing their changes before and after employing advanced oxidation using a combination of nontarget analysis and a total oxidizable precursor (TOP) assay. Thirty-four target PFAS were detected in soil samples. Trifluoroacetic acid (TFA) and hexafluoropropylene oxide dimer acid (HFPO-DA) were the dominant PFAS. Twenty-three novel PFAS of 14 classes were identified, including 8 precursors, 11 products, and 4 stable PFAS characterized by the TOP assay. Particularly, three per-/polyfluorinated alcohols were identified for the first time, and hexafluoroisopropanol (HFIP) quantified up to 657 ng/g dw is a novel precursor for TFA. Bistriflimide (NTf2) potentially associated with an oil refinery was also reported for the first time in the soil samples. This study highlighted the advantage of embedding the TOP assay in nontarget analysis to reveal not only the presence of unknown PFAS but also their roles in environmental processes. Overall, this approach provides an efficient way to uncover contamination profiles of PFAS especially in source-impacted areas.

Adding open spectral data to MassBank and PubChem using open source tools to support non-targeted exposomics of mixtures

The term "exposome" is defined as a comprehensive study of life-course environmental exposures and the associated biological responses. Humans are exposed to many different chemicals, which can pose a major threat to the well-being of humanity. Targeted or non-targeted mass spectrometry techniques are widely used to identify and characterize various environmental stressors when linking exposures to human health. However, identification remains challenging due to the huge chemical space applicable to exposomics, combined with the lack of sufficient relevant entries in spectral libraries. Addressing these challenges requires cheminformatics tools and database resources to share curated open spectral data on chemicals to improve the identification of chemicals in exposomics studies. This article describes efforts to contribute spectra relevant for exposomics to the open mass spectral library MassBank (https://www.massbank.eu) using various open source software efforts, including the R packages RMassBank and Shinyscreen. The experimental spectra were obtained from ten mixtures containing toxicologically relevant chemicals from the US Environmental Protection Agency (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT). Following processing and curation, 5582 spectra from 783 of the 1268 ENTACT compounds were added to MassBank, and through this to other open spectral libraries (e.g., MoNA, GNPS) for community benefit. Additionally, an automated deposition and annotation workflow was developed with PubChem to enable the display of all MassBank mass spectra in PubChem, which is rerun with each MassBank release. The new spectral records have already been used in several studies to increase the confidence in identification in non-target small molecule identification workflows applied to environmental and exposomics research.

Per- and Polyfluoroalkyl Substances (PFAS) in PubChem: 7 Million and Growing

Per- and polyfluoroalkyl substances (PFAS) are of high concern, with calls to regulate them as a class. In 2021, the Organisation for Economic Co-operation and Development (OECD) revised the definition of PFAS to include any chemical containing at least one saturated CF2 or CF3 moiety. The consequence is that one of the largest open chemical collections, PubChem, with 116 million compounds, now contains over 7 million PFAS under this revised definition. These numbers are several orders of magnitude higher than previously established PFAS lists (typically thousands of entries) and pose an incredible challenge to researchers and computational workflows alike. This article describes a dynamic, openly accessible effort to navigate and explore the >7 million PFAS and >21 million fluorinated compounds (September 2023) in PubChem by establishing the "PFAS and Fluorinated Compounds in PubChem" Classification Browser (or "PubChem PFAS Tree"). A total of 36500 nodes support browsing of the content according to several categories, including classification, structural properties, regulatory status, or presence in existing PFAS suspect lists. Additional annotation and associated data can be used to create subsets (and thus manageable suspect lists or databases) of interest for a wide range of environmental, regulatory, exposomics, and other applications.

Uncovering per- and polyfluoroalkyl substances (PFAS) with nontargeted ion mobility spectrometry-mass spectrometry analyses

Because of environmental and health concerns, legacy per- and polyfluoroalkyl substances (PFAS) have been voluntarily phased out, and thousands of emerging PFAS introduced as replacements. Traditional analytical methods target a limited number of mainly legacy PFAS; therefore, many species are not routinely assessed in the environment. Nontargeted approaches using high-resolution mass spectrometry methods have therefore been used to detect and characterize unknown PFAS. However, their ability to elucidate chemical structures relies on generation of informative fragments, and many low concentration species are not fragmented in typical data-dependent acquisition approaches. Here, a data-independent method leveraging ion mobility spectrometry (IMS) and size-dependent fragmentation was developed and applied to characterize aquatic passive samplers deployed near a North Carolina fluorochemical manufacturer. From the study, 11 PFAS structures for various per- and polyfluorinated ether sulfonic acids and multiheaded perfluorinated ether acids were elucidated in addition to 36 known PFAS. Eight of these species were previously unreported in environmental media, and three suspected species were validated.

Predicting the Diagnostic Information of Tandem Mass Spectra of Environmentally Relevant Compounds Using Machine Learning

Acquisition and processing of informative tandem mass spectra (MS2) is crucial for numerous applications, including library-based (tentative) identification, feature prioritization, and prediction of chemical and toxicological characteristics. However, for environmentally relevant compounds, approaches to automatically assess the quality of the MS2 spectra are missing. This work focused on developing a machine learning-based approach to automatically evaluate the diagnostic information of MS2 spectra (e.g., number, distribution, and intensity of diagnostic fragments) of environmentally relevant compounds analyzed with electrospray ionization. For this, approximately 1400 MS2 spectra of 204 environmental contaminants, acquired with different collision energies using liquid chromatography coupled to high-resolution mass spectrometry, were used to train a random forest classifier to distinguish between spectra providing good or poor diagnostic information. Prior to training, validation, and testing, spectra were manually labeled based on criteria such as number, intensity, range of fragments present, molecular ion intensity, and noise levels. Subsequently, feature engineering and selection were applied to retrieve relevant variables from raw MS2 spectra as inputs for the classifier. The optimal set of features based on model performances was selected and used to train a final model, which showed an accuracy of 84%, a precision of 88%, and a recall of 75%. Results show that the combination of selected features and the machine learning model used here can effectively distinguish between MS2 spectra providing good or poor diagnostic information according to the defined criteria. The developed model has the potential to improve a broad range of applications that rely on MS2 data.

ShinyTPs: Curating Transformation Products from Text Mining Results

Transformation product (TP) information is essential to accurately evaluate the hazards compounds pose to human health and the environment. However, information about TPs is often limited, and existing data is often not fully Findable, Accessible, Interoperable, and Reusable (FAIR). FAIRifying existing TP knowledge is a relatively easy path toward improving access to data for identification workflows and for machine-learning-based algorithms. ShinyTPs was developed to curate existing transformation information derived from text-mined data within the PubChem database. The application (available as an R package) visualizes the text-mined chemical names to facilitate the user validation of the automatically extracted reactions. ShinyTPs was applied to a case study using 436 tentatively identified compounds to prioritize TP retrieval. This resulted in the extraction of 645 reactions (associated with 496 compounds), of which 319 were not previously available in PubChem. The curated reactions were added to the PubChem Transformations library, which was used as a TP suspect list for identification of TPs using the open-source workflow patRoon. In total, 72 compounds from the library were tentatively identified, 18% of which were curated using ShinyTPs, showing that the app can help support TP identification in non-target analysis workflows.

Structural Diversity and Biological Activity of Cyanopeptolins Produced by Nostoc edaphicum CCNP1411

Cyanopeptolins (CPs) are one of the most commonly occurring class of cyanobacterial nonribosomal peptides. For the majority of these compounds, protease inhibition has been reported. In the current work, the structural diversity of cyanopeptolins produced by Nostoc edaphicum CCNP1411 was explored. As a result, 93 CPs, including 79 new variants, were detected and structurally characterized based on their mass fragmentation spectra. CPs isolated in higher amounts were additionally characterized by NMR. To the best of our knowledge, this is the highest number of cyanopeptides found in one strain. The biological assays performed with the 34 isolated CPs confirmed the significance of the amino acid located between Thr and the unique 3-amino-6-hydroxy-2-piperidone (Ahp) on the activity of the compounds against serine protease and HeLa cancer cells.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Simultaneously discovering the fate and biochemical effects of pharmaceuticals through untargeted metabolomics

Untargeted metabolomics is an established approach in toxicology for characterising endogenous metabolic responses to xenobiotic exposure. Detecting the xenobiotic and its biotransformation products as part of the metabolomics analysis provides an opportunity to simultaneously gain deep insights into its fate and metabolism, and to associate the internal relative dose directly with endogenous metabolic responses. This integration of untargeted exposure and response measurements into a single assay has yet to be fully demonstrated. Here we assemble a workflow to discover and analyse pharmaceutical-related measurements from routine untargeted UHPLC-MS metabolomics datasets, derived from in vivo (rat plasma and cardiac tissue, and human plasma) and in vitro (human cardiomyocytes) studies that were principally designed to investigate endogenous metabolic responses to drug exposure. Our findings clearly demonstrate how untargeted metabolomics can discover extensive biotransformation maps, temporally-changing relative systemic exposure, and direct associations of endogenous biochemical responses to the internal dose.

Metabolomics Reveals Strain-Specific Cyanopeptide Profiles and Their Production Dynamics in Microcystis aeruginosa and M. flos-aquae

Cyanobacterial blooms that release biologically active metabolites into the environment are increasing in frequency as a result of the degradation of freshwater ecosystems globally. The microcystins are one group of cyanopeptides that are extensively studied and included in water quality risk management frameworks. Common bloom-forming cyanobacteria produce incredibly diverse mixtures of other cyanopeptides; however, data on the abundance, distribution, and biological activities of non-microcystin cyanopeptides are limited. We used non-targeted LC-MS/MS metabolomics to study the cyanopeptide profiles of five Microcystis strains: four M. aeruginosa and one M. flos-aquae. Multivariate analysis and GNPS molecular networking demonstrated that each Microcystis strain produced a unique mixture of cyanopeptides. In total, 82 cyanopeptides from the cyanopeptolin (n = 23), microviridin (n = 18), microginin (n = 12), cyanobactin (n = 14), anabaenopeptin (n = 6), aeruginosin (n = 5), and microcystin (n = 4) classes were detected. Microcystin diversity was low compared with the other detected cyanopeptide classes. Based on surveys of the literature and spectral databases, most cyanopeptides represented new structures. To identify growth conditions yielding high amounts of multiple cyanopeptide groups, we next examined strain-specific cyanopeptide co-production dynamics for four of the studied Microcystis strains. When strains were cultivated in two common Microcystis growth media (BG-11 and MA), the qualitative cyanopeptides profiles remained unchanged throughout the growth cycle. For each of the cyanopeptide groups considered, the highest relative cyanopeptide amounts were observed in the mid-exponential growth phase. The outcomes of this study will guide the cultivation of strains producing common and abundant cyanopeptides contaminating freshwater ecosystems. The synchronous production of each cyanopeptide group by Microcystis highlights the need to make more cyanopeptide reference materials available to investigate their distributions and biological functions.

Efficient PFAS prioritization in non-target HRMS data: systematic evaluation of the novel MD/C-m/C approach

Non-target screening (NTS) based on high-resolution mass spectrometry (HRMS) is necessary to comprehensively characterize per- and polyfluoroalkyl substances (PFAS) in environmental, biological, and technical samples due to the very limited availability of authentic PFAS reference standards. Since in trace analysis, MS/MS information is not always achievable and only selected PFAS are present in homologous series, further techniques to prioritize measured HRMS data (features) according to their likelihood of being PFAS are highly desired due to the importance of efficient data reduction during NTS. Kaufmann et al. (J AOAC Int, 2022) presented a very promising approach to separate selected PFAS from sample matrix features by plotting the mass defect (MD) normalized to the number of carbons (MD/C) vs. mass normalized to the number of C (m/C). We systematically evaluated the advantages and limitations of this approach by using ~ 490,000 chemical formulas of organic chemicals (~ 210,000 PFAS, ~ 160,000 organic contaminants, and 125,000 natural organic matter compounds) and calculating how efficiently, and especially which, PFAS can be prioritized. While PFAS with high fluorine content (approximately: F/C > 0.8, H/F < 0.8, mass percent of fluorine > 55%) can be separated well, partially fluorinated PFAS with a high hydrogen content are more difficult to prioritize, which we discuss for selected PFAS. In the MD/C-m/C approach, even compounds with highly positive MDs above 0.5 Da and hence incorrectly assigned to negative MDs can still be separated from true negative mass defect features by the normalized mass (m/C). Furthermore, based on the position in the MD/C-m/C plot, we propose the estimation of the fluorine fraction in molecules for selected PFAS classes. The promising MD/C-m/C approach can be widely used in PFAS research and routine analysis. The concept is also applicable to other compound classes like iodinated compounds.

Wide-scope target screening characterization of legacy and emerging contaminants in the Danube River Basin by liquid and gas chromatography coupled with high-resolution mass spectrometry

A state-of-the-art wide-scope target screening of 2,362 chemicals and their transformation products (TPs) was performed in samples collected within the Joint Danube Survey 4 (JDS4) performed in 2019. The analysed contaminants of emerging concern (CECs) included three major categories: plant protection products (PPPs), industrial chemicals and pharmaceuticals and personal care products (PPCPs). In total, 586 CECs were detected in the samples including 158 PPPs, 71 industrial chemicals, 348 PPCPs, and 9 other chemicals. A wide-variety of sample matrices were collected including influent and effluent wastewater, groundwater, river water, sediment and biota. Forty-five CECs (19 PPPs, 8 industrial chemicals, 18 PPCPs) were detected at levels above their ecotoxicological thresholds (lowest predicted no-effect concentration (PNEC) values) in one or more of the investigated environmental compartments, indicating potential adverse effects on the impacted ecosystems. Among them 12 are legacy substances; 33 are emerging and qualify as potential Danube River Basin Specific Pollutants (RBSPs). Moreover, the efficiency of the wastewater treatment plants (WWTPs) was evaluated using 20 selected performance indicator chemicals. WWTPs showed effective removal (removal rate ≥80%) and medium removal (removal rate 25-80%) for 6 and 8 of the indicator chemicals, respectively. However, numerous contaminants passed the WWTPs with a lower removal rate. Further investigation on performance of WWTPs is suggested at catchment level to improve their removal efficiency. WWTP effluents are proven to be one of the major sources of contaminants in the Danube River Basin (DRB). Other sources include sewage discharges, industrial and agricultural activities. Continuous monitoring of the detected CECs is suggested to ensure water quality of the studied area.

Environmental Hazard Screening of Heterocyclic Polyaromatic Hydrocarbons: Physicochemical Data and In Silico Models

Heterocyclic polyaromatic hydrocarbons (heterocyclic PAHs) are frequently found in the environment yet, compared to homocyclic PAHs, little attention has been paid to their environmental behavior and a comprehensive hazard assessment has not been undertaken. Surprisingly, the physicochemical data necessary to perform at least a screening-level assessment are also limited. To address this, we began by experimentally determining the physicochemical properties of heterocyclic PAHs, namely, water solubility (S_w), n-octanol-water partition coefficients (K_ow), and organic carbon-water partition coefficients (K_oc). The physicochemical data obtained in this study allowed for the development of clear structure-property relationships and evaluation of the predictive power of in silico models including conductor-like screening model for realistic solvation, the poly-parameter linear solvation energy relationship, and the quantitative structure-property relationship. Finally, heterocyclic and homocyclic PAHs were evaluated in terms of persistence, bioaccumulation, mobility, and toxicity to perform a screening-level comparative hazard assessment by integrating the data and evidence from multiple sources.

Wide-Scope Target and Suspect Screening of Antibiotics in Effluent Wastewater from Wastewater Treatment Plants in Europe

The occurrence of antibiotics in the environment could result in the development of antibiotic-resistant bacteria, which could result in a public health crisis. The occurrence of 676 antibiotics and the main transformation products (TPs) was investigated in the 48 wastewater treatment plants (WWTPs) from 11 countries (Germany, Romania, Serbia, Croatia, Slovenia, Hungary, Slovakia, Czechia, Austria, Cyprus, and Greece) by target and suspect screening. Target screening involved the investigation of antibiotics with reference standards (40 antibiotics). Suspect screening covered 676 antibiotics retrieved from the NORMAN Substance Database (antibiotic list on NORMAN network). Forty-seven antibiotics were detected in effluent wastewater samples: thirty-two by target screening and fifteen additional ones by suspect screening. An ecotoxicological risk assessment was performed based on occurrence data and predicted no effect concentration (PNEC), which involved the derivation of frequency of appearance (FoA), frequency of PNEC exceedance (FoE), and extent of PNEC exceedance (EoE). Azithromycin, erythromycin, clarithromycin, ofloxacin, and ciprofloxacin were prioritized as the calculated risk score was above 1. The median of antibiotics' load to freshwater ecosystems was 0.59 g/day/WWTP. The detection of antibiotics across countries indicates the presence of antibiotics in the ecosystems of Europe, which may trigger unwanted responses from the ecosystem, including antibiotic resistance.

An algorithm to classify homologous series within compound datasets

Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH₂ repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues .