NORMAN guidance on suspect and non-target screening in environmental monitoring

Environmental Sciences Europe, Год журнала: 2023, Номер 35(1)

Опубликована: Сен. 4, 2023

Abstract Increasing production and use of chemicals awareness their impact on ecosystems humans has led to large interest for broadening the knowledge chemical status environment human health by suspect non-target screening (NTS). To facilitate effective implementation NTS in scientific, commercial governmental laboratories, as well acceptance managers, regulators risk assessors, more harmonisation is required. address this, NORMAN Association members involved activities have prepared this guidance document, based current state knowledge. The document intended provide performing high quality studies data interpretation while increasing promise but also pitfalls challenges associated with these techniques. Guidance provided all steps; from sampling sample preparation analysis chromatography (liquid gas—LC GC) coupled via various ionisation techniques high-resolution tandem mass spectrometry (HRMS/MS), through evaluation reporting context NTS. Although most experience within network still involves water polar compounds using LC–HRMS/MS, other matrices (sediment, soil, biota, dust, air) instrumentation (GC, ion mobility) are covered, reflecting rapid development extension field. Due ongoing developments, different questions addressed manifold use, feel that no standard operation process can be at stage. However, appropriate analytical methods, processing databases commonly compiled workflows introduced, limitations discussed recommendations cases provided. Proper assurance, quantification without reference standards results clear confidence identification assignment complete together a glossary definitions. community greatly supports sharing experiences open science hopes guideline effort.

Язык: Английский

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Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment

Journal of Hazardous Materials, Год журнала: 2025, Номер 487, С. 137071 - 137071

Опубликована: Янв. 10, 2025

Язык: Английский

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MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data

Journal of Cheminformatics, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 31, 2025

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, generates 490-bit bioactivity fingerprint used as basis prioritization, focusing time-consuming identification efforts features most likely to cause adverse effects. The practical advantages are demonstrated groundwater data. Among 874 which were derived spectra, including 630 nontargets, 185 spectral matches, 59 targets, around 4% feature/endpoint relationship pairs predicted be active. Cross-checking predictions targets matches with invitroDB data confirmed 120 active 6791 nonactive while mislabeling 88 56 non-active relationships. By filtering according probability, endpoint scores, similarity training data, number potentially toxic was reduced by at least one order magnitude. This refinement makes analytical confirmation feasible, offering significant benefits cost-efficient risk assessment.Scientific Contribution:In contrast classical ML-based approaches toxicity prediction, predicts (i.e., distinct m/z signals) based MS2 fragmentation spectra rather than identified features. While original proof concept study accompanied release v1 KNIME workflow, this study, we v2 package, which, addition automation, expands functionality include predicting structures, cleaning up generating fingerprints, customizing models, retraining custom Furthermore, result improvements processing, realized concurrently released pytcpl package processing input MLinvitroTox, current introduces enhancements model accuracy, coverage biological mechanistic overall interpretability.

Язык: Английский

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Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

Analytical and Bioanalytical Chemistry, Год журнала: 2024, Номер 416(9), С. 2125 - 2136

Опубликована: Фев. 1, 2024

Abstract This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods data evaluation, qualification, quantification, and assurance (QA/QC). It highlights the evolution NTS processing, where open-source platforms address challenges result comparability complexity. Advanced chemometrics machine learning (ML) are pivotal identification correlation analysis, with a growing emphasis automated workflows robust classification models. The also discusses rigorous QA/QC measures essential NTS, such as internal standards, batch effect monitoring, matrix assessment. examines progress quantitative (qNTS), noting ionization efficiency-based quantification predictive modeling despite sample variability analytical standards. Selected studies illustrate NTS’s role combining high-resolution mass spectrometry chromatographic techniques enhanced chemical exposure addresses prioritization challenges, highlighting integration database searches computational tools efficiency. Finally, outlines future research needs including establishing comprehensive guidelines, improving measures, reporting results. underscores potential to integrate multivariate chemometrics, AI/ML tools, multi-way into combine various sources understand ecosystem health protection comprehensively.

Язык: Английский

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Quinolone Antibiotics Inhibit the Rice Photosynthesis by Targeting Photosystem II Center Protein: Generational Differences and Mechanistic Insights

Environmental Science & Technology, Год журнала: 2024, Номер 58(26), С. 11280 - 11291

Опубликована: Июнь 20, 2024

Soil antibiotic pollution profoundly influences plant growth and photosynthetic performance, yet the main disturbed processes underlying mechanisms remain elusive. This study explored toxicity of quinolone antibiotics across three generations on rice plants clarified through experimental computational studies. Marked variations were noted in their impact photosynthesis with level inhibition intensifying from second to fourth generation. Omics analyses consistently targeted light reaction phase as primary process impacted, emphasizing particular vulnerability photosystem II (PS II) stress, manifested by significant interruptions photon-mediated electron transport O2 production. PS center D2 protein (psbD) was identified target tested antibiotics, fourth-generation quinolones displaying highest binding affinity psbD. A predictive machine learning method constructed pinpoint substructures that conferred enhanced affinity. As evolve, positive contribution carbonyl carboxyl groups 4-quinolone core ring interaction gradually intensified. research illuminates toxicities generations, offering insights for risk assessment highlighting potential threats carbon fixation agroecosystems.

Язык: Английский

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

Analytical and Bioanalytical Chemistry, Год журнала: 2024, Номер unknown

Опубликована: Июль 12, 2024

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

Язык: Английский

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Beyond target chemicals: updating the NORMAN prioritisation scheme to support the EU chemicals strategy with semi-quantitative suspect/non-target screening data

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Июнь 12, 2024

Abstract Background Prioritisation of chemical pollutants is a major challenge for environmental managers and decision-makers alike, which essential to help focus the limited resources available monitoring mitigation actions on most relevant chemicals. This study extends original NORMAN prioritisation scheme beyond target chemicals, presenting integration semi-quantitative data from retrospective suspect screening expansion existing exposure risk indicators. The utilises retrieved automatically Database System (NDS), including candidate substances prioritisation, data, ecotoxicological effect physico-chemical other properties. Two complementary workflows using are applied first group into six action categories then rank exposure, hazard results ‘target’ ‘suspect screening’ can be combined as multiple lines evidence support decision-making regulatory research actions. Results As proof-of-concept, new was dataset data. To this end, > 65,000 NDS, 2579 supported by wastewater were retrospectively screened in 84 effluent samples, totalling 11 million points. final identified 677 high priority further actions, 7455 medium 326 with potentially lower Among remaining substances, ca. 37,000 should considered uncertainty, while it not possible conclude 19,000 due insufficient information uncertainty identification screening. A degree agreement observed between assigned via analysis screening-based prioritisation. Suspect valuable approach analysis, helping prioritise thousands that insufficiently investigated current programmes. Conclusions updated workflow responds increasing use techniques. It adapted different compartments obligations, specific river basins marine environments, well confirmation occurrence levels predicted modelling tools. Graphical

Язык: Английский

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Combining Nontargeted Analysis with Computer-Based Hazard Comparison Approaches to Support Prioritization of Unregulated Organic Contaminants in Biosolids

Environmental Science & Technology, Год журнала: 2024, Номер 58(27), С. 12135 - 12146

Опубликована: Июнь 25, 2024

Biosolids are a byproduct of wastewater treatment that can be beneficially applied to agricultural land as fertilizer. While U.S. regulations limit metals and pathogens in biosolids intended for applications, no organic contaminants currently regulated. Novel techniques aid detection, evaluation, prioritization biosolid-associated (BOCs). For example, nontargeted analysis (NTA) detect broad range chemicals, producing data sets representing thousands measured analytes combined with computational toxicological tools support human ecological hazard assessment prioritization. We NTA computer-based tool from the EPA, Cheminformatics Hazard Comparison Module (HCM), identify prioritize BOCs present Canadian (

Язык: Английский

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High-Throughput Effect-Directed Analysis of Androgenic Compounds in Hospital Wastewater: Identifying Effect Drivers through Non-Target Screening Supported by Toxicity Prediction

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

The increasing number of contaminants released into the environment necessitates innovative strategies for their detection and identification, particularly in complex environmental matrices like hospital wastewater. Hospital effluents contain both natural synthetic hormones that might significantly contribute to endocrine disruption aquatic ecosystems. In this study, HT-EDA has been implemented identify main effect-drivers (testosterone, androsterone norgestrel) from effluent using microplate fractionation, AR-CALUX bioassay an efficient data processing workflow. Through nontargeted screening, over 5000 features (ESI+) were initially detected, but our workflow's prioritization based on androgenic activity prediction reduced requiring further analysis by 95%, streamlining workload. addition, semiquantitative nontarget allowed calculation contribution identified compound total sample without need reference standards. While was low (∼4.3%) applicable only one (1,4-androstadiene-3,17-dione), it presents first approach calculating such contributions relying Compared available alternatives workflow demonstrates clear relevance enhancing more identification effluents, can be adapted address other threats mixtures.

Язык: Английский

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Machine Learning-based Classification for the Prioritization of Potentially Hazardous Chemicals with Structural Alerts in Nontarget Screening

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Март 7, 2025

Nontarget screening (NTS) with liquid chromatography high-resolution mass spectrometry (LC-HRMS) is commonly used to detect unknown organic micropollutants in the environment. One of main challenges NTS prioritization relevant LC-HRMS features. A novel strategy based on structural alerts select features that correspond potentially hazardous chemicals presented here. This leverages raw tandem spectra (MS2) and machine learning models predict probability alerts. The were trained fragments neutral losses from experimental MS2 data. feasibility this approach evaluated for two groups: aromatic amines organophosphorus neural network classification model achieved an Area Under Curve Receiver Operating Characteristics (AUC-ROC) 0.97 a true positive rate 0.65 test set. random forest AUC-ROC value 0.82 0.58 successfully applied prioritize surface water samples, showcasing high potential develop implement further.

Язык: Английский

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