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Malm L, Liigand J, Aalizadeh R, Alygizakis N, Ng K, Fro̷kjær EE, Nanusha MY, Hansen M, Plassmann M, Bieber S, Letzel T, Balest L, Abis PP, Mazzetti M, Kasprzyk-Hordern B, Ceolotto N, Kumari S, Hann S, Kochmann S, Steininger-Mairinger T, Soulier C, Mascolo G, Murgolo S, Garcia-Vara M, López de Alda M, Hollender J, Arturi K, Coppola G, Peruzzo M, Joerss H, van der Neut-Marchand C, Pieke EN, Gago-Ferrero P, Gil-Solsona R, Licul-Kucera V, Roscioli C, Valsecchi S, Luckute A, Christensen JH, Tisler S, Vughs D, Meekel N, Talavera Andújar B, Aurich D, Schymanski EL, Frigerio G, Macherius A, Kunkel U, Bader T, Rostkowski P, Gundersen H, Valdecanas B, Davis WC, Schulze B, Kaserzon S, Pijnappels M, Esperanza M, Fildier A, Vulliet E, Wiest L, Covaci A, Macan Schönleben A, Belova L, Celma A, Bijlsma L, Caupos E, Mebold E, Le Roux J, Troia E, de Rijke E, Helmus R, Leroy G, Haelewyck N, Chrastina D, Verwoert M, Thomaidis NS, Kruve A. Quantification Approaches in Non-Target LC/ESI/HRMS Analysis: An Interlaboratory Comparison. Anal Chem 2024; 96:16215-16226. [PMID: 39353203 PMCID: PMC11483430 DOI: 10.1021/acs.analchem.4c02902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 09/16/2024] [Accepted: 09/16/2024] [Indexed: 10/04/2024]
Abstract
Nontargeted screening (NTS) utilizing liquid chromatography electrospray ionization high-resolution mass spectrometry (LC/ESI/HRMS) is increasingly used to identify environmental contaminants. Major differences in the ionization efficiency of compounds in ESI/HRMS result in widely varying responses and complicate quantitative analysis. Despite an increasing number of methods for quantification without authentic standards in NTS, the approaches are evaluated on limited and diverse data sets with varying chemical coverage collected on different instruments, complicating an unbiased comparison. In this interlaboratory comparison, organized by the NORMAN Network, we evaluated the accuracy and performance variability of five quantification approaches across 41 NTS methods from 37 laboratories. Three approaches are based on surrogate standard quantification (parent-transformation product, structurally similar or close eluting) and two on predicted ionization efficiencies (RandFor-IE and MLR-IE). Shortly, HPLC grade water, tap water, and surface water spiked with 45 compounds at 2 concentration levels were analyzed together with 41 calibrants at 6 known concentrations by the laboratories using in-house NTS workflows. The accuracy of the approaches was evaluated by comparing the estimated and spiked concentrations across quantification approaches, instrumentation, and laboratories. The RandFor-IE approach performed best with a reported mean prediction error of 15× and over 83% of compounds quantified within 10× error. Despite different instrumentation and workflows, the performance was stable across laboratories and did not depend on the complexity of water matrices.
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Affiliation(s)
- Louise Malm
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 11418 Stockholm, Sweden
| | | | - Reza Aalizadeh
- Laboratory
of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
- Department
of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut 06510, United States
| | - Nikiforos Alygizakis
- Laboratory
of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
- Environmental
Institute, Okružná
784/42, 97241 Koš, Slovak Republic
| | - Kelsey Ng
- Environmental
Institute, Okružná
784/42, 97241 Koš, Slovak Republic
- RECETOX,
Faculty of Science, Masaryk University, Kamenice 753/5, Building D29, 62500 Brno, Czech Republic
| | - Emil Egede Fro̷kjær
- Environmental
Metabolomics Lab, Aarhus University, Frederiksborgsvej 399, 4000 Roskilde, Denmark
| | - Mulatu Yohannes Nanusha
- Environmental
Metabolomics Lab, Aarhus University, Frederiksborgsvej 399, 4000 Roskilde, Denmark
| | - Martin Hansen
- Environmental
Metabolomics Lab, Aarhus University, Frederiksborgsvej 399, 4000 Roskilde, Denmark
| | - Merle Plassmann
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 11418 Stockholm, Sweden
| | - Stefan Bieber
- Analytisches
Forschungsinstitut für Non-Target Screening GmbH (AFIN-TS), Am Mittleren Moos 48, 86167 Augsburg, Germany
| | - Thomas Letzel
- Analytisches
Forschungsinstitut für Non-Target Screening GmbH (AFIN-TS), Am Mittleren Moos 48, 86167 Augsburg, Germany
| | - Lydia Balest
- Acquedotto
Pugliese SpA - Direzione Laboratori e Controllo Igienico Sanitario
(DIRLC), 70123 Bari, Italy
| | - Pier Paolo Abis
- Acquedotto
Pugliese SpA - Direzione Laboratori e Controllo Igienico Sanitario
(DIRLC), 70123 Bari, Italy
| | - Michele Mazzetti
- Agenzia
Regionale per l’Ambiente Toscana, Via G. Marradi 114, 57126 Livorno, Italy
| | - Barbara Kasprzyk-Hordern
- Department
of Chemistry, University of Bath, Bath BA2 7AY, U.K.
- Institute
for Sustainability, Bath BA2 7AY, U.K.
| | - Nicola Ceolotto
- Department
of Chemistry, University of Bath, Bath BA2 7AY, U.K.
- Institute
for Sustainability, Bath BA2 7AY, U.K.
| | - Sangeeta Kumari
- Department
of Chemistry, Vienna, BOKU University, Muthgasse 18, 1190 Vienna, Austria
| | - Stephan Hann
- Department
of Chemistry, Vienna, BOKU University, Muthgasse 18, 1190 Vienna, Austria
| | - Sven Kochmann
- Department
of Chemistry, Vienna, BOKU University, Muthgasse 18, 1190 Vienna, Austria
| | | | - Coralie Soulier
- BRGM, 3 avenue Claude
Guillemin, BP36009, 45060 Orléans Cedex 2, France
| | - Giuseppe Mascolo
- Water Research
Institute (IRSA), National Research Council
(CNR), Via F. De Blasio,
5, 70132 Bari, Italy
- Research
Institute for Geo-Hydrological Protection (IRPI), National Research Council (CNR), Via Amendola, 122/I, 70126 Bari, Italy
| | - Sapia Murgolo
- Water Research
Institute (IRSA), National Research Council
(CNR), Via F. De Blasio,
5, 70132 Bari, Italy
| | - Manuel Garcia-Vara
- Water,
Environmental and Food Chemistry Unit, Institute
of Environmental Assessment and Water Research, C/Jordi Girona 18-26, ES 08034 Barcelona, Spain
| | - Miren López de Alda
- Water,
Environmental and Food Chemistry Unit, Institute
of Environmental Assessment and Water Research, C/Jordi Girona 18-26, ES 08034 Barcelona, Spain
| | - Juliane Hollender
- Eawag,
Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Katarzyna Arturi
- Eawag,
Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Gianluca Coppola
- White
Lab Srl, Via Mons. Rodolfi
22, 36022 San Giuseppe
de Cassola (VI), Italy
| | - Massimo Peruzzo
- White
Lab Srl, Via Mons. Rodolfi
22, 36022 San Giuseppe
de Cassola (VI), Italy
| | - Hanna Joerss
- Department
for Organic Environmental Chemistry, Helmholtz
Centre Hereon, Max-Planck-Str.
1, 21502 Geesthacht, Germany
| | | | - Eelco N. Pieke
- Het Waterlaboratorium, J.W. Lucasweg 2, 2031 BE Haarlem, The Netherlands
| | - Pablo Gago-Ferrero
- Human Exposure
to Organic Pollutants Unit, Institute of
Environmental Assessment and Water Research, C/Jordi Girona 18-26, ES 08034 Barcelona, Spain
| | - Ruben Gil-Solsona
- Human Exposure
to Organic Pollutants Unit, Institute of
Environmental Assessment and Water Research, C/Jordi Girona 18-26, ES 08034 Barcelona, Spain
| | - Viktória Licul-Kucera
- Institute
for Analytical Research, Hochschulen Fresenius gem. Trägergesellschaft mbH, 65510 Idstein, Germany
- Institute
for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1012 WP Amsterdam, Netherlands
| | - Claudio Roscioli
- Water Research
Institute (IRSA), National Research Council
of Italy (CNR), via del
Mulino, 19, 20861 Brugherio, MB, Italy
| | - Sara Valsecchi
- Water Research
Institute (IRSA), National Research Council
of Italy (CNR), via del
Mulino, 19, 20861 Brugherio, MB, Italy
| | - Austeja Luckute
- Analytical
Chemistry Group, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsenvej 40, 1871 Frederiksberg, Denmark
| | - Jan H. Christensen
- Analytical
Chemistry Group, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsenvej 40, 1871 Frederiksberg, Denmark
| | - Selina Tisler
- Analytical
Chemistry Group, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsenvej 40, 1871 Frederiksberg, Denmark
| | - Dennis Vughs
- KWR Water
Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands
| | - Nienke Meekel
- KWR Water
Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands
| | - Begoña Talavera Andújar
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, 6, Avenue
du Swing, L-4367 Belvaux, Luxembourg
| | - Dagny Aurich
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, 6, Avenue
du Swing, L-4367 Belvaux, Luxembourg
| | - Emma L. Schymanski
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, 6, Avenue
du Swing, L-4367 Belvaux, Luxembourg
| | - Gianfranco Frigerio
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, 6, Avenue
du Swing, L-4367 Belvaux, Luxembourg
- Center
for Omics Sciences (COSR), IRCCS San Raffaele
Scientific Institute, 20132 Milan, Italy
| | - André Macherius
- Bavarian
Environment Agency, Bürgermeister-Ulrich-Str. 160, 86179 Augsburg, Germany
| | - Uwe Kunkel
- Bavarian
Environment Agency, Bürgermeister-Ulrich-Str. 160, 86179 Augsburg, Germany
| | - Tobias Bader
- Laboratory
for Operation Control and Research, Zweckverband
Landeswasserversorgung, Am Spitzigen Berg 1, 89129 Langenau, Germany
| | | | | | | | - W. Clay Davis
- US National
Institute of Standards and Technology, 331 Fort Johnson Rd, 29412 Charleston, South Carolina, United States
| | - Bastian Schulze
- Queensland
Alliance for Environmental Health Sciences, The University of Queensland, Woolloongabba, Queensland 4102, Australia
| | - Sarit Kaserzon
- Queensland
Alliance for Environmental Health Sciences, The University of Queensland, Woolloongabba, Queensland 4102, Australia
| | - Martijn Pijnappels
- Ministry
of Infrastructure and Water Management, Rijkswaterstaat Laboratory, Zuiderwagenplein 2, 8224 AD Lelystad, The Netherlands
| | - Mar Esperanza
- SUEZ-CIRSEE, 38 rue
du president Wilson, 78230 Le Pecq, France
| | - Aurélie Fildier
- Universite
Claude Bernard Lyon 1, CNRS, ISA, UMR5280, 5 rue de la Doua, F-69100 Villeurbanne, France
| | - Emmanuelle Vulliet
- Universite
Claude Bernard Lyon 1, CNRS, ISA, UMR5280, 5 rue de la Doua, F-69100 Villeurbanne, France
| | - Laure Wiest
- Universite
Claude Bernard Lyon 1, CNRS, ISA, UMR5280, 5 rue de la Doua, F-69100 Villeurbanne, France
| | - Adrian Covaci
- Toxicological
Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | | | - Lidia Belova
- Toxicological
Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Alberto Celma
- Environmental
and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, 12006 Castelló, Spain
- Department
of Aquatic Sciences and Assessment, Swedish
University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - Lubertus Bijlsma
- Environmental
and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, 12006 Castelló, Spain
| | - Emilie Caupos
- LEESU, Univ Paris Est Creteil, Ecole des
Ponts, F-94010 Creteil, France
- Univ Paris
Est Creteil, CNRS, OSU-EFLUVE, F-94010 Creteil, France
| | | | - Julien Le Roux
- LEESU, Univ Paris Est Creteil, Ecole des
Ponts, F-94010 Creteil, France
| | - Eugenie Troia
- IBED Environmental
Chemistry and Mass Spectrometry Laboratories, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Eva de Rijke
- IBED Environmental
Chemistry and Mass Spectrometry Laboratories, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Rick Helmus
- IBED Environmental
Chemistry and Mass Spectrometry Laboratories, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Gaëla Leroy
- VEOLIA
Recherche et Innovation, Chemin de la Digue, 78600 Maisons-Laffitte, France
| | - Niels Haelewyck
- Vlaamse
Milieumaatschappij, Raymonde de Larochelaan 1, 9051 Gent, Sint-Denijs-Westerem, Belgium
| | - David Chrastina
- T. G.
Masaryk Water Research Institute, p. r. i., Macharova 5, 70200 Ostrava, Czech Republic
| | - Milan Verwoert
- WLN, Rijksstraatweg
85, 9756 AD Glimmen,
Groningen, The Netherlands
| | - Nikolaos S. Thomaidis
- Laboratory
of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 11418 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 11418 Stockholm, Sweden
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2
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Titkare N, Chaturvedi S, Borah S, Sharma N. Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery. Biomed Chromatogr 2024:e6019. [PMID: 39370857 DOI: 10.1002/bmc.6019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 08/25/2024] [Accepted: 09/03/2024] [Indexed: 10/08/2024]
Abstract
Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.
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Affiliation(s)
- Nikhil Titkare
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
| | - Sachin Chaturvedi
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
| | - Sapan Borah
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
| | - Nitish Sharma
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
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3
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Tarábek P, Leonova N, Konovalova O, Kirchner M. Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries. CHEMOSPHERE 2024; 366:143489. [PMID: 39374668 DOI: 10.1016/j.chemosphere.2024.143489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 09/02/2024] [Accepted: 10/04/2024] [Indexed: 10/09/2024]
Abstract
Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment - complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018-2023. The results may be used as valuable inputs for future water quality monitoring programs.
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Affiliation(s)
- Peter Tarábek
- Water Research Institute, Nábr. arm. gen. L. Svobodu 5, 81249, Bratislava, Slovakia.
| | - Nataliia Leonova
- Water Research Institute, Nábr. arm. gen. L. Svobodu 5, 81249, Bratislava, Slovakia
| | - Olga Konovalova
- Water Research Institute, Nábr. arm. gen. L. Svobodu 5, 81249, Bratislava, Slovakia
| | - Michal Kirchner
- Water Research Institute, Nábr. arm. gen. L. Svobodu 5, 81249, Bratislava, Slovakia
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Aggerbeck MR, Frøkjær EE, Johansen A, Ellegaard-Jensen L, Hansen LH, Hansen M. Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool. ENVIRONMENTAL RESEARCH 2024; 257:119242. [PMID: 38821457 DOI: 10.1016/j.envres.2024.119242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/25/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.
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Affiliation(s)
- Marie Rønne Aggerbeck
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark.
| | - Emil Egede Frøkjær
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark
| | - Anders Johansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark; Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark; Aarhus University Centre for Circular Bioeconomy, Aarhus University, 8830 Tjele, Denmark
| | - Lea Ellegaard-Jensen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark
| | - Lars Hestbjerg Hansen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark
| | - Martin Hansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark.
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Sworen JC, Morken PA, Smith AP, Boyle JE, Cervantes Garcia MD, Kramer J, Wadsley MP, Davis MC. Interrogation of a fluoropolymer dispersion manufactured with a non-fluorinated polymerization aid for targeted and non-targeted fluorinated residuals by liquid chromatography high resolution mass spectrometry. J Chromatogr A 2024; 1736:465369. [PMID: 39288502 DOI: 10.1016/j.chroma.2024.465369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 09/09/2024] [Accepted: 09/10/2024] [Indexed: 09/19/2024]
Abstract
Recent advances in fluoropolymer polymerization have focused on replacing perfluorinated polymerization aids (PAs) with hydrocarbon-based alternatives. Hydrocarbon PAs are vulnerable to fluorinated radicals during polymerization, leading to the creation of hundreds of process-specific polyfluorinated residuals. These residuals, which include low molecular weight extractable or leachable impurities, are challenging to detect at trace levels. This study investigates a polytetrafluoroethylene (PTFE) dispersion prepared with a hydrocarbon-based surfactant (DOSS) to measure these process-specific fluorinated residues. Liquid chromatography high resolution mass spectrometry is one of the few analytical methods that offers the sensitivity and selectivity required to detect these residuals in complex matrices at concentrations as low as parts per billion. The results indicate that using a hydrocarbon PA during emulsion polymerization produces numerous polyfluorinated residuals. These must be identified and monitored to develop effective abatement strategies, ensuring responsible fluoropolymer manufacturing.
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Affiliation(s)
- John C Sworen
- The Chemours Company, 201 Discovery Blvd, Newark, DE, USA
| | - Peter A Morken
- The Chemours Company, 201 Discovery Blvd, Newark, DE, USA
| | - Adam P Smith
- The Chemours Company, 8480 DuPont Road, Washington, WV, USA
| | - Jill E Boyle
- The Chemours Company, 201 Discovery Blvd, Newark, DE, USA
| | | | - Jordyn Kramer
- The Chemours Company, 201 Discovery Blvd, Newark, DE, USA
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Harahap Y, Mulyadi CA, Muliawan HS, Wahab HA. Determination of warfarin in volumetric absorptive microsampling by liquid chromatography-tandem mass spectrometry. Heliyon 2024; 10:e34500. [PMID: 39130442 PMCID: PMC11315077 DOI: 10.1016/j.heliyon.2024.e34500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 07/05/2024] [Accepted: 07/10/2024] [Indexed: 08/13/2024] Open
Abstract
Objective This study aims to develop and validate bioanalytical method for quantifying warfarin in VAMS samples using liquid chromatography tandem mass spectrometry (LC-MS/MS), directly implementing the method to patients receiving warfarin therapy. Methods The UPLC-MS/MS method was developed and optimized, with quercetin as the internal standard. Sample preparation was carried out using protein precipitation with methanol-acetonitrile (1:3 v/v). Results Chromatographic separation was achieved using Acquity® UPLC BEH C18 column with 0.1 % formic acid-acetonitrile-methanol (30:69:1 v/v) as mobile phase, in isocratic elution. Multiple Reaction Monitoring (MRM) detection was done using m/z values of 307.10 → 161.06 for warfarin and 301.03 → 150.98 for quercetin as internal standard, using Electrospray Ionization (ESI) negative ion source. The clinical application of the bioanalytical method was carried out on 25 patients receiving warfarin therapy at Universitas Indonesia Hospital and warfarin levels were well within the calibration range from 6.05 to 431.39 ng/mL. Conclusion A novel method has been developed to analyze warfarin in VAMS samples. This method has been fully validated according to guideline from FDA 2022 and is linear in the range of 5-500 ng/mL and the value of r ≥ 0.9977, and successfully applied for the analysis of warfarin in VAMS samples of clinical patients.
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Affiliation(s)
- Yahdiana Harahap
- Faculty of Pharmacy, Universitas Indonesia, Depok, 16424, Indonesia
- Faculty of Military Pharmacy, the Republic of Indonesia Defense University, Bogor, 16810, Indonesia
| | | | - Hary Sakti Muliawan
- Department of Cardiology and Vascular Medicine, Faculty of Medicine-Universitas Indonesia Hospital, Depok, 16424, Indonesia
| | - Habibah A. Wahab
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia
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Wang S, Argikar UA, Chatzopoulou M, Cho S, Crouch RD, Dhaware D, Gu TJ, Heck CJS, Johnson KM, Kalgutkar AS, Liu J, Ma B, Miller GP, Rowley JA, Seneviratne HK, Zhang D, Khojasteh SC. Bioactivation and reactivity research advances - 2023 year in review. Drug Metab Rev 2024:1-38. [PMID: 38963129 DOI: 10.1080/03602532.2024.2376023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 06/28/2024] [Indexed: 07/05/2024]
Abstract
Advances in the field of bioactivation have significantly contributed to our understanding and prediction of drug-induced liver injury (DILI). It has been established that many adverse drug reactions, including DILI, are associated with the formation and reactivity of metabolites. Modern methods allow us to detect and characterize these reactive metabolites in earlier stages of drug development, which helps anticipate and circumvent the potential for DILI. Improved in silico models and experimental techniques that better reflect in vivo environments are enhancing predictive capabilities for DILI risk. Further, studies on the mechanisms of bioactivation, including enzyme interactions and the role of individual genetic differences, have provided valuable insights for drug optimizations. Cumulatively, this progress is continually refining our approaches to drug safety evaluation and personalized medicine.
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Affiliation(s)
- Shuai Wang
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - Upendra A Argikar
- Non-clinical Development, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, USA
| | | | - Sungjoon Cho
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - Rachel D Crouch
- Department of Pharmacy and Pharmaceutical Sciences, Lipscomb University College of Pharmacy, Nashville, TN, USA
| | | | - Ting-Jia Gu
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - Carley J S Heck
- Medicine Design, Pfizer Worldwide Research, Development and Medical, Groton, CT, USA
| | - Kevin M Johnson
- Drug Metabolism and Pharmacokinetics, Inotiv, Maryland Heights, MO, USA
| | - Amit S Kalgutkar
- Medicine Design, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Joyce Liu
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - Bin Ma
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - Grover P Miller
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | - Herana Kamal Seneviratne
- Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Donglu Zhang
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - S Cyrus Khojasteh
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
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8
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Souihi A, Kruve A. Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS. Anal Chem 2024; 96:11263-11272. [PMID: 38959408 PMCID: PMC11256014 DOI: 10.1021/acs.analchem.4c01002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 06/15/2024] [Accepted: 06/18/2024] [Indexed: 07/05/2024]
Abstract
Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C18 LC method.
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Affiliation(s)
- Amina Souihi
- Department
of Environmental and Materials Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Environmental and Materials Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
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9
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Alvarez-Mora I, Arturi K, Béen F, Buchinger S, El Mais AER, Gallampois C, Hahn M, Hollender J, Houtman C, Johann S, Krauss M, Lamoree M, Margalef M, Massei R, Brack W, Muz M. Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA? Anal Bioanal Chem 2024:10.1007/s00216-024-05424-4. [PMID: 38992177 DOI: 10.1007/s00216-024-05424-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
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.
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Affiliation(s)
- Iker Alvarez-Mora
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany.
- Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Katarzyna Arturi
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Frederic Béen
- KWR Water Research Institute, Nieuwegein, the Netherlands
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sebastian Buchinger
- Department of Biochemistry and Ecotoxicology, Federal Institute of Hydrology (BfG), Koblenz, Germany
| | | | | | - Meike Hahn
- Department of Biochemistry and Ecotoxicology, Federal Institute of Hydrology (BfG), Koblenz, Germany
| | - Juliane Hollender
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zürich, Switzerland
| | - Corine Houtman
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- The Water Laboratory, Haarlem, the Netherlands
| | - Sarah Johann
- Department of Evolutionary Ecology and Environmental Toxicology, Goethe University Frankfurt, Frankfurt Am Main, Germany
| | - Martin Krauss
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
| | - Marja Lamoree
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Maria Margalef
- Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Riccardo Massei
- Department of Monitoring and Exploration Technologies, Research Data Management Team (RDM), Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
- Department of Ecotoxicology, Group of Integrative Toxicology (iTox), Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
| | - Werner Brack
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
- Department of Evolutionary Ecology and Environmental Toxicology, Goethe University Frankfurt, Frankfurt Am Main, Germany
| | - Melis Muz
- Department of Exposure Science, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
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10
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Nanusha MY, Frøkjær EE, Søndergaard J, Mørk Larsen M, Schwartz Glottrup C, Bruun Nicolaisen J, Hansen M. Quantitative Non-targeted Screening to Profile Micropollutants in Sewage Sludge Used for Agricultural Field Amendments. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9850-9862. [PMID: 38758285 PMCID: PMC11155239 DOI: 10.1021/acs.est.4c01441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
A considerable number of micropollutants from human activities enter the wastewater network for removal. However, at the wastewater treatment plant (WWTP), some proportion of these compounds is retained in the sewage sludge (biosolids), and due to its high content of nutrients, sludge is widely applied as an agricultural fertilizer and becomes a means for the micropollutants to be introduced to the environment. Accordingly, a holistic semiquantitative nontarget screening was performed on sewage sludges from five different WWTPs using nanoflow liquid chromatography coupled to high-resolution Orbitrap mass spectrometry. Sixty-one inorganic elements were measured using inductively coupled plasma mass spectrometry. Across all sludges, the nontarget analysis workflow annotated >21,000 features with chemical structures, and after strict prioritization and filtering, 120 organic micropollutants with diverse chemical structures and applications such as pharmaceuticals, pesticides, flame retardants, and industrial and natural compounds were identified. None of the tested sludges were free from organic micropollutants. Pharmaceuticals contributed the largest share followed by pesticides and natural products. The predicted concentration of identified contaminants ranged between 0.2 and 10,881 ng/g dry matter. Through quantitative nontarget analysis, this study comprehensively demonstrated the occurrence of cocktails of micropollutants in sewage sludges.
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Affiliation(s)
- Mulatu Y. Nanusha
- Department
of Environmental Science, Environmental Metabolomics Lab, Aarhus University, Frederiksborgvej 399, Roskilde DK-4000, Denmark
| | - Emil Egede Frøkjær
- Department
of Environmental Science, Environmental Metabolomics Lab, Aarhus University, Frederiksborgvej 399, Roskilde DK-4000, Denmark
| | - Jens Søndergaard
- Department
of EcoScience, Aarhus University, Frederiksborgvej 399, Roskilde DK-4000, Denmark
| | - Martin Mørk Larsen
- Department
of EcoScience, Aarhus University, Frederiksborgvej 399, Roskilde DK-4000, Denmark
| | | | | | - Martin Hansen
- Department
of Environmental Science, Environmental Metabolomics Lab, Aarhus University, Frederiksborgvej 399, Roskilde DK-4000, Denmark
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11
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Kalinski JCJ, Noundou XS, Petras D, Matcher GF, Polyzois A, Aron AT, Gentry EC, Bornman TG, Adams JB, Dorrington RA. Urban and agricultural influences on the coastal dissolved organic matter pool in the Algoa Bay estuaries. CHEMOSPHERE 2024; 355:141782. [PMID: 38548083 DOI: 10.1016/j.chemosphere.2024.141782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/28/2024] [Accepted: 03/22/2024] [Indexed: 04/08/2024]
Abstract
While anthropogenic pollution is a major threat to aquatic ecosystem health, our knowledge of the presence of xenobiotics in coastal Dissolved Organic Matter (DOM) is still relatively poor. This is especially true for water bodies in the Global South with limited information gained mostly from targeted studies that rely on comparison with authentic standards. In recent years, non-targeted tandem mass spectrometry has emerged as a powerful tool to collectively detect and identify pollutants and biogenic DOM components in the environment, but this approach has yet to be widely utilized for monitoring ecologically important aquatic systems. In this study we compared the DOM composition of Algoa Bay, Eastern Cape, South Africa, and its two estuaries. The Swartkops Estuary is highly urbanized and severely impacted by anthropogenic pollution, while the Sundays Estuary is impacted by commercial agriculture in its catchment. We employed solid-phase extraction followed by liquid chromatography tandem mass spectrometry to annotate more than 200 pharmaceuticals, pesticides, urban xenobiotics, and natural products based on spectral matching. The identification with authentic standards confirmed the presence of methamphetamine, carbamazepine, sulfamethoxazole, N-acetylsulfamethoxazole, imazapyr, caffeine and hexa(methoxymethyl)melamine, and allowed semi-quantitative estimations for annotated xenobiotics. The Swartkops Estuary DOM composition was strongly impacted by features annotated as urban pollutants including pharmaceuticals such as melamines and antiretrovirals. By contrast, the Sundays Estuary exhibited significant enrichment of molecules annotated as agrochemicals widely used in the citrus farming industry, with predicted concentrations for some of them exceeding predicted no-effect concentrations. This study provides new insight into anthropogenic impact on the Algoa Bay system and demonstrates the utility of non-targeted tandem mass spectrometry as a sensitive tool for assessing the health of ecologically important coastal ecosystems and will serve as a valuable foundation for strategizing long-term monitoring efforts.
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Affiliation(s)
| | - Xavier Siwe Noundou
- Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa; Department of Pharmaceutical Sciences, Sefako Makgatho Health Sciences University, Pretoria, South Africa
| | - Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, USA; Department of Biochemistry, University of California Riverside, Riverside, USA; CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
| | - Gwynneth F Matcher
- Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa; South African Institute for Aquatic Biodiversity, 6139, Makhanda, South Africa
| | - Alexandros Polyzois
- Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa; Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, 14853, United States
| | - Allegra T Aron
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, USA; Department of Chemistry and Biochemistry, University of Denver, Denver, CO, 80210, United States
| | - Emily C Gentry
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, USA; Department of Chemistry, Virginia Tech, Blacksburg, VA, 24061, United States
| | - Thomas G Bornman
- Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa; South African Environmental Observation Network SAEON, Elwandle Coastal Node, Gqeberha, South Africa; Institute for Coastal and Marine Research, Nelson Mandela University, Gqeberha, South Africa
| | - Janine B Adams
- DSI/NRF Research Chair, Shallow Water Ecosystems, Department of Botany and Institute for Coastal and Marine Research, Nelson Mandela University, Gqeberha, South Africa; Department of Botany, Institute for Coastal and Marine Research CMR, Nelson Mandela University, Gqeberha, South Africa
| | - Rosemary A Dorrington
- Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa; South African Institute for Aquatic Biodiversity, 6139, Makhanda, South Africa.
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12
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Phelps D, Parkinson LV, Boucher JM, Muncke J, Geueke B. Per- and Polyfluoroalkyl Substances in Food Packaging: Migration, Toxicity, and Management Strategies. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5670-5684. [PMID: 38501683 PMCID: PMC10993423 DOI: 10.1021/acs.est.3c03702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/28/2024] [Accepted: 02/28/2024] [Indexed: 03/20/2024]
Abstract
PFASs are linked to serious health and environmental concerns. Among their widespread applications, PFASs are known to be used in food packaging and directly contribute to human exposure. However, information about PFASs in food packaging is scattered. Therefore, we systematically map the evidence on PFASs detected in migrates and extracts of food contact materials and provide an overview of available hazard and biomonitoring data. Based on the FCCmigex database, 68 PFASs have been identified in various food contact materials, including paper, plastic, and coated metal, by targeted and untargeted analyses. 87% of these PFASs belong to the perfluorocarboxylic acids and fluorotelomer-based compounds. Trends in chain length demonstrate that long-chain perfluoroalkyl acids continue to be found, despite years of global efforts to reduce the use of these substances. We utilized ToxPi to illustrate that hazard data are available for only 57% of the PFASs that have been detected in food packaging. For those PFASs for which toxicity testing has been performed, many adverse outcomes have been reported. The data and knowledge gaps presented here support international proposals to restrict PFASs as a group, including their use in food contact materials, to protect human and environmental health.
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Affiliation(s)
- Drake
W. Phelps
- Independent
Consultant, Raleigh, North Carolina 27617, United States
| | | | | | - Jane Muncke
- Food
Packaging Forum Foundation, 8045 Zürich, Switzerland
| | - Birgit Geueke
- Food
Packaging Forum Foundation, 8045 Zürich, Switzerland
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13
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Ao X, Zhang X, Sun W, Linden KG, Payne EM, Mao T, Li Z. What is the role of nitrate/nitrite in trace organic contaminants degradation and transformation during UV-based advanced oxidation processes? WATER RESEARCH 2024; 253:121259. [PMID: 38377923 DOI: 10.1016/j.watres.2024.121259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/01/2024] [Accepted: 02/03/2024] [Indexed: 02/22/2024]
Abstract
The effectiveness of UV-based advanced oxidation processes (UV-AOPs) in degrading trace organic contaminants (TrOCs) can be significantly influenced by the ubiquitous presence of nitrate (NO3-) and nitrite (NO2-) in water and wastewater. Indeed, NO3-/NO2- can play multiple roles of NO3-/NO2- in UV-AOPs, leading to complexities and conflicting results observed in existing research. They can inhibit the degradation of TrOCs by scavenging reactive species and/or competitively absorbing UV light. Conversely, they can also enhance the elimination of TrOCs by generating additional •OH and reactive nitrogen species (RNS). Furthermore, the presence of NO3-/NO2- during UV-AOP treatment can affect the transformation pathways of TrOCs, potentially resulting in the nitration/nitrosation of TrOCs. The resulting nitro(so)-products are generally more toxic than the parent TrOCs and may become precursors of nitrogenous disinfection byproducts (N-DBPs) upon chlorination. Particularly, since the impact of NO3-/NO2- in UV-AOPs is largely due to the generation of RNS from NO3-/NO2- including NO•, NO2•, and peroxynitrite (ONOO-/ONOOH), this review covers the generation, properties, and detection methods of these RNS. From kinetic, mechanistic, and toxicologic perspectives, future research needs are proposed to advance the understanding of how NO3-/NO2- can be exploited to improve the performance of UV-AOPs treating TrOCs. This critical review provides a comprehensive framework outlining the multifaceted impact of NO3-/NO2- in UV-AOPs, contributing insights for basic research and practical applications of UV-AOPs containing NO3-/NO2-.
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Affiliation(s)
- Xiuwei Ao
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, International Science and Technology Cooperation Base for Environmental and Energy Technology of MOST, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xi Zhang
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, International Science and Technology Cooperation Base for Environmental and Energy Technology of MOST, University of Science and Technology Beijing, Beijing, 100083, China
| | - Wenjun Sun
- School of Environment, Tsinghua University, Beijing 100084, China; Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China.
| | - Karl G Linden
- Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States.
| | - Emma M Payne
- Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Ted Mao
- Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; MW Technologies, Inc., Ontario L8N1E, Canada
| | - Zifu Li
- School of Energy and Environmental Engineering, Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, International Science and Technology Cooperation Base for Environmental and Energy Technology of MOST, University of Science and Technology Beijing, Beijing, 100083, China
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14
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Vosough M, Schmidt TC, Renner G. Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives. Anal Bioanal Chem 2024; 416:2125-2136. [PMID: 38300263 PMCID: PMC10951028 DOI: 10.1007/s00216-024-05153-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/02/2024]
Abstract
This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.
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Affiliation(s)
- Maryam Vosough
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, North Rhine-Westphalia, Germany.
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, 45141, North Rhine-Westphalia, Germany.
- Department of Clean Technologies, Chemistry and Chemical Engineering Research Center of Iran, P.O. Box 14335-186, Tehran, Iran.
| | - Torsten C Schmidt
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, North Rhine-Westphalia, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, 45141, North Rhine-Westphalia, Germany
- IWW Water Centre, Moritzstr. 26, Mülheim an der Ruhr, 45476, North Rhine-Westphalia, Germany
| | - Gerrit Renner
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, 45141, North Rhine-Westphalia, Germany.
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, 45141, North Rhine-Westphalia, Germany.
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15
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Sanz C, Sunyer-Caldú A, Casado M, Mansilla S, Martinez-Landa L, Valhondo C, Gil-Solsona R, Gago-Ferrero P, Portugal J, Diaz-Cruz MS, Carrera J, Piña B, Navarro-Martín L. Efficient removal of toxicity associated to wastewater treatment plant effluents by enhanced Soil Aquifer Treatment. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133377. [PMID: 38237439 DOI: 10.1016/j.jhazmat.2023.133377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 02/08/2024]
Abstract
The regeneration of wastewater has been recognized as an effective strategy to counter water scarcity. Nonetheless, Wastewater Treatment Plant (WWTP) effluents still contain a wide range of contaminants of emerging concern (CECs) even after water depuration. Filtration through Soil Aquifer Treatment (SAT) systems has proven efficient for CECs removal although the attenuation of their associated biological effects still remains poorly understood. To evaluate this, three pilot SAT systems were monitored, two of them enhanced with different reactive barriers. SATs were fed with secondary effluents during two consecutive campaigns. Fifteen water samples were collected from the WWTP effluent, below the barriers and 15 m into the aquifer. The potential attenuation of effluent-associated biological effects by SATs was evaluated through toxicogenomic bioassays using zebrafish eleutheroembryos and human hepatic cells. Transcriptomic analyses revealed a wide range of toxic activities exerted by the WWTP effluents that were reduced by more than 70% by SAT. Similar results were observed when HepG2 hepatic cells were tested for cytotoxic and dioxin-like responses. Toxicity reduction appeared partially determined by the barrier composition and/or SAT managing and correlated with CECs removal. SAT appears as a promising approach to efficiently reduce effluent-associated toxicity contributing to environmental and human health preservation.
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Affiliation(s)
- Claudia Sanz
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Adrià Sunyer-Caldú
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Marta Casado
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Sylvia Mansilla
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Lurdes Martinez-Landa
- Associated Unit: Hydrogeology Group (UPC-CSIC), Spain; Dept. of Civil and Environmental Engineering. Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Cristina Valhondo
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain; Associated Unit: Hydrogeology Group (UPC-CSIC), Spain; Geosciences Montpellier, Université de Montpellier, CNRS, Montpellier, France
| | - Ruben Gil-Solsona
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Jose Portugal
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - M Silvia Diaz-Cruz
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Jesús Carrera
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain; Associated Unit: Hydrogeology Group (UPC-CSIC), Spain
| | - Benjamin Piña
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain
| | - Laia Navarro-Martín
- Institute of Environmental Assessment and Water Research, IDAEA-CSIC, Barcelona 08034, Spain.
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16
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Castaño-Ortiz JM, Gago-Ferrero P, Barceló D, Rodríguez-Mozaz S, Gil-Solsona R. HRMS-based suspect screening of pharmaceuticals and their transformation products in multiple environmental compartments: An alternative to target analysis? JOURNAL OF HAZARDOUS MATERIALS 2024; 465:132974. [PMID: 38218030 DOI: 10.1016/j.jhazmat.2023.132974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/31/2023] [Accepted: 11/08/2023] [Indexed: 01/15/2024]
Abstract
The comprehensive monitoring of pharmaceutically active compounds (PhACs) in the environment is challenging given the myriad of substances continuously discharged, the increasing number of new compounds being produced (and released), or the variety of the associated human metabolites and transformation products (TPs). Approaches such as high-resolution mass spectrometry (HRMS)-based suspect analysis have emerged to overcome the drawbacks of classical target analytical methods, e.g., restricted chemical coverage. In this study, we assess the readiness of HRMS-based suspect screening to replace or rather complement target methodologies by comparing the performance of both approaches in terms of i) detection of PhACs in various environmental samples (water, sediments, biofilm, fish plasma, muscle and liver) in a field study; ii) PhACs (semi)quantification and iii) prediction of their environmental risks. Our findings revealed that target strategies alone significantly underestimate the variety of PhACs potentially impacting the environment. However, relying solely on suspect strategies can misjudge the presence and risk of low-level but potentially risky PhACs. Additionally, semiquantitative approaches, despite slightly overestimating concentrations, can provide a realistic overview of PhACs concentrations. Hence, it is recommended to adopt a combined strategy that first evaluates suspected threats and subsequently includes the relevant ones in the established target methodologies.
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Affiliation(s)
- Jose M Castaño-Ortiz
- Catalan Institute for Water Research (ICRA-CERCA), C/ Emili Grahit 101, 17003 Girona, Spain; University of Girona, Girona, Spain
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC) Severo Ochoa Excellence Centre, Department of Environmental Chemistry, C/ Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Damià Barceló
- Catalan Institute for Water Research (ICRA-CERCA), C/ Emili Grahit 101, 17003 Girona, Spain; University of Girona, Girona, Spain; Institute of Environmental Assessment and Water Research (IDAEA-CSIC) Severo Ochoa Excellence Centre, Department of Environmental Chemistry, C/ Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Sara Rodríguez-Mozaz
- Catalan Institute for Water Research (ICRA-CERCA), C/ Emili Grahit 101, 17003 Girona, Spain; University of Girona, Girona, Spain.
| | - Ruben Gil-Solsona
- Catalan Institute for Water Research (ICRA-CERCA), C/ Emili Grahit 101, 17003 Girona, Spain; University of Girona, Girona, Spain; Institute of Environmental Assessment and Water Research (IDAEA-CSIC) Severo Ochoa Excellence Centre, Department of Environmental Chemistry, C/ Jordi Girona 18-26, 08034 Barcelona, Spain.
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17
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Li Y, Lu Z, Zhang X, Wang J, Zhao S, Dai Y. Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates. ENVIRONMENTAL RESEARCH 2024; 243:117806. [PMID: 38043899 DOI: 10.1016/j.envres.2023.117806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/05/2023]
Abstract
Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 μg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.
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Affiliation(s)
- Yubo Li
- Shanghai Municipal Engineering Design Institute (Group) Co. LTD., Shanghai, 200092, PR China
| | - Zhibo Lu
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, Shanghai, 200092, PR China.
| | - Xin Zhang
- Shanghai Municipal Engineering Design Institute (Group) Co. LTD., Shanghai, 200092, PR China
| | - Juan Wang
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, Shanghai, 200092, PR China
| | - Shuiqian Zhao
- Shanghai Municipal Engineering Design Institute (Group) Co. LTD., Shanghai, 200092, PR China
| | - Yuxuan Dai
- Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology, Hong Kong, 999077, PR China
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18
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Lauria MZ, Sepman H, Ledbetter T, Plassmann M, Roos AM, Simon M, Benskin JP, Kruve A. Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:2458-2467. [PMID: 38270113 PMCID: PMC10851419 DOI: 10.1021/acs.est.3c07220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/28/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024]
Abstract
High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.
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Affiliation(s)
- Mélanie Z. Lauria
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden
| | - Helen Sepman
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 106
91 Stockholm, Sweden
| | - Thomas Ledbetter
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 106
91 Stockholm, Sweden
| | - Merle Plassmann
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden
| | - Anna M. Roos
- Department
of Environmental Research and Monitoring, Swedish Museum of Natural History, 104 05 Stockholm, Sweden
| | - Malene Simon
- Greenland
Climate Research Centre, Greenland Institute
of Natural Resources, 3900 Nuuk, Greenland
| | - Jonathan P. Benskin
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 10691 Stockholm, Sweden
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 106
91 Stockholm, Sweden
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19
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Pu S, McCord JP, Bangma J, Sobus JR. Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances. Anal Bioanal Chem 2024; 416:1249-1267. [PMID: 38289355 PMCID: PMC10850229 DOI: 10.1007/s00216-023-05117-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 02/09/2024]
Abstract
Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration "surrogates," which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from "global" chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using "global" surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using "expert-selected" surrogates (n = 3) instead of "global" surrogates (n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases.
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Affiliation(s)
- Shirley Pu
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
| | - James P McCord
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
| | - Jacqueline Bangma
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA
| | - Jon R Sobus
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA.
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20
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Johnson TA, Abrahamsson DP. Quantification of chemicals in non-targeted analysis without analytical standards - Understanding the mechanism of electrospray ionization and making predictions. CURRENT OPINION IN ENVIRONMENTAL SCIENCE & HEALTH 2024; 37:100529. [PMID: 38312491 PMCID: PMC10836048 DOI: 10.1016/j.coesh.2023.100529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
The constant creation and release of new chemicals to the environment is forming an ever-widening gap between available analytical standards and known chemicals. Developing non-targeted analysis (NTA) methods that have the ability to detect a broad spectrum of compounds is critical for research and analysis of emerging contaminants. There is a need to develop methods that make it possible to identify compound structures from their MS and MS/MS information and quantify them without analytical standards. Method refinements that utilize machine learning algorithms and chemical descriptors to estimate the instrument response of particular compounds have made progress in recent years. This narrative review seeks to summarize the current state of the field of non-targeted analysis (NTA) toward quantification of unknowns without the use of analytical standards. Despite the limited accumulation of validation studies on real samples, the ongoing enhancement in data processing and refinement of machine learning tools could lead to more comprehensive chemical coverage of NTA and validated quantitative NTA methods, thus boosting confidence in their usage and enhancing the utility of quantitative NTA.
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Affiliation(s)
- Trevor A Johnson
- Division of Environmental Pediatrics, Department of Pediatrics, Grossman School of Medicine, New York University
| | - Dimitri P Abrahamsson
- Division of Environmental Pediatrics, Department of Pediatrics, Grossman School of Medicine, New York University
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21
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Tisler S, Kilpinen K, Pattison DI, Tomasi G, Christensen JH. Quantitative Nontarget Analysis of CECs in Environmental Samples Can Be Improved by Considering All Mass Adducts. Anal Chem 2024; 96:229-237. [PMID: 38128072 PMCID: PMC10782417 DOI: 10.1021/acs.analchem.3c03791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Quantitative nontarget analysis (qNTA) for liquid chromatography coupled to high-resolution mass spectrometry enables a more comprehensive assessment of environmental samples. Previous studies have shown that correlations between a compound's ionization efficiency and a range of molecular descriptors can predict the compound's concentration within a factor of 5. In this study, the qNTA approach was further improved by considering all mass adducts instead of only the protonated ion. The model was based on a quantitative structure-property relationship (QSPR), including 216 contaminants of emerging concern (CECs), of which 80 exhibited adduct formation that accounted for >10% of the total peak intensity. When all mass adducts were included, the test set coefficient of determination improved to Q2 = 0.855 compared to Q2 = 0.670 when only the protonated ions were considered (test set median RF error factor 1.6). The inclusion of all adducts was also important to transfer the RF QSPR model reliably. It was assumed that RF variations are sequence-dependent; therefore, a second QSPR model for the prediction of the transferability factor was built for each sequence. For validation, samples were analyzed up to two years apart. The median prediction fold change was 1.74 for analytical standards (63 compounds) and 2.4 for enriched wastewater effluent samples (41 compounds), with 80% of the compounds predicted within a fold change of 2.4 and 3.3, respectively. The model was also validated on a second instrument, where 80% of the 26 compounds in wastewater effluent were predicted within a factor of 3.8.
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Affiliation(s)
- Selina Tisler
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Kristoffer Kilpinen
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
- Eurofins
Miljø Denmark A/S, Ladelundvej 85, 6600 Vejen, Denmark
| | - David I. Pattison
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Giorgio Tomasi
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Jan H. Christensen
- Analytical
Chemistry Group, Department of Plant and Environmental Science, Faculty
of Science, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
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22
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Mattoli L, Proietti G, Fodaroni G, Quintiero CM, Burico M, Gianni M, Giovagnoni E, Mercati V, Santi C. Suspect screening analysis to improve untargeted and targeted UHPLC-qToF approaches: the biodegradability of a proton pump inhibitor medicine and a natural medical device. Sci Rep 2024; 14:51. [PMID: 38167521 PMCID: PMC10761695 DOI: 10.1038/s41598-023-49948-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
Suspect screening and untargeted analysis using UHPLC-qToF are two advanced analytical approaches now used to achieve an extensive chemical profile of samples, which are then typically confirmed through targeted analysis. These techniques can detect a large number of chemical features simultaneously and are currently being introduced into the study of contaminants of emerging concern (CECs) and into the study of the extent of human chemical exposure (the exposome). Here is described the use of these techniques to characterize chemical mixtures derived from the OECD 301F ready biodegradability test (RBT) of a chemical and natural formulation currently used to treat reflux disease and functional dyspepsia. Untargeted analysis clearly evidenced a different behavior between formulations containing only natural products with respect to that containing synthetic and non-naturally occurring substances. Suspect screening analysis improved the untargeted analysis of the omeprazole-based medicine, leading to the tentative identification of a number of omeprazole-derived transformation products, thereby enabling their preliminary quali-quantitative evaluation. Targeted analysis was then performed to confirm the preliminary data gained from the suspect screening approach. The validation of the analytical method for the quantitative determination of omeprazole and its major metabolite, omeprazole sulphide, has provided robust data to evaluate the behavior of omeprazole during the OECD 301F test. Using advanced analytical approaches, the RBT performed on the two products under investigation confirmed that omeprazole is not readily biodegradable, while the medical device made of natural substances has proven to be readily biodegradable.
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Affiliation(s)
- Luisa Mattoli
- Metabolomics and Analytical Sciences, Aboca SpA, Sansepolcro, AR, Italy
| | - Giacomo Proietti
- Metabolomics and Analytical Sciences, Aboca SpA, Sansepolcro, AR, Italy
| | - Giada Fodaroni
- Metabolomics and Analytical Sciences, Aboca SpA, Sansepolcro, AR, Italy
| | | | - Michela Burico
- Metabolomics and Analytical Sciences, Aboca SpA, Sansepolcro, AR, Italy
| | - Mattia Gianni
- Metabolomics and Analytical Sciences, Aboca SpA, Sansepolcro, AR, Italy
| | | | - Valentino Mercati
- Metabolomics and Analytical Sciences, Aboca SpA, Sansepolcro, AR, Italy
| | - Claudio Santi
- Group of Catalysis, Synthesis and Organic Green Chemistry, Department of Pharmaceutical Sciences, University of Perugia, Via del Liceo 1, 06123, Perugia, Italy.
- Centro di Eccellenza Materiali Innovativi Nanostrutturati (CEMIN), University of Perugia, Via Elce di Sotto 8, 06123, Perugia, Italy.
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23
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Gutiérrez-Martín D, Restrepo-Montes E, Golovko O, López-Serna R, Aalizadeh R, Thomaidis NS, Marquès M, Gago-Ferrero P, Gil-Solsona R. Comprehensive profiling and semi-quantification of exogenous chemicals in human urine using HRMS-based strategies. Anal Bioanal Chem 2023; 415:7297-7313. [PMID: 37946034 PMCID: PMC10684428 DOI: 10.1007/s00216-023-04998-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/04/2023] [Accepted: 10/09/2023] [Indexed: 11/12/2023]
Abstract
Chemicals infiltrate our daily experiences through multiple exposure pathways. Human biomonitoring (HBM) is routinely used to comprehensively understand these chemical interactions. Historically, HBM depended on targeted screening methods limited to a relatively small set of chemicals with triple quadrupole instruments typically. However, recent advances in high-resolution mass spectrometry (HRMS) have facilitated the use of broad-scope target, suspect, and non-target strategies, enhancing chemical exposome characterization within acceptable detection limits. Despite these advancements, establishing robust and efficient sample treatment protocols is still essential for trustworthy broad-range chemical analysis. This study sought to validate a methodology leveraging HRMS-based strategies for accurate profiling of exogenous chemicals and related metabolites in urine samples. We evaluated five extraction protocols, each encompassing various chemical classes, such as pharmaceuticals, plastic additives, personal care products, and pesticides, in terms of their extraction recoveries, linearity, matrix effect, sensitivity, and reproducibility. The most effective protocol was extensively validated and subsequently applied to 10 real human urine samples using wide-scope target analysis encompassing over 2000 chemicals. We successfully identified and semi-quantified a total of 36 chemicals using an ionization efficiency-based model, affirming the methodology's robust performance. Notably, our results dismissed the need for a deconjugation step, a typically labor-intensive and time-consuming process.
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Affiliation(s)
- Daniel Gutiérrez-Martín
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), 08034, Barcelona, Spain
- Institute of Sustainable Processes (ISP), Dr. Mergelina S/N, 47011, Valladolid, Spain
- Department of Analytical Chemistry, Faculty of Sciences, University of Valladolid, Paseo de Belén 7, 47011, Valladolid, Spain
| | - Esteban Restrepo-Montes
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), 08034, Barcelona, Spain
| | - Oksana Golovko
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), 75007, Uppsala, Sweden
| | - Rebeca López-Serna
- Institute of Sustainable Processes (ISP), Dr. Mergelina S/N, 47011, Valladolid, Spain
- Department of Analytical Chemistry, Faculty of Sciences, University of Valladolid, Paseo de Belén 7, 47011, Valladolid, Spain
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Montse Marquès
- Universitat Rovira I Virgili, Laboratory of Toxicology and Environmental Health, School of Medicine, IISPV, Sant LLorenç 21, 43201, Reus, Catalonia, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Pablo Gago-Ferrero
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), 08034, Barcelona, Spain
| | - Rubén Gil-Solsona
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), 08034, Barcelona, Spain.
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24
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Rutz A, Wolfender JL. Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:18010-18023. [PMID: 37949451 PMCID: PMC10683005 DOI: 10.1021/acs.jafc.3c03099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 11/12/2023]
Abstract
Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.
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Affiliation(s)
- Adriano Rutz
- School
of Pharmaceutical Sciences, University of
Geneva, 1211 Geneva, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva, Switzerland
- Institute
of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland
| | - Jean-Luc Wolfender
- School
of Pharmaceutical Sciences, University of
Geneva, 1211 Geneva, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva, Switzerland
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25
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Arturi K, Hollender J. Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18067-18079. [PMID: 37279189 PMCID: PMC10666537 DOI: 10.1021/acs.est.3c00304] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/15/2023] [Accepted: 05/15/2023] [Indexed: 06/08/2023]
Abstract
Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.
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Affiliation(s)
- Katarzyna Arturi
- Department
of Environmental Chemistry, Swiss Federal
Institute of Aquatic Science and Technology (Eawag), Ueberlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Juliane Hollender
- Department
of Environmental Chemistry, Swiss Federal
Institute of Aquatic Science and Technology (Eawag), Ueberlandstrasse 133, 8600 Dübendorf, Switzerland
- Institute
of Biogeochemistry and Pollution Dynamics, Eidgenössische Technische Hochschule Zürich (ETH Zurich), Rämistrasse 101, 8092 Zürich, Switzerland
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26
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Egede Frøkjær E, Rüsz Hansen H, Hansen M. Non-targeted and suspect screening analysis using ion exchange chromatography-Orbitrap tandem mass spectrometry reveals polar and very mobile xenobiotics in Danish drinking water. CHEMOSPHERE 2023; 339:139745. [PMID: 37558003 DOI: 10.1016/j.chemosphere.2023.139745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/21/2023] [Accepted: 08/04/2023] [Indexed: 08/11/2023]
Abstract
Non-targeted and suspect screening analysis is gaining approval across the scientific and regulatory community to monitor the chemical status in the environment and thus environmental quality. These holistic screening analyses provides the means to perform suspect screening and go beyond to discover previously undescribed chemical pollutants in environmental samples. In a case study, we developed and optimized a high-resolution tandem mass spectrometry platform hyphenated with anion exchange chromatography to screen drinking water samples in Denmark. The optimized non-targeted screening method was able to detect anionic and polar compounds and was successfully applied to drinking water from two drinking water facilities. Following a data analysis pipeline optimization, anionic pesticide residues and other environmental contaminants were detected at confidence identification level 1 such as dimethachlor ESA, mecoprop, and dichlorprop in drinking water. In addition to these three substances, it was possible to detect another 1662 compounds, of which 97 were annotated at confidence identification level 2. More research is urgently needed to health risk prioritize the detected substances and to determine their concentrations.
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Affiliation(s)
- Emil Egede Frøkjær
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
| | - Helle Rüsz Hansen
- Danish Environmental Protection Agency, Tolderlundsvej 5, 5000, Odense C, Denmark
| | - Martin Hansen
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
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27
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Morales A, Candreva J, Jayarathne T, Esterman AL, Voruganti S, Flagg SC, Slaney T, Liu P, Adamo M, Patel S, Das TK, Zeng M, Li X. A comprehensive strategy for the identification of biologics by liquid-chromatography-mass spectrometry for release testing in a regulated environment. J Pharm Biomed Anal 2023; 234:115580. [PMID: 37478550 DOI: 10.1016/j.jpba.2023.115580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Identification (ID) testing is a regulatory requirement for biopharmaceutical manufacturing, requiring robust, GMP-qualified assays that can distinguish the therapeutic from any other in the facility. Liquid Chromatography-Mass Spectrometry (LC-MS) is a powerful analytical tool used to identify and characterize biologics. While routinely leveraged for characterization, LC-MS is relatively rare in Quality Control (QC) settings due to its perceived complexity and scarcity of MS-trained personnel. However, employing LC-MS for identification of drug products has many advantages versus conventional ID techniques, including but not limited to its high specificity, rapid turn-around time, and ease of method execution. In this work, we outline the development and implementation of a comprehensive LC-MS based ID strategy for biologics release testing. Two main workflows (WFs) were developed: i) WF1, a subunit-based assay measuring the molecular weight of the light chain (LC) and heavy chain (HC) of an antibody upon reduction, and ii) WF2, intact mass measurement of the biologic upon N-deglycosylation by PNGase F. The proposed strategy is shown to be applicable for over 40 diverse model biologics including monoclonal antibodies (mAbs), biobetters such as antibody prodrugs/afucosylated mAbs, fusion proteins, multi-specific antibodies, Fabs, and large peptides, all with excellent mass accuracy (error typically < 20 ppm) and precision. It requires a single-step sample preparation and a single click to run and process the data upon method setup. This strategy has been successfully implemented for release testing in GMP labs. Challenges and considerations for the establishment of QC-friendly methods are discussed. It is also shown that these methods can be applied to the ID of more analytically complex biotherapeutics, such as fixed-dose combination (FDC) and drug products co-formulated with trace-level additives.
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Affiliation(s)
- Anna Morales
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Jason Candreva
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Thilina Jayarathne
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Abbie L Esterman
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Sudhakar Voruganti
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Shannon C Flagg
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Thomas Slaney
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Peiran Liu
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Michael Adamo
- Analytical Strategy and Operations, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Saileshkumar Patel
- Analytical Strategy and Operations, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Tapan K Das
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Ming Zeng
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States
| | - Xue Li
- Biologics Development, Bristol Myers Squibb, New Brunswick, NJ, United States.
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28
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Ruan T, Li P, Wang H, Li T, Jiang G. Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective. Chem Rev 2023; 123:10584-10640. [PMID: 37531601 DOI: 10.1021/acs.chemrev.3c00056] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
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.
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Affiliation(s)
- Ting Ruan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pengyang Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haotian Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingyu Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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29
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Sepman H, Malm L, Peets P, MacLeod M, Martin J, Breitholtz M, Kruve A. Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS 2 Data. Anal Chem 2023; 95:12329-12338. [PMID: 37548594 PMCID: PMC10448440 DOI: 10.1021/acs.analchem.3c01744] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 07/23/2023] [Indexed: 08/08/2023]
Abstract
Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.
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Affiliation(s)
- Helen Sepman
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Louise Malm
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Pilleriin Peets
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Matthew MacLeod
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Jonathan Martin
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Magnus Breitholtz
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
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30
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Manz KE, Feerick A, Braun JM, Feng YL, Hall A, Koelmel J, Manzano C, Newton SR, Pennell KD, Place BJ, Godri Pollitt KJ, Prasse C, Young JA. Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:524-536. [PMID: 37380877 PMCID: PMC10403360 DOI: 10.1038/s41370-023-00574-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023]
Abstract
Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).
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Affiliation(s)
- Katherine E Manz
- School of Engineering, Brown University, Providence, RI, 02912, USA.
| | - Anna Feerick
- Agricultural & Environmental Chemistry Graduate Group, University of California, Davis, Davis, CA, 95616, USA
| | - Joseph M Braun
- Department of Epidemiology, Brown University, Providence, RI, 02912, USA
| | - Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Amber Hall
- Department of Epidemiology, Brown University, Providence, RI, 02912, USA
| | - Jeremy Koelmel
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Carlos Manzano
- Department of Chemistry, Faculty of Science, University of Chile, Santiago, RM, Chile
- School of Public Health, San Diego State University, San Diego, CA, USA
| | - Seth R Newton
- Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Kurt D Pennell
- School of Engineering, Brown University, Providence, RI, 02912, USA
| | - Benjamin J Place
- National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, 20899, USA
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Carsten Prasse
- Department of Environmental Health & Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Risk Sciences and Public Policy Institute, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Joshua A Young
- Division of Biology, Chemistry and Materials Science, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
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31
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Bieber S, Letzel T, Kruve A. Electrospray Ionization Efficiency Predictions and Analytical Standard Free Quantification for SFC/ESI/HRMS. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37358930 DOI: 10.1021/jasms.3c00156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Supercritical fluid chromatography (SFC) is a promising, sustainable, and complementary alternative to liquid chromatography (LC) and has often been coupled with high resolution mass spectrometry (HRMS) for nontarget screening (NTS). Recent developments in predicting the ionization efficiency for LC/ESI/HRMS have enabled quantification of chemicals detected in NTS even if the analytical standards of the detected and tentatively identified chemicals are unavailable. This poses the question of whether analytical standard free quantification can also be applied in SFC/ES/HRMS. We evaluate both the possibility to transfer an ionization efficiency predictions model, previously trained on LC/ESI/HRMS data, to SFC/ESI/HRMS as well as training a new predictive model on SFC/ESI/HRMS data for 127 chemicals. The response factors of these chemicals ranged over 4 orders of magnitude in spite of a postcolumn makeup flow, expectedly enhancing the ionization of the analytes. The ionization efficiency values were predicted based on a random forest regression model from PaDEL descriptors and predicted values showed statistically significant correlation with the measured response factors (p < 0.05) with Spearman's rho of 0.584 and 0.669 for SFC and LC data, respectively. Moreover, the most significant descriptors showed similarities independent of the chromatography used for collecting the training data. We also investigated the possibility to quantify the detected chemicals based on predicted ionization efficiency values. The model trained on SFC data showed very high prediction accuracy with median prediction error of 2.20×, while the model pretrained on LC/ESI/HRMS data yielded median prediction error of 5.11×. This is expected, as the training and test data for SFC/ESI/HRMS have been collected on the same instrument with the same chromatography. Still, the correlation observed between response factors measured with SFC/ESI/HRMS and predicted with a model trained on LC data hints that more abundant LC/ESI/HRMS data prove useful in understanding and predicting the ionization behavior in SFC/ESI/HRMS.
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Affiliation(s)
- Stefan Bieber
- AFIN-TS GmbH (Analytisches Forschungsinstitut für Non-Target Screening), Am Mittleren Moos 48, 86167 Augsburg, Germany
| | - Thomas Letzel
- AFIN-TS GmbH (Analytisches Forschungsinstitut für Non-Target Screening), Am Mittleren Moos 48, 86167 Augsburg, Germany
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 10691 Stockholm, Sweden
- Department of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, 10691 Stockholm, Sweden
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32
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Šebela M. The use of matrix-assisted laser desorption/ionization mass spectrometry in enzyme activity assays and its position in the context of other available methods. MASS SPECTROMETRY REVIEWS 2023; 42:1008-1031. [PMID: 34549449 DOI: 10.1002/mas.21733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
Activity assays are indispensable for studying biochemical properties of enzymes. The purposes of measuring activity are wide ranging from a simple detection of the presence of an enzyme to kinetic experiments evaluating the substrate specificity, reaction mechanisms, and susceptibility to inhibitors. Common activity assay methods include spectroscopy, electrochemical sensors, or liquid chromatography coupled with various detection techniques. This review focuses on the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) as a growing and modern alternative, which offers high speed of analysis, sensitivity, versatility, possibility of automation, and cost-effectiveness. It may reveal reaction intermediates, side products or measure more enzymes at once. The addition of an internal standard or calculating the ratios of the substrate and product peak intensities and areas overcome the inherent inhomogeneous distribution of analyte and matrix in the sample spot, which otherwise results in a poor reproducibility. Examples of the application of MALDI-TOF MS for assaying hydrolases (including peptidases and β-lactamases for antibiotic resistance tests) and other enzymes are provided. Concluding remarks summarize advantages and challenges coming from the present experience, and draw future perspectives such as a screening of large libraries of chemical compounds for their substrate or inhibitory properties towards enzymes.
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Affiliation(s)
- Marek Šebela
- Department of Biochemistry, Faculty of Science, and CATRIN, Palacký University, Olomouc, Czech Republic
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33
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Hu J, Lyu Y, Chen H, Cai L, Li J, Cao X, Sun W. Integration of target, suspect, and nontarget screening with risk modeling for per- and polyfluoroalkyl substances prioritization in surface waters. WATER RESEARCH 2023; 233:119735. [PMID: 36801580 DOI: 10.1016/j.watres.2023.119735] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/09/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
Though thousands of per- and polyfluoroalkyl substances (PFAS) have been on the global market, most research focused on only a small fraction, potentially resulting in underestimated environmental risks. Here, we used complementary target, suspect, and nontarget screening for quantifying and identifying the target and nontarget PFAS, respectively, and developed a risk model considering their specific properties to prioritize the PFAS in surface waters. Thirty-three PFAS were identified in surface water in the Chaobai river, Beijing. The suspect and nontarget screening by Orbitrap displayed a sensitivity of > 77%, indicating its good performance in identifying the PFAS in samples. We used triple quadrupole (QqQ) under multiple-reaction monitoring for quantifying PFAS with authentic standards due to its potentially high sensitivity. To quantify the nontarget PFAS without authentic standards, we trained a random forest regression model which presented the differences up to only 2.7 times between measured and predicted response factors (RFs). The maximum/minimum RF in each PFAS class was as high as 1.2-10.0 in Orbitrap and 1.7-22.3 in QqQ. A risk-based prioritization approach was developed to rank the identified PFAS, and four PFAS (i.e., perfluorooctanoic acid, hydrogenated perfluorohexanoic acid, bistriflimide, 6:2 fluorotelomer carboxylic acid) were flagged with high priority (risk index > 0.1) for remediation and management. Our study highlighted the importance of a quantification strategy during environmental scrutiny of PFAS, especially for nontarget PFAS without standards.
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Affiliation(s)
- Jingrun Hu
- State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
| | - Yitao Lyu
- State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
| | - Huan Chen
- Department of Environmental Engineering and Earth Sciences, Clemson University, SC 29634, USA.
| | - Leilei Cai
- College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
| | - Jie Li
- State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
| | - Xiaoqiang Cao
- College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
| | - Weiling Sun
- State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China.
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34
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Lerner R, Baker D, Schwitter C, Neuhaus S, Hauptmann T, Post JM, Kramer S, Bindila L. Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples. Nat Commun 2023; 14:937. [PMID: 36806650 PMCID: PMC9941096 DOI: 10.1038/s41467-023-36520-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 02/03/2023] [Indexed: 02/22/2023] Open
Abstract
Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.
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Affiliation(s)
- Raissa Lerner
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Dhanwin Baker
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Claudia Schwitter
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Sarah Neuhaus
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Tony Hauptmann
- Data Mining, Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, 55128, Mainz, Germany
| | - Julia M Post
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany
| | - Stefan Kramer
- Data Mining, Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, 55128, Mainz, Germany
| | - Laura Bindila
- Clinical Lipidomics Unit, Institute of Physiological Chemistry, University Medical Center, Duesbergweg 6, 55128, Mainz, Germany.
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35
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Lee JY, Han Y, Styczynski MP. Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data. Mol Omics 2023; 19:126-136. [PMID: 36374123 PMCID: PMC9974747 DOI: 10.1039/d2mo00168c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.
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Affiliation(s)
- Justin Y Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Yue Han
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Mark P Styczynski
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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36
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Visconti G, Boccard J, Feinberg M, Rudaz S. From fundamentals in calibration to modern methodologies: A tutorial for small molecules quantification in liquid chromatography-mass spectrometry bioanalysis. Anal Chim Acta 2023; 1240:340711. [PMID: 36641149 DOI: 10.1016/j.aca.2022.340711] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022]
Abstract
Over the last two decades, liquid chromatography coupled to mass-spectrometry (LC‒MS) has become the gold standard to perform qualitative and quantitative analyses of small molecules. When quantitative analysis is developed, an analyst usually refers to international guidelines for analytical method validation. In this context, the design of calibration curves plays a key role in providing accurate results. During recent years and along with instrumental advances, strategies to build calibration curves have dramatically evolved, introducing innovative approaches to improve quantitative precision and throughput. For example, when a labeled standard is available to be spiked directly into the study sample, the concentration of the unlabeled analog can be easily determined using the isotopic pattern deconvolution or the internal calibration approach, eliminating the need for multipoint calibration curves. This tutorial aims to synthetize the advances in LC‒MS quantitative analysis for small molecules in complex matrices, going from fundamental aspects in calibration to modern methodologies and applications. Different work schemes for calibration depending on the sample characteristics (analyte and matrix nature) are distinguished and discussed. Finally, this tutorial outlines the importance of having international guidelines for analytical method validation that agree with the advances in calibration strategies and analytical instrumentation.
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Affiliation(s)
- Gioele Visconti
- School of Pharmaceutical Sciences, University of Geneva, CMU - Rue Michel-Servet 1, 1211, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU - Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, CMU - Rue Michel-Servet 1, 1211, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU - Rue Michel-Servet 1, 1211, Geneva, Switzerland
| | | | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, CMU - Rue Michel-Servet 1, 1211, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU - Rue Michel-Servet 1, 1211, Geneva, Switzerland.
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Black G, Lowe C, Anumol T, Bade J, Favela K, Feng YL, Knolhoff A, Mceachran A, Nuñez J, Fisher C, Peter K, Quinete NS, Sobus J, Sussman E, Watson W, Wickramasekara S, Williams A, Young T. Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool. Anal Bioanal Chem 2023; 415:35-44. [PMID: 36435841 PMCID: PMC10010115 DOI: 10.1007/s00216-022-04434-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/30/2022] [Accepted: 11/09/2022] [Indexed: 11/28/2022]
Abstract
Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as "chemical space." Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.
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Affiliation(s)
- Gabrielle Black
- Department of Civil & Environmental Engineering, University of California Davis, Davis, CA, USA.
| | - Charles Lowe
- U.S. EPA, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA
| | - Tarun Anumol
- Agilent Technologies, Inc., Santa Clara, CA, USA
| | - Jessica Bade
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Yong-Lai Feng
- Exposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Ann Knolhoff
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, MD, USA
| | | | - Jamie Nuñez
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Christine Fisher
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, College Park, MD, USA
| | - Kathy Peter
- Center for Urban Waters, University of Washington Tacoma, Tacoma, WA, 98421, USA
| | - Natalia Soares Quinete
- Department of Chemistry and Biochemistry, Institute of Environment, Florida International University, North Miami, FL, USA
| | - Jon Sobus
- U.S. EPA, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA
| | | | | | - Samanthi Wickramasekara
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA
| | - Antony Williams
- U.S. EPA, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA
| | - Tom Young
- Department of Civil & Environmental Engineering, University of California Davis, Davis, CA, USA
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38
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Nanusha MY, Frøkjær EE, Liigand J, Christensen MR, Hansen HR, Hansen M. Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 315:120346. [PMID: 36202272 DOI: 10.1016/j.envpol.2022.120346] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 μg/L), valsartan (14.4 μg/L), and ibuprofen (12.7 μg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.
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Affiliation(s)
- Mulatu Yohannes Nanusha
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark
| | - Emil Egede Frøkjær
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark
| | - Jaanus Liigand
- Quantem Analytics OÜ, Narva mnt 149-8, Tartu, 51008, Estonia
| | | | - Helle Rüsz Hansen
- Danish Environmental Protection Agency, Tolderlundsvej 5, 5000, Odense C, Denmark
| | - Martin Hansen
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
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Tammiku-Taul J, Burk P. Nonempirical Prediction of the Relative Electrospray Ionization Efficiencies of Nitroanilines by Combined CBS-QB3 and SCC-DFTB Calculations. J Phys Chem A 2022; 126:8939-8944. [DOI: 10.1021/acs.jpca.2c05420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Jaana Tammiku-Taul
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Peeter Burk
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
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Zhang CY, Li X, Flor S, Ruiz P, Kruve A, Ludewig G, Lehmler HJ. Metabolism of 3-Chlorobiphenyl (PCB 2) in a Human-Relevant Cell Line: Evidence of Dechlorinated Metabolites. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12460-12472. [PMID: 35994059 PMCID: PMC9573771 DOI: 10.1021/acs.est.2c03687] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Lower chlorinated polychlorinated biphenyls (LC-PCBs) and their metabolites make up a class of environmental pollutants implicated in a range of adverse outcomes in humans; however, the metabolism of LC-PCBs in human models has received little attention. Here we characterize the metabolism of PCB 2 (3-chlorobiphenyl), an environmentally relevant LC-PCB congener, in HepG2 cells with in silico prediction and nontarget high-resolution mass spectrometry. Twenty PCB 2 metabolites belonging to 13 metabolite classes, including five dechlorinated metabolite classes, were identified in the cell culture media from HepG2 cells exposed for 24 h to 10 μM or 3.6 nM PCB 2. The PCB 2 metabolite profiles differed from the monochlorinated metabolite profiles identified in samples from an earlier study with PCB 11 (3,3'-dichlorobiphenyl) under identical experimental conditions. A dechlorinated dihydroxylated metabolite was also detected in human liver microsomal incubations with monohydroxylated PCB 2 metabolites but not PCB 2. These findings demonstrate that the metabolism of LC-PCBs in human-relevant models involves the formation of dechlorination products. In addition, untargeted metabolomic analyses revealed an altered bile acid biosynthesis in HepG2 cells. Our results indicate the need to study the disposition and toxicity of complex PCB 2 metabolites, including novel dechlorinated metabolites, in human-relevant models.
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Affiliation(s)
- Chun-Yun Zhang
- Hubei
Key Laboratory of Regional Development and Environmental Response,
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Xueshu Li
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Susanne Flor
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Patricia Ruiz
- Office
of Innovation and Analytics, Simulation Science Section, Agency for Toxic Substances and Disease Registry, Atlanta, Georgia 30333, United States
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 10691 Stockholm, Sweden
| | - Gabriele Ludewig
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
| | - Hans-Joachim Lehmler
- Department
of Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa 52242, United States
- Phone: (319) 335-4981. Fax: (319) 335-4290.
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Chao A, Grossman J, Carberry C, Lai Y, Williams AJ, Minucci JM, Purucker ST, Szilagyi J, Lu K, Boggess K, Fry RC, Sobus JR, Rager JE. Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia. ENVIRONMENT INTERNATIONAL 2022; 167:107385. [PMID: 35952468 PMCID: PMC9552572 DOI: 10.1016/j.envint.2022.107385] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology. OBJECTIVES We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures. METHODS A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia. RESULTS A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes. CONCLUSIONS These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.
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Affiliation(s)
- Alex Chao
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, Research Triangle Park, NC, USA
| | | | - Celeste Carberry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yunjia Lai
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Antony J. Williams
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, Research Triangle Park, NC, USA
| | - Jeffrey M. Minucci
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Public Health and Environmental Systems Division, Research Triangle Park, NC, USA
| | - S. Thomas Purucker
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Research Triangle Park, NC, USA
| | - John Szilagyi
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kim Boggess
- Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C. Fry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jon R. Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, Research Triangle Park, NC, USA
| | - Julia E. Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods. Anal Bioanal Chem 2022; 414:6455-6471. [PMID: 35796784 PMCID: PMC9411239 DOI: 10.1007/s00216-022-04203-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/17/2022] [Accepted: 06/24/2022] [Indexed: 11/06/2022]
Abstract
Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.
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43
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Uncertainty estimation strategies for quantitative non-targeted analysis. Anal Bioanal Chem 2022; 414:4919-4933. [PMID: 35699740 DOI: 10.1007/s00216-022-04118-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/13/2022] [Accepted: 05/04/2022] [Indexed: 11/01/2022]
Abstract
Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.
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Salionov D, Ludwig C, Bjelić S. Standard-Free Quantification of Dicarboxylic Acids: Case Studies with Salt-Rich Effluents and Serum. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:932-943. [PMID: 35511053 DOI: 10.1021/jasms.1c00377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The present study evaluates the ionization efficiency (IE) of linear and branched C2-C14 dicarboxylic acids (DCAs) by electrospray ionization (ESI) under different conditions. The influence of the concentration of organic modifier (MeOH); mobile phase additive; and its concentration, pH, and DCA structure on IE values is studied using flow injection analysis. The IE values of DCAs increase with the increase of MeOH concentration but also decrease with an increase of pH. The former is due to the increase in solvent evaporation rates; the latter is caused by an ion-pairing between the diacid and the cation (ammonium), which is confirmed by the study with different amines. The investigation of DCA ionization in the presence of different acidic mobile phase additives showed that a significant improvement in the (-)ESI responses of analytes was achieved in the presence of weak hydrophobic carboxylic acids, such as butyric or propanoic acid. Conversely, the use of strong carboxylic acids, such as trichloroacetic acid, was found to cause signal suppression. The results of the IE studies were used to develop the liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method that provided instrumental limits of detection in the range from 6 to 180 pg. Furthermore, upon applying the nonparametric Gaussian process, a model for the prediction of IE values was developed, which contains the number of carbons in the molecule and MeOH concentration as model parameters. As a case study, dicarboxylic acids are quantified in salt-rich effluent and blood serum samples using the developed LC-HRMS method.
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Affiliation(s)
- Daniil Salionov
- Laboratory for Bioenergy and Catalysis, Paul Scherrer Institut PSI, 5232 Villigen, Switzerland
- Environmental Engineering Institute (IIE, GR-LUD), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 6, CH-1015 Lausanne, Switzerland
| | - Christian Ludwig
- Laboratory for Bioenergy and Catalysis, Paul Scherrer Institut PSI, 5232 Villigen, Switzerland
- Environmental Engineering Institute (IIE, GR-LUD), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 6, CH-1015 Lausanne, Switzerland
| | - Saša Bjelić
- Laboratory for Bioenergy and Catalysis, Paul Scherrer Institut PSI, 5232 Villigen, Switzerland
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Tadić Đ, Manasfi R, Bertrand M, Sauvêtre A, Chiron S. Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27103167. [PMID: 35630644 PMCID: PMC9146997 DOI: 10.3390/molecules27103167] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 11/16/2022]
Abstract
Different groups of organic micropollutants including pharmaceuticals and pesticides have emerged in the environment in the last years, resulting in a rise in environmental and human health risks. In order to face up and evaluate these risks, there is an increasing need to assess their occurrence in the environment. Therefore, many studies in the past couple of decades were focused on the improvements in organic micropollutants’ extraction efficiency from the different environmental matrices, as well as their mass spectrometry detection parameters and acquisition modes. This paper presents different sampling methodologies and high-resolution mass spectrometry-based non-target screening workflows for the identification of pharmaceuticals, pesticides, and their transformation products in different kinds of water (domestic wastewater and river water). Identification confidence was increased including retention time prediction in the workflow. The applied methodology, using a passive sampling technique, allowed for the identification of 85 and 47 contaminants in the wastewater effluent and river water, respectively. Finally, contaminants’ prioritization was performed through semi-quantification in grab samples as a fundamental step for monitoring schemes.
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Affiliation(s)
- Đorđe Tadić
- Hydrosciences Montpellier, University Montpellier, CNRS, IRD, 34090 Montpellier, France; (R.M.); (S.C.)
- Correspondence:
| | - Rayana Manasfi
- Hydrosciences Montpellier, University Montpellier, CNRS, IRD, 34090 Montpellier, France; (R.M.); (S.C.)
| | - Marine Bertrand
- Hydrosciences Montpellier, University Montpellier, IMT Mines Ales, CNRS, IRD, 30100 Ales, France; (M.B.); (A.S.)
| | - Andrés Sauvêtre
- Hydrosciences Montpellier, University Montpellier, IMT Mines Ales, CNRS, IRD, 30100 Ales, France; (M.B.); (A.S.)
| | - Serge Chiron
- Hydrosciences Montpellier, University Montpellier, CNRS, IRD, 34090 Montpellier, France; (R.M.); (S.C.)
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Khabazbashi S, Engelhardt J, Möckel C, Weiss J, Kruve A. Estimation of the concentrations of hydroxylated polychlorinated biphenyls in human serum using ionization efficiency prediction for electrospray. Anal Bioanal Chem 2022; 414:7451-7460. [PMID: 35507099 PMCID: PMC9482908 DOI: 10.1007/s00216-022-04096-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/11/2022] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
Hydroxylated PCBs are an important class of metabolites of the widely distributed environmental contaminants polychlorinated biphenyls (PCBs). However, the absence of authentic standards is often a limitation when subject to detection, identification, and quantification. Recently, new strategies to quantify compounds detected with non-targeted LC/ESI/HRMS based on predicted ionization efficiency values have emerged. Here, we evaluate the impact of chemical space coverage and sample matrix on the accuracy of ionization efficiency-based quantification. We show that extending the chemical space of interest is crucial in improving the performance of quantification. Therefore, we extend the ionization efficiency-based quantification approach to hydroxylated PCBs in serum samples with a retraining approach that involves 14 OH-PCBs and validate it with an additional four OH-PCBs. The predicted and measured ionization efficiency values of the OH-PCBs agreed within the mean error of 2.1 × and enabled quantification with the mean error of 4.4 × or better. We observed that the error mostly arose from the ionization efficiency predictions and the impact of matrix effects was of less importance, varying from 37 to 165%. The results show that there is potential for predictive machine learning models for quantification even in very complex matrices such as serum. Further, retraining the already developed models provides a timely and cost-effective solution for extending the chemical space of the application area.
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Affiliation(s)
- Sara Khabazbashi
- Department of Materials and Environmental Science, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden
| | - Josefin Engelhardt
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91, Stockholm, Sweden
| | - Claudia Möckel
- Department of Materials and Environmental Science, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden
| | - Jana Weiss
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91, Stockholm, Sweden
| | - Anneli Kruve
- Department of Materials and Environmental Science, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden. .,Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91, Stockholm, Sweden.
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47
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Costalunga R, Tshepelevitsh S, Sepman H, Kull M, Kruve A. Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS. Anal Chim Acta 2022; 1204:339402. [PMID: 35397906 DOI: 10.1016/j.aca.2021.339402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/07/2021] [Accepted: 12/23/2021] [Indexed: 11/01/2022]
Abstract
Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibility to facilitate identification of isomers relies on different ionic species formed in electrospray. In positive ESI mode, both protonated molecules and adducts can be formed; however, not all isomeric structures form the same ionic species. The complicated mechanism of adduct formation has hindered the use of this molecular characteristic in the structural elucidation in non-targeted screening. Here, we have studied the adduct formation for 94 small molecules with ion mobility spectra and compared collision cross-sections of the respective ions. Based on the results we developed a fast support vector machine classifier with polynomial kernels for accurately predicting the sodium adduct formation in ESI/HRMS. The model is trained on five independent data sets from different laboratories and uses the graph-based connectivity of functional groups and PubChem fingerprints to predict the sodium adduct formation in ESI/HRMS. The validation of the model showed an accuracy of 74.7% (balanced accuracy 70.0%) on a dataset from an independent laboratory, which was not used in the training of the model. Lastly, we applied the classification algorithm to the SusDat database by NORMAN network to evaluate the proportion of isomeric compounds that could be distinguished based on predicted sodium adduct formation. It was observed that sodium adduct formation probability can provide additional selectivity for about one quarter of the exact masses and, therefore, shows practical utility for structural assignment in non-targeted screening.
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Affiliation(s)
- Riccardo Costalunga
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden; Department of Food and Drug, University of Parma, via Università, 12, I 43121, Parma, Italy
| | - Sofja Tshepelevitsh
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia
| | - Helen Sepman
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden
| | - Meelis Kull
- Institute of Computer Science, University of Tartu, Narva mnt 18, 51009, Tartu, Estonia
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden.
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48
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Aalizadeh R, Nikolopoulou V, Alygizakis N, Slobodnik J, Thomaidis NS. A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS. Anal Bioanal Chem 2022; 414:7435-7450. [PMID: 35471250 DOI: 10.1007/s00216-022-04084-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/31/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022]
Abstract
There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .
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Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece.
| | - Varvara Nikolopoulou
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Nikiforos Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
- Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | | | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece.
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49
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Minkus S, Bieber S, Letzel T. Spotlight on mass spectrometric non-target screening analysis: Advanced data processing methods recently communicated for extracting, prioritizing and quantifying features. ANALYTICAL SCIENCE ADVANCES 2022; 3:103-112. [PMID: 38715638 PMCID: PMC10989605 DOI: 10.1002/ansa.202200001] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 06/13/2024]
Abstract
Non-target screening of trace organic compounds complements routine monitoring of water bodies. So-called features need to be extracted from the raw data that preferably represent a chemical compound. Relevant features need to be prioritized and further be interpreted, for instance by identifying them. Finally, quantitative data is required to assess the risks of a detected compound. This review presents recent and noteworthy contributions to the processing of non-target screening (NTS) data, prioritization of features as well as (semi-) quantitative methods that do not require analytical standards. The focus lies on environmental water samples measured by liquid chromatography, electrospray ionization and high-resolution mass spectrometry. Examples for fully-integrated data processing workflows are given with options for parameter optimization and choosing between different feature extraction algorithms to increase feature coverage. The regions of interest-multivariate curve resolution method is reviewed which combines a data compression alternative with chemometric feature extraction. Furthermore, prioritization strategies based on a confined chemical space for annotation, guidance by targeted analysis and signal intensity are presented. Exploiting the retention time (RT) as diagnostic evidence for NTS investigations is highlighted by discussing RT indexing and prediction using quantitative structure-retention relationship models. Finally, a seminal technology for quantitative NTS is discussed without the need for analytical standards based on predicting ionization efficiencies.
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Affiliation(s)
- Susanne Minkus
- AFIN‐TS GmbHAugsburgGermany
- Technical University of Munich (Chair of Urban Water Systems Engineering)MunichGermany
| | | | - Thomas Letzel
- AFIN‐TS GmbHAugsburgGermany
- Technical University of Munich (Chair of Urban Water Systems Engineering)MunichGermany
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50
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Sussman EM, Oktem B, Isayeva IS, Liu J, Wickramasekara S, Chandrasekar V, Nahan K, Shin HY, Zheng J. Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices. ACS Biomater Sci Eng 2022; 8:939-963. [PMID: 35171560 DOI: 10.1021/acsbiomaterials.1c01119] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.
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Affiliation(s)
- Eric M Sussman
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Berk Oktem
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Irada S Isayeva
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jinrong Liu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Samanthi Wickramasekara
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Vaishnavi Chandrasekar
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Keaton Nahan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Hainsworth Y Shin
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jiwen Zheng
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
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