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Malm L, Liigand J, Aalizadeh R, Alygizakis N, Ng K, Fro Kjær 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. [PMID: 39353203 DOI: 10.1021/acs.analchem.4c02902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [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 Fro Kjær
- 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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Marden S, Campbell JM, Adams N, Coelho R, Foti C, Franca JR, Hostyn S, Huang Z, Ultramari M, Zelesky T, Baertschi SW. Mass Balance in Pharmaceutical Stress Testing: A Review of Principles and Practical Applications. AAPS J 2024; 26:96. [PMID: 39174806 DOI: 10.1208/s12248-024-00961-3] [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: 05/01/2024] [Accepted: 07/24/2024] [Indexed: 08/24/2024] Open
Abstract
Stress testing (also known as forced degradation) of pharmaceutical drug substances and products is a critical part of the drug development process, providing insight into the degradation pathways of drug substances and drug products. This information is used to support the development of stability-indicating methods (SIMs) capable of detecting pharmaceutically relevant degradation products that might potentially be observed during manufacturing, long-term storage, distribution, and use. Assessing mass balance of stressed samples is a key aspect of developing SIMs and is a regulatory expectation. However, the approaches to measure, calculate, and interpret mass balance can vary among different pharmaceutical companies. Such disparities also pose difficulties for health authorities when reviewing mass balance assessments, which may result in the potential delay of drug application approvals. The authors have gathered input from 10 pharma companies to map out a practical review of science-based approaches and technical details to assess and interpret mass balance results. Key concepts of mass balance are introduced, various mass balance calculations are demonstrated, and recommendations on how to investigate poor mass balance results are presented using real-world case studies. Herein we provide a single source reference on the topic of mass balance in pharmaceutical forced degradation for small molecule drug substances and drug products in support of regulatory submissions with the goal of facilitating a shared understanding among pharmaceutical scientists and health authorities.
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Affiliation(s)
- Stacey Marden
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D, Astrazeneca, Boston, 35 Gatehouse Dr., Waltham, Massachusetts, 02451, USA.
| | - John M Campbell
- Analytical Development, GSK, 1250 South Collegeville Rd, Mail Stop UP1400, Upper Providence, Pennsylvania, 19426, USA.
| | - Neal Adams
- Scientific and Laboratory Services-Analytical Sciences, Pfizer Inc, Kalamazoo, Michigan, USA
| | - Ronan Coelho
- Regulatory Affairs, Eurofarma Laboratórios SA, Itapevi, São Paulo, Brazil
| | - Chris Foti
- Analytical Development and Operations, Gilead Sciences Inc., Foster City, California, USA
| | | | - Steven Hostyn
- Janssen Research & Development, LLC, a Johnson & Johnson Company, Predictive Analytics & Stability Sciences Coe, Beerse, Belgium
| | - Zongyun Huang
- Drug Product Development, Bristol-Myers Squibb Co., New Brunswick, New Jersey, USA
| | - Mariah Ultramari
- Spektra Soluções Científico-Regulatórias Ltda, São Paulo, Brazil
| | - Todd Zelesky
- Analytical Research & Development, Pfizer Inc., Groton, Connecticut, USA
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3
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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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Nie CZ, Liu H, Huang XH, Zhou DY, Wang XS, Qin L. Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms. Analyst 2024; 149:3140-3151. [PMID: 38629585 DOI: 10.1039/d4an00301b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.
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Affiliation(s)
- Cheng-Zhen Nie
- School of Food Science and Technology, State Key Laboratory of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
| | - Hao Liu
- School of Food Science and Technology, State Key Laboratory of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
| | - Xu-Hui Huang
- School of Food Science and Technology, State Key Laboratory of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
| | - Da-Yong Zhou
- School of Food Science and Technology, State Key Laboratory of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
| | - Xu-Song Wang
- School of Food Science and Technology, State Key Laboratory of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
| | - Lei Qin
- School of Food Science and Technology, State Key Laboratory of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
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Brueck CL, Xin X, Lupolt SN, Kim BF, Santo RE, Lyu Q, Williams AJ, Nachman KE, Prasse C. (Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:3690-3701. [PMID: 38350027 PMCID: PMC11293618 DOI: 10.1021/acs.est.3c09106] [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] [Indexed: 02/15/2024]
Abstract
This study investigated the presence and human hazards associated with pesticides and other anthropogenic chemicals identified in kale grown in urban and rural environments. Pesticides and related compounds (i.e., surfactants and metabolites) in kale samples were evaluated using a nontargeted data acquisition for targeted analysis method which utilized a pesticide mixture containing >1,000 compounds for suspect screening and quantification. We modeled population-level exposures and assessed noncancer hazards to DEET, piperonyl butoxide, prometon, secbumeton, terbumeton, and spinosyn A using nationally representative estimates of kale consumption across life stages in the US. Our findings indicate even sensitive populations (e.g., pregnant women and children) are not likely to experience hazards from these select compounds were they to consume kale from this study. However, a strictly nontargeted chemical analytical approach identified a total of 1,822 features across all samples, and principal component analysis revealed that the kale chemical composition may have been impacted by agricultural growing practices and environmental factors. Confidence level 2 compounds that were ≥5 times more abundant in the urban samples than in rural samples (p < 0.05) included chemicals categorized as "flavoring and nutrients" and "surfactants" in the EPA's Chemicals and Products Database. Using the US-EPA's Cheminformatics Hazard Module, we identified that many of the nontarget compounds have predicted toxicity scores of "very high" for several end points related to human health. These aspects would have been overlooked using traditional targeted analysis methods, although more information is needed to ascertain whether the compounds identified through nontargeted analysis are of environmental or human health concern. As such, our approach enabled the identification of potentially hazardous compounds that, based on their hazard assessment score, merit follow-up investigations.
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Affiliation(s)
- Christopher L. Brueck
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
| | - Xiaoyue Xin
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
| | - Sara N. Lupolt
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
- Risk Sciences and Public Policy Institute, Johns Hopkins University, MD, USA
- Center for a Livable Future, Johns Hopkins University, MD, USA
| | - Brent F. Kim
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
- Center for a Livable Future, Johns Hopkins University, MD, USA
| | - Raychel E. Santo
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
- Center for a Livable Future, Johns Hopkins University, MD, USA
| | - Q. Lyu
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, NC, USA
| | - Keeve E. Nachman
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
- Risk Sciences and Public Policy Institute, Johns Hopkins University, MD, USA
- Center for a Livable Future, Johns Hopkins University, MD, USA
| | - Carsten Prasse
- Department of Environmental Health and Engineering, Johns Hopkins University, MD, USA
- Risk Sciences and Public Policy Institute, Johns Hopkins University, MD, USA
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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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7
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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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8
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Liang W, Feng X, Su W, Zhong L, Li P, Wang H, Li T, Ruan T, Jiang G. Suspect screening and effect evaluation for small-molecule agonists of the antioxidant response element pathway in fine particulate matter. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168266. [PMID: 37952677 DOI: 10.1016/j.scitotenv.2023.168266] [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: 08/15/2023] [Revised: 09/27/2023] [Accepted: 10/30/2023] [Indexed: 11/14/2023]
Abstract
Oxidative stress is an important mechanism by which fine particulate matter (PM2.5) generates toxicity. However, few inducers have been identified, and the combined effects of the contributing chemicals have rarely been examined. In this study, the occurrence of small-molecule agonists of the antioxidant response element (ARE) pathway was explored in 59 PM2.5 samples from urban Beijing over a one-year period via target and suspect screening analysis. In total, 31 chemicals with diverse structures and use categories were identified and quantified, among which polycyclic aromatic hydrocarbons (PAHs), pesticides, and phytochemicals were the most abundant chemical groups in terms of cumulative concentrations. PAHs and organonitrogen pesticides were also prioritized as the predominant contributors to the cumulative effect of ARE pathway activation, accounting for 55 % and 37 %, respectively. A combination of the prioritized chemicals (i.e., benzo[b]fluoranthene, benzo[k]fluoranthene, pyridaben, and acetochlor) was mixed at a fixed dosing ratio according to the measured average concentrations in samples, and synergism was revealed as the mode of mixture interaction. These findings highlight the importance of high-throughput chemical screening for identifying hazardous components in complex environmental samples, and also expand the knowledge regarding the components contributing to PM2.5-induced oxidative stress.
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Affiliation(s)
- Wenqing Liang
- 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
| | - Xiaoxia Feng
- 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
| | - Wenyuan Su
- 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
| | - Laijin Zhong
- 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
| | - 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.
| | - 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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9
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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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10
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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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11
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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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12
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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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13
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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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14
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Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts. SEPARATIONS 2022. [DOI: 10.3390/separations9120415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.
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15
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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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16
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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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17
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Aalizadeh R, Nikolopoulou V, Alygizakis NA, Thomaidis NS. First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry. Anal Chem 2022; 94:9766-9774. [PMID: 35760399 PMCID: PMC9280717 DOI: 10.1021/acs.analchem.2c01432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
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The ionization efficiency
of emerging contaminants was modeled
for the first time in gas chromatography-high-resolution mass spectrometry
(GC-HRMS) which is coupled to an atmospheric pressure chemical ionization
source (APCI). The recent chemical space has been expanded in environmental
samples such as soil, indoor dust, and sediments thanks to recent
use of high-resolution mass spectrometric techniques; however, many
of these chemicals have remained unquantified. Chemical exposure in
dust can pose potential risk to human health, and semiquantitative
analysis is potentially of need to semiquantify these newly identified
substances and assist with their risk assessment and environmental
fate. In this study, a rigorously tested semiquantification workflow
was proposed based on GC-APCI-HRMS ionization efficiency measurements
of 78 emerging contaminants. The mechanism of ionization of compounds
in the APCI source was discussed via a simple connectivity index and
topological structure. The quantitative structure–property
relationship (QSPR)-based model was also built to predict the APCI
ionization efficiencies of unknowns and later use it for their quantification
analyses. The proposed semiquantification method could be transferred
into the household indoor dust sample matrix, and it could include
the effect of recovery and matrix in the predictions of actual concentrations
of analytes. A suspect compound, which falls inside the application
domain of the tool, can be semiquantified by an online web application,
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 A 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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18
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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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19
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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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20
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Palm E, Kruve A. Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS. Molecules 2022; 27:1013. [PMID: 35164283 PMCID: PMC8840743 DOI: 10.3390/molecules27031013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/24/2022] [Accepted: 01/27/2022] [Indexed: 11/16/2022] Open
Abstract
LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches- one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.
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Affiliation(s)
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18 Stockholm, Sweden;
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21
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McCord JP, Groff LC, Sobus JR. Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization. ENVIRONMENT INTERNATIONAL 2022; 158:107011. [PMID: 35386928 PMCID: PMC8979303 DOI: 10.1016/j.envint.2021.107011] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.
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Affiliation(s)
- James P. McCord
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Corresponding author. (J.P. McCord)
| | - Louis C. Groff
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Jon R. Sobus
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
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22
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Le MT, Morato NM, Kaerner A, Welch CJ, Cooks RG. Fragmentation of Polyfunctional Compounds Recorded Using Automated High-Throughput Desorption Electrospray Ionization. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2261-2273. [PMID: 34280312 DOI: 10.1021/jasms.1c00176] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Using desorption electrospray ionization (DESI) as part of an automated high-throughput system, tandem mass spectra of the compounds in a pharmaceutical library were recorded in the positive mode under standardized conditions. Quality control filtering yielded an MS/MS library of 16 662 spectra. Fragmentation of subsets of the compounds in the library chosen to contain a single instance of a particular functional group (amide, piperazine, sulfonamide) was predicted by experts, and the results were compared with the experimental data. Expert performance was good to excellent for all the cases evaluated. Substituents on the functional groups were found to exert important secondary control over the fragmentation, with the main effect observed being product ion stabilization by aromatic substitution, which was consistent across the different groups evaluated. These substituent effects are generally explicable in terms of standard physical organic chemistry considerations of product ion stability as controlling fragmentation. A somewhat unexpected feature was the incidence of homolytic cleavages, driven by the stability of substituted amine radical cations. The findings of this study are intended to lay the groundwork for machine learning approaches to performing MS/MS spectrum → structure and structure → MS/MS spectrum operations on the same experimental data set. The effort involved and the success achieved in computer-aided interpretation, now underway, will be compared with the expert performance as described here.
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Affiliation(s)
- MyPhuong T Le
- Department of Chemistry and Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, United States
| | - Nicolás M Morato
- Department of Chemistry and Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, United States
| | - Andreas Kaerner
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285, United States
| | - Christopher J Welch
- Indiana Consortium for Analytical Science and Engineering (ICASE), Indianapolis, Indiana 46202, United States
| | - R Graham Cooks
- Department of Chemistry and Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, United States
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23
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Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS. Molecules 2021; 26:molecules26123524. [PMID: 34207787 PMCID: PMC8228683 DOI: 10.3390/molecules26123524] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/01/2021] [Accepted: 06/04/2021] [Indexed: 11/17/2022] Open
Abstract
Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.
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24
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Aalizadeh R, Panara A, Thomaidis NS. Development and Application of a Novel Semi-quantification Approach in LC-QToF-MS Analysis of Natural Products. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1412-1423. [PMID: 34027658 DOI: 10.1021/jasms.1c00032] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Use of high-resolution mass spectrometry (HRMS) including a MS calibration method has enabled simultaneous identification and quantification of knowns/unknowns. This has expanded our knowledge about the existing sample relevant chemical space in a way beyond reconciliation with a quantification task. This is largely due to fact that reference standards are not always available to achieve quantitative analysis. In this scenario, a semi-quantitative approach can fill the gap and provide a rough estimation of concentration. This research aimed to develop and compare several semi-quantification approaches based on chemical similarity or properties. The ionization efficiency scale was created for several groups of natural products. Advanced modeling approach based on a support vector machine was conducted to learn from the experimental ionization efficiency and apply it to unknowns or suspected compounds to predict their ionization efficiency in electrospray ionization mode. The developed semi-quantification workflows could be useful in most HRMS based "omics" areas, especially in natural products discovery.
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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
| | - Anthi Panara
- 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
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25
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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal Chim Acta 2021; 1161:338403. [DOI: 10.1016/j.aca.2021.338403] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023]
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26
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Pérez-Cova M, Jaumot J, Tauler R. Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116207] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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27
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Krettler CA, Thallinger GG. A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics. Brief Bioinform 2021; 22:6184408. [PMID: 33758925 DOI: 10.1093/bib/bbab073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/12/2021] [Indexed: 12/27/2022] Open
Abstract
Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.
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Affiliation(s)
- Christoph A Krettler
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
| | - Gerhard G Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
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28
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Kruve A, Kiefer K, Hollender J. Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater. Anal Bioanal Chem 2021; 413:1549-1559. [PMID: 33506334 PMCID: PMC7921029 DOI: 10.1007/s00216-020-03109-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/03/2020] [Accepted: 12/02/2020] [Indexed: 11/29/2022]
Abstract
A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.
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Affiliation(s)
- Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, 106 91, Stockholm, Sweden.
| | - Karin Kiefer
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH, 8092, Zürich, Switzerland
| | - Juliane Hollender
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH, 8092, Zürich, Switzerland
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29
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Liigand P, Liigand J, Kaupmees K, Kruve A. 30 Years of research on ESI/MS response: Trends, contradictions and applications. Anal Chim Acta 2020; 1152:238117. [PMID: 33648645 DOI: 10.1016/j.aca.2020.11.049] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/27/2020] [Accepted: 11/28/2020] [Indexed: 11/29/2022]
Abstract
The variation of ionization efficiency for different compounds has puzzled researchers since the invention of the electrospray mass spectrometry (ESI/MS). Ionization depends on the properties of the compound, eluent, matrix, and instrument. Despite significant research, some aspects have remained unclear. For example, research groups have reached contradicting conclusions regarding the ionization processes. One of the best-known is the significance of the logP value for predicting the ionization efficiency. In this tutorial review, we analyse the methodology used for ionization efficiency measurements as well as the most important trends observed in the data. Additionally, we give suggestions regarding the measurement methodology and modelling strategies to yield meaningful and consistent ionization efficiency data. Finally, we have collected a wide range of ionization efficiency values from the literature and evaluated the consistency of these data. We also make this collection available for everyone for downloading as well as for uploading additional and new ionization efficiency data. We hope this GitHub based ionization efficiency repository will allow a joined community effort to collect and unify the current knowledge about the ionization processes.
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Affiliation(s)
- Piia Liigand
- Institute of Chemistry, Faculty of Science and Technology, University of Tartu, Ravila 14A, 50411, Tartu, Estonia
| | - Jaanus Liigand
- Institute of Chemistry, Faculty of Science and Technology, University of Tartu, Ravila 14A, 50411, Tartu, Estonia; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Karl Kaupmees
- Institute of Chemistry, Faculty of Science and Technology, University of Tartu, Ravila 14A, 50411, Tartu, Estonia
| | - Anneli Kruve
- Institute of Chemistry, Faculty of Science and Technology, University of Tartu, Ravila 14A, 50411, Tartu, Estonia; Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 106 91, Stockholm, Sweden.
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