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Fučík J, Fučík S, Rexroth S, Sedlář M, Gargošová HZ, Mravcová L. Pharmaceutical metabolite identification in lettuce (Lactuca sativa) and earthworms (Eisenia fetida) using liquid chromatography coupled to high-resolution mass spectrometry and in silico spectral library. Anal Bioanal Chem 2024:10.1007/s00216-024-05515-2. [PMID: 39251428 DOI: 10.1007/s00216-024-05515-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/20/2024] [Accepted: 08/22/2024] [Indexed: 09/11/2024]
Abstract
Pharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded a total of 3762 pharmaceutical metabolites, and an in silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p < 0.05), with DDA + and DDA - outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types.
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Affiliation(s)
- Jan Fučík
- Institute of Chemistry and Technology of Environmental Protection, Faculty of Chemistry, Brno University of Technology, Purkyňova 118, 612 00, Brno, Czech Republic.
| | - Stanislav Fučík
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 3058/10, 616 00, Brno, Czech Republic
| | - Sascha Rexroth
- Shimadzu Europa GmbH, Albert-Hahn-Straße 6, 472 69, Duisburg, Germany
| | - Marian Sedlář
- CEITEC Brno University of Technology, Purkyňova 656/123, 612 00, Brno, Czech Republic
| | - Helena Zlámalová Gargošová
- Institute of Chemistry and Technology of Environmental Protection, Faculty of Chemistry, Brno University of Technology, Purkyňova 118, 612 00, Brno, Czech Republic
| | - Ludmila Mravcová
- Institute of Chemistry and Technology of Environmental Protection, Faculty of Chemistry, Brno University of Technology, Purkyňova 118, 612 00, Brno, Czech Republic
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De Somer T, Nguyen Luu Minh T, Roosen M, Nachtergaele P, Manhaeghe D, Van Laere T, Schlummer M, Van Geem KM, De Meester S. Application of chemometric tools in the QSAR development of VOC removal in plastic waste recycling. CHEMOSPHERE 2024; 350:141069. [PMID: 38160949 DOI: 10.1016/j.chemosphere.2023.141069] [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: 09/21/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 01/03/2024]
Abstract
Deodorization and, in a broader sense, the removal of volatile organic compounds (VOCs) from plastic waste have become increasingly important in the field of plastic recycling, and various new decontamination techniques have been developed. Both in research and industrial practice, the selection of VOCs has been random or unsubstantiated, making it difficult to compare studies and assess decontamination processes objectively. Thus, this study proposes the use of Statistical Molecular Design (SMD) and Quantitative Structure - Activity Relationship (QSAR) as chemometric tools for the selection of representative VOCs, based on physicochemical properties. Various algorithms are used for SMD; hence, several frequently used D-Optimal Onion Design (DOOD) and Space-Filling (SF) algorithms were assessed. Hereby, it was validated that DOOD, by dividing the layers based on the equal-distance approach without so-called 'Adjacent Layer Bias', results in the most representative selection of VOCs. QSAR models that describe VOC removal by water-based washing of plastic waste as a function of molecular weight, polarizability, dipole moment and Hansen Solubility Parameters Distance were successfully established. An adjusted-R2 value of 0.77 ± 0.09 and a mean absolute error of 24.5 ± 4 % was obtained. Consequently, by measuring a representative selection of VOCs compiled using SMD, the removal of other unanalyzed VOCs was predicted on the basis of the QSAR. Another advantage of the proposed chemometric selection procedure is its flexibility. SMD allows to extend or modify the considered dataset according to the available analytical techniques, and to adjust the considered physicochemical properties according to the intended process.
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Affiliation(s)
- Tobias De Somer
- Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
| | - Thien Nguyen Luu Minh
- Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
| | - Martijn Roosen
- Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
| | - Pieter Nachtergaele
- Research Group Sustainable Systems Engineering (STEN), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Gent, Belgium
| | - Dave Manhaeghe
- Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
| | - Tine Van Laere
- Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
| | - Martin Schlummer
- Fraunhofer-Institut für Verfahrenstechnik und Verpackung IVV, Giggenhauser Str. 35, 85354, Freising, Germany
| | - Kevin M Van Geem
- Laboratory for Chemical Technology (LCT), Department of Materials, Textiles and Chemical Engineering, Faculty of Engineering & Architecture, Ghent University, Technologiepark 125, B-9052 Zwijnaarde, Belgium
| | - Steven De Meester
- Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium.
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Brückner I, Classen S, Hammers-Wirtz M, Klaer K, Reichert J, Pinnekamp J. Tool for selecting indicator substances to evaluate the impact of wastewater treatment plants on receiving water bodies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 745:140746. [PMID: 32758737 DOI: 10.1016/j.scitotenv.2020.140746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 07/02/2020] [Accepted: 07/02/2020] [Indexed: 06/11/2023]
Abstract
The elimination of organic micropollutants (OMPs) from wastewater could in future become mandatory for operators of wastewater treatment plants (WWTPs). Indicator substances are a great help and a cost-efficient way in monitoring the pollution of water bodies with OMPs caused by the discharge of WWTPs. However, with the still increasing number of OMPs in our environment, the selection of suitable indicator substances presents a challenge. A concept was developed to help identify representative indicator substances. The derived indicator substances are not only used to assess water pollution, but can also be used to calculate elimination efficiencies of WWTPs. In the present investigations, the indicator substances were used to evaluate the reduction of OMPs in the water body on the basis of the expansion of a WWTP with an ozonation plant. The transferability of the tool was verified with a second WWTP. Furthermore, the impact of the number of measurements was analysed via statistical combinatorics. With the tool, 36 substances were classified, leading to the identification of 9 suggested indicator substances. Among them ibuprofen and diclofenac attracted attention due to their ecotoxicological relevance. Detailed data analyses were carried out using principal component analysis (PCA) and loads.
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Affiliation(s)
- Ira Brückner
- Eifel-Rur Waterboard, Eisenbahnstr. 5, 52353 Düren, Germany.
| | - Silke Classen
- gaiac Research Institute for Ecosystem Analysis and Assessment, RWTH Aachen University, Kackertstr. 10, 52072 Aachen, Germany
| | - Monika Hammers-Wirtz
- gaiac Research Institute for Ecosystem Analysis and Assessment, RWTH Aachen University, Kackertstr. 10, 52072 Aachen, Germany
| | | | | | - Johannes Pinnekamp
- Institute of Environmental Engineering, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany
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Bodhaguru M, Santhiyagu P, Lakshmanan M, Ramasamy R, Kumari AN, Ethiraj K, Arunachalam P, Grasian I. In vitro biomedicinal properties of Pyrrolidine-2,4-Dione derived from a novel actinobacterium Streptomyces rochei, a green approach. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2019. [DOI: 10.1016/j.bcab.2019.101244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Jin X, Peldszus S. Selection of representative emerging micropollutants for drinking water treatment studies: a systematic approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2012; 414:653-63. [PMID: 22142647 DOI: 10.1016/j.scitotenv.2011.11.035] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2011] [Revised: 11/10/2011] [Accepted: 11/13/2011] [Indexed: 05/11/2023]
Abstract
Micropollutants remain of concern in drinking water, and there is a broad interest in the ability of different treatment processes to remove these compounds. To gain a better understanding of treatment effectiveness for structurally diverse compounds and to be cost effective, it is necessary to select a small set of representative micropollutants for experimental studies. Unlike other approaches to-date, in this research micropollutants were systematically selected based solely on their physico-chemical and structural properties that are important in individual water treatment processes. This was accomplished by linking underlying principles of treatment processes such as coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration to compound characteristics and corresponding molecular descriptors. A systematic statistical approach not commonly used in water treatment was then applied to a compound pool of 182 micropollutants (identified from the literature) and their relevant calculated molecular descriptors. Principal component analysis (PCA) was used to summarize the information residing in this large dataset. D-optimal onion design was then applied to the PCA results to select structurally representative compounds that could be used in experimental treatment studies. To demonstrate the applicability and flexibility of this selection approach, two sets of 22 representative micropollutants are presented. Compounds in the first set are representative when studying a range of water treatment processes (coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration), whereas the second set shows representative compounds for ozonation and advanced oxidation studies. Overall, selected micropollutants in both lists are structurally diverse, have wide-ranging physico-chemical properties and cover a large spectrum of applications. The systematic compound selection approach presented here can also be adjusted to fit individual research needs with respect to type of micropollutants, treatment processes and number of compounds selected.
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Affiliation(s)
- Xiaohui Jin
- NSERC Chair in Water Treatment, Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada.
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A Multivariate Chemical Similarity Approach to Search for Drugs of Potential Environmental Concern. J Chem Inf Model 2011; 51:1788-94. [DOI: 10.1021/ci200107b] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Rännar S, Andersson PL. A Novel Approach Using Hierarchical Clustering To Select Industrial Chemicals for Environmental Impact Assessment. J Chem Inf Model 2010; 50:30-6. [DOI: 10.1021/ci9003255] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Stefan Rännar
- Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
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Bae HK, Olson BH, Hsu KL, Sorooshian S. Identification and application of physical and chemical parameters to predict indicator bacterial concentration in a small Californian creek. WATER ENVIRONMENT RESEARCH : A RESEARCH PUBLICATION OF THE WATER ENVIRONMENT FEDERATION 2009; 81:633-640. [PMID: 19601429 DOI: 10.2175/106143008x390843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
This study of Aliso Creek in California aimed to identify physical and chemical parameters that could be measured instantly to be used in a model to serve as surrogates for indicator bacterial concentrations during dry season flow. In this study, a new data smoothing technique and ranking/categorizing analysis was used to reduce variation to allow better delineation of the relationships between adopted variables and concentrations of indicator bacteria. The ranking/categorizing approach clarified overall trends between physico-chemical data and the indicators and suggested sources of the bacteria. This study also applied a principle component regression model to the data. Although the model was promising for predicting concentrations of total and fecal coliforms, it was somewhat weaker in predicting enteroccocci.
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Affiliation(s)
- Hun-Kyun Bae
- Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA.
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