1
|
Rojas C, Sarmiento N, Ayora E, Pis Diez R. Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography-mass spectrometry. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:6724-6732. [PMID: 38551410 DOI: 10.1002/jsfa.13499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/19/2024] [Accepted: 03/29/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography-mass spectrometry (LC-MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure-property relationships (QSPRs), which were used in this study. RESULTS A database of 122 antibiotics was selected in which the tR was measured using a Hypersil GOLD column. An optimal three-feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root-mean-square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in-depth explanation of the mechanism of action of each descriptor in predicting the tR is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics. CONCLUSION The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol-water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC-MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co-operation and Development. © 2024 Society of Chemical Industry.
Collapse
Affiliation(s)
- Cristian Rojas
- Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Cuenca, Ecuador
| | - Nicole Sarmiento
- Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Cuenca, Ecuador
| | - Emilia Ayora
- Grupo de Investigación en Quimiometría y QSAR, Facultad de Ciencia y Tecnología, Universidad del Azuay, Cuenca, Ecuador
| | - Reinaldo Pis Diez
- CEQUINOR, Centro de Química Inorgánica (CONICET, UNLP), Departamento de Química, Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
| |
Collapse
|
2
|
Li X, Wang J, Yang Y, Jin H, Wang H, Zhang Z, Li X, Cui Y, Wang X, Yan J. Sequential Reductive Dechlorination of Triclosan by Sediment Microbiota Harboring Organohalide-Respiring Dehalococcoidia. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:14843-14854. [PMID: 39106339 DOI: 10.1021/acs.est.4c04594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
Aquatic ecosystems represent a prominent reservoir of xenobiotic compounds, including triclosan (TCS), a broad-spectrum biocide extensively used in pharmaceuticals and personal care products. As a biogeochemical hotspot, the potential of aquatic sediments for the degradation of TCS remains largely unexplored. Here, we demonstrated anaerobic biotransformation of TCS in a batch microcosm established with freshwater sediment. The initial 43.4 ± 2.2 μM TCS was completely dechlorinated to diclosan, followed by subsequent conversion to 5-chloro-2-phenoxyphenol, a monochlorinated TCS (MCS) congener. Analyses of community profile and population dynamics revealed substrate-specific, temporal-growth of Dehalococcoides and Dehalogenimonas, which are organohalide-respiring bacteria (OHRB) affiliated with class Dehalococcoidia. Dehalococcoides growth was linked to the formation of diclosan but not MCS, yielding 3.6 ± 0.4 × 107 cells per μmol chloride released. A significant increase in Dehalogenimonas cells, from 1.5 ± 0.4 × 104 to 1.5 ± 0.3 × 106 mL-1, only occurred during the reductive dechlorination of diclosan to MCS. Dehalococcoidia OHRB gradually disappeared following consecutive transfers, likely due to the removal of sediment materials with strong adsorption capacity that could alleviate TCS's antimicrobial toxicity. Consequently, a solid-free, functionally stable TCS-dechlorinating consortium was not obtained. Our results provide insights into the microbial determinants controlling the environmental fate of TCS.
Collapse
Affiliation(s)
- Xiuying Li
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- Liaoning Technical Innovation Center for Ecological Restoration of Polluted Environment, Shenyang, Liaoning 110016, China
| | - Jingjing Wang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- Liaoning Technical Innovation Center for Ecological Restoration of Polluted Environment, Shenyang, Liaoning 110016, China
| | - Yi Yang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- Liaoning Technical Innovation Center for Ecological Restoration of Polluted Environment, Shenyang, Liaoning 110016, China
| | - Huijuan Jin
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- Liaoning Technical Innovation Center for Ecological Restoration of Polluted Environment, Shenyang, Liaoning 110016, China
| | - Hongyan Wang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhipeng Zhang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China
| | - Xiaocui Li
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiru Cui
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Wang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Yan
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
- Liaoning Technical Innovation Center for Ecological Restoration of Polluted Environment, Shenyang, Liaoning 110016, China
| |
Collapse
|
3
|
Kang Z, Duan L, Zahmatkesh S. Optimizing removal of antiretroviral drugs from tertiary wastewater using chlorination and AI-based prediction with response surface methodology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:172931. [PMID: 38703847 DOI: 10.1016/j.scitotenv.2024.172931] [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: 02/05/2024] [Revised: 04/12/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024]
Abstract
Chemical and pharmaceutical chemicals found in water sources create substantial risks to human health and the environment. The presence of pharmaceutical contaminants in water can cause antibiotic resistance development, toxicity to aquatic organisms, and endocrine disruption. Hence, the elimination of chemicals and other contaminants from wastewater prior to its release is a burgeoning concern in the domains of engineering and science. The use of treatment technologies in wastewater treatment plants can remove pharmaceutical contaminants through the oxidation process. However, many traditional wastewater treatment plants lack the advanced monitoring tools required to detect low concentrations of pharmaceuticals. Without the ability to detect these compounds, it's challenging to treat them effectively. The goal of this study was to use Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) algorithms to model and improve how Nevirapine and Efavirenz break down in different chlorination conditions. The RSM analysis revealed statistically significant models (F-values: Nevirapine, pH-t: 108.15, T-t: 76.55, ICC-t: 110.84), indicating a strong correlation between operational parameters (pH, temperature, and initial chlorine concentration) and degradation behavior. The ANN model accurately predicted the degradation of both Nevirapine and Efavirenz under various chlorination conditions, as confirmed by analyzing actual-predicted graphs, residual plots, and Mean Squared Error (MSE) values. The ANN model using ICC-t achieved the highest MOD value of 31.31 % for Nevirapine. The ANN model based on ICC-t yielded a maximum MOD value of 16.06 % for Efavirenz. These findings provide valuable insights into optimizing chlorination processes for better removal of these pharmaceutical contaminants from water.
Collapse
Affiliation(s)
- Zhenhua Kang
- Department of Colorectal & Anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun 130021, China
| | - Lian Duan
- Faculty of Pediatrics, the Chinese PLA General Hospital, Beijing 100700, China; Department of Pediatric Surgery, the Seventh Medical Center of PLA General Hospital, Beijing 100700, China.
| | - Sasan Zahmatkesh
- Tecnologico de Monterrey, Escuela de Ingenieríay Ciencias, Puebla, Mexico; Faculty of Health and Life Sciences, INTI International University, 71800 Nilai, Negeri Sembilan, Malaysia
| |
Collapse
|
4
|
Song D, Tang T, Wang R, Liu H, Xie D, Zhao B, Dang Z, Lu G. Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123763. [PMID: 38492749 DOI: 10.1016/j.envpol.2024.123763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/26/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.
Collapse
Affiliation(s)
- Dehao Song
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Ting Tang
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China.
| | - Rui Wang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - He Liu
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Danping Xie
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Bo Zhao
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Zhi Dang
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China
| | - Guining Lu
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China
| |
Collapse
|
5
|
Parinet J, Makni Y, Diallo T, Guérin T. Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry. Talanta 2024; 267:125214. [PMID: 37734288 DOI: 10.1016/j.talanta.2023.125214] [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: 06/16/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 09/23/2023]
Abstract
The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.
Collapse
Affiliation(s)
- Julien Parinet
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France.
| | - Yassine Makni
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France
| | - Thierno Diallo
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France
| | - Thierry Guérin
- ANSES, Strategy and Programmes Department, 94701, Maisons-Alfort, France
| |
Collapse
|
6
|
Li X, Yang Y, Wang J, Jin H, Zhang Y, Cui Y, Song Y, Yan J. Organohalide Respiration with Diclofenac by Dehalogenimonas. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:11266-11276. [PMID: 35921385 DOI: 10.1021/acs.est.1c08824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Diclofenac (DCF) is a pharmaceutically active contaminant frequently found in aquatic ecosystems. The transformation pathways and microbiology involved in the biodegradation of DCF, particularly under anoxic conditions, remain poorly understood. Here, we demonstrated microbially mediated reductive dechlorination of DCF in anaerobic enrichment culture derived from contaminated river sediment. Over 90% of the initial 76.7 ± 3.6 μM DCF was dechlorinated at a maximum rate of 1.8 ± 0.3 μM day-1 during a 160 days' incubation. Mass spectrometric analysis confirmed that 2-(2-((2-chlorophenyl)amino)phenyl)acetic acid (2-CPA) and 2-anilinophenylacetic acid (2-APA) were formed as the monochlorinated and nonchlorinated DCF transformation products, respectively. A survey of microbial composition and Sanger sequencing revealed the enrichment and dominance of a new Dehalogenimonas population, designated as Dehalogenimonas sp. strain DCF, in the DCF-dechlorinating community. Following the stoichiometric conversion of DCF to 2-CPA (76.0 ± 2.1 μM) and 2-APA (3.7 ± 0.8 μM), strain DCF cell densities increased by 24.4 ± 4.4-fold with a growth yield of 9.0 ± 0.1 × 108 cells per μmol chloride released. Our findings expand the metabolic capability in the genus Dehalogenimonas and highlight the relevant roles of organohalide-respiring bacteria for the natural attenuation of halogenated contaminants of emerging concerns (e.g., DCF).
Collapse
Affiliation(s)
- Xiuying Li
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
| | - Yi Yang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
| | - Jingjing Wang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
| | - Huijuan Jin
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaozhi Zhang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiru Cui
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufang Song
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
| | - Jun Yan
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Room 512 South Building, 72 Wenhua Road, Shenyang, Liaoning 110016, China
| |
Collapse
|
7
|
Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
Collapse
|
8
|
Parinet J. Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks. Heliyon 2021; 7:e08563. [PMID: 34950792 PMCID: PMC8671870 DOI: 10.1016/j.heliyon.2021.e08563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/26/2021] [Accepted: 12/03/2021] [Indexed: 11/29/2022] Open
Abstract
To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.
Collapse
Affiliation(s)
- Julien Parinet
- Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France
| |
Collapse
|
9
|
Omagari R, Nakayama T, Miyawaki T, Yagishita M, Hashimoto S, Kadokami K, Nakajima D. Evaluation of identification accuracy using AIQS for GC-MS for measuring heavily contaminated samples. CHEMOSPHERE 2021; 285:131401. [PMID: 34265717 DOI: 10.1016/j.chemosphere.2021.131401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 06/17/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
Automated identification and quantification systems with gas chromatography-mass spectrometry (GC-MS) (i.e., AIQS-GC) are used as a simple and comprehensive method for screening chemicals existing in the environment and are expected to be useful for emergency surveys in the event of a disaster. However, reports on the potential of AIQS-GC in heavily contaminated samples (HCSs) are limited. In this study, the identification performance of AIQS-GC was confirmed by comparing the exact mass of the targets identified by AIQS-GC with the measured accurate mass using GC-quadrupole-time-of-flight MS (GC-QTofMS) and by employing firefighting wastewater as HCS. In HCS, the mass spectrum interference was determined to cause false positives. The GC-QTofMS method revealed the presence of false positives and the false rate of AIQS-GC in HCS. Herein, AIQS-GC showed high identification accuracy in a normal sample such as river water. Conversely, in HCS, AIQS-GC may lead to incorrect evaluations. The combination of AIQS-GC and support method using GC-QTofMS, which can avoid the false positive is extremely useful for the rapid and easy analysis of HCS.
Collapse
Affiliation(s)
- Ryo Omagari
- Health and Environmental Risk Division, National Institute for Environmental Studies, Japan.
| | - Takashi Nakayama
- Health and Environmental Risk Division, National Institute for Environmental Studies, Japan
| | - Takashi Miyawaki
- Faculty of Environmental Engineering, The University of Kitakyushu, Japan
| | - Mayuko Yagishita
- Department of Life and Environmental Science, Prefectural University of Hiroshima, Japan
| | - Shunji Hashimoto
- Health and Environmental Risk Division, National Institute for Environmental Studies, Japan
| | - Kiwao Kadokami
- Institute of Environmental Science and Technology, The University of Kitakyushu, Japan
| | - Daisuke Nakajima
- Health and Environmental Risk Division, National Institute for Environmental Studies, Japan; Graduate School of Pharmaceutical Sciences, Chiba University, Japan
| |
Collapse
|
10
|
Parinet J. Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets. CHEMOSPHERE 2021; 275:130036. [PMID: 33676277 DOI: 10.1016/j.chemosphere.2021.130036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring of pesticides, their metabolites and transformation products. The development of quantitative structure-retention relationship models require selecting the most adequate and best set of molecular descriptors and the best machine-learning algorithm. Here, we used seven molecular descriptor sets extracted from four well-known studies and applied them to roughly 800 pesticides and their chromatographic reversed-phase retention times. We used and optimized five different machine-learning algorithms with these descriptor sets to carry out predictions. Our results show that a support-vector machine regression algorithm with only eight molecular descriptors gave the best compromise between the number of molecular descriptors, processing time and model complexity to optimize prediction performance for this specific gradient LC method.
Collapse
Affiliation(s)
- Julien Parinet
- Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France.
| |
Collapse
|
11
|
Hybrid data-intelligence algorithms for the simulation of thymoquinone in HPLC method development. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2021. [DOI: 10.1007/s13738-020-02124-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
|
12
|
Yu Y, Yao C, Guo DA. Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry. Acta Pharm Sin B 2021; 11:1469-1492. [PMID: 34221863 PMCID: PMC8245813 DOI: 10.1016/j.apsb.2021.02.017] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/02/2021] [Accepted: 02/22/2021] [Indexed: 12/21/2022] Open
Abstract
Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.
Collapse
Key Words
- BS, background subtraction
- CCS, collision cross section
- CE, collision energy
- CID, collision-induced dissociation
- DDA, data-dependent acquisition
- DE, dynamic exclusion
- DIA, data-independent acquisition
- DIF, diagnostic ion filtering
- DM, database matching
- Data acquisition
- Data post-processing
- EL, exclusion list
- EMS, enhanced mass spectrum
- EPI, enhanced product ion
- FS, full scan
- HCD, high-energy C-trap dissociation
- IDA, information dependent acquisition
- IM, ion mobility
- IPF, isotope pattern filtering
- ISCID, in-source collision-induced dissociation
- LC, liquid chromatography
- LTQ-Orbitrap, linear ion-trap/orbitrap
- Liquid chromatography−mass spectrometry
- MDF, mass defect filtering
- MIM, multiple ion monitoring
- MN, molecular networking
- MRM, multiple reaction monitoring
- MS, mass spectrometry
- MTSF, mass spectral trees similarity filter
- NL, neutral loss
- NLF, neutral loss filtering
- NLS, neutral loss scan
- NRF, nitrogen rule filtering
- PCA, principal component analysis
- PIL, precursor ion list
- PIS, precursor ion scan
- PLS-DA, partial least square-discriminant analysis
- Q-TRAP, hybrid triple quadrupole-linear ion trap
- QSRR, quantitative structure retention relationship
- QqQ, triple quadrupole
- Qualitative analysis
- RT, retention time
- SA, statistical analysis
- TCM, traditional Chinese medicine
- Traditional Chinese medicine
- UHPLC, ultra-high performance liquid chromatography
- cMRM, conventional multiple reaction monitoring
- sMRM, scheduled multiple reaction monitoring
Collapse
Affiliation(s)
- Yang Yu
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Changliang Yao
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - De-an Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
13
|
Bride E, Heinisch S, Bonnefille B, Guillemain C, Margoum C. Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling. JOURNAL OF HAZARDOUS MATERIALS 2021; 409:124652. [PMID: 33277075 DOI: 10.1016/j.jhazmat.2020.124652] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 10/02/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.
Collapse
Affiliation(s)
- Eloi Bride
- INRAE, UR RiverLy, F-69625 Villeurbanne, France
| | - Sabine Heinisch
- Université de Lyon, Institut des Sciences Analytiques, UMR 5280, CNRS, F-69100 Villeurbanne, France
| | | | | | | |
Collapse
|
14
|
Richardson AK, Chadha M, Rapp-Wright H, Mills GA, Fones GR, Gravell A, Stürzenbaum S, Cowan DA, Neep DJ, Barron LP. Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:595-606. [PMID: 33427827 DOI: 10.1039/d0ay02013c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balanced (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5 min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng L-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.
Collapse
Affiliation(s)
- Alexandra K Richardson
- Dept. Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, UK
| | | | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Ng KT, Rapp-Wright H, Egli M, Hartmann A, Steele JC, Sosa-Hernández JE, Melchor-Martínez EM, Jacobs M, White B, Regan F, Parra-Saldivar R, Couchman L, Halden RU, Barron LP. High-throughput multi-residue quantification of contaminants of emerging concern in wastewaters enabled using direct injection liquid chromatography-tandem mass spectrometry. JOURNAL OF HAZARDOUS MATERIALS 2020; 398:122933. [PMID: 32768824 PMCID: PMC7456777 DOI: 10.1016/j.jhazmat.2020.122933] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/23/2020] [Accepted: 05/10/2020] [Indexed: 05/30/2023]
Abstract
A rapid quantitative method for 135 contaminants of emerging concern (CECs) in untreated wastewater enabled with direct injection liquid chromatography-tandem mass spectrometry is presented. All compounds were analysed within 5 min on a short biphenyl cartridge using only 10 μL of filtered sample per injection. Up to 76 compounds were monitored simultaneously during the gradient (including mostly two transitions per compound and stable isotope-labelled analogues) while yielding >10 data points per peak. Evaluation of seven solid phase extraction sorbents showed no advantage for wastewater matrix removal. Excellent linearity, range, accuracy and precision was achieved for most compounds. Matrix effects were <11 % and detection limits were <30 ng L-1 on average. Application to untreated wastewater samples from three wastewater treatment works in the UK, USA and Mexico, enabled quantification of 56 compounds. Banned and EU 'watch-list' substances are critically discussed, including pesticides, macrolide antibiotics, diclofenac, illicit drugs as well as multiple pharmaceuticals and biocides. This high-throughput method sets a new standard for the speedy and confident determination of over a hundred CECs in wastewater at the part-per-trillion level, as demonstrated by performing over 260 injections per day.
Collapse
Affiliation(s)
- Keng Tiong Ng
- Dept. Analytical, Environmental & Forensic Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom
| | - Helena Rapp-Wright
- Dept. Analytical, Environmental & Forensic Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom; DCU Water Institute and School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Melanie Egli
- Dept. Analytical, Environmental & Forensic Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom
| | - Alicia Hartmann
- Dept. Analytical, Environmental & Forensic Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom; Hochschule Fresenius, Limburger Straße 2, Idstein, Hessen, Germany
| | - Joshua C Steele
- Biodesign Center for Environmental Health Engineering, The Biodesign Institute, Arizona State University, 1001 S. McAllister Avenue, Tempe, AZ 85287-8101, USA; School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA; AquaVitas, LLC, 9260 E. Raintree Dr., Ste 140, Scottsdale, AZ 85260, USA
| | - Juan Eduardo Sosa-Hernández
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Campus Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo Leon 64849, Mexico
| | - Elda M Melchor-Martínez
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Campus Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo Leon 64849, Mexico
| | - Matthew Jacobs
- DCU Water Institute and School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Blánaid White
- DCU Water Institute and School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Fiona Regan
- DCU Water Institute and School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Roberto Parra-Saldivar
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Campus Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo Leon 64849, Mexico
| | - Lewis Couchman
- Analytical Services International, St George's University of London, London, United Kingdom
| | - Rolf U Halden
- Biodesign Center for Environmental Health Engineering, The Biodesign Institute, Arizona State University, 1001 S. McAllister Avenue, Tempe, AZ 85287-8101, USA; School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA; OneWaterOneHealth, Arizona State University Foundation, 1001 S. McAllister Avenue, Tempe, AZ 85287-8101, USA; AquaVitas, LLC, 9260 E. Raintree Dr., Ste 140, Scottsdale, AZ 85260, USA
| | - Leon P Barron
- Dept. Analytical, Environmental & Forensic Sciences, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom; Environmental Research Group, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom.
| |
Collapse
|
16
|
Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03690-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
|
17
|
Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
| |
Collapse
|
18
|
Usman AG, Işik S, Abba SI. A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development. Chromatographia 2020. [DOI: 10.1007/s10337-020-03912-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
19
|
Irlam RC, Parkin MC, Brabazon DP, Beardah MS, O'Donnell M, Barron LP. Improved determination of femtogram-level organic explosives in multiple matrices using dual-sorbent solid phase extraction and liquid chromatography-high resolution accurate mass spectrometry. Talanta 2019; 203:65-76. [DOI: 10.1016/j.talanta.2019.05.047] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 10/26/2022]
|
20
|
Mayer M, Baeumner AJ. A Megatrend Challenging Analytical Chemistry: Biosensor and Chemosensor Concepts Ready for the Internet of Things. Chem Rev 2019; 119:7996-8027. [DOI: 10.1021/acs.chemrev.8b00719] [Citation(s) in RCA: 146] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Michael Mayer
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93040 Regensburg, Germany
| | - Antje J. Baeumner
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93040 Regensburg, Germany
| |
Collapse
|
21
|
Munro K, Martins CPB, Loewenthal M, Comber S, Cowan DA, Pereira L, Barron LP. Evaluation of combined sewer overflow impacts on short-term pharmaceutical and illicit drug occurrence in a heavily urbanised tidal river catchment (London, UK). THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 657:1099-1111. [PMID: 30677877 DOI: 10.1016/j.scitotenv.2018.12.108] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 12/07/2018] [Accepted: 12/08/2018] [Indexed: 05/12/2023]
Abstract
The occurrence of pharmaceutical and illicit drug residues potentially arising from combined sewer overflows (CSOs) in the Central London portion of the Thames Estuary is presented. Approximately 39 million tonnes of untreated sewage enter the River Thames at 57 CSO points annually. Differential analysis of influents and effluents in a major wastewater treatment plant identified seven potential drug-related CSO markers based on removal rates. Three were present in influent at concentrations >1 μg L-1 (caffeine, cocaine and benzoylecgonine). During dry weather, analysis of hourly samples of river water revealed relatively consistent concentrations for most drugs, including CSO markers, over a tidal cycle. River water was monitored over a week in January and July and then daily across six consecutive weeks in November/December 2014. Out of 31 compounds monitored, 27 drug residues were determined in the River Thames and, combined, ranged between ~1000-3500 ng L-1. Total drug concentration generally declined during extended periods of drier weather. For CSO markers, short-term increases in caffeine, cocaine and benzoylecgonine concentration were observed ~24 h after CSO events (especially those occurring at low tide) and generally within one order of magnitude. Timings of elevated occurrence also correlated well with ammonium ion and dissolved oxygen data following CSOs. This work also represents an important study of pharmaceutical occurrence before a major 'Super Sewer' infrastructure upgrade in London aiming to reduce CSOs by 95%.
Collapse
Affiliation(s)
- Kelly Munro
- King's Forensics, Dept. Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, SE1 9NH London, UK
| | | | - Matthew Loewenthal
- Environment Agency, National Water Quality Instrumentation Service, Bristol, UK
| | - Sean Comber
- Dept. Environmental Science, Plymouth University, Drake Circus, Plymouth, PL4 8AA, UK
| | - David A Cowan
- King's Forensics, Dept. Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, SE1 9NH London, UK
| | - Luisa Pereira
- Thermo Fisher Scientific, Manor Park, Tudor Road, Runcorn, UK
| | - Leon P Barron
- King's Forensics, Dept. Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, SE1 9NH London, UK.
| |
Collapse
|
22
|
Manda A, Walker RB, Khamanga SMM. An Artificial Neural Network Approach to Predict the Effects of Formulation and Process Variables on Prednisone Release from a Multipartite System. Pharmaceutics 2019; 11:E109. [PMID: 30866418 PMCID: PMC6470535 DOI: 10.3390/pharmaceutics11030109] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 02/06/2019] [Accepted: 02/08/2019] [Indexed: 12/22/2022] Open
Abstract
The impact of formulation and process variables on the in-vitro release of prednisone from a multiple-unit pellet system was investigated. Box-Behnken Response Surface Methodology (RSM) was used to generate multivariate experiments. The extrusion-spheronization method was used to produce pellets and dissolution studies were performed using United States Pharmacopoeia (USP) Apparatus 2 as described in USP XXIV. Analysis of dissolution test samples was performed using a reversed-phase high-performance liquid chromatography (RP-HPLC) method. Four formulation and process variables viz., microcrystalline cellulose concentration, sodium starch glycolate concentration, spheronization time and extrusion speed were investigated and drug release, aspect ratio and yield were monitored for the trained artificial neural networks (ANN). To achieve accurate prediction, data generated from experimentation were used to train a multi-layer perceptron (MLP) using back propagation (BP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) 57 training algorithm until a satisfactory value of root mean square error (RMSE) was observed. The study revealed that the in-vitro release profile of prednisone was significantly impacted by microcrystalline cellulose concentration and sodium starch glycolate concentration. Increasing microcrystalline cellulose concentration retarded dissolution rate whereas increasing sodium starch glycolate concentration improved dissolution rate. Spheronization time and extrusion speed had minimal impact on prednisone release but had a significant impact on extrudate and pellet quality. This work demonstrated that RSM can be successfully used concurrently with ANN for dosage form manufacture to permit the exploration of experimental regions that are omitted when using RSM alone.
Collapse
Affiliation(s)
- Arthur Manda
- Division of Pharmaceutics, Faculty of Pharmacy, Rhodes University, Grahamstown 6140, South Africa.
| | - Roderick B Walker
- Division of Pharmaceutics, Faculty of Pharmacy, Rhodes University, Grahamstown 6140, South Africa.
| | - Sandile M M Khamanga
- Division of Pharmaceutics, Faculty of Pharmacy, Rhodes University, Grahamstown 6140, South Africa.
| |
Collapse
|
23
|
Aalizadeh R, Nika MC, Thomaidis NS. Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants. JOURNAL OF HAZARDOUS MATERIALS 2019; 363:277-285. [PMID: 30312924 DOI: 10.1016/j.jhazmat.2018.09.047] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 09/16/2018] [Accepted: 09/17/2018] [Indexed: 05/13/2023]
Abstract
Hydrophilic interaction liquid chromatography (HILIC) and reversed phase LC (RPLC) coupled to high resolution mass spectrometry (HRMS) are widely used for the identification of suspects and unknown compounds in the environment. For the identification of unknowns, apart from mass accuracy and isotopic fitting, retention time (tR) and MS/MS spectra evaluation is required. In this context, a novel comprehensive workflow was developed to study the tR behavior of large groups of emerging contaminants using Quantitative Structure-Retention Relationships (QSRR). 682 compounds were analyzed by HILIC-HRMS in positive Electrospray Ionization mode (ESI). Moreover, an extensive dataset was built for RPLC-HRMS including 1830 and 308 compounds for positive and negative ESI, respectively. Support Vector Machines (SVM) was used to model the tR data. The applicability domains of the models were studied by Monte Carlo Sampling (MCS) methods. The MCS method was also used to calculate the acceptable error windows for the predicted tR from various LC conditions. This paper provides validated models for predicting tR in HILIC/RPLC-HRMS platforms to facilitate identification of new emerging contaminants by suspect and non-target HRMS screening, and were applied for the identification of transformation products (TPs) of emerging contaminants and biocides in wastewater and sludge.
Collapse
Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Maria-Christina Nika
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece.
| |
Collapse
|
24
|
Safdel F, Safa F. Atom-Type-Based AI Topological Indices for Artificial Neural Network Modeling of Retention Indices of Monomethylalkanes. J Chromatogr Sci 2019; 57:1-8. [PMID: 30169788 DOI: 10.1093/chromsci/bmy081] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Indexed: 11/14/2022]
Abstract
In this work, a combination of Xu and atom-type-based AI topological indices (TIs) were employed for quantitative structure-retention relationship (QSRR) study of monomethylalkanes (MMAs). A total of 196 temperature-programmed gas chromatographic retention indices corresponding to all C4-C30 MMAs on OV-1 stationary phase have been used in QSRR modeling. Results of the study showed that an artificial neural network (ANN) with 4-9-1 topology and Levenberg-Marquardt training algorithm can predict the retention indices with high degree of accuracy. The statistics of root-mean-square error for the training, validation and test sets were 0.200, 0.316 and 0.215, respectively. The proposed model resulted in a maximum relative error of 0.24% suggesting the TIs as excellent alternative for estimating retention indices of MMAs. According to the obtained results, relative importance of the TIs decreased in the order of AI(-CH3)> AI(-CH2-)> AI(>CH-)> Xu showing significant role of molecular branching, steric factor and molecular size as effective structural features on retention indices of MMAs.
Collapse
Affiliation(s)
- Farah Safdel
- Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, I.R. Iran
| | - Fariba Safa
- Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, I.R. Iran
| |
Collapse
|
25
|
Miller TH, Bury NR, Owen SF, MacRae JI, Barron LP. A review of the pharmaceutical exposome in aquatic fauna. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 239:129-146. [PMID: 29653304 PMCID: PMC5981000 DOI: 10.1016/j.envpol.2018.04.012] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/26/2018] [Accepted: 04/02/2018] [Indexed: 05/20/2023]
Abstract
Pharmaceuticals have been considered 'contaminants of emerging concern' for more than 20 years. In that time, many laboratory studies have sought to identify hazard and assess risk in the aquatic environment, whilst field studies have searched for targeted candidates and occurrence trends using advanced analytical techniques. However, a lack of a systematic approach to the detection and quantification of pharmaceuticals has provided a fragmented literature of serendipitous approaches. Evaluation of the extent of the risk for the plethora of human and veterinary pharmaceuticals available requires the reliable measurement of trace levels of contaminants across different environmental compartments (water, sediment, biota - of which biota has been largely neglected). The focus on pharmaceutical concentrations in surface waters and other exposure media have therefore limited both the characterisation of the exposome in aquatic wildlife and the understanding of cause and effect relationships. Here, we compile the current analytical approaches and available occurrence and accumulation data in biota to review the current state of research in the field. Our analysis provides evidence in support of the 'Matthew Effect' and raises critical questions about the use of targeted analyte lists for biomonitoring. We provide six recommendations to stimulate and improve future research avenues.
Collapse
Affiliation(s)
- Thomas H Miller
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom.
| | - Nicolas R Bury
- Faculty of Science, Health and Technology, University of Suffolk, James Hehir Building, University Avenue, Ipswich, Suffolk, IP3 0FS, UK; Division of Diabetes and Nutritional Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London, SE1 9NH, UK
| | - Stewart F Owen
- AstraZeneca, Global Environment, Alderley Park, Macclesfield, Cheshire SK10 4TF, UK
| | - James I MacRae
- Metabolomics Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Leon P Barron
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom
| |
Collapse
|
26
|
Hernández F, Castiglioni S, Covaci A, de Voogt P, Emke E, Kasprzyk‐Hordern B, Ort C, Reid M, Sancho JV, Thomas KV, van Nuijs AL, Zuccato E, Bijlsma L. Mass spectrometric strategies for the investigation of biomarkers of illicit drug use in wastewater. MASS SPECTROMETRY REVIEWS 2018; 37:258-280. [PMID: 27750373 PMCID: PMC6191649 DOI: 10.1002/mas.21525] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Accepted: 09/30/2016] [Indexed: 05/04/2023]
Abstract
The analysis of illicit drugs in urban wastewater is the basis of wastewater-based epidemiology (WBE), and has received much scientific attention because the concentrations measured can be used as a new non-intrusive tool to provide evidence-based and real-time estimates of community-wide drug consumption. Moreover, WBE allows monitoring patterns and spatial and temporal trends of drug use. Although information and expertise from other disciplines is required to refine and effectively apply WBE, analytical chemistry is the fundamental driver in this field. The use of advanced analytical techniques, commonly based on combined chromatography-mass spectrometry, is mandatory because the very low analyte concentration and the complexity of samples (raw wastewater) make quantification and identification/confirmation of illicit drug biomarkers (IDBs) troublesome. We review the most-recent literature available (mostly from the last 5 years) on the determination of IDBs in wastewater with particular emphasis on the different analytical strategies applied. The predominance of liquid chromatography coupled to tandem mass spectrometry to quantify target IDBs and the essence to produce reliable and comparable results is illustrated. Accordingly, the importance to perform inter-laboratory exercises and the need to analyze appropriate quality controls in each sample sequence is highlighted. Other crucial steps in WBE, such as sample collection and sample pre-treatment, are briefly and carefully discussed. The article further focuses on the potential of high-resolution mass spectrometry. Different approaches for target and non-target analysis are discussed, and the interest to perform experiments under laboratory-controlled conditions, as a complementary tool to investigate related compounds (e.g., minor metabolites and/or transformation products in wastewater) is treated. The article ends up with the trends and future perspectives in this field from the authors' point of view. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:258-280, 2018.
Collapse
Affiliation(s)
- Félix Hernández
- Research Institute for Pesticides and WaterUniversity Jaume ICastellónSpain
| | - Sara Castiglioni
- Department of Environmental Health SciencesIRCCS—Istituto di Ricerche Farmacologiche Mario NegriMilanItaly
| | - Adrian Covaci
- Toxicological CenterUniversity of AntwerpAntwerpBelgium
| | - Pim de Voogt
- KWR Watercycle Research InstituteNieuwegeinthe Netherlands
- IBED—University of AmsterdamAmsterdamthe Netherlands
| | - Erik Emke
- KWR Watercycle Research InstituteNieuwegeinthe Netherlands
| | | | - Christoph Ort
- Swiss Federal Institute of Aquatic Science and Technology (Eawag)DübendorfSwitzerland
| | - Malcolm Reid
- Norwegian Institute for Water Research (NIVA)OsloNorway
| | - Juan V. Sancho
- Research Institute for Pesticides and WaterUniversity Jaume ICastellónSpain
| | | | | | - Ettore Zuccato
- Department of Environmental Health SciencesIRCCS—Istituto di Ricerche Farmacologiche Mario NegriMilanItaly
| | - Lubertus Bijlsma
- Research Institute for Pesticides and WaterUniversity Jaume ICastellónSpain
| |
Collapse
|
27
|
Gallidabino MD, Hamdan L, Murphy B, Barron LP. Suspect screening of halogenated carboxylic acids in drinking water using ion exchange chromatography – high resolution (Orbitrap) mass spectrometry (IC-HRMS). Talanta 2018; 178:57-68. [DOI: 10.1016/j.talanta.2017.08.092] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 08/25/2017] [Accepted: 08/29/2017] [Indexed: 10/18/2022]
|
28
|
A comparison of three liquid chromatography (LC) retention time prediction models. Talanta 2018; 182:371-379. [PMID: 29501166 DOI: 10.1016/j.talanta.2018.01.022] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 01/08/2018] [Accepted: 01/09/2018] [Indexed: 11/20/2022]
Abstract
High-resolution mass spectrometry (HRMS) data has revolutionized the identification of environmental contaminants through non-targeted analysis (NTA). However, chemical identification remains challenging due to the vast number of unknown molecular features typically observed in environmental samples. Advanced data processing techniques are required to improve chemical identification workflows. The ideal workflow brings together a variety of data and tools to increase the certainty of identification. One such tool is chromatographic retention time (RT) prediction, which can be used to reduce the number of possible suspect chemicals within an observed RT window. This paper compares the relative predictive ability and applicability to NTA workflows of three RT prediction models: (1) a logP (octanol-water partition coefficient)-based model using EPI Suite™ logP predictions; (2) a commercially available ACD/ChromGenius model; and, (3) a newly developed Quantitative Structure Retention Relationship model called OPERA-RT. Models were developed using the same training set of 78 compounds with experimental RT data and evaluated for external predictivity on an identical test set of 19 compounds. Both the ACD/ChromGenius and OPERA-RT models outperformed the EPI Suite™ logP-based RT model (R2 = 0.81-0.92, 0.86-0.83, 0.66-0.69 for training-test sets, respectively). Further, both OPERA-RT and ACD/ChromGenius predicted 95% of RTs within a ± 15% chromatographic time window of experimental RTs. Based on these results, we simulated an NTA workflow with a ten-fold larger list of candidate structures generated for formulae of the known test set chemicals using the U.S. EPA's CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard), RTs for all candidates were predicted using both ACD/ChromGenius and OPERA-RT, and RT screening windows were assessed for their ability to filter out unlikely candidate chemicals and enhance potential identification. Compared to ACD/ChromGenius, OPERA-RT screened out a greater percentage of candidate structures within a 3-min RT window (60% vs. 40%) but retained fewer of the known chemicals (42% vs. 83%). By several metrics, the OPERA-RT model, generated as a proof-of-concept using a limited set of open source data, performed as well as the commercial tool ACD/ChromGenius when constrained to the same small training and test sets. As the availability of RT data increases, we expect the OPERA-RT model's predictive ability will increase.
Collapse
|
29
|
Bottoni P, Caroli S. Presence of residues and metabolites of pharmaceuticals in environmental compartments, food commodities and workplaces: A review spanning the three-year period 2014–2016. Microchem J 2018. [DOI: 10.1016/j.microc.2017.06.016] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
30
|
Miller TH, Bury NR, Owen SF, Barron LP. Uptake, biotransformation and elimination of selected pharmaceuticals in a freshwater invertebrate measured using liquid chromatography tandem mass spectrometry. CHEMOSPHERE 2017; 183:389-400. [PMID: 28554023 PMCID: PMC5476196 DOI: 10.1016/j.chemosphere.2017.05.083] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 04/28/2017] [Accepted: 05/13/2017] [Indexed: 05/19/2023]
Abstract
Methods were developed to assess uptake and elimination kinetics in Gammarus pulex of nine pharmaceuticals (sulfamethazine, carbamazepine, diazepam, temazepam, trimethoprim, warfarin, metoprolol, nifedipine and propranolol) using targeted LC-MS/MS to determine bioconcentration factors (BCFs) using a 96 h toxicokinetic exposure and depuration period. The derived BCFs for these pharmaceuticals did not trigger any regulatory thresholds and ranged from 0 to 73 L kg-1 (sulfamethazine showed no bioconcentration). Metabolism of chemicals can affect accurate BCF determination through parameterisation of the kinetic models. The added selectivity of LC-MS/MS allowed us to develop confirmatory methods to monitor the biotransformation of propranolol, carbamazepine and diazepam in G. pulex. Varying concentrations of the biotransformed products; 4-hydroxypropranolol sulphate, carbamazepine-10,11-epoxide, nordiazepam, oxazepam and temazepam were measured following exposure of the precursor compounds. For diazepam, the biotransformation product nordiazepam was present at higher concentrations than the parent compound at 94 ng g-1 dw. Overall, the results indicate that pharmaceutical accumulation is low in these freshwater amphipods, which can potentially be explained by the rapid biotransformation and excretion.
Collapse
Affiliation(s)
- Thomas H Miller
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom
| | - Nicolas R Bury
- Division of Diabetes and Nutritional Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London, SE1 9NH, United Kingdom
| | - Stewart F Owen
- AstraZeneca, Global Environment, Alderley Park, Macclesfield, Cheshire, SK10 4TF, United Kingdom
| | - Leon P Barron
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, United Kingdom.
| |
Collapse
|
31
|
Bijlsma L, Bade R, Celma A, Mullin L, Cleland G, Stead S, Hernandez F, Sancho JV. Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis. Anal Chem 2017; 89:6583-6589. [DOI: 10.1021/acs.analchem.7b00741] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Lubertus Bijlsma
- Research
Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat
s/n, E-12071 Castellón, Spain
| | - Richard Bade
- Research
Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat
s/n, E-12071 Castellón, Spain
- School
of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
| | - Alberto Celma
- Research
Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat
s/n, E-12071 Castellón, Spain
| | - Lauren Mullin
- Waters Corporation, 34 Maple Street, Milford, Massachusetts 01757, United States
| | - Gareth Cleland
- Waters Corporation, 34 Maple Street, Milford, Massachusetts 01757, United States
| | - Sara Stead
- Waters Corporation, 34 Maple Street, Milford, Massachusetts 01757, United States
| | - Felix Hernandez
- Research
Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat
s/n, E-12071 Castellón, Spain
| | - Juan V. Sancho
- Research
Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat
s/n, E-12071 Castellón, Spain
| |
Collapse
|
32
|
Rapp-Wright H, McEneff G, Murphy B, Gamble S, Morgan R, Beardah M, Barron L. Suspect screening and quantification of trace organic explosives in wastewater using solid phase extraction and liquid chromatography-high resolution accurate mass spectrometry. JOURNAL OF HAZARDOUS MATERIALS 2017; 329:11-21. [PMID: 28119193 DOI: 10.1016/j.jhazmat.2017.01.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 01/03/2017] [Accepted: 01/04/2017] [Indexed: 06/06/2023]
Abstract
The first comprehensive assessment of 34 solid phase extraction sorbents is presented for organic explosive residues in wastewater prior to analysis with liquid chromatography-high resolution accurate mass spectrometry (LC-HRMS). A total of 18 explosives were selected including nitramines, nitrate esters, nitroaromatics and organic peroxides. Three polymeric divinylbenzene-based sorbents were found to be most suitable and one co-polymerised with n-vinyl pyrrolidone offered satisfactory recoveries for 14 compounds in fortified wastewater (77-124%). Limits of detection in matrix ranged from 0.026-23μgL-1 with R2≥0.98 for most compounds. The method was applied to eight 24-h composite wastewater samples from a London wastewater works and one compound, 2,4-dinitrotoluene, was determined over five days between 332 and 468g day-1 (225-303ngL-1). To further exploit the suspect screening capability, 17 additional explosives, precursors and transformation products were screened in spiked wastewater samples. Of these, 14 were detected with recoveries from 62 to 92%, highlighting the broad applicability of the method. To our knowledge, this represents the first screen of explosives-related compounds in wastewater from a major European city. This method also allows post-analysis detection of new or emerging compounds using full-scan HRMS datasets to potentially identify and locate illegal manufacture of explosives via wastewater analysis.
Collapse
Affiliation(s)
- Helena Rapp-Wright
- Analytical & Environmental Sciences Division, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Gillian McEneff
- Analytical & Environmental Sciences Division, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Bronagh Murphy
- Analytical & Environmental Sciences Division, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Sally Gamble
- UCL Department of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, UK
| | - Ruth Morgan
- UCL Department of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, UK
| | - Matthew Beardah
- Forensic Explosives Laboratory, Dstl Fort Halstead, TN14 7BP, UK
| | - Leon Barron
- Analytical & Environmental Sciences Division, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK.
| |
Collapse
|
33
|
Zhang Y, Guo W, Yue Z, Lin L, Zhao F, Chen P, Wu W, Zhu H, Yang B, Kuang Y, Wang J. Rapid determination of 54 pharmaceutical and personal care products in fish samples using microwave-assisted extraction—Hollow fiber—Liquid/solid phase microextraction. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1051:41-53. [DOI: 10.1016/j.jchromb.2017.01.026] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 01/11/2017] [Accepted: 01/19/2017] [Indexed: 11/15/2022]
|
34
|
Bade R, Causanilles A, Emke E, Bijlsma L, Sancho JV, Hernandez F, de Voogt P. Facilitating high resolution mass spectrometry data processing for screening of environmental water samples: An evaluation of two deconvolution tools. THE SCIENCE OF THE TOTAL ENVIRONMENT 2016; 569-570:434-441. [PMID: 27351148 DOI: 10.1016/j.scitotenv.2016.06.162] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 06/13/2016] [Accepted: 06/20/2016] [Indexed: 06/06/2023]
Abstract
A screening approach was applied to influent and effluent wastewater samples. After injection in a LC-LTQ-Orbitrap, data analysis was performed using two deconvolution tools, MsXelerator (modules MPeaks and MS Compare) and Sieve 2.1. The outputs were searched incorporating an in-house database of >200 pharmaceuticals and illicit drugs or ChemSpider. This hidden target screening approach led to the detection of numerous compounds including the illicit drug cocaine and its metabolite benzoylecgonine and the pharmaceuticals carbamazepine, gemfibrozil and losartan. The compounds found using both approaches were combined, and isotopic pattern and retention time prediction were used to filter out false positives. The remaining potential positives were reanalysed in MS/MS mode and their product ions were compared with literature and/or mass spectral libraries. The inclusion of the chemical database ChemSpider led to the tentative identification of several metabolites, including paraxanthine, theobromine, theophylline and carboxylosartan, as well as the pharmaceutical phenazone. The first three of these compounds are isomers and they were subsequently distinguished based on their product ions and predicted retention times. This work has shown that the use deconvolution tools facilitates non-target screening and enables the identification of a higher number of compounds.
Collapse
Affiliation(s)
- Richard Bade
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Ana Causanilles
- KWR Watercycle Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands
| | - Erik Emke
- KWR Watercycle Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands
| | - Lubertus Bijlsma
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Juan V Sancho
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Felix Hernandez
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Pim de Voogt
- KWR Watercycle Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands; Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam, The Netherlands.
| |
Collapse
|
35
|
Miller T, Baz-Lomba JA, Harman C, Reid MJ, Owen SF, Bury NR, Thomas KV, Barron LP. The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:7973-81. [PMID: 27363449 PMCID: PMC5089532 DOI: 10.1021/acs.est.6b01407] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(-1) (RTD-model) and 0.03 ± 0.03 L d(-1) (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d(-1) and 0.0437 ± 0.02 L d(-1), respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.
Collapse
Affiliation(s)
- Thomas
H. Miller
- Analytical
& Environmental Sciences Division, Faculty of Life Sciences and
Medicine, King’s College London, 150 Stamford Street, London, SE1 9NH United
Kingdom
| | - Jose A. Baz-Lomba
- Norwegian
Institute for Water Research (NIVA), Oslo, NO-0349, Norway
| | - Christopher Harman
- Norwegian
Institute for Water Research (NIVA), Grimstad, NO-4879, Norway
| | - Malcolm J. Reid
- Norwegian
Institute for Water Research (NIVA), Oslo, NO-0349, Norway
| | - Stewart F. Owen
- AstraZeneca,
Global
Environment, Alderley Park, Macclesfield, Cheshire SK10 4TF, United Kingdom
| | - Nicolas R. Bury
- Division
of Diabetes and Nutritional Sciences, Faculty of Life Sciences and
Medicine, King’s College London, Franklin Wilkins Building, 150 Stamford
Street, London, SE1 9NH, United Kingdom
| | - Kevin V. Thomas
- Norwegian
Institute for Water Research (NIVA), Oslo, NO-0349, Norway
| | - Leon P. Barron
- Analytical
& Environmental Sciences Division, Faculty of Life Sciences and
Medicine, King’s College London, 150 Stamford Street, London, SE1 9NH United
Kingdom
- Phone: +44 20 7848 3842; fax: +44 20 7848 4980; e-mail:
| |
Collapse
|
36
|
Aalizadeh R, Thomaidis NS, Bletsou AA, Gago-Ferrero P. Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples. J Chem Inf Model 2016; 56:1384-98. [PMID: 27266383 DOI: 10.1021/acs.jcim.5b00752] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens , Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens , Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Anna A Bletsou
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens , Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Pablo Gago-Ferrero
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens , Panepistimiopolis Zografou, 15771 Athens, Greece
| |
Collapse
|
37
|
Golubović J, Protić A, Otašević B, Zečević M. Quantitative structure–retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans. Talanta 2016; 150:190-7. [DOI: 10.1016/j.talanta.2015.12.035] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 12/03/2015] [Accepted: 12/11/2015] [Indexed: 10/22/2022]
|
38
|
Nontarget Analysis of Environmental Samples Based on Liquid Chromatography Coupled to High Resolution Mass Spectrometry (LC-HRMS). APPLICATIONS OF TIME-OF-FLIGHT AND ORBITRAP MASS SPECTROMETRY IN ENVIRONMENTAL, FOOD, DOPING, AND FORENSIC ANALYSIS 2016. [DOI: 10.1016/bs.coac.2016.01.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
|
39
|
Prasse C, Ternes T. Application of Orbitrap Mass Spectrometry for the Identification of Transformation Products of Trace Organic Contaminants Formed in the Environment. APPLICATIONS OF TIME-OF-FLIGHT AND ORBITRAP MASS SPECTROMETRY IN ENVIRONMENTAL, FOOD, DOPING, AND FORENSIC ANALYSIS 2016. [DOI: 10.1016/bs.coac.2016.02.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
40
|
Barron LP, McEneff GL. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods. Talanta 2016; 147:261-70. [DOI: 10.1016/j.talanta.2015.09.065] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 09/22/2015] [Accepted: 09/27/2015] [Indexed: 12/01/2022]
|
41
|
Bade R, Bijlsma L, Miller TH, Barron LP, Sancho JV, Hernández F. Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2015; 538:934-41. [PMID: 26363605 DOI: 10.1016/j.scitotenv.2015.08.078] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 08/14/2015] [Accepted: 08/14/2015] [Indexed: 04/14/2023]
Abstract
The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (tR) and mass spectral comparisons. Chromatographic tR prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for tR prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound tR dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85% of all compounds to within 2min of their measured tR for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95% prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.
Collapse
Affiliation(s)
- Richard Bade
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain
| | - Lubertus Bijlsma
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain
| | - Thomas H Miller
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Leon P Barron
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Juan Vicente Sancho
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain
| | - Felix Hernández
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain.
| |
Collapse
|
42
|
Stanstrup J, Neumann S, Vrhovšek U. PredRet: prediction of retention time by direct mapping between multiple chromatographic systems. Anal Chem 2015; 87:9421-8. [PMID: 26289378 DOI: 10.1021/acs.analchem.5b02287] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org , a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.
Collapse
Affiliation(s)
- Jan Stanstrup
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) , Via E. Mach 1, 38010 San Michele all'Adige, Trentiono, Italy
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Weinberg 3, 06120 Halle, Germany
| | - Urška Vrhovšek
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) , Via E. Mach 1, 38010 San Michele all'Adige, Trentiono, Italy
| |
Collapse
|