From Pesticides to Per- and Polyfluoroalkyl Substances: An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics

Dodds, James N.; Alexander, Nancy Lee M; Kirkwood, Kaylie I; Foster, MaKayla; Hopkins, Zachary R.; Knappe, Detlef R.U.; Baker, Erin

doi:10.1021/acs.analchem.0c04359

Cited by 25 publications

(17 citation statements)

References 160 publications

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“…While these analyses are commonly performed for PFAS, Figure illustrates the various relationships that result for the mass defect and KMD analyses. To evaluate the mass defect and KMDs for the xenobiotics studied, eqs – were utilized. − ,,− The scalar factor in eq is obtained as the nominal mass of the target compound divided by its exact mass, so it varies for each repeating unit or functional group of interest. For example, for CF 2 , it is 50/49.9968 and for CH 2 , it is 14/14.0156 (showing the exact mass to 4 decimal points).…”

Section: Resultsmentioning

confidence: 99%

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

Foster

Rainey

Watson

et al. 2022

Environ. Sci. Technol.

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The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS–MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)–IMS–MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.

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Section: Resultsmentioning

confidence: 99%

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

Foster

Rainey

Watson

et al. 2022

Environ. Sci. Technol.

Self Cite

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“…Lately, different sorbents combined in one or more SPE column(s) have been used to extract numerous compounds with different physico-chemical properties [31]. Furthermore, advanced analytical techniques, such as LC and/or gas chromatography coupled to HRMS are used to get a comprehensive overview of organic contaminants in water systems [11,32,33]. Hence, this study used a general approach based on four different sorbents combined in one cartridge, as extraction procedure and an HRMS wide-scope screening method covering a very broad range of pesticides.…”

Section: Resultsmentioning

confidence: 99%

“…A comprehensive study of all pesticides and their transformation products and/or metabolites (TP/M) in waters would be necessary to get a complete overview of this group of substances. However, thousands of pesticides are registered and this number is continuously increasing [11]. Therefore, it would be difficult to develop an analytical method to include all compounds.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Environmental Pollution and Human Exposure to Pesticides by Wastewater Analysis in a Seven-Year Study in Athens, Greece

Rousis

Denardou

Alygizakis

et al. 2021

Toxics

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Pesticides have been used in large amounts around the world for decades and are responsible for environmental pollution and various adverse effects on human health. Analysis of untreated wastewater can deliver useful information on pesticides’ use in a particular area and allow the assessment of human exposure to certain substances. A wide-scope screening method, based on liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry, was applied, using both target and suspect screening methodologies. Daily composite influent wastewater samples were collected for seven or eight consecutive days in Athens between 2014 and 2020 and analyzed for 756 pesticides, their environmental transformation products and their human metabolites. Forty pesticides were quantified at mean concentrations up to 4.9 µg/L (tralkoxydim). The most abundant class was fungicides followed by herbicides, insect repellents, insecticides and plant growth regulators. In addition, pesticide transformation products and/or metabolites were detected with high frequency, indicating that research should be focused on them. Human exposure was evaluated using the wastewater-based epidemiology (WBE) approach and 3-ethyl-carbamoyl benzoic acid and cis-1,2,3,6-tetrahydrophthalimide were proposed as potential WBE biomarkers. Wastewater analysis revealed the presence of unapproved pesticides and indicated that there is an urgent need to include more transformation products in target databases.

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“…Typical NIAS include impurities, oligomers, and degradation products of material components, For example, organophosphate esters can result from the oxidation of organophosphite antioxidants in plastics and have been detected in indoor dust. , If plastic products are made from recycled plastics, NIAS can also include contaminants resulting from the previous use of the material or from the recycling process itself . In recent years, the presence of perfluoroalkyl substances in plastic products has also attracted the attention of food safety and environmental authorities. − …”

Section: Introductionmentioning

confidence: 99%

Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products

Song

Dreolin

Canellas

et al. 2022

Environ. Sci. Technol.

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The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H] + adducts and 95.0% of CCS values for [M + Na] + adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H] + and [M + Na] + adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.

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From Pesticides to Per- and Polyfluoroalkyl Substances: An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics

Cited by 25 publications

References 160 publications

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning

Assessment of Environmental Pollution and Human Exposure to Pesticides by Wastewater Analysis in a Seven-Year Study in Athens, Greece

Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products

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