Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using <i>In Silico</i> and Experimental Collision Cross-Section Values

Connolly, John R F B; Munoz-Muriedas, Jordi; Lapthorn, Cris; Higton, David; Vissers, Johannes P.C.; Webb, Alison; Beaumont, Claire; Dear, Gordon J.

doi:10.1021/jasms.0c00427

Cited by 13 publications

(10 citation statements)

References 65 publications

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“…However, CCS prediction tools cannot be used to confirm the identification of an unknown compound. Connolly and coworkers have shown that predicted CCS values cannot accurately describe the difference of CCS values for isomeric glucuronide pairs . Technological developments are on-going though, and as the accuracy and reproducibility of experimentally obtained CCS values improves, a similar improvement can be expected in the accuracy of predicted CCS values.…”

Section: Resultsmentioning

confidence: 99%

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials

Song

Canellas

Dreolin

et al. 2022

J. Agric. Food Chem.

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The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen ( TW CCS N2 ) values for extractables and leachables from FCMs. The database contains a total of 1038 TW CCS N2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The TW CCS N2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H] + , [M + Na] + , and [M – H] − adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental TW CCS N2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The TW CCS N2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

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Section: Resultsmentioning

confidence: 99%

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials

Song

Canellas

Dreolin

et al. 2022

J. Agric. Food Chem.

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“…Later, the same ML model was compared with QM approaches using MOBCAL on glucuronides. 177 As it turned out, the overall difference between the experimental TW CCS N2 and the predicted CCS using the ML approach was lower compared with the QM approach. More compounds with the ML approach were within a 2% difference (Figure 3).…”

Section: Metccs and Lipidccsmentioning

confidence: 93%

“…The web server for this model is accessible through CCSondemand (waters.com). Later, the same ML model was compared with QM approaches using MOBCAL on glucuronides 177 . As it turned out, the overall difference between the experimental TW CCS N2 and the predicted CCS using the ML approach was lower compared with the QM approach.…”

Section: Ccs Modelsmentioning

confidence: 99%

“…Orange bars show the difference between the experimental and machine learning CCS values, while blue bars show the difference between the experimental and quantum mechanics approaches in CCS values. Source : Adapted with permission from reference, 177 Copyright (2021) American Chemical Society.…”

Section: Ccs Modelsmentioning

confidence: 99%

“…In general, DeepCCS gave excellent accuracy compared with the experimental values with a correlation coefficient and MRE greater than 0.97 and less than 2.6%, respectively. 49 However, the limitation of DeepCCS is that it can predict CCS only from known features contained in the datasets, suggesting that it is applicable to all kinds of molecules similar to the molecules in the training set, such as metabolites, 149,177 lipids, amino acids, some peptides, and some proteins. to achieve great results in language processing, image analysis, 186 gene expression, 187 and protein folding.…”

Section: Ann Modelsmentioning

confidence: 99%

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Collision cross section measurement and prediction methods in omics

Kartowikromo,

Olajide,

Hamid

2023

J Mass Spectrom

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Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant‐independent and calibrant‐dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

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Potential of measured relative shifts in collision cross section values for biotransformation studies

Lanshoeft,

Schütz,

Lozac’h

et al. 2023

Anal Bioanal Chem

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Ion mobility spectrometry–mass spectrometry (IMS-MS) separates gas phase ions due to differences in drift time from which reproducible and analyte-specific collision cross section (CCS) values can be derived. Internally conducted in vitro and in vivo metabolism (biotransformation) studies indicated repetitive shifts in measured CCS values (CCSmeas) between parent drugs and their metabolites. Hence, the purpose of the present article was (i) to investigate if such relative shifts in CCSmeas were biotransformation-specific and (ii) to highlight their potential benefits for biotransformation studies. First, mean CCSmeas values of 165 compounds were determined (up to n = 3) using a travelling wave IMS-MS device with nitrogen as drift gas (TWCCSN2, meas). Further comparison with their predicted values (TWCCSN2, pred, Waters CCSonDemand) resulted in a mean absolute error of 5.1%. Second, a reduced data set (n = 139) was utilized to create compound pairs (n = 86) covering eight common types of phase I and II biotransformations. Constant, discriminative, and almost non-overlapping relative shifts in mean TWCCSN2, meas were obtained for demethylation (− 6.5 ± 2.1 Å2), oxygenation (hydroxylation + 3.8 ± 1.4 Å2, N-oxidation + 3.4 ± 3.3 Å2), acetylation (+ 13.5 ± 1.9 Å2), sulfation (+ 17.9 ± 4.4 Å2), glucuronidation (N-linked: + 41.7 ± 7.5 Å2, O-linked: + 38.1 ± 8.9 Å2), and glutathione conjugation (+ 49.2 ± 13.2 Å2). Consequently, we propose to consider such relative shifts in TWCCSN2, meas (rather than absolute values) as well for metabolite assignment/confirmation complementing the conventional approach to associate changes in mass-to-charge (m/z) values between a parent drug and its metabolite(s). Moreover, the comparison of relative shifts in TWCCSN2, meas significantly simplifies the mapping of metabolites into metabolic pathways as demonstrated. Graphical Abstract

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Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using In Silico and Experimental Collision Cross-Section Values

Cited by 13 publications

References 65 publications

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials

Collision cross section measurement and prediction methods in omics

Potential of measured relative shifts in collision cross section values for biotransformation studies

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