Application of Predicted Collisional Cross Section to Metabolome Databases to Probabilistically Describe the Current and Future Ion Mobility Mass Spectrometry

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling

The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

Strategy strengthens structural identification through hyphenating full collision energy ramp-MS2 and full exciting energy ramp-MS3 spectra: An application for metabolites identification of rosmarinic acid

"MS/MS spectrum to structure" analysis is the most challenging task for MS/MS-relied qualitative characterization. The conventional database- and computation-assisted strategies cannot reach confirmative identification, notably for isomers. Hence, an advanced strategy was proposed here through tackling the two determinant obstacles such as the transformation from elemental compositions to fragment ion structures and the linkage style amongst substructures. As typical conjugated structures, esters were measured for strategy illustration, and metabolite identification of a famous natural antioxidant namely rosmarinic acid (RosA) in rat was undertaken for applicability justification. Through programming online energy-resolved (ER)-MS for the first collision cell of Qtrap-MS device, full collision energy ramp (FCER)-MS2 spectrum was configured for [M-H]- ion of each ester to provide optimal collision energies (OCEs) for all concerned diagnostic fragment ions (DFIs), i.e. a-, b-, c-, y-, and z-type ions. The linear correlations between masses and OCEs were built for each ion type to facilitate DFIs recognition from chaotic MS2 spectrum. To identify 1st-generation fragment ions, full exciting energy ramp (FEER)-MS3 spectra were configured for key DFIs via programming the second ER-MS in the latter collision chamber. FEER-MS3 spectrum of 1st-generation fragment ion for ester was demonstrated to be identical with FEER-MS2 spectrum of certain hydrolysis product when sharing the same structure. After applying the advanced strategy to recognize DFIs and identify 1st-generation fragment ions, a total of forty metabolites (M1-M40), resulted from hydrolysis, methylation, sulfation, and glucuronidation, were unambiguously identified for RosA after oral administration. Together, the advanced bottom-up strategy hyphenating FCER-MS2 and FEER-MS3 spectra, is meaningful to strengthen "MS/MS spectrum to structure" analysis through recognizing and identifying fragment ions.

The rapid separation and characterization of sulfates of tyrosine and its metabolites in reaction mixtures and human urine using a cyclic ion mobility device and mass spectrometry

Ion mobility (IM) separations, especially when combined with mass spectrometry, offer the opportunity for the rapid analysis and characterization of mixtures. However, the limited resolution afforded by many IM systems means that in practice applications may be limited. Here we have employed an IM separation on a high-resolution cyclic IM device with MS/MS to separate and characterize mixtures of sulfated isomers of tyrosine and associated metabolites containing multiple sulfated isoforms present in reaction mixtures. The cIMS device allowed ions, not resolved using a single pass, to be subjected to multiple passes, enabling the resolution of those with similar collision cross sections (CCS). Predicted single pass CCS values calculated for the isomers likely to be present in these mixtures showed only small differences between them, ranging between of between 0.1 - 0.7 % depending on structure. These small differences highlight the high degree of mobility resolution required for separating the isomers. Experimentally different isoforms of tyrosine sulfate and sulfated tyrosine metabolites could be sufficiently resolved via multipass separations (3-35 passes). This degree of separation provided resolving powers of up to 384 CCS/ΔCCS for sulfated dopamine which enabled good MS/MS spectra to be generated. In human urine the presence of a single sulfated form of tyrosine was detected and identified as the O-sulfate after 3 passes based on the synthetic standard. Of the other tyrosine-related sulfates for which synthetic standards had been prepared only dopamine sulfate was detected in this sample.

Increasing coverage of the urinary polar metabolome using ultra high-performance hydrophobic interaction liquid chromatography combined with linear and cyclic travelling wave ion mobility and mass spectrometry

The use of HILIC-based separations for the analysis of polar metabolites in metabolic phenotyping studies is well established. Here, we demonstrate the increased coverage of the polar metabolome obtained by travelling wave (TW) ion mobility (IM) instruments combined with HILIC and mass spectrometry (MS) for metabotyping rat and mouse urine samples. Profiling was performed using either a linear TW IM-MS based instrument with a path length of 40 cm or an instrument with a cyclic travelling wave analyser (cIM) with a path length of 95 cm. Due to the added resolution afforded by using both the linear and cyclic IM geometries with MS detection (IM-MS) significant increases in feature count (m/z-tR pairs) were generally obtained compared to HILIC-MS alone. In addition, the use of both linear and cyclic IM-MS improved the quality of the mass spectra obtained as a result of the separation of co-eluting analytes. As would be expected from the increased path length of the cyclic IM-MS instrument compared to the linear device, the largest gains in feature detection were obtained for the HILIC-cIM-MS combination. By increasing the resolution of coeluting components, the cyclic IM-MS instrumentation also provided the largest improvement in the quality of the mass spectral data obtained. When applied to mouse urines obtained from both control and gefitinib-dosed mice, time-related changes were detected in those obtained from the treated animals that were not seen in the controls. Polar metabolites affected by drug administration included, but were not limited to, hypoxanthine, 1,3-dimethyluracil and acetylcarnitine. The changes seen in the relative concentrations of these endogenous metabolites appeared to be related to drug concentrations in the plasma and urine suggesting a pharmacometabodynamic link.

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

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.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Two-dimensional liquid chromatography and ion mobility-mass spectrometry for the multicomponent characterization of different parts of the medicinal plant Gynostemma longipes

Herba Gynostemma (Jiaogulan) is an herbaceous plant of the genus Gynostemma in the family Cucurbitaceae. Gynostemma longipes has lipid-lowering activity, thus, it is used as a medicinal material. However, its medicinal using parts have been recorded as whole plants or aerial parts in different provincial quality standards; therefore, it is necessary to conduct a comprehensive compositional analysis of the different parts of G. longipes (rhizomes, stems, and leaves) used in traditional medicine. In this study, offline two-dimensional liquid chromatography-ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS) was used to analyze the different parts of G. longipes obtained from Shaanxi province, China. By combining the retention times, mass fragments, collision cross-section values, reference standards, and information concerning literature compounds, 396 components were identified from the three parts of the plant, including 94 groups of isomers, and 217 components were identified or tentatively identified as new compounds. In the rhizomes, leaves, and stems, 240, 220, and 168 compounds, respectively, were identified. Differential analysis of the compounds in the rhizomes and aerial parts was also carried out, and 36 differential components were identified, of which 32 had higher contents in the rhizomes. Therefore, these findings indicate that the number of chemical components and the content of major differential components are higher in the rhizomes than the leaves and stems of G. longipes from the Maobaling Planting Base in Pingli county, Shaanxi province. Thus, the rhizomes of G. longipes are also an important part for medicinal use. These results will contribute to the establishment of quality control methods for G. longipes.

Collision cross section measurement and prediction methods in omics

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.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Collision cross-section as a universal molecular descriptor in the analysis of PFAS and use of ion mobility spectrum filtering for improved analytical sensitivities

The massive usage of per- and polyfluoroalkyl substances (PFAS), as well as their high chemical stability, have led to their ubiquitous presence in environmental matrices and direct human exposure through contaminated food, particularly fish. In the analysis of this large group of substances, the use of ion mobility coupled to mass spectrometry is of particular relevance because it uses an additional descriptor, the collision cross-section (CCS), which results in increased selectivity. In the present work, the ^TWCCS_N2 of 24 priority PFAS were experimentally obtained, and the reproducibility of these measurements was evaluated over seven weeks. The average values were employed to critically assess previously reported data and theoretical calculations. This gain in selectivity made it possible to increase the sensitivity of the detection on complex matrices (biota, food and human serum) by using the drift time associated to each analyte as a filter, thus reducing the interferences and background noise and allowing their detection at trace levels.

Understanding mobile phase buffer composition and chemical structure effects on electrospray ionization mass spectrometry response

Mobile phase selection is of critical importance in liquid chromatography - mass spectrometry (LC-MS) based studies, since it affects retention, chromatographic selectivity, ionization, limits of detection and quantification, and linear dynamic range. Generalized LC-MS mobile phase selection criteria, suitable for a broad class of chemical compounds, do not exist thus far. Here we have performed a large-scale qualitative assessment of the effect of solvent composition used for reversed-phase LC separations on electrospray ionization (ESI) response for 240 small molecular weight drugs, representing various chemical compound classes. Of these 240 analytes 224 were detectable using ESI. The main chemical structural features affecting ESI response were found to all be surface area or surface charge-related. Mobile phase composition was found to be less differentiating, although for some compounds a pH effect was noted. Unsurprisingly, chemical structure was found to be the dominant factor for ESI response for the majority of the investigated analytes, representing about 85% of the replicating detectable complement of the sample data set. A weak correlation between ESI response and structure complexity was observed. Solvents based on isopropanol, and those containing phosphoric or di- and trifluoracetic acids, performed relatively poorly in terms of chromatographic or ESI response, whilst the best performing 'generic' LC solvents were based on methanol, acetonitrile using formic acid and ammonium acetate as buffer components, consistent with current practice in many laboratories.

Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section

The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics

Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools

The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such compounds. This study presents a workflow to identify nonvolatile migrates from FCMs based on liquid chromatography-ion mobility-high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. The applicability of this workflow was evaluated by screening the chemicals that migrated from polyamide (PA) spatulas. The number of candidate compounds was reduced by approximately 75% and 29% on applying RT and CCS prediction filters, respectively. A total of 95 compounds were identified in the PA spatulas of which 54 compounds were confirmed using reference standards. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.

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

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.

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

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 (^TWCCS_N2) values for extractables and leachables from FCMs. The database contains a total of 1038 ^TWCCS_N2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The ^TWCCS_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 ^TWCCS_N2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The ^TWCCS_N2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.

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

Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.

Meeting report of the first European biotransformation workshop

1. Challenges and opportunities in the field of biotransformation were presented and discussed at the 1st European Biotransformation workshop which was conducted virtually in collaboration with the DMDG 27 January 2021. Here we summarize the presentations and discussions from this workshop.The following topics were covered:2. Needs for radiolabel for IND filing versus quantitation without standards.3. Applications of cyclic ion mobility in the field of biotransformation.4. Computational predictions of xenobiotic metabolism.5. Future (outsourcing) needs in biotransformation.6. Genotoxicity risk assessment of metabolites and qualification of impurities using metabolite data.7. Regulatory aspects of MIST.

Use of Cyclic Ion Mobility Spectrometry (cIM)-Mass Spectrometry to Study the Intramolecular Transacylation of Diclofenac Acyl Glucuronide

1-β-O-Acyl-glucuronides (AGs) are common metabolites of carboxylic acid-containing xenobiotics, including, e.g., many nonsteroidal anti-inflammatory drugs (NSAIDs). They are of concern to regulatory authorities because of the association of these metabolites with the hepatotoxicity that has resulted in drug withdrawal. One factor in assessing the potential risk posed by AGs is the rate of transacylation of the biosynthetic 1-β-O-acyl form to the 2-, 3-, and 4-O-acyl isomers. While transacylation can be measured using ¹H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS), the process can be time consuming and involve significant method development. The separation of these positional isomers by ion mobility spectrometry (IMS) has the potential to allow their rapid analysis, but conventional instruments lacked the resolving power to do this. Prediction of the collision cross section (CCS) using a machine learning model suggested that greater IMS resolution might be of use in this area. Cyclic IMS was evaluated for separating mixtures of isomeric AGs of diclofenac and was compared with a conventional ultraperformance liquid chromatography (UPLC)-MS method as a means for studying transacylation kinetics. The resolution of isomeric AGs was not seen using a conventional traveling wave IMS device; however, separation was seen after several passes around a cyclic IMS. The cyclic IMS enabled the degradation of the 1-β-O-acyl-isomer to be analyzed much more rapidly than by LC-MS. The ability of cyclic IMS to monitor the rate of AG transacylation at different pH values, without the need for a prior chromatographic separation, should allow high-throughput, real-time, monitoring of these types of reactions.