Ion mobility-mass spectrometry (IM-MS) has gained increased applications in the characterization and identification of drugs and drug metabolites, largely owning to the complementary separation of ...analyte ions based on their gas-phase size and shape in the IM dimension in addition to their mass-to-charge ratios. In this review, we discuss recent advances in such applications. We first introduce various types of IM techniques, focusing on those that allow the measurement of collision cross section (CCS), the physical property of an ion that reflects its gas-phase size and shape. Next, we discuss the IM-MS landscape of the large chemical space of drugs and multimodal distributions of certain drugs in IM separation due to the presence of protomers. We then review drug metabolism reactions and discuss the application of IM-MS in separation and identification of isomeric drug metabolites. Subsequently, we discuss various approaches to generate theoretical and predicted CCS data, including theory-based calculation methods and data-driven prediction models, and currently available resources on these approaches. Finally, current limitations and future directions of application of IM-MS are discussed.
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•IM-MS landscape of the large chemical space of drugs.•Multimodal distribution of protomers of drugs in IM separation.•Characterization and identification of isomeric drug metabolites.•Theoretical approaches for the calculation and prediction of collision cross sections.
Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid ...two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications.
Ion mobility-mass spectrometry (IM-MS) can provide orthogonal information, i.e., m/z and collision cross section (CCS), for the identification of drugs and drug metabolites. However, only a small ...number of CCS values are available for drugs, which limits the use of CCS as an identification parameter and the assessment of structure–function relationships of drugs using IM-MS. Here, we report the development of a rapid workflow for the measurement of CCS values of a large number of drug or drug-like molecules in nitrogen on the widely available traveling wave IM-MS (TWIM-MS) platform. Using a combination of small molecule and polypeptide CCS calibrants, we successfully determined the nitrogen CCS values of 1425 drug or drug-like molecules in the MicroSource Discovery Systems’ Spectrum Collection using flow injection analysis of 384-well plates. Software was developed to streamline data extraction, processing, and calibration. We found that the overall drug collection covers a wide CCS range for the same mass, suggesting a large structural diversity of these drugs. However, individual drug classes appear to occupy a narrow and unique space in the CCS–mass 2D spectrum, suggesting a tight structure–function relationship for each class of drugs with a specific target. We observed bimodal distributions for several antibiotic species due to multiple protomers, including the known fluoroquinolone protomers and the new finding of cephalosporin protomers. Lastly, we demonstrated the utility of the high-throughput method and drug CCS database by quickly and confidently confirming the active component in a pharmaceutical product.
Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics ...experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses (“stats” module), generating informative plots (“plotting” module), identifying lipid species at different confidence levels (“identification” module), and carrying out all functions using a user-friendly text-based interface (“interactive” module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.
Conventional strategies for drug metabolite identification employ a combination of liquid chromatography-mass spectrometry (LC-MS), which offers higher throughput but provides limited structural ...information, and nuclear magnetic resonance spectroscopy, which can achieves the most definitive identification but lacks throughput. Ion mobility-mass spectrometry (IM-MS) is a rapid, two-dimensional analysis that separates ions on the basis of their gas-phase size and shape (reflected by collision cross section, CCS) and their mass-to-charge (m/z) ratios. The rapid nature of IM separation combined with the structural information provided by CCS make IM-MS a promising technique for obtaining more structural information on drug metabolites without sacrificing analytical throughput. Here, we present an in vitro biosynthesis coupled with IM-MS strategy for rapid generation and analysis of drug metabolites. Drug metabolites were generated in vitro using pooled subcellular fractions derived from human liver and analyzed using a rapid flow injection–IM-MS method. We measured CCS values for 19 parent drugs and their 37 metabolites generated in vitro (78 values in total), representing a wide variety of metabolic modifications. Post-IM fragmentation and computational modeling were used to support metabolite identifications and explore the structural characteristics driving behaviors observed in IM separation. Overall, we found the effects of metabolic modifications on the gas-phase structures of the metabolites to be highly dependent upon the structural characteristics of the parent compounds and the specific position of the modification. This in vitro biosynthesis coupled with rapid IM-MS analysis workflow represents a promising platform for rapid and high-confidence identification of drug metabolites, applicable at a large scale.
Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry ...(IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion’s gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8–2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.
The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological ...understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.
The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the ...clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography–ion mobility–tandem mass spectrometry (LC–IM–MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC–IM–MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.
Dalbavancin is a lipoglycopeptide active against methicillin-resistant Staphylococcus aureus (MRSA). Its long half-life (8.5–16 days) allows for once-weekly or single-dose treatments but could ...prolong the mutant selection window, promoting resistance and cross-resistance to related antimicrobials such as vancomycin. The objective of this study was to evaluate the capacity of post-distributional pharmacokinetic exposures of dalbavancin to select for resistance and cross-resistance in MRSA.
We simulated average, post-distributional exposures of single-dose (1500 mg) dalbavancin (fCmax 9.9 μg/mL, β-elimination t1/2 204 h) in an in vitro pharmacokinetic/pharmacodynamic (PK/PD) model for 28 days (672 h) against five MRSA strains and one methicillin-susceptible strain (MSSA). Samples were collected at least daily, and surviving colonies were enumerated and screened for resistance on drug-free and dalbavancin-supplemented medium respectively. Isolates from resistance screening plates were subjected to whole-genome sequencing (WGS) and susceptibly testing against dalbavancin, vancomycin, daptomycin, and six β-lactams with varying penicillin-binding protein (PBP) affinities.
Dalbavancin was bactericidal against most strains for days 1–4 before regrowth of less susceptible subpopulations occurred. Isolates with eight-fold increases in dalbavancin MIC were detected as early as day 4 but increased 64–128-fold in all models by day 28. Vancomycin and daptomycin MICs increased 4–16-fold, exceeding the susceptibly breakpoints for both antibiotics; β-lactam MICs generally decreased by two-to eight-fold, suggesting a dalbavancin–β-lactam seesaw effect, but increased by eight-fold or more in certain isolates. Resistant isolates carried mutations in a variety of genes, most commonly walKR, apt, stp1, and atl.
In our in vitro system, post-distributional dalbavancin exposures selected for stable mutants with reduced susceptibility to dalbavancin, vancomycin, and daptomycin, and generally increased susceptibility to β-lactams in all strains of MRSA tested. The clinical significance of these findings remains unclear, but created an opportunity to genotype a unique collection of dalbavancin-resistant strains for the first time. Mutations involved genes previously associated with vancomycin intermediate susceptibility and daptomycin non-susceptibility, most commonly walKR-associated genes.
Vaginal brachytherapy (VBT) is an essential component of curative intent treatment for many patients with endometrial cancer. The prevalence of trauma history in this population is unknown and ...important to understand considering VBT requires patients to have an instrument vaginally inserted while in the vulnerable lithotomy position. We aim to identify patients treated with intracavitary VBT and collect survey data to assess trauma endpoints.
We retrospectively identified patients with endometrial cancer who underwent intracavitary VBT at our institution between 01/2017 and 08/2022. Patients were mailed and/or electronically mailed a survey that included demographics, psychosocial background, and validated trauma surveys to be filled out as they relate to their trauma experiences prior to VBT and again considering any trauma symptomatology related to VBT. Electronic medical record review was performed. Descriptive statistics as well as multivariate analysis were performed.
206 patients met inclusion criteria, 66 (32.1%) of whom returned the survey and were included for analysis. Thirty-two percent of patients self-reported a personal history of any prior mental health diagnosis. Eighty-eight percent of patients screened positive for a history of trauma exposure, 23% endorsed symptoms of PTSD related to their VBT experience, and 5% screened positive for a likely PTSD diagnosis from VBT.
A majority of included patients had a history of trauma exposure prior to VBT. In a subset of patients, VBT re-induced trauma and was considered to be an independent traumatic event. This study highlights the importance of practicing trauma informed care, particularly in this patient population.
•88% of patients screened positive for trauma exposure on the BTQ, 20% indicated history of unwanted sexual contact.•11% of patients indicated VBT triggered an increase in thoughts or flashbacks to traumatic event(s).•VBT was considered to independently induce trauma symptoms in 23% of patients.•36% of patients would have accepted referral to psychiatric services.
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