High-resolution Mass Spectrometry Data Research Articles

A 1,8-Naphthalimide-based Tripodal Fluorescent Chemosensor to Selectively Detect Copper Ions.

A 1,8-naphthalimide-based tripodal fluorescent ligand (L3) was synthesized through the copper (I) catalyzed Huisgen azide-alkyne cycloaddition reaction of 2-(2-azidoethyl)-6-morpholino-1H-benzo[de]isoquinoline-1,3(2H)-dione with triproparagylamine. Naphthalimide acts as the fluorophore while the triazole and amine nitrogens chelate the metal ion. L3 showed a selective fluorescence turn-off for Cu(II) over other metal ions in aqueous acetonitrile solution. A Job's plot, Benesi-Hildbrand plot and high-resolution mass spectrometry data confirm a 1:1 binding stoichiometry with a binding constant of 7.8 х105 M- 1 while addition of disodium EDTA demonstrates its reversibility. The structure and stability of the complex was supported by theoretical calculations. The limit of detection for Cu(II) was calculated to be 0.3 µM which is considerably lower than WHO recommended Cu(II) limit in drinking water.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Regular Tetrahedron Model for the Assessment of High-Resolution Mass Spectrometry Data of Four-Way Fractionated Dissolved Organic Matter.

A regular tetrahedron model was established to pierce the fractionation of dissolved organic matter (DOM) among quaternary components by using high-resolution mass spectrometry. The model can stereoscopically visualize molecular formulas of DOM to show the preference to each component according to the position in a regular tetrahedron. A classification method was subsequently developed to divide molecular formulas into 15 categories related to fractionation ratios, the relative change of which was demonstrated to be convergent with the uncertainty of mass peak area. The practicality of the regular tetrahedron model was verified by seven kinds of sludge from waste leachate treatment and sewage wastewater treatment plants by using stratification of extracellular polymeric substances coupled with Orbitrap MS as an example, presenting the DOM chemodiversity in stratified sludge flocs. Sensitivity analysis proved that classification results were relatively stable with the perturbation of four model parameters. Multinomial logistic regression analysis could further help identify the effect of molecular properties on the fractionation of DOM based on the classification results of the regular tetrahedron model. This model offers a methodology for the assessment of specificity of sequential extraction on DOM from solid or semisolid components and simplifies the complex mathematical expression of fractionation coefficients for quaternary components.

Multilevel Screening Strategy to Identify the Hydrophobic Organic Components of Ambient PM2.5 Associated with Hepatocellular Steatosis.

Hepatic steatosis is the first step in a series of events that drives hepatic disease and has been considerably associated with exposure to fine particulate matter (PM2.5). Although the chemical constituents of particles matter in the negative health effects, the specific components of PM2.5 that trigger hepatic steatosis remain unclear. New strategies prioritizing the identification of the key components with the highest potential to cause adverse effects among the numerous components of PM2.5 are needed. Herein, we established a high-resolution mass spectrometry (MS) data set comprising the hydrophobic organic components corresponding to 67 PM2.5 samples in total from Taiyuan and Guangzhou, two representative cities in North and South China, respectively. The lipid accumulation bioeffect profiles of the above samples were also obtained. Considerable hepatocyte lipid accumulation was observed in most PM2.5 extracts. Subsequently, 40 of 695 components were initially screened through machine learning-assisted data filtering based on an integrated bioassay with MS data. Next, nine compounds were further selected as candidates contributing to hepatocellular steatosis based on absorption, distribution, metabolism, and excretion evaluation and molecular dockingin silico. Finally, seven components were confirmed in vitro. This study provided a multilevel screening strategy for key active components in PM2.5 and provided insight into the hydrophobic PM2.5 components that induce hepatocellular steatosis.

Comparison of data processing strategies using commercial vs. open-source software in GC-Orbitrap-HRMS untargeted metabolomics analysis for food authentication: thyme geographical differentiation and marker identification as a case study

Untargeted analysis of gas chromatography–high-resolution mass spectrometry (GC-HRMS) data is a key and time-consuming challenge for identifying metabolite markers in food authentication applications. Few studies have been performed to evaluate the capability of untargeted data processing tools for feature extraction, metabolite annotation, and marker selection from untargeted GC-HRMS data since most of them are focused on liquid chromatography (LC) analysis. In this framework, this study provides a comprehensive evaluation of data analysis tools for GC-Orbitrap-HRMS plant metabolomics data, including the open-source MS-DIAL software and commercial Compound Discoverer™ software (designed for Orbitrap data processing), applied for the geographical discrimination and search for thyme markers (Spanish vs. Polish differentiation) as the case study. Both approaches showed that the feature detection process is highly affected by unknown metabolites (Levels 4–5 of identification confidence), background signals, and duplicate features that must be carefully assessed before further multivariate data analysis for reliable putative identification of markers. As a result, Compound Discoverer™ and MS-DIAL putatively annotated 52 and 115 compounds at Level 2, respectively. Further multivariate data analysis allowed the identification of differential compounds, showing that the putative identification of markers, especially in challenging untargeted analysis, heavily depends on the data processing parameters, including available databases used during compound annotation. Overall, this method comparison pointed out both approaches as good options for untargeted analysis of GC-Orbitrap-HRMS data, and it is presented as a useful guide for users to implement these data processing approaches in food authenticity applications depending on their availability.Graphical

Postmortem metabolomics as a high-throughput cause-of-death screening tool for human death investigations

Autopsy rates are declining globally, impacting cause-of-death (CoD) diagnoses and quality control. Postmortem metabolomics was evaluated for CoD screening using 4,282 human cases, encompassing CoD groups: acidosis, drug intoxication, hanging, ischemic heart disease (IHD), and pneumonia. Cases were split 3:1 into training and test sets. High-resolution mass spectrometry data from femoral blood were analyzed via orthogonal-partial least squares discriminant analysis (OPLS-DA) to discriminate CoD groups. OPLS-DA achieved an R2= 0.52 and Q2= 0.30, with true-positive prediction rates of 68% and 65% for training and test sets, respectively, across all groups. Specificity-optimized thresholds predicted 56% of test cases with a unique CoD, average 45% sensitivity, and average 96% specificity. Prediction accuracies varied: 98.7% for acidosis, 80.5% for drug intoxication, 81.6% for hanging, 73.1% for IHD, and 93.6% for pneumonia. This study demonstrates the potential of large-scale postmortem metabolomics for CoD screening, offering high specificity and enhancing throughput and decision-making in human death investigations.

Targeted and untargeted screening for impurities in losartan tablets marketed in Germany by means of liquid chromatography/high resolution mass spectrometry

Technical advances in the field of quality analysis allow an increasingly deeper look into the impurity profile of drugs. The ability to detect unexpected impurities in addition to known impurities ensures the supply of high-quality drugs and can prevent recalls due to the detection of harmful unexpected impurities, as has happened recently with the N-nitrosamine and azido impurities in losartan (LOS) drug products. In the present study, the LC-MS/HRMS approach described by Backer et al. was applied to an even more complex system, being the investigation of 35 LOS drug products and combination preparations purchased in 2018 and 2022 in German pharmacies. The film-coated tablets were analysed by means of four LC-MS/HRMS method variants. For the separation a Zorbax RR StableBond C18 column (3.0 ×100 mm, particle size of 3.5 µm, pore size of 80 Å), a gradient elution and for mass spectrometric detection a qTOF mass spectrometer with electrospray ionization in positive and negative mode was used. An information-dependent acquisition method was applied for the acquisition of high-resolution mass spectrometry data. The combination of an untargeted and a targeted screening approach revealed the finding of eight impurities in total. Beside the five LOS related compounds, LOS impurity F, J, K, L, M, and related compound D from amlodipine besilate, LOS azide and an unknown derivative thereof were detected. Identification and structure elucidation, respectively, were successfully performed using in silico fragmentation. Differences in the impurity profiles of drug products from 2018 and 2022 could be observed. This study shows that broad screening approaches like this are applicable to the analysis of drug products and can be an important enhancement of the quality assurance of medicinal products.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.Graphical

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors.

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

Metabolites rapid-annotation in mice by comprehensive method of virtual polygons and Kendric mass loss filtering: A case study of Dendrobium nobile Lindl

With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Identification and characterization of impurities in an insecticide, commercial chlorantraniliprole using liquid chromatography-tandem mass spectrometry.

Ensuring the global safety and effectiveness of agrochemicals has become imperative. An in-depth understanding of impurity profiles of products is crucial, especially for high-demand agrochemicals, where impurities may be more toxic and persistent than original agrochemicals. This study focuses on the detection and identification of impurities in a commercial chlorantraniliprole (CAP), an anthranilic diamide class broad-spectrum insecticide. Commercial CAP was collected from an agrochemical supplier in India and was analyzed using a high-performance liquid chromatography-photodiode array (HPLC-PDA) (Agilent 1260; wavelength, 220 nm) with a Zorbax RP SB-C18 (250 × 4.6mm, 5μm) column and liquid chromatography-mass spectrometry (LC-MS) (Agilent 6545 quadrupole time of flight (Q-TOF)) techniques to identify the impurities. The impurities were isolated by preparative HPLC using a Zorbax-DB C18 (250 × 9.4mm, 5μm) column. liquid chromatography- tandem mass spectrometry (LC-MS/MS) experiments (Q-TOF) were performed on CAP and its impurities to obtain their structural data. HPLC-PDA analysis of CAP showed four major impurities (IM-1 to IM-4) ranging from 0.76% to 4.1%. The positive ion electrospray ionization (ESI) mass spectra of CAP and its impurities showed dominant [M + H]+ ions in addition to [M + Na]+ , [M + K]+ , and [2M + Na]+ ions. High-resolution mass spectrometry (HRMS) data provided the elemental composition of the compounds, and isotopic distribution patterns revealed the number of Cl and/or Br atoms present in them. The structures of impurities were proposed based on the LC-MS/MS) data and further confirmed by nuclear magnetic resonance (NMR) data on isolated impurities/synthesis. The quality and impurities of CAP, a popular insecticide, must be assessed and described for its efficacy and safety. In this study, four impurities of CAP were detected using HPLC and successfully characterized using LC-HRMS, LC-MS/MS, and NMR data. The method is useful for verifying the purity of CAP as well as helping in the identification of its possible impurities.

Cycloartane-type triterpenoids from the leaves of Sandoricum koetjape and their efficacy on α-glucosidase inhibition activity.

The preliminary α-glucosidase inhibitory activity of the methanol extract of the leaves of Sandoricum koetjape Merr. exhibited promising results. The leaves was extracted with methanol to obtain the methanol extract that was continuedly partitioned with hexane and ethyl acetate. Those fractions were further purified by various chromatographic techniques. The isolation of the potent fractions furnished two new cycloartane-type triterpenoids (1 and 2) along with ten known compounds (3-12). Their chemical structures were unambiguously established by interpretation of NMR (1 D & 2 D) and high-resolution electrospray ionization mass spectrometry (HRESIMS) data. Furthermore, the configurations of two new compounds were determined by using NOESY spectrum as well as comparing their NMR data to the reference. These compounds were evaluated against α-glucosidase. All tested compounds revealed potent activity with IC50 value in the range of 2.17-49.2µM compared to that of acarbose (IC50 100.6µM). Compound 10 showed the lowest IC50 value. This compound was reported as a mixed-type inhibitor. Compound 3 possessed the second strong activity with an IC50 value of 14.0μM and was further investigated on kinetic analysis which revealed as a mixed-type inhibitor with Ki and Ki' values of 59.1 and 155.2μM, respectively.

Identification of clobromazolam in Australian emergency department intoxications using data-independent high-resolution mass spectrometry and the HighResNPS.com database.

The proliferation of novel psychoactive substances (NPSs) continues to challenge toxicology laboratories. In particular, the United Nations Office on Drugs and Crime considers designer benzodiazepines to be a current primary threat among all NPSs. Herein, we report detection of a new emerging designer benzodiazepine, clobromazolam, using high-resolution mass spectrometry and untargeted data acquisition in combination with a "suspect screening" method built from the crowd-sourced HighResNPS.com database. Our laboratory first detected clobromazolam in emergency department presenting intoxications included within the Emerging Drugs Network of Australia-Victoria project in the state of Victoria, Australia, from April 2022 to March 2023. Clobromazolam was the most frequent designer benzodiazepine detected in this cohort (100/993 cases, 10%). No patients reported intentional administration of clobromazolam, although over half reported exposure to alprazolam, which was detected in only 7% of cases. Polydrug use was prevalent (98%), with phenazepam (45%), methylamphetamine (71%) and other benzodiazepines (60%) most frequently co-detected. This is the first case series published in the literature concerning clobromazolam in clinical patients. The identification of clobromazolam in patients presenting to emergency departments in Victoria demonstrates how high-resolution mass spectrometry coupled with the HighResNPS.com database can be a valuable tool to assist toxicology laboratories in keeping abreast of emerging psychoactive drug use.

Molecular characteristics of eight lignites based on the big data obtained from Orbitrap mass spectrometry

Eight lignites from different regions were selected for thermal dissolution (TD) sequentially using cyclohexane, methanol and ethanol. The TD extracts of the eight coal samples were characterized using atmospheric pressure chemical ionization source (APCI)-Orbitrap mass spectrometry (MS). Chemometric methods were employed to deeply explore the molecular structural and composition information of the organic compounds detected by Orbitrap MS. The longest distance method in systematic clustering of chemometrics was employed to ascertain the optimal number of groups, which was found to be 7 by clustering 24 soluble portions (SPs) using Euclidean distance. Taking one group as an example, the similarities and differences of SPs within the group were derived based on the compositional class of O1N1, N1, O2N1 and O2, and heteroatom distribution patterns of the SPs were proposed and compared. The integration of high-resolution MS data and chemometric methods not only exposes the existence of heteroatomic compounds in lignite but also furnishes evidence for the origin and evolution of lignite, as well as a methodology for the high-value utilization of lignites from different sources.

A widely targeted metabolite modificomics strategy for modified metabolites identification in tomato.

The structural and functional diversity of plant metabolites is largely created via chemical modification of a basic backbone. However, metabolite modifications in plants have still not been thoroughly investigated by metabolomics approaches. In this study, a widely targeted metabolite modificomics (WTMM) strategy was developed based on ultra-high performance liquid chromatography-quadrupole-linear ion trap (UHPLC-Q-Trap) and UHPLC-Q-Exactive-Orbitrap (UHPLC-QE-Orbitrap), which greatly improved the detection sensitivity and the efficiency of identification of modified metabolites. A metabolite modificomics study was carried out using tomato as a model, and over 34,000 signals with MS2 information were obtained from approximately 232 neutral loss transitions. Unbiased metabolite profiling was also performed by utilizing high-resolution mass spectrometry data to annotate a total of 2,118 metabolites with 125 modification types; of these, 165 modified metabolites were identified in this study. Next, the WTMM database was used to assess diseased tomato tissues and 29 biomarkers were analyzed. In summary, the WTMM strategy is not only capable of large-scale detection and quantitative analysis of plant-modified metabolites in plants, but also can be used for plant biomarker development.

Chalcogen- and Halogen-Bond-Donating Cyanoborohydrides Provide Imine Hydrogenation.

Sulfonium, selenonium, telluronium, and iodonium cyanoborohydrides have been synthesized, isolated, and fully characterized by various methods, including single-crystal X-ray diffraction (XRD) analysis. The quantum theory of atoms in molecules' analysis based on the XRD data indicated that the hydride···σ-hole short contacts observed in the crystal structures of each compound have a purely noncovalent nature. The telluronium and iodonium cyanoborohydrides provide a significantly higher rate of the model reaction of imine hydrogenation compared with sodium and tetrabutylammonium cyanoborohydrides. Based on the NMR and high-resolution electrospray ionization mass spectrometry data indicating that the reaction progress is accompanied by the cation reduction, a mechanism involving intermediate formation of elusive onium hydrides has been proposed as an alternative to conventional electrophilic activation of the imine moiety by its ligation to the cation's σ-hole.

Comparative Analysis of Glycoform Profiles Between Biosimilar and Originator Monoclonal Antibodies by Liquid Chromatography–Mass Spectrometry

Glycosylation is considered as a critical quality attribute for monoclonal antibodies (mAbs) and needs routine monitoring during production. This study aims to compare the glycoform profiles of biosimilar and four originator mAbs using ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization-quadrupole time of flight-mass spectrometry (ESI/Q-TOF MS). The resultant mass spectrum showed that seven different glycoform pairs, including G0F–GN/G0, G0F–GN/G0F, G0F/G0F, G0F/G1F, G1F/G1F, G1F/G2F, and G2F/G2F were identified via intact mass analysis for all tested mAb samples. The correct identification of each glycoform pair was achieved by comparing the observed mass with its theoretical mass using high-resolution mass spectrometry data (with mass accuracies of less than 100 ppm). The most abundant paired glycoforms detected at the intact protein level are G0F/G0F and G0F/G1F, with relative abundance ranges of 38.45 – 43.43% and 19.32 – 22.20%, respectively. The obtained data demonstrated that biosimilar and originators have the same types of glycoform pairs, and the relative abundances of each pair were comparable among biosimilar and four originator mAb samples. Additionally, the reduced mass analysis revealed that five different glycans (G0F–GN, G0, G0F, G1F, and G2F) were attached to the heavy chain of the mAb, and the relative abundance of G0F ranged from 75.21 to 77.90%. The detected mass accuracies for reduced mass analysis were below 25 ppm. The results of the intact and reduced mass analyses showed that the biosimilar is similar to its originator in terms of glycoform percentages and molecular masses.

Application of advanced high resolution mass spectrometric techniques for the analysis of losartan potassium regarding known and unknown impurities

Recalls of medicinal products can cause supply bottlenecks. This is often due to the findings of unexpected impurities that pose a health risk to patients. A recent example is losartan potassium which was contaminated with azido-impurities. The choice of the analytical method determines which substances can be detected and thus controlled. In this study a combination of an untargeted screening approach for impurities and a targeted evaluation of high-resolution mass spectrometry data was applied to search for impurities not described so far, leaving out a precise quantification. Six losartan potassium samples were studied regarding known and unknown impurities and hence highlight the applicability and capability of the approach. For separation a Zorbax RR StableBond C18 column (3.0 ×100 mm, particle size of 3.5 µm, pore size of 80 Å), a gradient elution and an electrospray ionization in positive and negative mode for mass spectrometric detection was used. An information-dependent acquisition method was applied for the measurement of losartan potassium samples. The untargeted data evaluation using general unknown comparative screening revealed the presence of N-methyl-2-pyrrolidone (NMP) and another impurity from synthesis. The identity of NMP was corroborated by a spiking experiment and the amount was estimated by means of standard addition. A targeted data evaluation by generating extracted ion chromatograms resulted in finding of four additional impurities. Combined approaches like this are needed to detect and respond to changes in the quality of drugs precociously.