The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.
In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.
A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3\',4\'-didehydro-3\'-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients\' levels.
Finally, we demonstrate the added value of the kynurenic acid/tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.

We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk.
This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60-40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out.
LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%.
Our study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development. Trials registration NCT00924937.

The SARS-CoV-2 virus sets up a global catastrophe, and countries all around the world made significant efforts to halt the spread. Nirmatrelvir (NMV) was lately approved by the FDA as a safe and well-tolerated oral direct-acting antiviral medication for SARS-CoV-2 virus infection. Therefore, a fast completely validated stability indicating method was established-for the first time- for NMV determination. The study used NaOH, HCl, neutral, H2O2, and sunlight to test NMV stability under various stress conditions followed by kinetics degradation investigation and derivation of Arrhenius plot. The analysis was performed using Agilent Zorbax Eclipse-C18 column (5 µm, 4.6 × 250 mm) with a mobile phase consisting of acetonitrile: 50 mM ammonium acetate, pH = 5 (50:50, v/v, respectively) at a flow rate of 1.0 mL/min with 5 min run time. Diode array detector (DAD) was set at 225 nm to quantify NMV at the concentration range of 5-500 µg/mL with LOD and LOQ of 0.6 and 2 µg/mL, respectively. Method\'s greenness was assessed using different metrics including Analytical Eco-Scale, Greenness Assessment Procedure Index, GAPI, and Analytical Greenness, AGREE. A thorough study of stress stability revealed that NMV was more susceptible to alkaline hydrolysis compared with acid hydrolysis. In contrast, it was found that NMV remained stable when subjected to oxidative, neutral, and sun-induced degradation conditions. Moreover, acid and alkali-induced hydrolysis were found to follow pseudo first order kinetics. Consequently, the half lifetime of the studied degradation conditions at room temperature were calculated using the Arrhenius plot. The mechanism of the degradation pathways under stress circumstances was proposed using LC-MS-UV. Toxicities of the proposed degradation products were assessed using ProTox-II, along with the parent medication NMV, and were shown to be hardly hazardous.

Azacitidine (Vidaza®, AZA) is a mainstay for treating acute myeloid leukemia (AML) in patients unfit for standard induction and other myelodysplastic syndromes (MDS). However, only half of the patients usually respond to this drug and almost all patients will eventually relapse. Predictive markers for response to AZA are yet to be identified. AZA is metabolized in the liver by a single enzyme, cytidine deaminase (CDA). CDA is a ubiquitous enzyme coded by a highly polymorphic gene, with subsequent great variability in resulting activities in the liver. The quantitative determination of AZA in plasma is challenging due the required sensitivity and because of the instability in the biological matrix upon sampling, possibly resulting in erratic values.
We have developed and validated following EMA standards a simple, rapid, and cost-effective liquid chromatography-tandem mass spectrometry method for the determination of azacitidine in human plasma.
After a simple and rapid precipitation step, analytes were successfully separated and quantitated over a 5-500 ng/mL range. The performance and reliability of this method were tested as part of an investigational study in MDS/AML patients treated with standard azacitidine (75 mg/m2 for 7 days a week every 28 days).
Overall, this new method meets the requirements of current bioanalytical guidelines and could be used to monitor drug levels in MDS/AML patients.

BACKGROUND: Too little sleep and the consequences thereof are a heavy burden in modern societies. In contrast to alcohol or illicit drug use, there are no quick roadside or workplace tests for objective biomarkers for sleepiness. We hypothesize that changes in physiological functions (such as sleep-wake regulation) are reflected in changes of endogenous metabolism and should therefore be detectable as a change in metabolic profiles. This study will allow for creating a reliable and objective panel of candidate biomarkers being indicative for sleepiness and its behavioral outcomes.
METHODS: This is a monocentric, controlled, randomized, crossover, clinical study to detect potential biomarkers. Each of the anticipated 24 participants will be allocated in randomized order to each of the three study arms (control, sleep restriction, and sleep deprivation). These only differ in the amount of hours slept per night. In the control condition, participants will adhere to a 16/8 h wake/sleep regime. In both sleep restriction and sleep deprivation conditions, participants will accumulate a total sleep deficit of 8 h, achieved by different wake/sleep regimes that simulate real-life scenarios. The primary outcome is changes in the metabolic profile (i.e., metabolome) in oral fluid. Secondary outcome measures will include driving performance, psychomotor vigilance test, d2 Test of Attention, visual attention test, subjective (situational) sleepiness, electroencephalographic changes, behavioral markers of sleepiness, changes in metabolite concentrations in exhaled breath and finger sweat, and correlation of metabolic changes among biological matrices.
CONCLUSIONS: This is the first trial of its kind that investigates complete metabolic profiles combined with performance monitoring in humans over a multi-day period involving different sleep-wake schedules. Hereby, we aim to establish a candidate biomarker panel being indicative for sleepiness and its behavioral outcomes. To date, there are no robust and easily accessible biomarkers for the detection of sleepiness, even though the vast damage on society is well known. Thus, our findings will be of high value for many related disciplines.
BACKGROUND: ClinicalTrials.gov Identifier NCT05585515, released on 18.10.2022; Swiss National Clinical Trial Portal SNCTP000005089, registered on 12 August 2022.

OBJECTIVE: To identify fasting serum metabolites associated with WG intake in a free-living population adjusted for potential confounders.
METHODS: We selected fasting serum samples at baseline from a subset (n = 364) of the prospective population-based Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD) cohort. The samples were analyzed using nontargeted metabolomics with liquid chromatography coupled with mass spectrometry (LC-MS). Association with WG intake was investigated using both random forest followed by linear regression adjusted for age, BMI, smoking, physical activity, energy and alcohol consumption, and partial Spearman correlation adjusted for the same covariates. Features selected by any of these models were shortlisted for annotation. We then checked if we could replicate the findings in an independent subset from the same cohort (n = 200).
RESULTS: Direct associations were observed between WG intake and pipecolic acid betaine, tetradecanedioic acid, four glucuronidated alkylresorcinols (ARs), and an unknown metabolite both in discovery and replication cohorts. The associations remained significant (FDR<0.05) even after adjustment for the confounders in both cohorts. Sinapyl alcohol was positively correlated with WG intake in both cohorts after adjustment for the confounders but not in linear models in the replication cohort. Some microbial metabolites, such as indolepropionic acid, were positively correlated with WG intake in the discovery cohort, but the correlations were not replicated in the replication cohort.
CONCLUSIONS: The identified associations between WG intake and the seven metabolites after adjusting for confounders in both discovery and replication cohorts suggest the potential of these metabolites as robust biomarkers of WG consumption.

This work presents a comparative study for the analysis of carbohydrates for four common chromatographic methods, each coupled to mass spectrometry. Supercritical fluid chromatography (SFC), hydrophilic interaction liquid chromatography (HILIC), reversed-phase liquid chromatography (RP-LC) and gas chromatography (GC) with detection by triple quadrupole mass spectrometer (QqQ-MS) are compared. It is shown that gas chromatography and reversed-phase liquid chromatography, each after derivatisation, are superior to the other two methods in terms of separation performance. Furthermore, comparing the different working modes of the mass spectrometer, it can be determined that a targeted analysis, i.e. moving from full scan to single ion monitoring (SIM) and multiple reaction monitoring (MRM), results in an improvement in the sensitivity as well as the repeatability of the method, which has deficiencies especially in the analysis using HILIC. Overall, RP-LC-MS in MRM after derivatisation with 1-phenyl-3-methyl-5-pyrazolone (PMP) proved to be the most suitable method in terms of separation performance, sensitivity and repeatability for the analysis of monosaccharides. Detection limits in the nanomolar range were achieved, which corresponds to a mass concentration in the low µg/L range. The applicability of this method to different biological samples was investigated with various herbal liquors, pectins and a human glycoprotein.

Aim: Mass-selective quantitation is a powerful attribute of LC-MS as a platform for bioanalysis. Here, a sensitive LC-MS approach has been validated for an oligonucleotide having chemical modifications (e.g., N-acetylgalactosamine [GalNAc] conjugated), to distinguish between the conjugated and unconjugated forms of the oligonucleotide, thereby enabling a nuanced view of the pharmacokinetic profile. Results: A high-sensitivity methodology for mass-specific measurement of AZD8233, a GalNAc-conjugated 16-mer oligonucleotide, using LLE-SPE with optimized LC conditions and detection of a low-mass fragment ion was successfully validated in the range of 0.20-100 ng/ml in human plasma. Conclusion: The AZD8233 LC-MS methodology adds valuable insight on the GalNAc linker\'s in vivo stability to the program and should be broadly applicable to oligonucleotides requiring high sensitivity and mass-selective measurement for quantitative discrimination from metabolites and endogenous interferences.

Forced degradation study is a systemic characterization of degradation products of active pharmaceutical ingredient (API) at conditions which posses more harsh environment that accelerates degradation of API. Forced degradation and stability studies would be useful in selection of proper, packaging material and storage conditions of the API. These are also useful to demonstrate degradation pathways and degradation products of the API and further characterisation of the degradation products using mass spectrometry. TGR5 is a G protein-coupled receptor, activation of which promotes secretion of glucagon-like peptide-1 (GLP-1) and modulates insulin secretion. The potent and orally bioavailable TGR5 agonist, ZY12201, shows activation of TGR5 which increase secretion of GLP-1 and help in lowering blood glucose level in animal models. Hence it is necessary to establish and study degradation pathway and stability of API for better handling and regulatory approval. Force degradation studies of ZY12201 have shown presence of one oxidative impurity during oxidative degradation in HPLC analysis. The oxidized product is further characterized by LC-MS to elucidate structure of impurity and characterize its degradation pathway.
BACKGROUND: The online version contains supplementary material available at 10.1007/s42452-021-04660-y.