INTRODUCTION: Therapeutic drug monitoring of antiepileptic drugs is critical for optimizing clinical outcomes, minimizing toxicity, assessing drug compliance, and managing overdoses and drug interactions. OBJECTIVES: We developed and validated an LC-MS/MS method for the quantification of 15 antiepileptic drugs (ethosuximide, primidone, pentobarbital, carbamazepine-10,11 epoxide, pregabalin, gabapentin, zonisamide, lacosamide, rufinamide, felbamate, lamotrigine, topiramate, 10,11-dihydro-10-hydroxycarbamazepine, perampanel, and brivaracetam). METHODS: Antiepileptic drugs were extracted from plasma and serum using methanol-based protein precipitation, followed by dilution. A commercial ClinCal® 3-point calibrator was used for all analytes except pentobarbital, which used a 6-point in-house calibrator. Chromatographic separation was achieved using a reverse-phase C18 column with a 7.31 min elution gradient of water and methanol, both containing 2 mM of ammonium acetate. RESULTS: All analytes required a 5 µL injection volume, except ethosuximide and pentobarbital, which required 20 µL and 10 µL, respectively, for optimal performance. The method demonstrated excellent linearity for all 15 antiepileptic drugs within their respective concentration ranges, with acceptable selectivity, accuracy (80.0–118.3%), and intraday and interassay precision (CV < 7.4%). Stability studies showed that antiepileptic drugs were stable in serum and plasma for up to 24 h at room temperature, 7 days at 4 °C, and 30 days at −20 °C, and after extraction for 7 days at 4 °C. CONCLUSION: This LC-MS/MS method supports routine quantification of 15 antiepileptic drugs in a clinical laboratory using a common sample preparation and chromatography workflow across all measurements. It was operationalized into three instrument methods to accommodate the different injection volume and calibrator requirements.
BACKGROUND: Escherichia coli (E. coli) is a clinically significant pathogen. Early identification of carbapenem-resistant E. coli (CREC) is critical for reducing mortality. METHOD: Gas chromatography-ion mobility spectrometry (GC-IMS) combined with chemometric analysis was used to detect volatile organic compounds (VOCs) from 40 clinical E. coli isolates. A two-phase experimental design was implemented: an exploratory phase with six replicates and a validation phase with 40 isolates. Bacteria were cultured in BacT/ALERT® SA broth, and VOCs were analyzed at specific time points. Imipenem (IPM) was added to induce metabolic stress, whereas pyridine-2,6-dicarboxylic acid (DPA) was used to enhance New Delhi metallo-β-lactamase (NDM)-type CREC discrimination. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to identify key VOCs. RESULTS: GC-IMS analysis revealed 55 VOCs as detectable signals, of which 30 were tentatively identified. In the absence of IPM, only three VOCs showed significant differences between carbapenem-sensitive E. coli (CSEC) and CREC. However, upon the addition of IPM, the number of differentially expressed VOCs increased to 17. PCA and PLS-DA identified 13 key VOCs (including four ketones, two esters, one aldehyde, one pyrazine, and five unknown compounds) that effectively distinguished CSEC from CREC. The addition of DPA further enabled the detection of nine additional VOCs, which may serve as potential markers for identifying NDM-type CREC. CONCLUSION: GC-IMS combined with chemometrics analysis can quickly distinguish CREC from CSEC through analysis of VOC spectra. The addition of IPM and DPA increases metabolic differences, providing a promising method for the rapid detection of CREC.
OBJECTIVES: Direct oral anticoagulants (DOACs) are the first-line therapy for stroke prevention in non-valvular atrial fibrillation. Exposure-response analysis has shown correlations between drug concentrations and clinical outcomes, making therapeutic drug monitoring a valuable tool. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) enables sensitive and accurate quantification for clinical analysis. Volumetric absorptive microsampling (VAMS) is a minimally invasive technique for collecting dried blood specimens. However, the translation from conventional venous blood sampling to VAMS and the conversion between plasma and whole-blood concentrations remain unclear. METHODS: We developed an LC–MS/MS method for quantifying four DOACs—dabigatran, apixaban, rivaroxaban, and edoxaban—in VAMS samples and applied it to paired clinical specimens to compare venous and finger-prick blood and establish conversion factors. RESULTS: Validation results support accuracy and precision of VAMS analysis by LC–MS/MS. Comparative analysis demonstrated no significant differences in concentrations between finger-prick and venous blood for dabigatran, apixaban, and rivaroxaban. Using paired clinical samples, conversion factors were derived via weighted Deming regression: 1.88, 1.57, 1.64, and 1.08 for dabigatran (n = 30), rivaroxaban (n = 33), apixaban (n = 35), and edoxaban (n = 36), respectively. The hematocrit effect was statistically significant for dabigatran. Bland–Altman analysis showed that more than 80% of samples fell within ±20% of the mean between estimated and measured plasma concentrations. CONCLUSION: These findings support the potential clinical utility of VAMS with LC–MS/MS as an accurate and convenient tool for DOAC monitoring, facilitating future implementation of precision medicine in anticoagulation management to minimize bleeding and stroke events.
INTRODUCTION: Antimicrobial resistance is a leading contributor to global mortality. Among the most concerning pathogens is Staphylococcus aureus, a gram-positive bacterium capable of causing severe infections. Particularly problematic is methicillin-resistant S. aureus (MRSA), which exhibits extensive resistance to antibiotics. Rapid differentiation between resistant and susceptible strains is essential for early and accurate intervention, enabling timely administration of appropriate antibiotics. However, conventional diagnostic methods remain slow, labor-intensive, and costly. METHODS: In this study, we developed a method using flow injection analysis–ion mobility–high-resolution mass spectrometry for the rapid discrimination of MRSA from its susceptible counterpart, methicillin-susceptible S. aureus (MSSA). The total instrument run time is 2 min, and samples can be analyzed after 6 h of incubation. RESULTS: MRSA was distinguished from MSSA based on the detection of three peptide peaks at m/z 733.791, 763.176, and 769.129, proposed as marker peaks for differentiating MRSA from MSSA. These peptide peaks were identified as phenol-soluble modulins (PSMα1, PSMα2, and PSMα4) using HRMS by applying de novo sequencing from HRMS/MS data. CONCLUSION: Our study may facilitate the rapid detection and discrimination of S. aureus strains, potentially enabling timely intervention and therapeutic strategies for affected patients.
INTRODUCTION: Ganciclovir (GCV) and its prodrug valganciclovir (VGCV) are commonly used to prevent and treat cytomegalovirus (CMV) infections in solid organ transplant recipients. Because of high interindividual pharmacokinetic variability, therapeutic drug monitoring (TDM) is important for optimizing therapy. Ganciclovir-triphosphate (GCV-TP), the active intracellular metabolite, is linked to both efficacy and toxicity; however, clinical monitoring is limited by analytical challenges. OBJECTIVES: To develop and validate a simple and sensitive liquid chromatography–tandem mass spectrometry (LC–MS/MS) method for quantifying GCV-TP in human red blood cells (RBCs). METHODS: GCV-TP was extracted using protein precipitation, separated on a BioBasic AX column, and detected with tandem mass spectrometry using guanosine-13C10-5′-triphosphate as the internal standard. RESULTS: The assay was linear over 0.01–2.00 μg/mL (r2 ≥ 0.99), with intra- and inter-assay variability ≤7.99%. GCV-TP in RBC lysate was stable when stored at room temperature for 4 h or at 4 °C for 24 h. This method was applied to RBC samples from 27 renal transplant recipients receiving VGCV, with a median GCV-TP concentration of 180.8 pmol/8 ×109 RBC (IQR: 113.6–297.6 pmol/8 ×109 RBC). GCV-TP levels showed a significant correlation with VGCV dose (p = 0.020, r = 0.31) but not with plasma GCV concentrations (p > 0.05). CONCLUSION: This validated method enables reliable measurement of intracellular GCV-TP and may serve as a useful tool to support individualized VGCV therapy and pharmacodynamic monitoring in clinical practice.
JMSACL has partnered with Stanford University Press and moved to a non-profit, open access model to better support trustworthy, widely available research in laboratory medicine. Publishing and reading will remain free in 2026, with institutions helping fund open access from 2027. MSACL will retain ownership of the journal, meaning all existing relationships (e.g., PubMed Central indexing) and scoring histories (e.g., Impact Factor) will remain intact. The use of AI in collaboration with human oversight to expedite review speed, while maintaining the quality of published content, will be evaluated for incorporation into the publishing workflow.
Background:Detecting and monitoring monoclonal free light immunoglobulin chains in serum is important for managing patients with B-cell neoplasms. Established methods have relied on immunochemistry, with monoclonality determined through an abnormal ratio of free kappa and lambda chains and an increased concentration of the involved chain. This indirect approach has limitations. Mass spectrometric methods that directly demonstrate the monoclonal fraction have been described; however, all reported approaches so far are affinity-dependent. The aim of this study was to develop a non-affinity-dependent high-resolution mass spectrometry (HRMS) method. Methods:Samples were prepared using ultrafiltration, then separated by reversed-phase liquid chromatography and analyzed by HRMS. The performance of the method was evaluated, including a comparison with a nephelometric immunoassay for free light immunoglobulin chains. Results:Concordance between HRMS and the immunoassay in classifying a sample as containing a monoclonal free light chain or not was 84% (based on 100 unique patient samples). HRMS identified more samples containing a monoclonal free light chain than the immunoassay. Some monoclonal fractions were glycosylated and/or cysteinylated. Imprecision (CV) for the concentration measurements of monoclonal fractions ranged from 10 to 14%. Conclusions:The HRMS method presented can detect, isotype, and semi-quantify monoclonal monomeric and dimeric light chains in serum, as well as demonstrate post-translational modifications. It is a selective, non-affinity-dependent method with a simple workflow that has the potential to become a valuable tool in the management of B-cell diseases.
Introduction Severe infections and sepsis significantly impact military operational readiness and costs through loss of duty days, high treatment rates, and medical evacuations. Early diagnosis is critical for preventing sepsis progression and mortality, but it requires validated biomarkers to guide clinical decision-making. Study protocols for host biomarker discovery in infections usually require inactivation of high-risk pathogens prior to sample analysis, which limits the utility of metabolomics assays designed for untreated samples. Methods Matched blood plasma aliquots obtained from an international, observational sepsis cohort were analyzed to quantify metabolites following the commercial AbsoluteIDQ p180 protocol, with and without the addition of an organic solvent extraction method for metabolites, proteins, and lipids (MPLEx), previously validated for inactivating BSL-3/4 pathogens. We evaluated analyte detection rates and concentrations for each method, as well as differences in extraction efficiency. Results Levels of agreement between the unmodified AbsoluteIDQ p180 and combined MPLEx-p180 methods varied by metabolite class. Most targeted amino acids, glycerophospholipids, sphingolipids, and monosaccharides were reliably measured and correlated well between methods. However, the higher sample dilution in the MPLEx-p180 method significantly reduced detection rates for biogenic amines and acylcarnitines, and overall extraction efficiencies also differed. Conclusions This study extends the applicability of commercial metabolomics assays designed for untreated samples by improving their suitability for high-risk infectious disease studies. Differences in metabolite extraction efficiencies and detection rates, as well as data harmonization strategies, should be considered if results from both protocols are to be combined.
Introduction:Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant public health threat. Rapid detection of CRKP and its resistance mechanisms is essential for optimizing antibiotic therapy and infection control. However, clinical implementation faces several challenges. Methods:Machine learning classifiers were applied using MALDI-TOF MS data to discriminate KPC-type, NDM-type CRKP, and carbapenem-susceptible strains (CSKP). Model performance was validated across platforms and strain collections. SHapley Additive exPlanations (SHAP) analysis and phylogenetic reconstruction were used to interpret feature contributions and genetic determinants. Results:Significant spectral divergence was observed among K. pneumoniae phenotypes, particularly between KPC and non-KPC strains. Random forest (RF) classifiers demonstrated excellent performance, perfectly discriminating KPC from non-KPC strains (AUC = 1.00) and achieving robust classification between CRKP and CSKP isolates (AUC = 0.809). However, differentiation between NDM and CSKP isolates remained challenging, showing moderate diagnostic reliability (AUC = 0.67-0.87) and inconsistent performance across platforms. Optimization strategies did not yield significant improvements in NDM-CSKP classification, underscoring the minimal spectral differences. SHAP analysis identified the 4521.91 m/z peak as the key feature for KPC classification, whereas NDM strains lacked distinctive spectral features. Phylogenetic analysis revealed that KPC strains formed a distinct cluster, while NDM and CSKP strains were intermixed, emphasizing the difficulty of differentiating them based on MALDI-TOF MS profiles. Conclusion:This study developed models to classify KPC, NDM, and CSKP strains using MALDI-TOF MS combined with machine learning. KPC strains were effectively classified across platforms, whereas NDM and CSKP strains showed limited differentiation due to their close evolutionary relationship. Effective classification requires consideration of regional strain variation and periodic model updates informed by local epidemiology.
Background and aims:There is growing interest in replacing steroid immunoassays with liquid chromatography-tandem mass spectrometry-based methods in many clinical and research applications. The aim of the present work is to develop and validate an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based method using atmospheric pressure chemical ionisation (APCI) to quantify cortisol, androstenedione, testosterone, pregnenolone, progesterone, 17-hydroxyprogesterone, 17-hydroxypregnenolone, and dehydroepiandrosterone in paediatric serum samples. Methods:Sample preparation was performed by protein precipitation using 100 µL of sample. The analytical method was validated in accordance with FDA and EMA international guidelines. Commercial quality control materials and a reference material (NIST®SRM®1950) were analysed for external assessment. Clinical applicability was tested in 61 infants under three months of age (50 healthy infants, eight preterm neonates, and three patients with confirmed adrenal disorders). Results:The LLOQs were lower than 8 nmol/L; within- and between-run CVs were <12 %. Accuracy, in terms of recovery, was 89-111 %. Serum cortisol levels varied widely in healthy infants, and only testosterone levels exhibited sexual dimorphism (p < 0.0001). Androstenedione and 17-hydroxyprogesterone levels were significantly higher in the preterm group compared with babies born at term. Patients with confirmed adrenal pathologies exhibited abnormal steroid profiles. Conclusion:A novel, accurate, and sensitive UHPLC-APCI-MS/MS-based method for the simultaneous analysis of eight steroids in serum was developed, validated and successfully applied to infants younger than three months of age. The method enabled detection of abnormal values of precursors and steroid hormones in patients with adrenal disorders, with the potential for a large impact on paediatric patient care.
Background: Adalimumab (ADL), a monoclonal antibody targeting TNF-⍺, is widely used to treat autoimmune diseases, but its efficacy can diminish over time due to the development of antidrug antibodies (ADAs), particularly neutralizing or blocking ADAs. Immunoassays used for mAb drug TDM typically measure ADA concentrations rather than their functional impact. This article describes an attempt to establish a label-free blocking immunoassay (LF-BIA) to measure the ADA blocking activity in serum samples. Methods: The LF-BIA was designed to measure the ADA blocking activity against the interaction between ADL and its therapeutic target, TNF-⍺. The complete time course of ADL-ADAs and ADL-TNF-⍺ immune complex formation on each sensing probe was recorded as a sensorgram, and an ADA blocking rate was calculated from the sensorgram. Results: The LF-BIA detected ADA blocking activity in serum samples. A 1:25 dilution was selected to determine ADA blocking activity, and 42 patient samples were analyzed. The LF-BIA results showed partial concordance with ELISA results, likely reflecting methodological differences: LF-BIA measures blocking ADAs, whereas ELISA quantifies total ADAs. Conclusions: The LF-BIA may provide an efficient approach to specifically measure blocking ADAs rather than total ADAs. Alongside with previously reported applications of label-free immunoassays in clinical testing, the LF-BIA highlights a promising area in which label-free technologies such as BLI can play an important role.
Background:Typing Mycobacterium tuberculosis (MTB) isolates is important for identifying clusters and guiding infection control measures. Although whole-genome sequencing (WGS) and 24-loci mycobacterial interspersed repetitive units-variable-number tandem repeat (MIRU-VNTR) are widely used for molecular typing, they may not capture phenotypic or metabolic variation. Alternative approaches, such as mass spectrometric-based profiling, could provide complementary insights. Methods:A total of 247 clinical MTB isolates were analyzed by thermal desorption-electrospray ionization mass spectrometry (TD-ESI/MS). The resulting mass spectral profiles were evaluated using principal component analysis (PCA) and hierarchical clustering analysis (HCA). Results were compared with lineage classifications based on WGS and MIRU-VNTR to assess concordance. Results:While WGS and MIRU-VNTR were highly concordant for lineage classification (98.8%; 244/247), TD-ESI/MS revealed diverse spectral profiles that did not align consistently with genetic lineages. However, HCA showed isolate-level clustering, including among genetically similar strains, suggesting that TD-ESI/MS may detect metabolic or lipidomic differences not captured by genome-based methods. Conclusion:Although TD-ESI/MS does not generate similar results from MTB molecular typing, it shows potential for identifying specific spectral signatures. The technique may be useful in future investigations into phenotypic diversity and other clinically relevant features not captured by genotyping alone.
Background:Mass spectrometry is a powerful technique for tear fluid proteomics, offering critical insights into its complex molecular composition. Traditional data-dependent acquisition (DDA) often favors high-abundance proteins because it selects only the most intense precursor ions within a given window during each scan cycle. A newer approach, data-independent acquisition (DIA), addresses this by fragmenting all precursor ions within defined mass windows, offering broader coverage and improved quantification. This study presents a systematic comparison of DDA and DIA workflows to assess their relative performance in detecting tear fluid proteins. Methods:Tear fluid samples were collected from healthy individuals using Schirmer strips, processed using in-strip protein digestion, and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). DDA and DIA workflows were compared for proteomic depth, reproducibility, and data completeness. Quantification accuracy was assessed using serial dilutions of tear fluid in a complex biological matrix. Results:DIA identified 701 unique proteins and 2,444 peptides, outperforming DDA, which identified 396 unique proteins and 1,447 peptides. Across eight replicates, DIA exhibited greater data completeness (78.7% for proteins and 78.5% for peptides) compared with DDA (42% for proteins and 48% for peptides). Reproducibility was markedly improved with DIA, with a median coefficient of variation (CV) of 9.8% for proteins and 10.6% for peptides, compared to 17.3% and 22.3%, respectively, for DDA. Quantification accuracy was also enhanced, with superior consistency across the dilution series. Conclusion:Overall, DIA provides deeper, more reproducible, and more accurate proteome profiling of tear fluid than DDA, making it well suited for biomarker discovery.
Cancer is the second leading cause of death in the United States, and with ongoing population growth and aging, its annual incidence continues to rise. Glioma is a malignant tumor of the brain and central nervous system (CNS). Neurosurgical tumor resection is a critical component of glioma treatment, significantly affecting patient prognosis. However, due to the diffuse nature of gliomas, achieving gross total resection is challenging, and residual tumor cells often lead to recurrence and disease progression following surgery. Intraoperative cancer diagnosis using rapid and sensitive techniques, such as ambient ionization mass spectrometry (AIMS), can provide crucial molecular insights to guide surgical decision-making and potentially improve patient outcomes. AIMS techniques, including desorption electrospray ionization-mass spectrometry (DESI-MS), require minimal or no sample pretreatment, making them particularly advantageous for intraoperative applications where time efficiency is essential. Several AIMS methods have been investigated in brain cancer studies, either intraoperatively or offline, to analyze molecular alterations in cancerous tissues. Among these, DESI-MS is the most extensively reported AIMS technique in brain cancer research. This review focuses on the developments and applications of DESI-MS in both offline and intraoperative brain cancer diagnosis. Additionally, other AIMS methods employed in brain cancer research are discussed. The potential impact of AIMS techniques on glioma diagnosis is also explored.Abbreviations: 5-ALA, 5-Aminolevulinic Acid; 2HG, 2-Hydroxyglutaric Acid; AIMS, Ambient Ionization Mass Spectrometry; AI, Artificial Intelligence; Arg, Arginine; AUC, Area Under the Curve; BWH, Brigham and Women’s Hospital; CBS-MS, Coated Blade Spray Mass Spectrometry; CL, Cardiolipins; CNS, Central Nervous System; CT, Computed Tomography; CUSA, Cavitron Ultrasonic Surgical Aspirator; CUSA/SSI-MS, Cavitron Ultrasonic Surgical Aspiration/Sonic Spray Ionization Mass Spectrometry; DESI, Desorption Electrospray Ionization; DSC, Direct Sampling Cartridge; e.e.%, Enantiomeric Excess %;ESI, Electrospray Ionization; Extraction-nESI, Extraction-Nanoelectrospray Ionization; FA, Fatty Acid; FAIMS, High-Field Asymmetric Ion Mobility Spectrometry; GABA, Higher Gamma-Aminobutyric Acid; GalCer, Galactoceramides; GBM, Glioblastoma; Glu, Glutamate; PC, Glycerophosphocholines; PI, Glycerophosphoinositols; PG, Glycerophosphoglycerols; PS, Glycerophosphoserines; H&E, Hematoxylin and Eosin; HLB, Hydrophilic–Lipophilic Balance; HRMS, High Resolution Mass Spectrometry; HT, High-Throughput; ICE, Inline Cartridge Extraction; IC, Ion Counts; IDH, Isocitrate Dehydrogenase; IDH-mut, IDH-Mutant; IDH-wt, IDH-Wildtype; iKnife, Intelligent Knife; LASSO, Least Absolute Shrinkage and Selection Operator; LC, Liquid Chromatography; LDA, Linear Discriminant Analysis; LIT, Linear Ion Trap; LMJ-SSP, Liquid Micro-Junction Surface Sampling Probe; MALDI, Matrix-Assisted Laser Desorption Ionization; MB, Medulloblastoma; ML, Machine Learning; MRI, Magnetic Resonance Imaging; MRM, Multiple Reaction Monitoring; MRS, Magnetic Resonance Spectroscopy; MS, Mass Spectrometry; MS/MS, Tandem Mass Spectrometry; MSI, Mass Spectrometry Imaging; NAA, N-Acetyl-Aspartic Acid; NF2, Neurofibromatosis Type 2; NSCLC, Non-Small Cell Lung Cancer; OCT, Optical Coherence Tomography; PCA, Principal Component Analysis; PCA-LDA, Principal Component Analysis/Linear Discriminant Analysis; PE, Plasmenylethanolamines; PESI-MS, Probe Electrospray Ionization Mass Spectrometry; PG, Phosphatidylglycerol; PI, Phosphatidylinositol; PIRL-MS, Picosecond Infrared Laser Mass Spectrometry; Plasmenyl-PE, Plasmenyl Glycerophosphoethanolamines; PLS-DA, Partial Least Squares-Discriminant Analysis; PLSR, Partial Least Squares Regression; PMMA, Polymethylmethacrylate; POC, Point-of-Care; PS, Phosphatidylserines; PTFE, Polytetrafluoroethylene; REIMS, Rapid Evaporative Ionization Mass Spectrometry; SIMS, Secondary Ion Mass Spectrometry; SM, Sphingomyelins; SPME, Solid-Phase Microextraction; SSI, Sonic Spray Ionization; ST, Sulfatides; SVM, Support Vector Machine; TCP, Tumor Cell Percentage; TQ, Triple Quadrupole; TS, Touch Spray.
Introduction: Image segmentation is an important challenge in mass spectrometry imaging data processing. Here, we report an unsupervised topological segmentation method adapted to the specific nature of mass spectrometry data. Unlike machine learning clustering algorithms, the proposed method retains the physical and chemical integrity of the mass spectrum, as no dimensionality reduction is required. Methods: Using the cosine similarity measure, we discard outliers, detect spectrally homogeneous regions, and filter pixels with mixed cell origin on the border of different tissue subtypes. Then, we evaluate the actual data manifold dimensionality to determine spectrally homogeneous regions within samples. The method was implemented to discriminate regions related to sections of aggressive human glial tumours analysed by MALDI-TOF mass spectrometry. Results: Analysis of parallel sections reveals correlated region allocation throughout the sample. The presence of tumour cells decreases progressively from the tumour core toward the sample edge. Filtering pixels with mixed cellular content is essential for investigating highly heterogeneous tumour tissues and their infiltration regions. Therefore, only homogeneous regions were selected using topological segmentation, as identifying metabolic alterations associated with tumour infiltration and metastasis in the native microenvironment is critical for cancer biology. Conclusions: Topological segmentation helps filter pixels from transition zones where cells of different types contribute comparably to the resulting signal. Consequently, the regions identified by spectral similarity are homogeneous data clusters that represent the characteristic molecular composition of the analyzed cells while preserving their natural variability.
Background:Congenital adrenal hyperplasia (CAH) represents a group of inherited disorders affecting steroidogenesis. Early and accurate diagnosis is crucial for effective treatment, particularly for preventing adrenal insufficiency and minimizing androgen excess. This study aims to develop and validate an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous quantification of 21 steroid hormones, including 11-oxygenated androgens, which are critical for diagnosing and monitoring various forms of CAH. Methods:We utilized a microbore column UPLC combined with hydroxylamine derivatization, which enabled excellent chromatographic separation and enhanced sensitivity of all target compounds. The method was evaluated for precision, linearity, recovery, ion suppression, and carryover according to FDA and CLSI guidelines. Steroid profiles from healthy controls and CAH patients were compared using Mann-Whitney tests. Results:The UPLC-MS/MS method demonstrated excellent precision (<20 % except for 11-ketoandrostenedione), linearity (R 2 > 0.99), low limits of detection and quantification, and satisfactory recovery (57-86 % absolute, 99-111 % relative). Our method showed good correlation with proficiency testing group means, although significant negative biases were noted for androstenedione, progesterone, and 11-deoxycortisol. In a clinical setting, significant increases in pregnenolone, progesterone, 17-hydroxyprogesterone, dehydroepiandrosterone, and other key steroids were observed in patients with 21-hydroxylase deficiency, while distinct profiles were identified for patients with 17-hydroxylase deficiency, cytochrome P450 oxidoreductase deficiency, and lipoid CAH. Conclusions:Our UPLC-MS/MS method provides a sensitive and specific tool for the comprehensive profiling of adrenal steroids, offering improved diagnostic accuracy for CAH. Its ability to differentiate between various CAH subtypes highlights its potential clinical utility in both diagnosis and monitoring.
Background:Epilepsy affects approximately 50 million people worldwide. Antiepileptic drugs (AEDs) are the mainstream treatment. Therapeutic drug monitoring (TDM) of AEDs is necessary to maximize efficacy and minimize toxicity. We report a simple, rapid, and cost-effective ultra-performance liquid chromatography-mass spectrometry method that can simultaneously measure seven AEDs/metabolites, including levetiracetam (LEV), lacosamide (LCM), zonisamide (ZON), lamotrigine (LMT), 10-hydroxycarbazepine (OXC-M1), clobazam (CLO), and N-desmethyl clobazam (N-CLB) in serum. Method:Only 20 µl of serum was used with simple protein precipitation and dilution. Analysis was performed on a SCIEX 6500 UHPLC-MS/MS in positive ion mode. Separation was performed on a C18 reversed-phase column using a gradient. The seven AEDs/metabolites were eluted in 4.5 min. Results:The assay was linear over the concentration ranges 0.4-100 µg/mL for LEV, 0.12-30 µg/mL for LMT, 0.12-30 µg/mL for LCM, 0.32-80 µg/mL for ZON, 0.28-70 µg/mL for OXC-M1, and 7.82-2000 ng/mL for CLO, 78.2-20000 ng/mL for N-CLB, respectively, with correlation coefficient greater than 0.99. Recovery was from 88 to 108 %. Intra and inter assay precision for three levels of quality controls were from 2.1 to 6.8 % and 4.2 to 10.9 %, respectively. The accuracy was evaluated by comparing with the College of American Pathologists survey results, and a correlation coefficient greater than 0.96 was observed. The absence of matrix effects was also confirmed. Conclusion:We have developed and validated a simple, rapid, and cost-effective UHPLC-MS/MS method for the simultaneous quantitation of seven AEDs/metabolites in serum within a 4.5-min analysis time. It has been implemented in our children's hospital with same-day turnaround time.
Objective: Tandem mass spectrometry (MS/MS) is highly specific in principle, but there is always the possibility of interference due to unexpected substances in the samples that have the identical mass transitions as the target analytes (isomeric/isobaric interferences). By recording the ion ratio (IR), clinical laboratories already widely attempt to identify such interferences in individual cases. To supplement this procedure, differential tuning effects can be assessed. We aimed to evaluate this approach experimentally. Methods: The detuning ratio (DR) is based on the differential influences of MS instrument settings on the ion yield of a respective target analyte; isomeric or isobaric interferences can lead to a shift of the DR for an affected sample. By determining the DR in samples in which known isomeric interference substances have been spiked to the target analyte, the applicability of DR detection was quantitatively investigated. Results: It was observed in two independent exemplary test systems (Cortisone / Prednisolone and O-Desmethylvenlafaxine / cis-Tramadol HCl) that a DR can indicate the presence of isomeric interferences. Conclusion: It was confirmed that a DR can be used as a method to obtain indications of the presence of isomeric or isobaric interferences in individual samples in an analytical LC-MS/MS system; the technique can be used in addition to the established method of IR detection to increase the analytical reliability of clinical MS analyses.Abbreviations: CE, collision energy; CID, collision induced dissociation; CXP, cell exit potential; CLSI, Clinical and Laboratory Standards Institute; DR, detuning ratio; ESI+, positive electrospray ionization; IR, ion ratio; IS, internal standard; LC, liquid chromatographic; LC-MS/MS, liquid chromatography tandem mass spectrometry; ME, matrix effects; MRM, multiple reaction monitoring; MS, mass spectrometry; m/z, mass-to-charge ratio; TIC, Total ion current.
