A universal surrogate matrix assay for urea measurement in clinical pharmacokinetic studies of respiratory diseases

In pharmacokinetic studies for respiratory diseases, urea is a commonly used dilution marker for volume normalization of various biological matrices, owing to the fact that urea diffuses freely throughout the body and is minimally affected by disease states. In this study, we developed a convenient liquid chromatography-tandem mass spectrometry (LC-MS/MS) surrogate matrix assay for accurate urea quantitation in plasma, serum and epithelial lining fluid. Different mass spectrometer platforms and ionization modes were compared in parallel. The LC method and mass spectrometer parameters were comprehensively optimized to reduce interferences, to smooth the baseline and to maximize the signal-to-noise ratio. Saline was selected as the surrogate matrix, and its suitability was confirmed by good parallelism and accurate quality control sample measurements. Reliable and robust assay performance was demonstrated by precision and accuracy, dilution integrity, sensitivity, recovery and stability, all of which met bioanalysis requirements to support clinical studies. The assay performance was also verified and better understood by comparing it with a colorimetric assay and to a surrogate analyte assay. The newly developed surrogate matrix assay has the potential to be further expanded for urea quantitation in numerous physiological matrices.

Smartphone-Based Platforms for Clinical Detections in Lung-Cancer-Related Exhaled Breath Biomarkers: A Review

Lung cancer has been studied for decades because of its high morbidity and high mortality. Traditional methods involving bronchoscopy and needle biopsy are invasive and expensive, which makes patients suffer more risks and costs. Various noninvasive lung cancer markers, such as medical imaging indices, volatile organic compounds (VOCs), and exhaled breath condensates (EBCs), have been discovered for application in screening, diagnosis, and prognosis. However, the detection of markers still relies on bulky and professional instruments, which are limited to training personnel or laboratories. This seriously hinders population screening for early diagnosis of lung cancer. Advanced smartphones integrated with powerful applications can provide easy operation and real-time monitoring for healthcare, which demonstrates tremendous application scenarios in the biomedical analysis region from medical institutions or laboratories to personalized medicine. In this review, we propose an overview of lung-cancer-related noninvasive markers from exhaled breath, focusing on the novel development of smartphone-based platforms for the detection of these biomarkers. Lastly, we discuss the current limitations and potential solutions.

Options of the Main Derivatization Approaches for Analytical ESI and MALDI Mass Spectrometry

The inclusion of preliminary chemical labeling (derivatization) in the analysis process by such powerful and widespread methods as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is a popular and widely used methodological approach. This is due to the need to remove some fundamental limitations inherent in these powerful analytic methods. Although a number of special reviews has been published discussing the utilization of derivatization approaches, the purpose of the present critical review is to comprehensively summarize, characterize and evaluate most of the previously developed and practically applied, as well as recently proposed representative derivatization reagents for ESI-MS and MALDI-MS platforms in their mostly sensitive positive ion mode and frequently hyphenated with separation techniques. The review is focused on the use of preliminary chemical labeling to facilitate the detection, identification, structure elucidation, quantification, profiling or MS imaging of compounds within complex matrices. Two main derivatization approaches, namely the introduction of permanent charge-fixed or highly proton affinitive residues into analytes are critically evaluated. In situ charge-generation, charge-switch and charge-transfer derivatizations are considered separately. The potential of using reactive matrices in MALDI-MS and chemical labeling in MS-based omics sciences is given.

Development of a multi-matrix LC-MS/MS method for urea quantitation and its application in human respiratory disease studies

In human respiratory disease studies, liquid samples such as nasal secretion (NS), lung epithelial lining fluid (ELF), or upper airway mucosal lining fluid (MLF) are frequently collected, but their volumes often remain unknown. The lack of volume information makes it hard to estimate the actual concentration of recovered active pharmaceutical ingredient or biomarkers. Urea has been proposed to serve as a sample volume marker because it can freely diffuse through most body compartments and is less affected by disease states. Here, we report an easy and reliable LC-MS/MS method for cross-matrix measurement of urea in serum, plasma, universal transfer medium (UTM), synthetic absorptive matrix elution buffer 1 (SAMe1) and synthetic absorptive matrix elution buffer 2 (SAMe2) which are commonly sampled in human respiratory disease studies. The method uses two stable-isotope-labeled urea isotopologues, [¹⁵N₂]-urea and [¹³C,¹⁵N₂]-urea, as the surrogate analyte and the internal standard, respectively. This approach provides the best measurement consistency across different matrices. The analyte extraction was individually optimized in each matrix. Specifically in UTM, SAMe1 and SAMe2, the unique salting-out assisted liquid-liquid extraction (SALLE) not only dramatically reduces the matrix interferences but also improves the assay recovery. The use of an HILIC column largely increases the analyte retention. The typical run time is 3.6min which allows for high throughput analysis.

Determining urea levels in exhaled breath condensate with minimal preparation steps and classic LC-MS

Exhaled breath condensate (EBC) provides a relatively easy, non-invasive method for measuring biomarkers of inflammation and oxidative stress in the airways. However, the levels of these biomarkers in EBC are influenced, not only by their levels in lung lining fluid but also by the volume of water vapor that also condenses during EBC collection. For this reason, the use of a biomarker of dilution has been recommended. Urea has been proposed and utilized as a promising dilution biomarker due to its even distribution throughout the body and relatively low volatility. Current EBC urea analytical methods either are not sensitive enough, necessitating large volumes of EBC, or are labor intensive, requiring a derivatization step or other pretreatment. We report here a straightforward and reliable LC-MS approach that we developed that does not require derivatization or large sample volume (∼36 µL). An Acclaim mixed-mode hydrophilic interaction chromatography column was selected because it can produce good peak symmetry and efficiently separate urea from other polar and nonpolar compounds. To achieve a high recovery rate, a slow and incomplete evaporation method was used followed by a solvent-phase exchange. Among EBC samples collected from 28 children, urea levels were found to be highly variable, with a relative standard deviation of 234%, suggesting high variability in dilution of the lung lining fluid component of EBC. The limit of detection was found to be 0.036 µg/mL.