The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company\'s proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN\'s SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

BACKGROUND: Euterpe oleracea Mart. (açaí) is a botanical of interest to many who seek functional foods that provide antioxidant and anti-inflammatory properties. Cancer patients are increasingly taking botanical dietary supplements containing açaí to complement their conventional therapeutics, which may lead to serious adverse events. Before testing our açaí extracts in vitro for botanical-drug interactions, the goal is to chemically characterize our extracts for compounds whose biological activity in açaí is unknown.
OBJECTIVE: The objective of this work was to develop a chemical fingerprinting method for untargeted characterization of açaí samples from a variety of sources, including food products and botanical dietary supplement capsules, made with multiple extraction solvents.
METHODS: An optimized LC-MS method was generated for in-depth untargeted fingerprinting of chemical constituents in açaí extracts. Statistical analysis models were used to describe relationships between the açaí extracts based on molecular features found in both positive and negative mode ESI.
RESULTS: In an attempt to elucidate the differences in metabolites among açaí extracts from different cultivars, we identified or tentatively identified 173 metabolites from the 16 extracts made from 6 different sources. Of these compounds, there are 138 reported in açaí for the first time. Statistical models showed similar yet distinct differences between the extracts tested based on the polarity of compounds present and the origin of the source material.
CONCLUSIONS: A high-resolution mass spectrometry method was generated that allowed us to greatly characterize 16 complex extracts made from different sources of açaí with different extraction solvent polarities.

Liquid chromatography paired with tandem mass spectrometry (LC-MS/MS) is the gold standard in measurement of endocannabinoid concentrations in biomatrices. We conducted a systematic review of literature to identify advances in targeted LC-MS/MS methods in the period 2017-2024. We found that LC-MS/MS methods for endocannabinoid quantification are relatively consistent both across time and across biomatrices. Recent advances have primarily been in three areas: (1) sample preparation techniques, specific to the chosen biomatrix; (2) the range of biomatrices tested, recently favoring blood matrices; and (3) the breadth of endocannabinoid and endocannabinoid-like analytes incorporated into assays. This review provides a summary of the recent literature and a guide for researchers looking to establish the best methods for quantifying endocannabinoids in a range of biomatrices.

Fecal metaproteomics is a useful approach to measure changes in microbial and host protein abundance and to infer which members of the gut microbiota are involved in specific functions and pathways. This chapter describes a protocol enabling analysis and characterization of fecal metaproteomes, successfully applied to human, mouse, and rat stool samples. The protocol combines mechanical and thermal treatments for protein extraction, a centrifugal filter-based procedure for cleanup and digestion, long-gradient liquid chromatography for peptide separation, and high-resolution mass spectrometry for peptide detection.

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Fingolimod is an oral drug for the escalation of treatment of relapsing-remitting multiple sclerosis in patients with persistent disease activity on first-line drugs or in patients with rapidly progressive severe relapsing-remitting multiple sclerosis. An ultra-high-performance liquid chromatography-tandem mass spectrometry method for determining the concentrations of fingolimod and its active metabolite fingolimod phosphate in whole blood has been developed and validated. The advantages of this method are the easy, fast and cheap sample preparation using protein precipitation from blood with a mixture of acetonitrile-methanol (40:60, v/v). Chromatographic separation was performed on a ultra-high performance liquid chromatography BEH C18 1.7 μm (100 × 2.1 mm) column. Two modes of ionization, electrospray ionization and atmospheric pressure chemical ionization, were tested and compared. For validation, the electrospray ionization mode was chosen. As internal standard, isotopically labeled fingolimod-D4 was used to quantify the analytes. The method was validated according to the rules of the European Medicines Agency. The coefficients of variation for fingolimod were in the range of 1.13-11.88%, and the recovery was 98.80-106.00%. The coefficients of variation for fingolimod phosphate were in the range of 2.73-9.31%, and the recovery was 90.08-107.00%. The method is quite easy and fast and can be used for routine analysis.

Nowadays, lipidomics plays a crucial role in the investigation of novel biomarkers of various diseases. Its implementation into the field of clinical analysis led to the identification of specific lipids and/or significant changes in their plasma levels in patients suffering from cancer, Alzheimer\'s disease, sepsis, and many other diseases and pathological conditions. Profiling of lipids and determination of their plasma concentrations could also be helpful in the case of drug therapy management, especially in combination with therapeutic drug monitoring (TDM). Here, for the first time, a combined approach based on the TDM of colistin, a last-resort antibiotic, and lipidomic profiling is presented in a case study of a critically ill male patient suffering from Pseudomonas aeruginosa-induced pneumonia. Implementation of innovative analytical approaches for TDM (online combination of capillary electrophoresis with tandem mass spectrometry, CZE-MS/MS) and lipidomics (liquid chromatography-tandem mass spectrometry, LC-MS/MS) was demonstrated. The CZE-MS/MS strategy confirmed the chosen colistin drug dosing regimen, leading to stable colistin concentrations in plasma samples. The determined colistin concentrations in plasma samples reached the required minimal inhibitory concentration of 1 μg/mL. The complex lipidomics approach led to monitoring 545 lipids in collected patient plasma samples during and after the therapy. Some changes in specific individual lipids were in good agreement with previous lipidomics studies dealing with sepsis. The presented case study represents a good starting point for identifying particular individual lipids that could correlate with antimicrobial and inflammation therapeutic management.

Herbal teas are blends of leaves, seeds, fruits, and flowers from various plants that provide relaxation, anti-inflammatory benefits, and immune system support for conditions such as diabetes and asthma. Despite their health benefits, comprehensive lipidomic data on herbal teas are limited in the literature. We used non-targeted liquid chromatography-linear ion trap orbitrap mass spectrometry to identify and correlate the lipid species in the following six herbal tea samples: fennel, ginger, juniper, lemon peel, orange peel, and rosehip. A total of 204 lipid molecular species were identified, and multivariate analysis revealed a significant difference between lipid species in herbal teas. Saturated fatty acids (SFAs) and polyunsaturated fatty acids (PUFAs) are significantly abundant in juniper, including ω-3 and ω-6 fatty acids, followed by fennel. Cluster correlations showed that ginger contained mainly sphingolipids and lysophospholipids, whereas fennel was rich in phospholipids. No significant variations in the content of triacylglycerols were observed in any of the herbal teas analyzed. The ratio of PUFAs to SFAs in herbal teas showed that orange peel had the highest ratio, followed by lemon peel and fennel, indicating their potential health benefits. In addition, using high-resolution mass spectrometry, various lipids such as fatty acid esters of hydroxy fatty acids and N-acyl-lysophosphatidylethanolamines were identified and characterized in these herbal teas. This study provides a comprehensive lipid analysis and detailed characterization of lipids in six herbal teas, highlighting their plausible applications in the field of nutrition and various food industries for the development of functional foods.

Enantioseparation of nineteen liquid crystalline racemic mixtures obtained based on (R,S)-2-octanol was studied in reversed-phase mode on an amylose tris(3-chloro-5-methylphenylcarbamate) (ReproSil Chiral-MIG) and a cellulose tris(3,5-dichlorophenylcarbamate) (ReproSil Chiral-MIC). These polysaccharide-based chiral stationary phase (CSP) columns for High-Performance Liquid Chromatography (HPLC) were highly effective in recognizing isomers of minor structural differences. The mobile phase (MP), which consists of acetonitrile (ACN)/water (H2O) at different volume ratios, was used. The mobile phases were pumped at a flow rate of 0.3, 0.5, or 1 mL·min-1 with a column temperature of 25 °C, using a UV detector at 254 nm. The order of the elution was also determined. The chromatographic parameters, such as resolution (Rs), selectivity (α), and the number of theoretical plates, i.e., column efficiency (N), were determined. The polysaccharide-based CSP columns have unique advantages in separation technology, and this study has shown the potential usefulness of the CSP columns in separating liquid crystalline racemic mixtures belonging to the same homologous series.

The identification of pediatric appendicitis is challenging due to the lack of specific markers thereby several factors are included in the diagnostic process such as abdominal pain, ultrasonography and altered laboratory parameters (C reactive protein, absolute neutrophil cell number and white blood cell number). The glycosylation pattern of serum N-glycome was analyzed in this study of 38 controls and 40 patients with pediatric appendicitis. The glycans were released by enzymatic deglycosylation followed by fluorescent labeling and solid-phase extraction. The prepared samples were analyzed by hydrophilic interaction liquid chromatography with fluorescence and mass-spectrometric detection. The generated data were analyzed by multiple statistical tests involving the most important laboratory parameters as well. Significant differences associated with the examined patient groups were revealed suggesting the potential use of glycosylation analysis supporting the detection of pediatric appendicitis.