bims-metlip 2021-10-31 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2021–10–31
eightteen papers selected by
Sofia Costa, Icahn School of Medicine at Mount Sinai

Metabolite discovery through global annotation of untargeted metabolomics data.
Review on in vivo profiling of drug metabolites with LC-MS/MS in the past decade.
Extending the Scope of 1H NMR-Based Blood Metabolomics for the Analysis of Labile Antioxidants: Reduced and Oxidized Glutathione.
Integrated LC-MS metabolomics with dual derivatization for quantification of free fatty acids in fecal samples of hepatocellular carcinoma patients.
Simultaneous toxicokinetics characterization of acrylamide and its primary metabolites using a novel microdialysis isotope-dilution liquid chromatography mass spectrometry method.
UniDecCD: Deconvolution of Charge Detection-Mass Spectrometry Data.
The value of coupling thin-layer chromatography to mass spectrometry in lipid research - a review.
Computational Framework for Machine-Learning-Enabled 13C Fluxomics.
HepG2 as promising cell-based model for biosynthesis of long-term metabolites - exemplified for metandienone.
A liquid chromatography tandem mass spectrometry method for semiquantitative screening of cellular acyl-CoA.
MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra.
Squaric acid as a new chemoselective moiety for mass spectrometry-based metabolomics analysis of amines.
Development of a novel UPLC-MS/MS method for the simultaneously quantification of polydatin and resveratrol in plasma: Application to a pharmacokinetic study in rats.
The state of the art in plant lipidomics.
A rapid, simple and sensitive LC-MS/MS method for lenvatinib quantification in human plasma for therapeutic drug monitoring.
Non-derivatization strategy for the comprehensive characterization of neutral monosaccharide isomers and neutral disaccharide isomers using hydrophilic interaction liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry.
Image Processing and Machine Learning for Automated Identification of Chemo-/Biomarkers in Chromatography-Mass Spectrometry.
A sensitive LC-MS/MS method for simultaneous quantification of ulotaront and its N-desmethyl metabolite in human plasma and application to a clinical study.

Nat Methods. 2021 Oct 28.

Metabolite discovery through global annotation of untargeted metabolomics data.

Li Chen, Wenyun Lu, Lin Wang, Xi Xing, Ziyang Chen, Xin Teng, Xianfeng Zeng, Antonio D Muscarella, Yihui Shen, Alexis Cowan, Melanie R McReynolds, Brandon J Kennedy, Ashley M Lato, Shawn R Campagna, Mona Singh, Joshua D Rabinowitz.

Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.

DOI: https://doi.org/10.1038/s41592-021-01303-3
Bioanalysis. 2021 Oct 26.

Review on in vivo profiling of drug metabolites with LC-MS/MS in the past decade.

Gangireddy Navitha Reddy, Chenkual Laltanpuii, Rajesh Sonti.

  Metabolite profiling is an indispensable part of drug discovery and development, enabling a comprehensive understanding of the drug's metabolic behavior. Liquid chromatography-mass spectrometry facilitates metabolite profiling by reducing sample complexity and providing high sensitivity. This review discusses the in vivo metabolite profiling involving LC-MS/MS and the utilization of QTOF, QQQ mass analyzers with a particular emphasis on a mass filter. Further, a summary of sample extraction procedures in biological matrices such as plasma, urine, feces, serum and hair as in vivo samples are outlined. toward the end, we present 15 case studies in biological matrices and their LC-MS/MS conditions to understand the metabolic disposition.

Keywords:  LC-MS; biotransformation; metabolite profiling; plasma; urine

DOI:  https://doi.org/10.4155/bio-2021-0144
Anal Chem. 2021 Oct 27.

Extending the Scope of 1H NMR-Based Blood Metabolomics for the Analysis of Labile Antioxidants: Reduced and Oxidized Glutathione.

G A Nagana Gowda, Vadim Pascua, Daniel Raftery.

Glutathione is a ubiquitous cellular antioxidant, which is critically required to protect cells from oxidative damage and free radical injury. It is practically impossible to analyze glutathione in its native form after isolation from biological mixtures since the active form (reduced glutathione, GSH) spontaneously gets converted to the oxidized form (oxidized glutathione, GSSG). To address this challenge, numerous highly sensitive detection methods, including mass spectrometry, have been used in conjunction with derivatization to block the oxidation of GSH. Efforts so far to quantitate GSH and GSSG using the nuclear magnetic resonance (NMR) spectroscopy method have remained unsuccessful. With a focus on addressing this challenge, in this study, we describe an extension to our recent whole blood analysis method [ Anal. Chem. 2017, 89, 4620-4627] that includes the important antioxidants GSH and GSSG. Fresh and frozen human whole blood specimens as well as standard GSH and GSSG were comprehensively investigated using NMR without and with derivatization using N-ethylmaleimide (NEM). NMR experiments detect two diastereomers, distinctly, for the derivatized GSH and enable the analysis of both GSH and GSSG in human whole blood with an accuracy of >99%. Interestingly, the excess (unreacted) NEM used for blocking the GSH can be removed from the samples during a drying step after extraction, with no need for additional processing. This is an important characteristic that offers an added advantage for simultaneous analysis of the antioxidants (GSH and GSSG), redox coenzymes (oxidized nicotinamide adenine dinucleotide (NAD+), reduced nicotinamide adenine dinucleotide (NADH), oxidized nicotinamide adenine dinucleotide phosphate (NADP+), reduced nicotinamide adenine dinucleotide phosphate (NADPH)), energy coenzymes (adenosine 5'-triphosphate (ATP), adenosine 5'-diphosphate (ADP), adenosine 5'-monophosphate (AMP)), and a large number of other blood metabolites using the same one-dimensional (1D) NMR spectrum. The presented method broadens the scope of global metabolite profiling and adds a new dimension to NMR-based blood metabolomics. Further, the method demonstrated here for human blood can be extended to virtually any biological specimen.

DOI: https://doi.org/10.1021/acs.analchem.1c03763
J Lipid Res. 2021 Oct 25. pii: S0022-2275(21)00125-5. [Epub ahead of print] 100143

Integrated LC-MS metabolomics with dual derivatization for quantification of free fatty acids in fecal samples of hepatocellular carcinoma patients.

Jiangang Zhang, Shuai Yang, Jingchun Wang, Yanquan Xu, Huakan Zhao, Juan Lei, Yu Zhou, Yu Chen, Lei Wu, Mingyue Zhou, Yan Li, Yongsheng Li.

  Free fatty acids (FFAs) display pleiotropic functions in human diseases. Short-, medium-, and long-chain fatty acids (SCFAs, MCFAs, and LCFAs) are derived from different origins, and precise quantification of these FFAs is critical for revealing their roles in biological processes. However, accessing stable isotope-labeled internal standards (SIL-IS) is difficult, and different chain lengths of FFAs challenge the chromatographic coverage. Here, we developed a metabolomics strategy to analyze FFAs based on isotope-free liquid chromatography-mass spectrometry-multiple reaction monitoring (LC-MS-MRM) integrated with dual derivatization. Samples and dual derivatization internal standards (DD-ISs) were synthesized using 2-dimethylaminoethylamine (DMED) or dansylhydrazine (Dns-Hz) as a "light" label under mild and efficient reaction conditions, and N, N-diethyl ethylene diamine (DEEA) or N, N-diethyldansulfonyl hydrazide (Dens-HZ) as a "heavy" label. General MRM parameters were designed to analyze these FFAs. The limit of detection (LOD) of SCFAs varied from 0.5 to 3 nM. Furthermore, we show this approach exhibits good linearity (R2=0.99374 to 0.99929), there is no serious substrate interference, and no quench steps are required, confirming the feasibility and reliability of the method. Using this method, we successfully quantified 15 types of SCFAs in fecal samples from hepatocellular carcinoma (HCC) patients and healthy individuals; among these, propionate, butyrate, isobutyrate, and 2-methylbutyrate were significantly decreased in the HCC group compared to the healthy control group. These results indicate that the integrated LC-MS metabolomics with isotope-free and dual derivatization is an efficient approach for quantifying FFAs, and may be useful for identifying lipid biomarkers of cancer.

Keywords:  2-dimethylaminoethylamine (DMED); Free fatty acids (FFAs); N, N-diethyl ethylene diamine (DEEA); N, N-diethyldansulfonyl hydrazide (Dens-HZ); dansylhydrazine (Dns-Hz); derivatization; hepatocellular carcinoma (HCC); isotope-free; liquid chromatography-mass spectrometry (LC-MS); short chain fatty acids (SCFAs)

DOI:  https://doi.org/10.1016/j.jlr.2021.100143
Environ Int. 2021 Oct 25. pii: S0160-4120(21)00579-1. [Epub ahead of print]158 106954

Simultaneous toxicokinetics characterization of acrylamide and its primary metabolites using a novel microdialysis isotope-dilution liquid chromatography mass spectrometry method.

Yu-Syuan Luo, Su-Yin Chiang, Tai-Ying Long, Tung-Hu Tsai, Kuen-Yuh Wu.

  Acrylamide (AA) is a toxicant in high-temperature processed foods and an animal carcinogen. Upon absorption, AA is metabolized to glycidamide (GA) or conjugates with glutathione (AA-GSH). Important advantages of microdialysis coupled with liquid chromatography-tandem mass spectrometry (MD-LC-MS/MS) include its minimization of potential losses during sample collection, storage and preparation, as well as an improvement in temporal resolution for toxicokinetics (TKs). We aimed to simultaneously study the TKs of AA and products of its primary metabolism using an isotope-dilution (ID) MD-LC-MS/MS method. MD probes implanted into the jugular vein/right atrium of anesthetized Sprague Dawley rats were connected to the ID-LC-MS/MS for continuous monitoring of AA, GA and AA-GSH in the blood every 15 min over 8 h following intraperitoneal AA administration (0.1 mg/kg or 5 mg/kg). AA, GA, and AA-GSH TKs followed linear kinetics: GA AUC/AA AUC = 0.11 and AA-GSH AUC/AA AUC = 0.011 at 5 mg/kg. Elimination half-life (Te1/2) values were 2.44 ± 0.70, 4.93 ± 2.37 and 3.47 ± 1.47 h for AA, GA and AA-GSH, respectively. GA TKs reached a plateau at 3-6 h, suggesting that metabolic saturation of AA and Te1/2 values of the analytes were prolonged with AA at 5 mg/kg. Our results demonstrate that oxidation of AA to GA overwhelmed the conjugation of AA with GSH. Our innovative MD-ID-LC-MS/MS method facilitates the simultaneous characterization of multiple TKs associated with toxicants and their active metabolites with excellent temporal resolution to capture metabolic saturation of AA to GA.

Keywords:  Acrylamide; Acrylamide-glutathione conjugate; Glycidamide; In vivo microdialysis; Isotope-dilution liquid chromatography-tandem mass spectrometry

DOI:  https://doi.org/10.1016/j.envint.2021.106954
Anal Chem. 2021 Oct 27.

UniDecCD: Deconvolution of Charge Detection-Mass Spectrometry Data.

Marius M Kostelic, Ciara K Zak, Yang Liu, Victor Shugui Chen, Zhuchun Wu, Jared Sivinski, Eli Chapman, Michael T Marty.

Native mass spectrometry (MS) has become a versatile tool for characterizing high-mass complexes and measuring biomolecular interactions. Native MS usually requires the resolution of different charge states produced by electrospray ionization to measure the mass, which is difficult for highly heterogeneous samples that have overlapping and unresolvable charge states. Charge detection-mass spectrometry (CD-MS) seeks to address this challenge by simultaneously measuring the charge and m/z for isolated ions. However, CD-MS often shows uncertainty in the charge measurement that limits the resolution. To overcome this charge state uncertainty, we developed UniDecCD (UCD) software for computational deconvolution of CD-MS data, which significantly improves the resolution of CD-MS data. Here, we describe the UCD algorithm and demonstrate its ability to improve the CD-MS resolution of proteins, megadalton viral capsids, and heterogeneous nanodiscs made from natural lipid extracts. UCD provides a user-friendly interface that will increase the accessibility of CD-MS technology and provide a valuable new computational tool for CD-MS data analysis.

DOI: https://doi.org/10.1021/acs.analchem.1c03181
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Oct 19. pii: S1570-0232(21)00482-7. [Epub ahead of print]1185 123001

The value of coupling thin-layer chromatography to mass spectrometry in lipid research - a review.

Kathrin M Engel, Jürgen Schiller.

  Mass spectrometry has emerged as an extremely powerful analytical tool, which is widely used in many fields. This broad application range became possible with the invention of MALDI and ESI as "soft ionization" techniques that keep fragmentation of the analyte to a minimum. However, when these techniques are applied to mixture analysis, less-sensitively detectable compounds may be suppressed by more sensitively detectable compounds, a process called "ion suppression". Thus, previous separation of the mixture into the individual lipid classes is necessary to be able to detect all compounds. This review summarizes the current knowledge in the field of combined TLC/MS and discusses the most important strengths and weaknesses of the different MS (particularly ionization) techniques with respect to phospholipids. This comprises techniques such as MALDI and ESI, but less established approaches such as plasma desorption will be also discussed.

Keywords:  HPTLC; Lipidomics; Phospholipids; TLC-MS coupling; Thin-layer chromatography

DOI:  https://doi.org/10.1016/j.jchromb.2021.123001
ACS Synth Biol. 2021 Oct 27.

Computational Framework for Machine-Learning-Enabled 13C Fluxomics.

Chao Wu, Jianping Yu, Michael Guarnieri, Wei Xiong.

  13C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

Keywords:  13C metabolic flux analysis; constrained flux balance analysis; machine learning; metabolic network decomposition; solvability of flux ratios

DOI:  https://doi.org/10.1021/acssynbio.1c00189
Drug Test Anal. 2021 Oct 27.

HepG2 as promising cell-based model for biosynthesis of long-term metabolites - exemplified for metandienone.

Annette Zschiesche, Zdenek Chundela, Detlef Thieme, Annekathrin M Keiler.

  In order to detect the abuse of substances in sports, the knowledge of their metabolism is of undisputable importance. As in vivo administration of compounds faces ethical problems and might even not be applicable for non-approved compounds, cell-based models might be a versatile tool for biotransformation studies. We co-incubated HepG2 cells with metandienone and D3 -epitestosterone for 14 days. Phase I and II metabolites were analyzed by HPLC-tandem mass spectrometry, and confirmed by GC-MS. The metandienone metabolites formed by HepG2 cells were comparable to those renally excreted by humans. HepG2 cells also generated the two long-term metabolites 17β-hydroxymethyl-17α-methyl-18-nor-androst-1,4,13-trien-3-one and 17α-hydroxymethyl-17β-methyl-18-nor-androst-1,4,13-trien-3-one used in doping analyses, though in an inverse ratio compared to that observed in human urine. In conclusion, we showed that HepG2 cells are suitable as model for the investigation of biotransformation of androgens, especially for the anabolic androgenic steroid metandienone. They further proved to cover phase I and II metabolic pathways, which combined with a prolonged incubation time with metandienone resulted in the generation of its respective long-term metabolites known from in vivo metabolism. Moreover, we showed the usability of D3 -epitestosterone as internal standard for the incubation. The method used herein appears to be suitable and advantageous compared to other models for the investigation of doping-relevant compounds, probably enabling the discovery of candidate metabolites for doping analyses.

Keywords:  GC-MS; HPLC-MS; HepG2; in vitro metabolism; metandienone

DOI:  https://doi.org/10.1002/dta.3184
Anal Biochem. 2021 Oct 21. pii: S0003-2697(21)00331-6. [Epub ahead of print] 114430

A liquid chromatography tandem mass spectrometry method for semiquantitative screening of cellular acyl-CoA.

Ryan W Pearce, Jillian V Kodger, Yana I Sandlers.

  This study describes LC-ESI-MS/MS method that covers the analysis of various cellular acyl-CoA in a single injection. The method is based on a quick extraction step eliminating LLE/SPE clean up. Method performance characteristics were determined after spiking acyl-CoA standards in different concentrations into a surrogate matrix. The extensive matrix effect for most acyl-CoA except for palmitoyl-CoA was compensated by using isotopically labeled internal standard and matrix-matched calibration. As a result of the high matrix effect, the accuracy for palmitoyl-CoA at the low concentration deviated from the target range of ±20%. The developed method was applied to identify twenty-one cellular acyl-CoA in SK-HEP-1 cells and screening for alterations in acyl-CoA levels post Mito Q antioxidant intervention.

Keywords:  Acyl-CoA; Liquid chromatography tandem mass spectrometry

DOI:  https://doi.org/10.1016/j.ab.2021.114430
J Cheminform. 2021 Oct 29. 13(1): 84

MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra.

Florian Huber, Sven van der Burg, Justin J J van der Hooft, Lars Ridder.

  Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.

Keywords:  Deep learning; Mass spectrometry; Metabolomics; Spectral similarity measure; Supervised machine learning

DOI:  https://doi.org/10.1186/s13321-021-00558-4
RSC Chem Biol. 2021 Oct 07. 2(5): 1479-1483

Squaric acid as a new chemoselective moiety for mass spectrometry-based metabolomics analysis of amines.

Weifeng Lin, Zhen Yang, Amanpreet Kaur, Annika Block, Miroslav Vujasinovic, J-Matthias Löhr, Daniel Globisch.

The investigation of microbiome-derived metabolites is important to understand metabolic interactions with their human host. New methodologies for mass spectrometric discovery of undetected metabolites with unknown bioactivity are required. Herein, we introduce squaric acid as a new chemoselective moiety for amine metabolite analysis in human fecal samples.

DOI: https://doi.org/10.1039/d1cb00132a
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Oct 19. pii: S1570-0232(21)00481-5. [Epub ahead of print]1185 123000

Development of a novel UPLC-MS/MS method for the simultaneously quantification of polydatin and resveratrol in plasma: Application to a pharmacokinetic study in rats.

Robin Sunsong, Ting Du, Imoh Etim, Yun Zhang, Dong Liang, Song Gao.

  The purpose of this study is to develop a sensitive LC-MS-MS method to simultaneously quantify polydatin and its metabolite, resveratrol, for its application in a pharmacokinetic (PK) study and to determine polydatin hydrolysis by microflora. A Shimadzu UHPLC system coupled to an AB Sciex QTrap 4000 mass spectrometer was used for the analysis. Separation was achieved using an Acquity BEH C18 column (2.1 × 50 mm) with acetonitrile and 0.1% formic acid as the mobile phases. Analysis was performed under negative ionization mode using the multiple reaction monitoring (MRM) approach. The method was linear in the range of 9.77-1250 nM for both resveratrol and polydatin with correlation coefficient values >0.99. Themethodhas been shown to be reproducible, with intra- and inter-day accuracy and precision ±10.4% of nominal values, for both analytes. The average extraction recovery rates were 81.78-98.3% for polydatin and 86.4-103.2% for resveratrol, respectively. Matrix effect was in the acceptable range (<15%). The analytes in plasma were found to be stable under bench-top, freeze-thaw, and storage (-4 °C) conditions. The metabolic studies showed that polydatin can be rapidly hydrolyzed by rat fecal S9 fractions and PK studies showed that both polydatin and resveratrol were exposed in the plasma and variable tissues. This novel UPLC-MS-MS method can quantify the levels of both polydatin and its major metabolite resveratrol in biological samples.

Keywords:  LC-MS/MS; PK; Polydatin; Resveratrol

DOI:  https://doi.org/10.1016/j.jchromb.2021.123000
Mol Omics. 2021 Oct 26.

The state of the art in plant lipidomics.

Cheka Kehelpannala, Thusitha Rupasinghe, Thomas Hennessy, David Bradley, Berit Ebert, Ute Roessner.

Lipids are a group of compounds with diverse structures that perform several important functions in plants. To unravel and better understand their in vivo functions, plant biologists have been using various lipidomic technologies including liquid-chromatography (LC)-mass spectrometry (MS). However, there are still significant challenges in LC-MS based plant lipidomics, which need to be addressed. In this review, we provide an overview of the key developments in LC-MS based lipidomic approaches to detect and identify plant lipids with emphasis on areas that can be further improved. Given that the cellular lipidome is estimated to contain hundreds of thousands of lipids,1,2 many of the lipid structures remain to be discovered. Furthermore, the plant lipidome is considered to be significantly more complex compared to that of mammals. Recent technical developments in mass spectrometry have made the detection of novel lipids possible; hence, approaches that can be used for plant lipid discovery are also discussed.

DOI: https://doi.org/10.1039/d1mo00196e
PLoS One. 2021 ;16(10): e0259137

A rapid, simple and sensitive LC-MS/MS method for lenvatinib quantification in human plasma for therapeutic drug monitoring.

Martina Zanchetta, Valentina Iacuzzi, Bianca Posocco, Giorgia Bortolin, Ariana Soledad Poetto, Marco Orleni, Giovanni Canil, Michela Guardascione, Luisa Foltran, Valentina Fanotto, Fabio Puglisi, Sara Gagno, Giuseppe Toffoli.

Lenvatinib (LENVA) is an oral antineoplastic drug used for the treatment of hepatocellular carcinoma and thyroid carcinoma. LENVA therapeutic drug monitoring (TDM) should be mandatory for a precision medicine to optimize the drug dosage. To this end, the development of a sensitive and robust quantification method to be applied in the clinical setting is essential. The aim of this work was to develop and validate a sensitive, rapid, and cost-effective LC-MS/MS method for the quantification of LENVA in human plasma. On this premise, sample preparation was based on a protein precipitation and the chromatographic separation was achieved on a Synergi Fusion RP C18 column in 4 min. The method was completely and successfully validated according to European Medicines Agency (EMA) and Food and Drug Administration (FDA) guidelines, with good linearity in the range of 0.50-2000 ng/mL (R≥0.9968). Coefficient of variation (CV) for intra- and inter-day precision was ≤11.3% and accuracy ranged from 96.3 to 109.0%, internal standard normalized matrix effect CV% was ≤2.8% and recovery was ≥95.6%. Successful results were obtained for sensitivity (signal to noise (S/N) ratio >21) and selectivity, dilution integrity (CV% ≤ 4.0% and accuracy 99.9-102%), and analyte stability under various handling and storage conditions both in matrix and solvents. This method was applied to quantify LENVA in patient's plasma samples and covered the concentration range achievable in patients. In conclusion, a sensitive and robust quantification method was developed and validated to be applied in the clinical setting.

DOI: https://doi.org/10.1371/journal.pone.0259137
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Oct 08. pii: S1570-0232(21)00453-0. [Epub ahead of print]1185 122972

Non-derivatization strategy for the comprehensive characterization of neutral monosaccharide isomers and neutral disaccharide isomers using hydrophilic interaction liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry.

Jing Liu, Jing Li, Dan Yi, Yanyang Liu, Rong Liu, Ying Xue, Qi Huang, Shao Liu, Yueping Jiang.

  Monosaccharide isomers and disaccharide isomers widely exist in nature, playing a key role in a number of important biological processes. However, due to high structural similarity and high polarity, the characterization of monosaccharide isomers, disaccharide isomers, as well as the analysis of monosaccharide composition of polysaccharides by a method that does not require derivatization is an ongoing challenge. Herein, we proposed a simple method for rapid discrimination of non-derivatized neutral monosaccharide, and disaccharide isomers using hydrophilic interaction liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry (HILIC-Q/TOF-MS). In this work, we optimized the experimental parameters, and detailed approaches to discriminate the precursor ions, deprotonated ions, and fragment ions are proposed, as well. To discriminate the various ions, the retention times, the relative abundance (RA) of precursor ions and fragment ions at different collision energies, the relative abundance ratio (RAR) of fragment ions to deprotonated ions or precursor ions were considered for characterization of neutral monosaccharide and disaccharide isomers. Finally, this strategy was successfully applied to analyzing the monosaccharide composition of neutral disaccharides, polysaccharides, and an aqueous extract of Moringa oleifera seeds. The experimental results revealed that the HILIC-Q/TOF-MS is an effective and convenient strategy for rapid differentiation of monosaccharide isomers and disaccharide isomers, which may serve as a general platform for the analysis of neutral polysaccharides, food, medicinal plants, and herbs.

Keywords:  Aqueous extract of Moringa oleifera seeds; Disaccharide isomers; HILIC-Q/TOF-MS; Monosaccharide isomers; Non-derivatization; Polysaccharides

DOI:  https://doi.org/10.1016/j.jchromb.2021.122972
Anal Chem. 2021 Oct 27.

Image Processing and Machine Learning for Automated Identification of Chemo-/Biomarkers in Chromatography-Mass Spectrometry.

Chaiyanut Jirayupat, Kazuki Nagashima, Takuro Hosomi, Tsunaki Takahashi, Wataru Tanaka, Benjarong Samransuksamer, Guozhu Zhang, Jiangyang Liu, Masaki Kanai, Takeshi Yanagida.

We present a method named NPFimg, which automatically identifies multivariate chemo-/biomarker features of analytes in chromatography-mass spectrometry (MS) data by combining image processing and machine learning. NPFimg processes a two-dimensional MS map (m/z vs retention time) to discriminate analytes and identify and visualize the marker features. Our approach allows us to comprehensively characterize the signals in MS data without the conventional peak picking process, which suffers from false peak detections. The feasibility of marker identification is successfully demonstrated in case studies of aroma odor and human breath on gas chromatography-mass spectrometry (GC-MS) even at the parts per billion level. Comparison with the widely used XCMS shows the excellent reliability of NPFimg, in that it has lower error rates of signal acquisition and marker identification. In addition, we show the potential applicability of NPFimg to the untargeted metabolomics of human breath. While this study shows the limited applications, NPFimg is potentially applicable to data processing in diverse metabolomics/chemometrics using GC-MS and liquid chromatography-MS. NPFimg is available as open source on GitHub (http://github.com/poomcj/NPFimg) under the MIT license.

DOI: https://doi.org/10.1021/acs.analchem.1c03163
J Pharm Biomed Anal. 2021 Oct 06. pii: S0731-7085(21)00515-X. [Epub ahead of print]207 114404

A sensitive LC-MS/MS method for simultaneous quantification of ulotaront and its N-desmethyl metabolite in human plasma and application to a clinical study.

Yu-Luan Chen, Yao Shi, Amber LaFayette, Lei Shi, Kenneth S Koblan, Gerald R Galluppi.

  Ulotaront (SEP-363856) is a novel non-D2-receptor-binding agent under development for the treatment of patients with schizophrenia. A highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with lower limit of quantitation of 0.0200 ng/mL (i.e. 20.0 pg/mL) was successfully developed and validated for the simultaneous quantitation of ulotaront and its N-desmethyl metabolite (M11A) in human plasma. Plasma samples were extracted by solid phase extraction with Oasis MCX 96-well plate, followed by a reversed phase LC separation coupled with MS/MS detection in positive mode (m/z 184.1 → 135.0 for ulotaront and 170.1 → 135.0 for M11A). Stable isotope-labeled compounds SEP-363856-d3 and M11A-d4 were used as internal standards (IS) for corresponding analytes. The validated calibration curve range was 0.0200-20.0 ng/mL for both analytes using a 0.200 mL plasma. Extraction recoveries were found to be 75.7% and 75.1% for ulotaront and IS1, and 82.7% and 83.9% for M11A and IS2, respectively. Frozen plasma samples were confirmed to be stable for up to 730 days at both -20 °C and -70 °C. The validated method has been successfully used to evaluate the pharmacokinetics (PK) of ulotaront and M11A in clinical studies. The application to the first-in-human PK study (single ascending dose) presented in this work demonstrated that ulotaront exhibited near dose proportionality for both Cmax (maximum concentration) and AUC (area under the curve) over the dose range from 5 to 125 mg. M11A was found as a minor metabolite with an exposure of about 2-3% of the parent compound.

Keywords:  LC-MS/MS; N-desmethyl metabolite; Pharmacokinetics; SEP-856); Single ascending dose; Solid phase extraction; Ulotaront (SEP-363856

DOI:  https://doi.org/10.1016/j.jpba.2021.114404