A systematic metabolite profiling approach has paramount importance in detecting, identifying, and characterizing drug metabolites. Till date, there is no report published on the comprehensive metabolic fate of flibanserin (FLB). In this study, the structure of entire potential metabolites of FLB has been elucidated by execution of in silico tool and high resolution mass spectrometry based metabolite profiling strategy employing data-dependent and data-independent approaches. In vitro metabolism profile was investigated after incubating FLB with liver microsomes (rat and human) and S9 fractions in presence of their respective co-factors. In vivo metabolites were identified from rat plasma, urine, feces, and brain tissue samples. An efficient extraction technique was developed that made it possible to identify the metabolites generated even in extremely low concentrations. Extraction was carried out by precipitating protein and thereafter solid-phase extraction to enrich their concentration in the sample before analysis. Fourteen new metabolites have been identified and characterized. Most of the metabolites of FLB were generated due to hydrolysis and oxidation followed by glucuronide, sulfate, and methyl conjugation. Additionally, a spiking study was employed to confirm the presence of N-oxide metabolite in human liver S9 fraction and rat urine samples. Moreover, we have established the probable biotransformation pathway of FLB and successfully analyzed the toxicity potential of the metabolites using Pro Tox-II software.
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http://dx.doi.org/10.1016/j.jchromb.2021.123011 | DOI Listing |
J Chromatogr Sci
January 2025
Division of Chemical and Material Metrology, Korea Research Institute of Standards and Science, 267, Gajeong-ro, Yuseong-gu, Daejeon, 34113Republic of Korea.
We developed a reversed-phased high-performance liquid chromatographic method combining ultraviolet detection and integrated pulsed amperometric detection for the simultaneous quantification of dopamine, 5-hydroxyindolacetic acid, homovanillic acid, serotonin, 3,4-dihydroxyphenylacetic acid, norepinephrine and epinephrine. All target components were completely separated in a C18 column with isocratic elution of 5% acetonitrile solution containing 8 mM HClO4 and 0.20 mM 1-octanesulfonic acid as an ion pairing reagent.
View Article and Find Full Text PDFJ Acquir Immune Defic Syndr
November 2024
University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC, USA.
Background: Incomplete adherence to daily tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) reduces effectiveness. Adherence biomeasures (i.e.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden.
Background: The causes of reduced aerobic exercise capacity (ExCap) in chronic kidney disease (CKD) are multifactorial, possibly involving the accumulation of tryptophan (TRP) metabolites such as kynurenine (KYN) and kynurenic acid (KYNA), known as kynurenines. Their relationship to ExCap has yet to be studied in CKD. We hypothesised that aerobic ExCap would be negatively associated with plasma levels of TRP, KYN and KYNA in CKD.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Plant Pathology, Key Laboratory of Plant Immunity, Key Laboratory of Integrated Management of Crop Diseases and Pests, College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China.
Bacterial-fungal interaction (BFI) has significant implications for the health of host plants. While the diffusible antibiotic metabolite-mediated competition in BFI has been extensively characterized, the impact of intercellular contact remains largely elusive. Here, we demonstrate that the intercellular contact is a prevalent mode of interaction between beneficial soil bacteria and pathogenic filamentous fungi.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Grupo Metabolômica, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, RJ, Brazil.
Metabolomics is the area of research, which strives to obtain complete metabolic fingerprints, to detect differences between them and to provide hypothesis to explain those differences (Schripsema J, Dagnino D, Handbook of chemical and biological plant analytical methods. Wiley, New York, 2015). However, obtaining complete metabolic fingerprints is not an easy task.
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