Metabolic profiling of ligands for the chemokine receptor CXCR3 by liquid chromatography-mass spectrometry coupled to bioaffinity assessment View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-09

AUTHORS

Marija Mladic, Danny J. Scholten, Maikel Wijtmans, David Falck, Rob Leurs, Wilfried M. A. Niessen, Martine J. Smit, Jeroen Kool

ABSTRACT

Chemokine receptors belong to the class of G protein-coupled receptors and are important in the host defense against infections and inflammation. However, aberrant chemokine signaling is linked to different disorders such as cancer, central nervous system and immune disorders, and viral infections [Scholten DJ et al. (2012) Br J Pharmacol 165(6):1617-1643]. Modulating the chemokine receptor function provides new ways of targeting specific diseases. Therefore, discovery and development of drugs targeting chemokine receptors have received considerable attention from the pharmaceutical industry in the past decade. Along with that, the determination of bioactivities of individual metabolites derived from lead compounds towards chemokine receptors is crucial for drug selectivity, pharmacodynamics, and potential toxicity issues. Therefore, advanced analytical methodologies are in high demand. This study is aimed at the optimization of a new analytical method for metabolic profiling with parallel bioaffinity assessment of CXCR3 ligands of the azaquinazolinone and piperazinyl-piperidine class and their metabolites. The method is based on mass spectrometric (MS) identification after liquid chromatographic (LC) separation of metabolic mixtures. The bioaffinity assessment is performed "at-line" via high-resolution nanofractionation onto 96-well plates allowing direct integration of radioligand binding assays. This new method enables identification of metabolites from lead compounds with associated estimation of their individual bioaffinity. Moreover, the identification of the metabolite structures via accurate mass measurements and MS(2) allows the identification of liable metabolic "hotspots" for further lead optimization. The efficient combination of chemokine receptor ligand binding assays with analytical techniques, involving nanofractionation as linking technology, allows implementation of comprehensive metabolic profiling in an early phase of the drug discovery process. More... »

PAGES

7067-7081

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-015-8867-z

DOI

http://dx.doi.org/10.1007/s00216-015-8867-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003670617

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26164305


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N-Triples is a line-based linked data format ideal for batch operations.

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00216-015-8867-z'


 

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