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Review

Liquid chromatography coupled to mass spectrometry for metabolite profiling in the field of drug discovery

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Pages 469-483 | Received 15 Nov 2018, Accepted 11 Feb 2019, Published online: 27 Feb 2019
 

ABSTRACT

Introduction: The identification and characterization of the metabolites during the early stages of discovery and development of new drug candidates are essential to establish the metabolic clearance as well as the potential pharmacological and/or toxicological effects. Hence, feasible methods of analysis, preferably rapid and simple, are required to satisfy the increasing demand of metabolite profiling studies.

Areas covered: This paper reviews the topic of metabolite profiling in drug discovery based on liquid chromatography, with especial emphasis on chromatographic modes and detectors. Features and possibilities of the different options are critically discussed.

Expert opinion: High performance analytical techniques are fundamental to gain unambiguous information on metabolites of new drugs. In this regard, liquid chromatography hyphenated to mass spectrometric detection is the most popular approach. The diversity of chromatographic modes and the great variety of separation columns available offer innumerable analytical possibilities to characterize and quantify compounds with a broad range of physicochemical properties.

Article highlights

  • Metabolite profiling is fundamental in drug discovery to assess activity and toxicity issues of new chemical entities.

  • Liquid chromatography coupled to mass spectrometry detection, and in particular the ultra-high performance UHPLC version, is the basic separation technique to lead this research.

  • Reversed phase and, in lesser extension, HILIC separation modes have shown an excellent performance to separate the vast majority of components of metabolic systems.

  • Structural elucidation and identification of unknown metabolites can be accomplished from the interpretation of MS and MS/MS spectra, especially those obtained with high resolution instruments.

  • HRMS have proved their feasibility for quantitative analysis.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose

Additional information

Funding

This manuscript has not been funded.

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