Data Acquisition and Data Mining Techniques for Metabolite Identification Using LC Coupled to High-Resolution MS

Abstract

Metabolite identification plays a pivotal role through all stages of drug discovery and development. The task of detecting and characterizing drug metabolites in complex biological matrices is very challenging, due in part to the co-existence of drug-related material with a large excess of endogenous material. Deciphering information on drug metabolites in these complex biological systems requires not only sophisticated LC–MS systems, but also software that can help differentiate drug-related compounds from endogenous material in the MS data. Fortunately, there have been considerable advances in high-resolution MS technologies with improved mass accuracy. The high resolution and mass accuracy capabilities have necessitated and augmented the development of integrated data acquisition methods, which have significantly facilitated metabolite detection and identification. In this review, we discuss various data-dependent and -independent acquisition methods in combination with accurate mass-based data mining tools for metabolite identification in drug discovery and development.

Financial & competing interests disclosure

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.

No writing assistance was utilized in the production of this manuscript.