Drug Metabolite Profiling and Identification by High-resolution Mass Spectrometry

W. Griffith Humphreys,Haiying Zhang,Mingshe Zhu

doi:10.1074/jbc.r110.200055

W. Griffith Humphreys, Haiying Zhang + Show 1 more

Open Access

https://doi.org/10.1074/jbc.r110.200055

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Abstract

Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.

Highlights

Subtraction—Background subtraction has been used for many years to attempt to find species that are present in a test sample but not in a control sample
To enable HR-MS to be routinely employed in drug metabolism laboratories, the MS platform must be capable of detecting unexpected metabolites via methods that are not dependent on the precursor ion (PI) and neutral loss (NL) scanning techniques
Mass Defect Filter (MDF)—MDF is one of the first processing methods developed for detection of metabolites using full-scan HR-MS data

Summary

Paradigm Shift in Drug Metabolite Identification

Modern HR-MS instruments, including quadrupole (Q) TOF- and Fourier transform-based instruments, provide ion measurements with high-resolution (Ͼ10,000 at full-width at half-maximum) and accurate mass (Ͻ5 ppm deviation) capabilities [24, 25] This enables collection of data that can distinguish drug metabolites from most if not all isobaric endogenous components and that can determine elemental compositions of metabolite ions and their fragments. To enable HR-MS to be routinely employed in drug metabolism laboratories, the MS platform must be capable of detecting unexpected metabolites via methods that are not dependent on the PI and NL scanning techniques To accomplish this goal, various HR-MS-based data acquisition and data mining technologies have been developed. The third step is to elucidate metabolite structures based on their accurate molecular masses, product ion spectra, and relevant drug biotransformation knowledge In this new approach, detection of drug metabolites is accomplished via post-acquisition data mining rather than direct PI and NL scans. HR-MS instruments with data mining techniques have the potential to greatly increase the speed, selectivity, sensitivity, accuracy, and comprehensive nature of metabolite detection and identification and to fundamentally change the way that many drug metabolism and disposition studies are conducted [26, 41,42,43]

Data Mining Methods for Finding Drug Metabolites

Applications and limitations

Background

Data Acquisition Methods Facilitating Drug Metabolite Identification

Application to Metabolite Identification Processes

Future Perspective