Metabolite identification by liquid chromatography-mass spectrometry
Introduction
Drug discovery and development typically involves screening new chemical entities (NCEs), optimization of leads and evaluation of potential candidates to convert them into successful drugs. The modern trend is to evaluate most of the candidate molecules early, so that the resources spent on poor ones can be minimized [1], [2], [3], [4]. In this context, drug metabolism and pharmacokinetics (DMPK) studies, supported by bioanalytical research, play a very useful role. Typical DMPK attributes investigated during early discovery include metabolic stability, cytochrome P450 (CYP)-reaction phenotyping, CYP inhibition and induction assays, detection of reactive metabolites, metabolite identification (Met ID), determination of in vitro permeability, and estimation of plasma-protein binding. The data not only help in screening potential candidates, but also provide good feedback on the improvement of properties of molecules under investigation (Table 1). For example, high metabolic clearance followed by identification of soft spots helps design NCEs with desirable bioavailability. Similarly, detection and identification of reactive intermediate(s) suggest the need for structural modifications in investigational compound(s) to avoid subsequent toxic consequences. Overall, the information obtained serves as a basis for taking key decisions in the pre-clinical and clinical-developmental phases [1], [2], [3], [4], [5], [6], [7].
For its part, drug metabolism is an extremely complex process, involving multiple enzymatic pathways that result in a variety of metabolites with concentrations ranging from trace to major. Traditionally, Met ID has been a challenging process, which has lately become less cumbersome due to the availability of coupled techniques. Liquid chromatography-mass spectrometry (LC-MS) has become most popular analytical platform for Met ID. The technique has been extensively used for qualitative and quantitative analyses of metabolites. Many reviews in the literature [2], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] highlight principles and usage of different MS instruments for Met ID. They also cover: sample-preparation procedures; the role of chromatographic conditions; the utility of various other LC detectors [e.g., photodiode-array (PDA), fluorescence, and polarimetric]; comparison of mass fragmentation profiles of the drug and its metabolite(s); and, use of accurate mass studies.
For the purposes of Met ID, a systematic strategy laid down by Clarke et al. [11] is generally followed by the drug industry, though with a few modifications. However, existing and newer developments are not yet integrated. Hence, we intend this article to provide comprehensive coverage of the approaches currently followed, and to combine them with recent advances, so as to offer an updated LC-MS-based strategy for Met ID.
Section snippets
Significance of Met ID studies
During the early stages of drug discovery, the emphasis of Met ID studies is to provide information on the nature and the number of metabolites formed by exploratory compounds. There are several other benefits, as summarized in Table 1.
Met ID studies provide information on the site(s) or functional groups that need to be blocked or modified in order to improve metabolic properties of the molecule(s) under consideration. For example, if a compound shows a higher rate of metabolism, that means it
Traditional approaches
In the past, Met ID studies were normally initiated when a molecule had cleared the discovery process and entered the development phase. By that time, good quantities of the compound would usually be available and there was enough time to spend on the metabolism studies. The metabolites were isolated from biological matrices and characterized using conventional spectral analyses. Alternatively, the predicted or identified metabolites were synthesized and their presence in biological samples was
Modern LC-MS approaches in Met ID
The employment of LC-MS tools for Met ID is in consonance with developments in software and hardware, as discussed above. We discuss below current applications of modern MS tools, incorporating examples from the literature.
New generation LC systems and mass ion sources
The new generation LC systems are of two types: ultra-high performance LC (UHPLC) and nano-flow LC. The major commercially available UHPLC systems are:
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UPLC (Ultra Performance LC, ACQUITY, Waters, Milford, USA);
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HSLC (High Speed LC, Accela, Thermo Electron Corp., San Jose, USA);
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UFLC (Ultra Fast LC, Prominence, Shimadzu, Tokyo, Japan);
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RRLC (Rapid Resolution LC, 1200 Series, Agilent Technologies, Palo Alto, USA);
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Fast LC (Varian 920 LC, Varian, Palo Alto, USA, now part of Agilent Technologies); and,
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Practices and advancements in metabolite quantitation
LC-MS is the most widely used tool for quantitation of drug molecules in biological matrices, for which purpose various selective modes are employed (e.g., MRM, SRM, SIM and EIC). However, the technique has very limited use with respect to quantitation of the metabolites, because it is very difficult to correlate relative responses of the drug and its metabolites by LC-MS unless pure metabolite standards are available. Most quantitative metabolism studies therefore employ radiolabeled compounds
Proposed strategy
As discussed above, there is much recent advancement in LC-MS hardware and software for Met ID. Most were introduced individually by different vendors, and some of the new tools are not expected to be available yet to users widely. Still, for the sake of understanding of biotransformation scientists on how accurate, high-throughput Met ID (as required during drug discovery and development) can be performed to encompass existing approaches and new advancements (coded A-Z, Table 9), a
Summary and future outlook
It is clearly evident from the above discussion that modern LC-MS systems are indispensable for Met ID studies. Fortunately, continual developments in MS hardware and software have simplified the Met ID exercise to a great extent, and, as of today, the tools meet the criteria of high-throughput, sensitive detection and low labor intensiveness.
With modernization continuing, we anticipate that future generation LC-MS systems will be more robust and easier to handle. Considering the recent overall
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These authors are with equal contribution.