Metabolite identification by liquid chromatography-mass spectrometry

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Metabolite identification by liquid chromatography-mass spectrometry Bhagwat Prasad, Amit Garg#, Hardik Takwani#, Saranjit Singh Metabolite identification (Met ID) is important during the early stages of drug discovery and development, as the metabolic products may be pharmacologically active or toxic in nature. Liquid chromatography-mass spectrometry (LC-MS) has a towering role in metabolism research. This review discusses current approaches and recent advances in using LC-MS for Met ID. We critically assess and compare various mass spectrometers, highlighting their strengths and limitations. Citing appropriate examples, we cover recent LC and ion sources, isotopic-pattern matching, hydrogen/deuterium-exchange MS, data dependent analyses, MSE, mass defect filter, 2D and 3D approaches for the elucidation of molecular formula, polarity switching, and background-subtraction and noise-reduction algorithms. A flow chart outlines a comprehensive strategy for Met ID, including a focus on reactive metabolites. ª 2010 Elsevier Ltd. All rights reserved. Keywords: Drug development; Drug discovery; Early stage drug discovery; Liquid chromatography-mass spectrometry (LC-MS); Mass spectrometer; Metabolism; Metabolite identification (Met ID); Pharmacologically active; Pharmacologically toxic; Reactive metabolite

Bhagwat Prasad, Amit Garg, Hardik Takwani, Saranjit Singh* Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, 160 062 Punjab, India

*

Corresponding author. Tel.: +91 172 2292031; Fax: +91 172 2214692 E-mail: [email protected]

#

These authors are with equal contribution.

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1. 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– 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 plasmaprotein 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 com-

pound(s) to avoid subsequent toxic consequences. Overall, the information obtained serves as a basis for taking key decisions in the pre-clinical and clinicaldevelopmental phases [1–7]. 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 [2,5–25] 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,

0165-9936/$ - see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trac.2010.10.014

Trends in Analytical Chemistry, Vol. 30, No. 2, 2011

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Table 1. Metabolism attributes studied in early drug discovery and their significant inferences [1–7] Metabolism attributes

Significant inferences

Rate of metabolism (metabolic stability) Identification of major metabolite(s) (Met ID)

Indicates need of modification of chemical structures to block or enhance metabolism Provides information on the sites to be modified or blocked Indicates possible new scaffolds Identification of metabolites generated in in vitro models in the pre-clinical stage helps in the prediction of what is likely to happen or be observed in the in vivo situation in clinical trials Predicts toxicity alerts Helps in selection of animal species for toxicological assessment Assists in understanding the mechanism of action of the drugs Highlights metabolism-induced toxicity potential Predicts appearance of idiosyncratic toxicities Guides in functional groups responsible for the formation of reactive intermediates Provides guidance in selecting animal species to be used for toxicological evaluation

Reactive metabolite formation

Rate and nature of metabolism in different in vitro species Enzyme inhibition and induction potential Reaction phenotyping Time-dependent enzyme inhibition

Suggests structural moieties responsible for high enzyme induction or inhibition potential Helps in designing of drug-drug interaction experiments during clinical trials Predicts drug-drug interaction potential Indicates structures leading to metabolism through polymorphic CYPs (e.g., CYP 2D6) Suggests metabolism-induced toxicity potential

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-MSbased strategy for Met ID.

2. 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 undergoes high first-pass metabolism, and hence such compounds can be modified to make them stable to metabolism to improve their bioavailability. Generally, it is also prudent to investigate the reasons for high metabolic degradation at this stage, as by this time, significant efforts might have been made in synthesis and therapeutic-activity testing. Also, there are chances that a metabolite may be equally good or even superior in pharmacological activity. There are many examples of these in the literature (e.g., procainamide metabolizes to N-acetyl procainamide, which is as active as the parent). Similarly, on biotransformation, codeine converts to the more potent morphine. Other examples of drugs that form active metabolites in the body include aspirin, carbamazepine, chlordiazepoxide, diazepam, enalapril, fluoxetine, encainide, imipramine, zidovudine,

and prednisone [26,27]. Thus, the identity of a metabolite becomes important early with respect to its use as a drug and for proposing better scaffolds. It has become routine in modern practice to isolate or to synthesize major metabolites in pure form and screen them for efficacy [26,27]. In addition, knowledge of the structure of metabolites is vital to predict their toxicity potential, for which commercially available in silico toxicity tools can be used at an early stage. During in vitro studies, investigations involve identifying electrophilic reactive metabolites, which can interact chemically with endogenous proteins, DNA or other molecules, and cause multiple toxicities [17,28,29]. Early detection and identification of reactive metabolites is considered best to avoid subsequent negative outcomes. Many adverse reactions, especially idiosyncratic reactions caused by drugs, are believed to be due to the reactive species, and adverse reactions have been responsible for multiple drug withdrawals in the past few years. The most commonly used approach employed for reactive metabolite screening is in vitro trapping using nucleophiles {e.g., thiols (glutathione (GSH) and N-acetylcysteine (NAC)), and amines (semicarbazide) [17,28–31]}. An indirect advantage of Met ID studies is information drawn from comparison of the type of metabolites formed in animal versus human in vitro systems (e.g., liver microsomes or hepatocytes). The same comparison helps to select the animal species for toxicity studies that shows similar or very near metabolite profile to the humans. This information becomes extremely important as the compound enters the pre-clinical development stage, where animals are employed for in vivo absorption, distribution, metabolism, elimination (ADME) and toxicological studies [31]. The identification of metabolites generated in vitro at the pre-clinical stage also helps prediction of what is likely to happen or would be http://www.elsevier.com/locate/trac

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observed in humans during clinical trials [7]. Lastly, Met ID studies have come into greater focus as a sequel to the release of the Metabolites in Safety Testing (MIST) guideline by the US Food and Drug Administration (FDA) in 2008 [32]. The guideline requires pre-clinical toxicity evaluation of disproportionate metabolites that appear uniquely in humans during Phase I clinical studies and may be absent or detected in minor-to-trace quantities in animal studies.

3. Tools for Met ID studies 3.1. 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 confirmed through retention time (RT) matching, spiking and/or comparison of UV spectra. One also finds the use of sophisticated analytical platforms for the purpose {e.g., gas chromatography-mass spectrometry (GC-MS), radiochromatography involving LC-radioflow detection (LC-RFD) and off-flow liquid-scintillation counting [16,19,31]}. GC-MS was popularly used for Met ID from the early 1970s until the late 1990s. However, its application decreased later due to its inherent disadvantages {e.g., the requirement for derivatization of the analyte(s) and frequent changes in chromatographic RT due to temperature fluctuations [33]}. Another reason was the advent of LC-MS systems towards the mid-1980s and the substantial increase in their use later. 3.2. Modern approaches 3.2.1. LC-MS and other modern coupled tools. A major leap in Met ID strategies has been possible by the availability of very sensitive, high-resolution LC-MS tools utilizing atmospheric pressure ionization (API) interfaces. Although the coupling of LC and MS was much more difficult than that of GC and MS, modern API-based ion-source technologies [e.g., electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI)] have been efficient at converting HPLC effluent containing drug and/or metabolites from LC to gasphase molecules at normal pressure conditions. Further, significant improvements have also been brought to mass analyzers in the last two-and-a-half decades. The first LC-MS was a single-stage quadrupole (SSQ) instrument, which was only able to give information on molecular ion peaks and fragment ions (formed in the 362

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ion source). Subsequent developments resulted in the launch of triple-stage quadrupole (TSQ) (also commonly known as triple quadrupole, QqQ), ion trap, time-of-flight (TOF), hybrid ion trap (Q-Trap), hybrid TOF (Q-TOF), Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap systems. Details of these modern MS systems are reviewed elsewhere [16,19,21]. However, Table 2 compares different modern MS instruments. In the early 1990s, the successful incorporation of a collision-induced dissociation (CID) cell between two quadrupole analyzers (commonly referred to as Q1 and Q3) resulted in the development of TSQ MS systems. This tandem mass spectrometry (MS2) helped in acquiring very selective data in the form of multiple-reaction monitoring (MRM) scan, precursor-ion scan (PIS) and constant neutral-loss scan (CNLS). Such instruments are successful in providing both qualitative and quantitative information for metabolism research. The next generation of mass systems were ion traps, which were capable of providing multiple-stage mass (MSn) experiments on the analytes. Essentially, these systems were discovered in three generations. The basic model was a three-dimensional (3D) trap, in which ion trapping could be executed by arranging three electrodes to form a hyperboloidal geometry [34]. However, as 3D trap was poor in ion-storage efficiency, linear twodimensional (2D) ion traps (LITs) were introduced in the late 1990s by eliminating one of the three quadrupole trapping fields. The third development in this series, which happened almost at the same time, was the QTrap, which combined axial-ejecting ion traps with TSQ. These systems enabled acquisition of PIS, CNLS, or MRM scans, together with the enhanced sensitivity of a full scan, and MS2-data acquisition [24,35,36]. In general, other than the MSn function, ion traps are sensitive and have the ability to detect even trace-level metabolites. They are 10 times more sensitive than TSQ in full-scan mode, while the sensitivity of LIT and Q-Trap are still greater than that of ion traps. By contrast TSQ is a more selective and sensitive detection tool in MRM mode. The development of TOF systems was a great achievement for structure elucidation scientists, as they were capable of acquiring accurate mass data in an on-line mode. However, the technique gained popularity in Met ID studies only after the advent of hybrid Q-TOF systems in the late 1990s. Hybrid Q-TOF could conduct fast data dependent acquisition (DDA) for MS and MS2, similar to the ion traps. Although the technique could not acquire MSn data, it has achieved the distinction of being the most cost-effective MS system for Met ID purposes [37]. Further improvement in the MS systems was the introduction of FT-ICR in 2004, which is the most sensitive MS system currently available. It has ultra-high resolution and provides both MSn and accurate mass data. However, its disadvantages are low throughput and high cost compared with other systems. The latest is

Feature 1. Detection sensitivity 2. Mass accuracy (ppm) 3. Mass resolution 4. Mass range 5. Data-acquisition rate 6. Linearity of response 7. PIS or CNLS 8. Possibility of efficient MDF, BgS and NoR 9. DDA mode 10. MSE application 11. Post-acquisition data processing option 12. Use in Met ID

13. Fragmentation capability 14. Size of machine

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15. Speed of analysis 16. Cost of instrument

TOF

Q-TOF

Ion Traps (3D)

TSQ

LIT/Q-Trap

Orbitrap

FT-ICR Medium Excellent (0.5–5) Excellent (1.5 · 105) >104 Low

High Good (1–20) Good (103–104) 105 High Moderate to good No Yes

High Very good (1–5) Very good (4 · 104) 105 High Moderate to good Post-acquisition Yes

Medium Poor (>50) Low (103–104) 2 · 104 Low to moderate Moderate to good Post-acquisition No

Low Poor (>100) Low (102–104) 4 · 103 Low to high Very good Yes No

Medium Poor (>50) Good (104) >103 Moderate to high Very good Yes* No

Moderate to high Excellent (0.5–5) Excellent (105–106) 6 · 103 Low to moderate Moderate Post-acquisition* Yes

No No Yes

Yes Yes# Yes

Yes No Yes

Yes No Less

Yes No Yes

Yes No Yes

No

Rapid postulation of metabolic changes by mass shifts

Rapid postulation of possible metabolic changes and the site of change (I)

Limited to MSn application

Limited to MS2 applications

Both I and II

Both I and II

No

Yes, up to MS2

Yes, up to MSn

Yes, up to MS2

Metabolite screening; identification of biotransformation sites (II) Yes, up to MSn

Yes, up to MSn

Yes, up to MSn

Bench-top to laboratory size 10–104 Hz Moderate to high

Bench-top to laboratory size 10–104 Hz High

Bench-top

Bench-top

Bench-top

Bench-top

Laboratory size

1–30 Hz Moderate

1–20 Hz Moderate

1–30 Hz High

1–4 Hz Very high

0.001–10 Hz Very high

Yes

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Table 2. Features of modern mass spectrometers used in Met ID [7,16,19,21]

*

PIS is impossible, while CNLS is post-acquisition in LIT and Orbitrap. MSE is limited to a few modern Q-TOF systems.

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the Orbitrap mass analyzer [18], which can acquire data with similar resolution and accuracy to FT-ICR, and is also equipped with MSn features. Moreover, it has higher speed than FT-ICR, and hence is finding favor in Met ID research. Overall, these modern LC-MS systems are inherently sensitive, widely applicable to a broad range of compounds, robust, rapid, and easily automated. The utility of LC-MS in Met ID research is facilitated further by incorporation of in-line radioactivity flow detectors between the LC and MS systems. The instrument provides simultaneous acquisition of radio-chromatograph and MS data and helps in Met ID studies through the use of radiolabeled compounds. The availability of miniaturized MS systems is a most recent development. The microfabricated ion sources, mass analyzers, and their applications are comprehensively reviewed by Sikanen et al. [38]. These micro-tonano-based MS systems are truly advantageous in Met ID and meet the speed and sensitivity required for modern discovery practices (e.g., analysis of microdialysis samples). 3.2.2. In silico tools. In addition to the instrument related developments, equal emphasis has been paid to in silico tools for drug metabolism studies. Advancements in this area until 2005 have already been reviewed [8]. Table 3 gives updated information on currently available commercial software. There are basically three categories of in silico tools that are presently used in metabolism research:  The first are the stand-alone prediction software [e.g., MetaSite from Molecular Discovery, which is a computational tool that predicts sites of metabolism by major human CYPs (i.e. 1A1, 1A2, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and 3A5)]. The positive aspect of this software is its ability to predict 3D interaction between the drug and the active site of the enzyme, which eliminates the chances of false positives.  The second category is LC-MS integrated software (e.g., MetWorks). Such software not only predicts metabolic products based on common biotransformation reactions, but also mines complex LC-MS data to facilitate detection and identification of the metabolite(s). The limitation of the latter is prediction of a large number of metabolite structures, with only a few being rational.  The third category of in silico tools comprises databases, which provide metabolism information based on biotransformation data in the literature (e.g., MDL Metabolite encompasses reports on xenobiotic metabolism in the literature since 1901).

4. 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 364

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discussed above. We discuss below current applications of modern MS tools, incorporating examples from the literature. 4.1. In silico metabolite prediction/detection There are several reports [39–42] on the use of in silico prediction and detection tools in Met ID. For example, Trunzer et al. explored MetaSite 3.0 for the identification of metabolic soft spots during lead optimization in early drug discovery [42]. The study included experimentally generated metabolic stability samples of 18 marketed drugs and 95 NCEs. The results showed that the highthroughput in silico method increased the likelihood of characterizing the right soft spot and it provided structural information on the metabolites more easily and correctly. In another interesting example [40], multiple software (viz Meteor, MetaboliteDetect and ACD/MS Fragmenter) were used together to predict and to detect Phase 1 metabolites of quetiapine in 10 autopsy urine samples. Of the 14 metabolites predicted by Meteor, eight were detected in urine with the help of MetaboliteDetect, which also detected five additional mono-oxygenated derivatives, not predicted by Meteor. Subsequently, ACD/ MS Fragmenter was used to obtain structural information on mass fragments, and the whole information was collated to assign structures convincingly to 13 of the 14 metabolites. 4.2. Tandem mass spectrometry (MS2) and multiplestage mass (MSn) scans Experimental Met ID using MS tools involves comparing molecular masses and MS fragmentation pathways of the parent and its metabolites. The mass fragmentation of the drug is first delineated by direct injection of its solution into MS2 or MSn systems. Traditionally, MSfragmentation studies were carried out on SSQ instruments, where in-source fragmentation at high source potential was the only means of collecting MS2 information. For this reason, some of the fragments were skipped and a complete sequence of fragments was not observed most of the time. Modern MS systems (e.g., TSQ, ion trap, LIT, Q-Trap, Q-TOF, Orbitrap or FT-ICR) are therefore more relied upon in the present times. These systems allow the selection of parent ion of interest, which is then fragmented in the CID cell to generate a complete spectrum, containing product ions with masses from low to high. Also, the MSn ability of ion trap, LIT, Q-Trap, Orbitrap and FT-ICR systems is used to advantage to obtain comprehensive fragmentation data [19–22,24,43]. The same applies to daughter ions formed even in trace quantities. The same is highlighted through a reported Met ID study on rifabutin (m/z 847) [43]. The MS2 spectrum of the drug yielded only two prominent fragments (of m/z 815 and 755). The trace fragments of m/z 730, 716, 712, 708, 688, 674, 656, 652, 573, 555, 545, 444 and 423 were

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Table 3. Commercial in silico tools available to support drug metabolism research Name

Manufacturer

A. Stand-alone software MetabolExpert

CompuDrug, USA

METEOR

Lhasa, UK

META

Case Western Reserve University, USA

Metabolism

Panlab, USA

ACD/MS MetaSite

Advanced Chemistry Development, Canada Molecular Discovery, UK

B. Software packages with LC-MS MetaboLynx

Micromass, UK

Metabolite ID

PE/Sciex, USA

MetaboliteTools

Bruker Daltonics, Germany

Metabolite Data Browser

Thermo Finnigan, USA

MetWorks

Thermo Electron Corp., USA

C. Metabolism databases MDL Metabolite

MDL Information Systems, USA

Accelrys Metabolism

Accelrys, USA

Metabolism

Panlab, USA

DRUGBANK

University of Alberta, Canada

visible only on zooming the spectrum by 10,000 times. Instead, all these ions were observed with reasonable relative intensities during an MSn study on a LIT system, which helped in deducing their origins. Mass fragmentation information on the metabolites is obtained by using LC-MS instruments, where the metabolites are first separated on the column and then sent to the ionization source individually. The common LC-MS methods for acquiring fragmentation data again are insource ionization and targeted MS2 or MSn. The latter are

Remark Includes a set of metabolic reactions obtained from the literature focusing on the biotransformation of toxic and druglike organic compounds Uses expert-knowledge rules in metabolism to predict the fate of chemicals. The predictions are presented in metabolic trees Predicts metabolic transformations that may be produced when the molecules are ingested or dumped in the environment Describes metabolic pathways of drugs, agrochemicals and industrial chemicals in different species Searches metabolites in the sample and assigns their structures using mass fragmentation analysis Predicts metabolic transformations related to CYP reactions Predicts structure of metabolites along with priority ranking

Screens MS chromatograms for the expected metabolites according to predicted mass gains and losses relative to the parent. Unique feature includes ‘‘structure intelligent dealkylation tool’’ combined with mass defect filter Almost similar to MetaboLynx. The additional feature is isotope pattern recognition It comprises two modules: MetabolitePredict and MetaboliteDetect. The first is used to predict list of metabolites, while the second detects metabolites in the sample Almost similar to MetaboLynx, but its unique feature is BgS or the application of an isotope cluster analysis Same as above. Special features include Graphical user interface (GUI) that maximizes the power of MMDF and enhances automatic isotope pattern recognition for accurate mass data

Contains data from original literature since 1901. Graphically searchable biotransformation and metabolic schemes from in vivo and in vitro studies Incorporates literature information from two journals (Biotransformations, and Metabolic Pathways of Agrochemicals). Focuses on the metabolic fate of pharmaceuticals, agrochemicals, food additives, environmental wastes and industrial chemicals in vertebrates, invertebrates and plants Covers the metabolic pathways of drugs, agrochemicals and industrial chemicals in various species Combines detailed drug data (i.e. chemical, pharmacological and pharmaceutical) with comprehensive information on drug target (i.e. sequence, structure, and pathway)

better, as they rely on the predicted and predefined molecular masses of the metabolites (i.e. the peaks of the targeted masses are fragmented one by one using CID parameters optimized for the drug). In modern LC-MS instruments, the targeted MS2 or MSn information can be obtained in a single run for multiple analytes and/or their fragments, even at different instrument parameters, due to high speed of data acquisition [18,19,22,24,40,44–47]. Once fragmentation profiles of both the parent and its metabolite(s) are established, there follows the http://www.elsevier.com/locate/trac

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determination of comparative mass shifts that are characteristic for various metabolic changes, as listed in Table 4. 4.3. High resolution mass spectrometry (HR-MS) Modern HR-MS systems (e.g., TOF, Q-TOF, FT-ICR, and Orbitrap) on coupling with LC yield accurate masses of a drug and its metabolites. The data permit calculation of accurate mass shifts of the metabolites, and also determination of their molecular formulae. As accurate mass differences for various metabolic changes are already known (Table 5) [13,21,43,48], they directly help in the prediction of the nature of the metabolites. For example, an experimental shift of 42.0066 Da from rifampicin (representing a loss of CH2CO, theoretical mass 42.0106 Da) was sufficient to confirm the metabolite as desacetylrifampicin [48]. Another advantage of accurate mass data in Met ID is that it can help to distinguish isobaric molecular ions. During a study on metabolism of rifabutin, two isobaric peaks were observed in TIC with a nominal mass shift of 56 Da [43]. Their accurate mass shift values were 56.0269 and 56.0633 Da, which revealed that the two metabolites were different in nature. The first was characterized as desacetyldemethylrifabutin due to the loss of C3H4O (theoretical mass 56.0262 Da), while the second, identified as N-dealkylrifabutin, appeared on elimination of C4H8 (theoretical mass 56.0626 Da).

Another benefit of HR-MS data is that it can help in determining the generation (i.e., 1 or >1) of drug metabolism [43], which can also be assessed from accurate mass-shift values for various metabolism reactions, as listed in Table 5. This strategy was used in Met ID of rifabutin [43], where a total of 23 metabolites were identified in rat urine. Of these, 14 metabolites were indicated to be the first generation, while the rest of them were second or higher. Similar to its application in identifying isobaric metabolites, HR-MS has been helpful even in assigning exact structures to fragments where two or more structures of the same nominal mass are possible {e.g., identification of nefazodone metabolites [18]}. Nefazodone upon MS fragmentation yielded ions of m/z 237, 197, 180, 154 and 140, for which structures were predicted in silico by Frontier 4.0. As evident from data in Table 6, it was easy to identify the correct structures from HR-MS results, which otherwise was not possible through unit resolution MS data. Another example (Fig. 1) depicts the advantage of HR-MS over LIT-MS in elucidating the structures of metabolite fragments. During the LIT run of rifampicin quinone, only one fragment peak of m/z 297.1 was observed (Fig. 1a). However, HR-MS analysis showed good separation of isobaric fragments with m/z 297.0540 and 297.1880 (Fig. 1b). These clearly pertained to two different structures, which were justified by comparing their theoretical and experimental masses, as shown in Fig. 1c.

Table 4. Metabolic reactions showing characteristic shifts on mass fragmentation [18,19,22,24,40,43,47,48] Metabolite reaction Aliphatic hydroxylation Aromatic hydroxylation Epoxidation followed by formation of dihydrodiols Carboxylic acid formed by oxidation of alcohol/aldehydes or by hydrolysis of esters O-Demethylation N-Oxidation Dehalogenation Glucuronide conjugate Glutathione conjugate

N-Acetylcysteine Amino acid hydrolysis to carboxylic acid Sulfate conjugation Taurine conjugates Acetylation Desacetylation Glycine conjugation Cysteine conjugation Glucosylation

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Characteristic shifts on mass fragmentation of the metabolites, compared to the parent Prominent loss of H2O (18 Da) Rare or less prominent loss of H2O Two consecutive H2O losses CO2 loss (44 Da) in negative ionization mode, loss of HCOOH (46 Da) and H2O in positive ionization mode Absence of CH3OH loss (32 Da), particularly if oxygen is linked to an aliphatic carbon Unusual loss of OH radical (17 Da) along with facile water loss, M + Cl adduct in negative ionization mode Absence of HX loss Loss of C6H8O6 (glucuronic acid, 176 Da) Loss of C5H7NO3 (pyroglutamic acid, 129 Da) and C2H5NO2 (pyroglycine, 75 Da) in negative ionization mode. Unique fragment of m/z 272 (C10H14N3O6-) in negativeionization mode Loss of CH2CO (ketene moiety, 42 Da) in positive ionization mode, loss of C5H7NO3 (2acetamidoacrylic acid, 129 Da) in negative ionization mode Loss of HCOOH moiety Loss of SO3 (80 Da) from phenolic sulfate in negative ionization mode. In negative ionization mode, aliphatic sulfonates give unique fragment of m/z 97 (HSO4) Unique fragment of m/z 126 (protonated taurine, C2H8NO3S+) Loss of CH2CO (ketene moiety, 42 Da) Absence of the loss of CH2CO (ketene moiety, 42 Da) Loss of CO + H2O (46 Da), loss of glycine moiety (57 Da) Loss of C3H7NO2S (cysteine, 121 Da), C3H7NO2 (alanine, 89 Da) or formic acid (HCOOH, 46 Da) Loss of C6H10O5 (dehydrated glucose, 162 Da) or C6H10O6 (glucose, 180 Da)

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Table 5. Common metabolic reactions and their corresponding theoretical accurate mass shifts, mass defect shifts, molecular formula of mass shifts and proposed RRT class of metabolites [7,13,21,43,48] Metabolic change Phase I reactions Reductive debenzylation Reductive debromination Oxidative debenzylation Loss of CF3 Oxidative debromination Debutylation Reductive loss of nitro group or hydrolysis of nitrate to alcohol Decarboxylation Depropylation Desacetylation Loss of HCl Reductive displacement of chlorine Desethylation Reductive loss of nitrile group Dehydration Reductive displacement of fluorine Oxidative displacement of chlorine Reduction of sulfoxide to thioether or reduction of hydroxylamine to amine Oxidative desulfuration Demethylation Aromatization of saturated ring Dehydrogenation (oxidation), alcohol oxidation to ketone/aldehyde, ring formation Oxidative displacement of fluorine Oxidative deamination to ketone/aldehyde Oxidative deamination or hydrolysis of amide to carboxyl Hydrogenation (reduction) Methylation Alcohol to carboxylic acid Ring opening by water addition or hydrolysis of nitrile to amide Epoxidation N/S-oxidation Hydroxylation Ketone formation Nitro reduction to amine Methyl oxidation to carboxylic acid S-Oxidation of thioether to sulfone (+O2) or dihydroxylation (+O2) Trihydroxylation or S-oxidation of thiol to sulfonic acid Epoxidation and hydration Phase II reactions Methylation Formyl conjugation Acetylation Propionyl conjugation Glycine conjugation Butyryl conjugation Phosphorylation Sulfate conjugation Taurine conjugation S-Cysteine conjugation Glutamine conjugation

Accurate mass shift (Da)

Mass defect shift (mDa)

Molecular formula of mass shift

Proposed RRT class

90.0470 77.9105 74.0521 67.9874 61.9156 56.0626 44.9851

47.0 89.5 52.1 12.6 84.4 62.6 14.9

–C7H6 –Br, +H –C7H6, +O –CF3, +H –Br, +OH –C4H8 –NO2, +H

1 1 1 1 1 1 1

43.9898 42.0470 42.0106 35.9767 33.9611 28.0313 24.9953 18.0106 17.9906 17.9662 15.9949

10.2 47.0 10.6 23.3 38.9 31.3 4.7 10.6 9.4 33.8 5.1

–CO2 –C3H6 –C2H2O –HCl –Cl, +H –C2H4 –CN, +H –H2O –F, +H –Cl, +OH –O

2 1 1 1 1 1 3 3 1 1 2

15.9772 14.0157 6.0470 2.0157

22.8 15.7 47.0 15.7

–S, +O –CH2 –H6 –H2

2 1 2 3

1.9957 1.0316 0.9840

4.3 31.6 16.0

–F, +OH –NH3, +O –NH3, +O

1 3 3

2.0157 14.0157 13.9792 18.0106

15.7 15.7 20.8 10.6

H2 CH2 –2H, +O H2O

3 2 3 1

15.9949 15.9949 15.9949 13.9792 29.9741 29.9741 31.9898

5.1 5.1 5.1 20.8 25.9 25.9 10.2

O O O –2H, +O –O2, +H2 O2, –H2 O2

3 3 1 3 2 1 1

47.9847

15.3

+O3

1

34.0055

5.5

H2O2

1

14.0157 27.9949 42.0106 56.0262 57.0215 70.0419 78.9585 79.9568 107.0041 119.0041 129.0426

15.7 5.1 10.6 26.2 21.5 41.9 41.5 43.2 4.1 4.1 42.6

CH2 CO C2H2O C3H4O C2H3NO C4H6O PO3 SO3 C2H5NO2S C3H5NO2S C5H7O2N

2 2 2 2 1 2 3 1 1 1 1 (continued on next page)

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Table 5. (continued) Metabolic change Carnitine conjugation Disulfation N-Acetylcysteine conjugation Glucosylation Glucuronide conjugation Indirect carbamate glucuronidation of amines S-Glutathione conjugation Diglucuronidation

Accurate mass shift (Da)

Mass defect shift (mDa)

Molecular formula of mass shift

Proposed RRT class

144.1025 159.9136 161.0147 162.0528 176.0321 220.0219 305.0682 352.0642

102.5 86.4 14.7 52.8 32.1 21.9 68.2 64.2

C7H14O2N S2O6 C5H7NO3S C6H10O5 C6H8O6 C7H8O8 C10H15N3O6S C12H16O12

1 1 1 1 1 1 1 1

4.4. Precursor-ion and constant neutral loss scans PIS and CNLS are the common methods for metabolite detection using MS2 data. If a drug metabolite produces a characteristic product ion upon dissociation, PIS can be employed as a sensitive tool for semi-targeted detection. CNLS finds application in those Met ID experiments where a constant neutral loss is observed during fragmentation of the parent or its metabolites. PIS features is available in TSQ and Q-Trap systems. It helps in the detection of those precursor ions that generate a common fragment in the ion chromatogram {e.g., irrespective of the molecular ion peak, conjugates of GSH and drug or metabolite(s) ionize into a common fragment of m/z 272 in the negative mode, due to the loss of R-SH from glutathionyl moiety (Fig. 2a) [49]}. PIS can also be used for the detection of Phase I metabolites where a part of the drug structure remains intact on metabolism, and the same is observed as a moderate-to-major fragment on MS2 analyses. Fig. 2b shows a typical case of a proprietary compound [47], where MS2 analysis of a monkey-liver microsomal incubate indicated a methylcarboxylpiperidine portion in the molecule as a metabolic soft spot. Accordingly, the stable ion of m/z 155 representing a tetrahydropyran moiety in the parent (Fig. 2b) was used in PIS for detection of the metabolites. The data obtained through MS2 resembled the results obtained by radioactivity detection. The CNLS approach has been more extensively applied in identification of glucuronide/sulfate/GSH conjugates, which often undergo common cleavages to generate specific neutral fragments upon CID [31] {e.g., GSH adducts predominantly yield a constant mass shift of 129 Da in positive MS mode on the loss of pyroglutamic acid (Fig. 2a)}. In CNLS experiments, there is always a possibility of false positives appearing in the MS chromatogram, which are common in in vivo samples. In this context, HR-MS has been applied successfully, wherein falsepositive peaks due to endogenous components are eliminated by searching for the exact neutral loss {e.g., 129.0426 Da in case of GSH adducts [50]}. This even leads to increase in the signal-to-noise ratio by 100–1000 times. There are examples that show that CNLS is not restricted to the identification of Phase II metabolites, and 368

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that it can be utilized even for the detection of Phase 1 metabolites, similar to PIS. For example, an MS2 study of an NME of m/z 439 [47] resulted in a prominent fragment of m/z 264 on the loss of metabolically stable trifluoromethylbenzylamine moiety of m/z 175 (Fig. 2c). This constant neutral loss was used further in Met ID of the parent, where two additional metabolites were revealed, over and above those observed on radioactivity analysis. Thus, both PIS and CNLS can be fruitfully used in early drug discovery, where applicability of the radiotracer technique is not yet feasible. These may also be of advantage even when loss of the radiolabeled moiety is observed on metabolism. Overall, CNLS is a better approach than PIS, as constant neutral losses are more common than the appearance of intact ions. However, both have a common limitation in that their utility is restricted to the loss or appearance of a unique fragment [47].

4.5. Selective reaction monitoring Modern mass spectrometers (i.e., TSQ, Q-Trap and LIT) are designed to allow single reaction monitoring (SRM) and multiple-reaction monitoring (MRM). In particular, the MRM mode allows selective monitoring of components in the metabolite matrix by simultaneous detection of single or multiple precursors, based on user-defined fragment ion(s) [47], which may even be in silico predicted metabolite motifs [51]. In addition, it is a high-throughput detection tool for low-level metabolites. Due to these capabilities, the approach is particularly valuable for Met ID of potential series of compounds at the lead-selection stage during drug discovery. Even in later stages, it can be gainfully used for Met ID investigations on the selected drug candidates. There are many recent reports in the literature [40,51,52] where MRM has been successfully utilized in Met ID studies. For example, MRM analysis was carried out on a test compound, an internal standard and 48 most common Phase I and II predicted biotransformation products in a sample by using unique virtual fragment ions [51]. The mode provided the necessary sensitivity to detect minor metabolites in a relevant therapeutic concentration range.

O Cl

N

N NH

Alternate fragments with same nominal mass a

O

N

Experimental mass (m/z)

b

Cl

O NH

N N

N

N

N

NH

Correct structure, associated mass error (ppm)

237.1148

a, 2.19

197.0831

a, 4.52

180.1128

b, 2.00

154.0971

b, 2.40

140.0815

b, 2.78

N

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Table 6. Structure of nefazodone, pair of fragments with same nominal masses, and identification of correct structure using HR-MS data [18]

m/z 237.1584

m/z 237.1153 Cl

O NH NH

m/z 197.0840

H2 N

NH

N

N

m/z 197.1397

Cl

O N HN

HN

m/z 180.0575

N

m/z 180.1131

Cl

O NH2 NH2

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m/z 154.0418 Cl

N

N

m/z 154.0975

O N NH

m/z 140.0262

HN

N

m/z 140.0818

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370 297.0540

b 70

297.1880

297.1

800

60

600

50

Intensity

40

400 30 20 298.0

200 296.1

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a

10

0

0 296.0

296.5

297.0

297.5

298.0

296.6

298.5

OH

c HO

297.2

297.4

m/z

H N Experimental Mass:297.0540 Theoretical Mass:297.0506 Error:11.44ppm

NH

OH

297.0

O O

O

OH

H O

O

O NH

O

N N

N

OH

O O

O O

O

Experimental Mass:297.1880 Theoretical Mass:297.1849 Error:10.43ppm

Rifampicin Quinone

Figure 1. Mass spectra showing appearance of isobaric fragments of rifampicin quinone employing (a) LIT and (b) Q-TOF-MS instruments. (c) The structures of riampicin quinone and isobaric fragments.

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O

O

296.8

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O

a

+ve mode

O

NH3

N H

+

OH

O

O H2N

S H N

O

R

N H

O 129 Da (Constant neutral loss)

CID O H2N

OH S

OH O

O R Glutathione conjugate, R=drug or metabolite

H N

O N H

-ve mode OH

O H2N

O

m/z 272 (Common fragment)

b

O

OH

O

OR'

R

H O

CID

N

R

R

OH m/z 173

m/z 155 (Characteristic fragment)

O m/z 574 (Parent compound)

c http://www.elsevier.com/locate/trac

Metabolically stable site O

O S

N H2 F3 C

S

CID

O

O O

O

m/z 439 (Parent compound)

N H

O

O

N H

m/z 264 (Prominent fragment)

NH2 + F3C 175 Da (Neutral loss)

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371

Figure 2. (a) MS2 profiles of glutathione conjugates and two proprietary compounds of m/z 574 and m/z 439 (b and c, respectively) leading to characteristic moieties suitable for PIS and CNLS, respectively.

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MRM (835→803) MRM (835→803)

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100

50

0 100

MRM (835→785) MRM (835→785)

50

0 100

50

0 0

2

4

6

8

10

12

14

Time (min) Figure 3. Representative MRM chromatograms of desacetylrifapentine, m/z 835. The first two transitions in the metabolite were due to losses of CH3OH (32 Da) and CH3OH + H2O, respectively, while the last transition was due to the loss of CH3OH + 2H2O.

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MRM (835→767) MRM (835→767)

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Our group employed a similar approach [unpublished data] to detect metabolites of rifapentine in the in vitro metabolic stability samples generated at a very low substrate concentration (1 lM). The metabolites were first predicted using MetWorks and MetabolitePredict, and by considering the metabolism reactions observed for rifabutin [43] and rifampicin [48]. The MS fragmentation profile of each potential metabolite was then virtually determined using Frontier 5.1 and at least three prominent transitions were selected for MRM study. These were chosen based on critical evaluation of the parallelism between the predicted fragmentation profile of the metabolites and the pathway established experimentally for rifapentine. The transitions were then explored in MRM mode using a LIT system. Fig. 3 shows the result for desacetylrifpentine (m/z 835), a predicted metabolite, where the fragments considered for MRM analyses were [M+H]-CH3OH (m/z 803), [M+H]-CH3OHH2O (m/z 785) and [M+H]-CH3OH-2H2O (m/z 767). As evident, a characteristic peak appeared at RT 6.4 min for all the three transitions, confirming the presence of the predicted metabolite. In this manner, a total of 11 metabolites were detected in the metabolic stability sample, by employing a single LC-MS run of just 15 min duration. For application of MRM in the late stages of drug discovery and development, we need to consider that uncommon metabolism happening in vivo may not be detected, as the mode involves search based on in silico prediction. The other general limitation of MRM is that metabolites (e.g., glucuronides and N-oxides), which get fragmented in source, may not be analyzed per se [14,23].

4.6. Additional approaches that complement LC-MS studies 4.6.1. Chromatographic retention based prediction of metabolic change. Metabolism is a process of elimination of drugs from the body; hence, metabolites ought to be polar by nature, so they usually elute before the drug on an RP-LC column. However, there are cases where biotransformation products are non-polar and elute later than the parent. A compilation of retention data on RPLC columns of several drugs and their metabolites, and correlation with metabolic changes was published recently [13]. Using information in the literature [13,21,43,48], Table 5 lists common metabolic reactions and proposed relative RTs (RRTs) for metabolites. In brief, the retention behavior of metabolites on an RP-LC column can be categorized into three classes:  Class 1 – significantly polar metabolites that elute at RRT <0.90;  Class 2 – border-line metabolites that elute with RRT 0.90–1.10; and,  Class 3 – metabolites that are noticeably more nonpolar than the drug and have RRT >1.10.

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As shown in Table 5, most of the metabolites, particularly those belonging to Phase 1, fall in Class 1. Those produced from metabolic reactions (e.g., methyl oxidation to aldehydes, N-oxidation and isomerization) belong to Class 2. The metabolites generated by metabolic reactions involving acetylation, methylation, ring formation, denitration, hydrogenation and hydration are categorized as Class 3. The products falling in RRT Classes 1 and 3 always elute before and later than the parent drug, respectively, irrespective of other LC conditions. However, Class 2 metabolites may behave polar or non-polar to the parent, depending upon the mobile phase and structure of the drug {e.g., the N-oxide metabolite of sulfadiazine resolved before the drug [53], while those of loratidine and imatinib eluted later than their parents [54,55]}. In that respect, RRT classification given in Table 5 is anticipated to play only a supporting role for Class 1 and 3 metabolites. 4.6.2. UV spectra based characterization of metabolites. Traditionally, UV spectra have helped well in structure elucidation of process impurities and drugdegradation products [56]. But, as biological matrices are complex in nature, UV spectral analysis has been of less utility in Met ID studies. However, the advent of new generation LC-PDA detectors with advanced flow-cell designs has made the collection of UV spectral data much easier for metabolites. Still, the practical benefits are obtained only when biotransformation leads to significant hypsochromic or hypochromic lambda shifts. The common biotransformation reactions resulting in significant change in UV spectra are hydrogenation or dehydrogenation, though other changes in UV spectra have been reported {e.g., following acetylation or desacetylation [13]}. Among typical examples, Cı´sarˇ et al. [57] utilized the distinguishing feature of UV-DAD to differentiate keto form of dimefluron, a new potential antineoplastic drug, and its reduced metabolites formed in rats. Similarly, Nobilis et al. [58] deduced the position of O-acetylation in a potential antifungal drug through changes in UV spectra, as acetylation of phenolic or alcoholic hydroxy groups was not discriminated using MS information. Another example is that of conversion of rifampicin to its quinone, where significant changes in spectra were observed, as shown in Fig. 4a and b. The spectral shifts occurred due to the elongation of double-bond conjugation. This was not the case with other major metabolites {viz desacetylrifampicin and demethylrifampicin, wherein the spectra was similar to the drug (Fig. 4c and d)}. Hence, UV detection may provide useful complementary information in select cases. 4.6.3. Chemical derivatization or hydrolysis. Traditionally, chemical derivatization has been used to enhance http://www.elsevier.com/locate/trac

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374 120000

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180000

100000 335.00

140000

90000 80000

uAU

120000

uAU

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100000 475.00

70000 60000

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40000

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50000 15000

40000 10000

30000 20000

5000 0 200

581.00 300

400 wavelength (nm)

500

600

10000 0 200

571.00 300

400 wavelength (nm)

500

Figure 4. UV spetra (200–600 nm) of (a) rifampicin and its metabolites: (b) rifampicin quinone, (c) desacetylrifampicin, and (d) demethylrifampicin.

600

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uAU

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Table 7. Common derivatization approaches used during Met ID Reagent

Functional group

Derivatized moiety

Mass shift

Dansyl chloride

Hydroxyl and amine

Dansyl conjugate

235

Acetic anhydride

Amine and phenolic OH

Acetylated derivative

42

Ethyl chloroformate

Aminophenolic group

73

Propionic or benzoic anhydride 3-Pyridylcarbinol

OH group

168

p-Toluenesulfonhydrazide TMPP-AcPFP

Carbonyl group

Ethoxycarbonyl – derivative Propionylated and benzoylated derivative Picolinyl ester derivatives Hydrazone

Alcohol

TMPP acetyl ester

573

N-Dansylaziridine

Thiol

176

Sodium ethoxide

Carbamoyl glucuronide

Dansylaziridine thiol derivative Ethyl carbamate derivative

Methoxylamine

Aldehyde intermediate

O-Methyloxime

29

TiCl3

N-oxide

Amine

16

Jones reagent

Aliphatic OH

Diazomethane or diazoethane Trimethylsilylimidozole

Phenolic OH or carboxylic acid Phenolic or aliphatic OH Secondary amine

Carboxylic acid (1 OH) Ketone (2 OH) Methyl and ethyl ethers or ester Silylated derivative

14 2 14 28 72

Thio urea derivative

134

Phenyl isothiocyanate

Carboxylic acid

53 104 182

148

Utility in LC-MS studies [Ref.] Improvement in chromatographic separation and mass ionization [60] Increase of non-polarity and thus improvement in chromatographic separation [59] Increase of retention time of highly-polar metabolites [59] Improvement of chromatographic elution pattern and ESI responses [61] Establishment of site of glucuronidation and improvement of ionization efficiency [63] Improvement of detection limit [64] Detection of alcohols (including sugars and steroids) [62] Detection of unstable thiols [67] Detection of carbamoyl glucuronide indirectly by determining the ethyl-carbamate derivative [65] Detection of unstable aldehyde intermediates by trapping [66] Differentiates N-oxide from hydroxylated metabolites [68,70] Differentiates between primary and secondary alcohol [71] Location of the positions of phenolic and carboxylic acids [73] Determination of the site of glucuronidation [72] Determination of the site of glucuronidation [68]

Key: TMPP-AcPFP = S-Pentafluorophenyl [tris(2,4,6-trimethoxyphenyl)]phosphonium acetate bromide. TMPP-PrG = (4-hydrazino-4-oxobutyl) [tris(2,4,6-trimethoxyphenyl)]phosphonium bromide.

the volatility of drugs and metabolites for GC-MS detection. The same has been exploited in LC-MS studies, but for different reasons, viz: (1) improvement of LC elution [29,59–61]; (2) enhancement of ionization and sensitivity of the analyte [60–64]; (3) stabilization of thermo-labile components [65–67]; and, (4) enhancement of structure elucidation efficiency by producing recognizable, differentiating MS fragments [63,68–73]. Table 7 lists the common derivatization reactions used in Met ID along with their applications. A typical example is that of the glucuronide conjugate of N-(3,5dichlorophenyl)-2-hydroxysuccinamic acid (2-NDHSA), where derivatization was used to enhance ionization efficiency and to understand the site of glucuronidation [63]. As 2-NDHSA contained both hydroxyl and carboxylic acid groups, there were two possible structures for the glucuronide conjugate. These could not be differentiated using MS tools directly. To confirm the metabolite, 3-pyridinylcarbinol was used as a derivatiz-

ing agent, and the metabolite was identified as an alcohol-linked glucuronide by examining the molecular ions and MS2 spectra of the derivatized products [63]. In another study, the detection of unstable thiols could be achieved by derivatizing them with N-dansylaziridine [67]. Other examples include use of TiCl3 to convert N-oxide to amines to differentiate them from hydroxylation [70], and improvement of chromatographic retention of polar hydroxyl and amine compounds by forming their dansyl conjugates [60]. As such, derivatization methods are laborious and time consuming. Some modifications have been attempted to overcome these drawbacks, including use of post-column gas-phase reactions that significantly reduce analysis time. For example, gas-phase molecular reactions of tri-(dimethylamino) borate (TDMAB) with protonated analyte were utilized to derivatize N-oxide metabolites selectively to identify them [74]. The utility of this approach was augmented by using a modified QTrap, which was fitted with an external reagent-mixing manifold that allowed consecutive ion-molecule reactions and CID [55]. This approach was used to

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distinguish N-oxides of aromatic and aliphatic tertiary amines. An alternative to derivatization is chemical or enzymatic hydrolysis of metabolites to elucidate their structures [75]. For example, N-glucuronide conjugates of primary, secondary and N-hydroxy amines were hydrolyzed to glucuronic acid and parent amines in mild acidic conditions, while the conjugates of quaternary ammonium compounds were subjected to facile hydrolysis in a basic medium [75]. Similarly, O- and N-glucuronic acid conjugates were hydrolyzed by b-glucuronidase. Acyl glucuronates were also hydrolyzed in the same manner, but, in this case, acyl migration resulted in isomers, which were then hydrolyzed by NaOH [76].

5. Recent trends in Met ID using LC-MS 5.1. New generation LC systems and mass ion sources The new generation LC systems are of two types: ultrahigh performance LC (UHPLC) and nano-flow LC. The major commercially available UHPLC systems are:  UPLC (Ultra Performance LC, ACQUITY, Waters, Milford, USA);  HSLC (High Speed LC, Accela, Thermo Electron Corp., San Jose, USA);  UFLC (Ultra Fast LC, Prominence, Shimadzu, Tokyo, Japan);  RRLC (Rapid Resolution LC, 1200 Series, Agilent Technologies, Palo Alto, USA);  Fast LC (Varian 920 LC, Varian, Palo Alto, USA, now part of Agilent Technologies); and,  RSLC (Rapid Separation LC, Ultimate 3000, Dionex, Idstein, Germany). In all these instruments, higher chromatographic resolution and sensitivity are achieved due to reduced particle size of the stationary phase. In addition, there is an advantage of reduced run time and robust performance over the traditional HPLC systems. Furthermore, due to their better separation efficiency, these systems require less cleaning of the complex matrices, thus avoiding involvement of time-consuming, laborious sample preparation methods. Due to all these benefits, the new generation of LC systems has been found to be very useful in Met ID experiments [37,77,78]. The only limitation with these is the smaller chromatographic peak width. For example, some of the peaks might not be picked up when UHPLC is connected to conventional MS systems, and the results are collected in data dependent analysis (DDA) mode. For this, the best solution is to use modern LC systems combined with high-speed MS instruments [50]. The nano-flow LC systems, with flow rates of 200– 1000 nL/min, have to date mainly been used in proteomics research. However, they have been applied to Met ID lately, in conjunction with MS [20,23,79,80]. In 376

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nano-flow LC-ESI-MS, miniaturized emitters of 10– 100 lm i.d. are used in the MS system to produce small droplets of the eluent. This not only increases the ionization efficiency, but also minimizes the chances of analyte degradation in the source. The systems also have advantages of greater sensitivity, enhanced dynamic range, low ion suppression, significantly reduced carry over, requirement for very little sample volume, and shorter analysis time [20,23]. More recently, there were reports on the use of the direct infusion nano-ESI-MS [80,81], which was employed to re-examine Met ID of prazosin in different biofluids [81]. The method involved initial sample cleanup from matrix components using pipette tips packed with reversed-phase material (ZipTips), followed by direct infusion nano-ESI analysis on an LTQ/Orbitrap MS system. Using this set-up, the metabolic fate of the drug was established quickly and even some new metabolites were identified. The main advantage of the direct infusion nanospray mode was the availability of extended analysis times, as signal averaging for 1 min or more was possible through direct infusion – especially valuable for low-level metabolites. The approach also gave information on the type of metabolism reactions involved, which could then be used for targeted LC-MS experiments [81]. However, there are a few limitations of direct infusion nano-ESI-MS in Met ID studies: (1) proneness to interferences from components that dissociate within the source (e.g., glucuronides, sulfates, and N-oxides); (2) high cost of analysis due to the single-use nature of MS nozzles; and, (3) failure to characterize isomeric metabolites [23,80,81]. 5.2. Isotopic pattern matching and stable isotope labeling Traditionally, natural isotopic pattern matching had been used in structure elucidation [82,83], but its applicability was restricted to compounds with characteristic isotopic groups (e.g., halogens). To widen the scope, fabricated or custom designed stable isotopic compounds are employed these days, which are combinations of natural and synthetically labeled molecules. The isotopes commonly used for designing of stable isotope labeled compounds are 2H, 13C, 15N, 18O and 34S [84]. The approach has also been encouraged in Met ID studies because of the benefits of significantly high selectivity and better signal-to-noise ratio [16,85,86]. A typical example is of isotopically labeled GSH (13C2 and 15N in the glycine moiety), which was used in equimolar ratio with natural GSH as a trapping agent to screen accurately the presence of reactive metabolites of four model compounds {viz 2-acetylthiophene, clozapine, troglitazone and 7-methylindole [87]}.

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The fabricated stable isotope labeling was also employed in the study of in vivo drug metabolism [16]. After dosing a mixture containing equal amounts of natural and isotope labeled (13C3) ribavirin in rats, two metabolites were identified by their unique production spectra.

Using the same approach, Tong et al. characterized unstable metabolites of a model compound [86]. Similarly, the combined application of HR-MS and isotopic simulation identified a unique metabolite of isotopically enriched 4-cyanoaniline has been reported [85].

a

F F

NH3

F

O

*

F

NH2

N

N

F

O N

N F

CF3

(I)

N

N

N

N

N N

H2 N *

F

CF3

F

CF3

N

N

F

Sitagliptin (m/z 408)

O (II) Two possibilities of dehydrogenated metabolites (m/z 406)

b H N

Cl

H N

Biotransformation N

Cl

S

NH

N

N Ziprasidone, m/z 413

H/D Exchange

D N

Cl N

SH NH

Metabolite, m/z 415

H/D Exchange

D N

NH

Cl

S

ND

N

ND

SD ND

N Metabolite, m/z 419

Ziprasidone, m/z 415

Fragmentation

Fragmentation

D N

D N

Cl

D2 N

Cl

-NH2D

N m/z 280 (283)

Cl

-C 4H8N

ND2

DN m/z 263 (265)

m/z 192 (194)

Figure 5. Utility of HDE-MS in structure elucidation of metabolites. (a) Sitagliptin metabolism through dehydrogenation resulted in two possible structures. (b) Biotransformation of ziprasidone through hydrogenation leading to a metabolite of m/z 415. The metabolite shows fragmentation to three characteristic fragments of m/z 280, m/z 263 and m/z 192. m/z values shown in brackets represent m/z of metabolite of fragment after D2O exchange. *Indicates enantiomeric carbon.

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HO

Cl

N NH 2 M1 (213.0789)

O Cl

N

N NH

N

O

N

Nefazodone (m/z 470.2317)

Cl

N NH 2 M2 (197.0840)

Figure 6. Metabolism of nefazodone (m/z 470) into two products of m/z 213 and 197 with higher than usual mass defects.

5.3. Hydrogen/deuterium exchange mass spectrometry (HDE-MS) Hydrogen/deuterium exchange (HDE)-MS has been employed widely to determine the presence, the number and the position of exchangeable hydrogen(s) in small organic molecules, including metabolites. The exchange of labile hydrogen with deuterium happens in solution or gas phase when groups [e.g., –OH, –SH, –N(R)H, –NH2, and –COOH] are present in the structure [2,47]. There are many ways of performing an HDE experiment during LC-MS studies. One is to use D2O as a mobile phase solvent – the simplest, most accurate strategy for on-line HDE. The second is gas phase exchange, where ND3, CH3OD or D2O is used as an ionization gas in chemical ionization (CI) mode [88]. In ESI mode, ND3 and D2O are used as nebulization gas and sheath liquid, respectively [89,90]. Gas phase HDE is a more cost-effective strategy for Met ID applications. The only negative aspect is a stronger possibility of partial exchange. HDE-MS is reported to be particularly useful for distinguishing mono-oxygenation metabolites of different drugs [91–93] {e.g., the technique was able to differentiate N- or S-oxides from hydroxylated metabolites of denopamine and promethazine [93]}. In another study, HDE-MS was helpful in understanding the nature of metabolites formed even through unpredicted pathways [94]. HDE-MS, followed by NMR was employed for the identification of two uncommon enantiomeric metabolites of sitagliptin with m/z value 2 Da lower than the drug. During structure elucidation, the first task was to establish the core structure of the metabolites, without considering their enantiomeric nature. For this, two possibilities were considered (I and II, Fig. 5a), based on the molecular ion mass and available fragmentation data. The first possibility was the formation of metabolite through dehydrogenation of C–C bond of the piperazine ring (I, Fig. 5a), while the second was conversion of primary amine to secondary amine 378

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via C–N bond formation, followed by cyclization (II, Fig. 5a). In the first case, the neutral molecule was expected to contain two exchangeable hydrogens from the primary amino group, while only one was possible in the second. The comparison of molecular masses of the metabolites in normal (m/z 406) and HDE (m/z 408) modes indicated towards the second possibility (II in Fig. 5a). A similar conclusion was obtained from comparison of fragmentation data in HDE-MS [94]. Eventually, NMR results were used to establish the enantiomeric configuration of the structure. Another example is shown in Fig. 5b, where HDE-MS was used to characterize a novel metabolite of ziprasidone [67]. The mass of the metabolite was m/z 415, which was 2 Da higher than the drug (m/z 413), indicating the former to be a hydrogenated product. The HDE-MS study revealed a molecular ion peak of m/z 419, with fragments having masses of m/z 283, 265 and 194, similar to the drug. In accordance with this data, the site of hydrogenation was established to be the benzisothiazole moiety, purportedly an unusual site. 5.4. Data dependent analyses DDA is a potential approach for Met ID. The feature exists in modern MS systems, as DDAs provide more than one type of MS information on each metabolite during a single run. In DDA, two kinds of scan are generally acquired consecutively. In the first ‘‘survey scan’’, full scan, PIS, CNLS, isotopic pattern recognition, MRM, or multiple-ion monitoring (MIM) is obtained on the metabolite as it elutes from the column. Later, based on the targeted parameter(s), a ‘‘data dependent scan’’ is triggered nonspecifically to acquire spectral data for individual metabolites. The dependent scan is again switched back to survey scan and the cycle is continued [95,96]. As this approach reduces the overall time spent on analysis, it has even been applied in Met ID studies for the samples containing multiple metabolites [51,52]. An

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interesting example is that of natural isotopic abundance of bromine in a new anti-tubercular compound (R207910) [97], where DDA was triggered by the appearance of a signal of the brominated species. The 79 Br/81Br isotope ratio was used in the search for metabolites, which allowed facile structural characterization of the main metabolites of the compound in complex extracts of rat and dog feces. The output from this type of data dependent scanning using the isotopic pattern of bromine has been termed a ÔbromatogramÕ. Further, in silico tools can be used to obtain masses of metabolites in order to facilitate their detection and structural elucidation in DDA mode [8]. In conventional DDA, the detection is usually limited to main components, whether of drug, its major metabolites or biological matrix. However, when the masses of predicted metabolites are included as a selection criterion during DDA, the detection of potential metabolites present even at low concentrations becomes possible. For example, this approach was employed to study the metabolism behavior of indinavir in human hepatic S9 incubate [39]. In total, two dealkylated, 11 monooxygenated, three deoxygenated and two dealkylated/monooxygenated metabolites were detected in a single LC-MSn run. The strategy has also been used for Met ID of several other drugs, including glyburide, tamoxifen, raloxifene and adatanserin [98]. Another new type of DDA application is the use of ‘‘Nin-one’’ strategy, which is specific to Q- Trap MS systems [99]. Here fast trap mode scan is utilized for Met ID. Initially, enhanced mass scan (EMS), a type of full scan in trap mode, is used as a survey scan to trigger multiple dependent enhanced product ion (EPI) scans. The output is a single file, which contains EMS survey data and also EPI data that are rich in structural information. The characteristic product ions are extracted from dependent EPI chromatograms. The approach obviates the need of PIS and CNLS analyses by giving the desired output in a single run. The same was utilized for detection and identification of uncommon metabolites of nefazodone from complex biological matrices [99]. A total of 13 metabolites were detected by extracting the first EPI chromatogram, while three trace-level metabolites were detected in the second EPI scan. 5.5. LC-MSE approach MSE, also known as the ‘‘all-in-one’’ or ‘‘parallel fragmentation’’ approach, is a new application of Q-TOF systems for Met ID [100,101], where superscript E represents collision energy (CE). Here, the unique fast scanning capability of Q-TOF systems is utilized to allow simultaneous acquisition of both molecular ion and fragmentation data in a single experiment. The system records two scans, first at a low CE (<5 eV) in a broad mass range (e.g., 100–1000 m/z) to obtain the molecular mass of the drug or its metabolite, and the second where CE is ramped from low to high voltage

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(e.g., 20–40 eV) and applied over the same mass range to obtain comprehensive fragmentation data of both stable and metastable ions. In this manner, MSE helps in mining an entire experimental data set to yield information on specific molecular masses, precursor ions, product ions, and neutral loss(es) [100,101]. In one reported study, LC-MSE mode was employed in a semi-automated manner, in conjunction with in silico tools, to identify metabolites at the lead optimization stage [102]. The drug, metabolite(s) and matrix peaks were well separated on an LC column (a basic requirement of MSE experiments) and then Q-TOF-MS was used simultaneously to generate elemental compositions and fragmentation data for both the drug and its metabolites. The metabolite prediction/detection software was used post-acquisition to determine structures of the most likely metabolites, based on elemental composition of the fragments and bond connections present in the parent molecule. The comparison of fragment profiles of the drug and its metabolites provided mass-shift values due to metabolism, thus helping to identify the sites of biotransformation. Overall, MSE and in silico tools together were able to produce and interpret data much more rapidly than the conventional Met ID approach, so this integrated application of Q-TOF has been proposed as a ‘‘first-line’’ approach for Met ID studies in early drug discovery. Rather modern Q-TOF-MS systems offer very good sensitivity, ultra-high resolution, a wide linear range, and at least one instrument has two CID cells for MS3 along with ionmobility features [37]. With DDA and MSE possibilities coupled to these, the systems allow very fast data acquisition and hence offer possibilities of highthroughput analysis, an essential requirement in the early stages of drug discovery. 5.6. Mass defect filters The term ‘‘mass defect’’ refers to the difference between the exact mass of an element (or a compound) and its closest integer value. For example, the theoretical mass defect of oxygen (nominal mass, 16 Da; exact mass, 15.9949 Da) is 0.0051 Da or 5.1 mDa. It means that the mass defect shift between parents and their oxidative metabolites must be 5.1 mDa, or a value very near to this figure when experimentally determined accurate mass is used. In general, mass defects for changes due to biotransformation are less than 50 mDa with a maximum of 89 mDa (Table 5). The mass defect filter (MDF) algorithm, integrated into software of HR-MS systems by major vendors [25,103], uses accurate mass data and determines the mass defects of the parent drug and its metabolites. Subsequently, post-run chromatograms are generated in a fixed, narrow mass defect range, yielding peaks selectively for the drug-related metabolites, and excluding non-metabolite matrix components. There are many literature reports http://www.elsevier.com/locate/trac

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highlighting the utility of the MDF method in Met ID studies [25,96,103–108]. More recently, the concept was extended to multiple MDFs (MMDFs), where several MDFs were used simultaneously to identify multiple metabolites of interest over a wide range of mass defects. MMDFs assume that drug metabolites of a single class have similar mass defects, and they are within a limited mass range with respect to the parent structures [104]. The commonly used MMDF templates are: (i) drug filter, (ii) substructure filter, and (iii) conjugate filter [25]. The drug filter is the basic template, where the mass defect of a drug only is considered, and it is used to monitor metabolites with minor changes in their molecular masses in comparison to the drug (e.g., oxidation, reduction, demethylation, and deethylation). The substructure filter includes a few core portions of a drug as filter templates, which are applied to detect metabolites that are significantly smaller than the parent drug (e.g., metabolites formed by hydrolytic cleavage of the parent drug into two smaller molecules). Such filters are also applicable in detecting metabolites of a prodrug, in which the active moiety can be used as a substructurefilter template. The third category uses typical drug conjugates as filter templates and is designed to detect different classes of conjugated metabolites. In addition to these common templates, some special filter templates can also be considered on a case-by-case basis [104]. Use of such MDF templates is more widely applicable than single MDF, because they are also applicable in the detection of metabolites with significantly different molecular weights. For example, in the case of nefazodone, two small metabolites (M1 and M2, Fig. 6) were formed with unusual mass defect shifts of 152.8 mDa and 147.7 mDa, respectively. These small metabolites could not be detected when the projected mass defect shifts between the drug and its metabolites were even increased to 100 mDA. However, the identification could be done easily using the dealkyl moiety as the substructure filter (Fig. 6). Generally, these hydrolytic metabolites pose significant challenges to Met ID scientists. To address them, modified software was introduced recently, using MDF in combination with an automatic dealkylation tool [106], which predicts cleavage sites in the drug, and picks up corresponding metabolite peaks by using the MDF principle. In general, MDF algorithm is applicable in Met ID of even unpredicted metabolites, compared to other approaches (e.g., MRM, CNLS, and PIS). 5.7. 2D and 3D approaches for elucidation of molecular formula During structure elucidation by MS studies, the HR-MS data are also employed to derive the molecular formula of unknown species. Unfortunately, as the m/z value increases, the number of possible molecular formulae 380

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rises exponentially, so there is always a chance of getting multiple numbers even if mass accuracy is less than 5 ppm. To address this difficulty and to obtain reliable molecular formulae from the list, there have been some advancements recently [109,110]. The new generation software tools intelligently combine accurate mass and isotopic pattern to compare theoretical and experimental data, resulting in a considerably shorter list of reliable formulae compared to that obtained using HR-MS data alone. Such 2D software algorithms have been incorporated into LC-MS systems by a few vendors [e.g., i-Fit (Waters) and SigmaFit, (Bruker Daltonics)]. Also, a novel mass spectral correlation method called ‘‘FuzzyFit’’ can be installed in any kind of MS system [85]. A further recent advancement is to combine fragmentation information with accurate mass and isotopic pattern (3D approach). For this, a novel software module (Smart Formula 3D) is supplied with Q-TOF-MS systems by Bruker Daltonics [111]. The module elucidates elemental composition of metabolites or any unknown chemical compound by gathering all the possible formulae for the precursor and product ions through application of both accurate-mass and isotopepattern filters. Later, all product ion formulae that are not a subset of the precursor ion formulae are filtered out (i.e. where the number of elements in the fragment is not less or equal to that in the precursor). Furthermore, every potential pair of precursor ion and fragment is crosschecked with corresponding neutral losses to verify exact elemental composition. The module involves mathematical procedures to generate elemental formulae automatically in a more robust, faster and more accurate manner. Another unique advantage of the software module is its universal applicability to any kind of HR-MS system. In that way, this is a significant advancement towards Met ID using LC-MS.

5.8. Polarity switching Polarity switching is a high-throughput screening technique where MS data are acquired in both positive and negative modes simultaneously. The option is mainly available in Q-Trap systems. Principally, it involves survey scan in one ionization mode and generation of a data dependent product-ion spectrum in the second. The technique has been primarily employed in Met ID of reactive metabolites [29,112,113]. During GSH-trapping assays for the identification of reactive metabolites, the detection of neutral loss of m/z 129 is done in positive ionization mode, while PIS for m/z 272 is carried out simultaneously in the negative ionization mode. This way overall acquisition time period is reduced. An example exists, where polarity switching has been used even for the identification of a stable oxidative metabolite [110].

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5.9. Background-subtraction and noise-reduction algorithms (BgS-NoRA) Background subtraction (BgS) is routinely practiced in LC-MS laboratories. The control file containing signals of background or matrix ions is subtracted from the analyte file having additional signals of the drug and its metabolites. This way, the analytes of interest are expressed in a subtracted file [78]. Recently, this subtraction step has attracted attention of biotransformation scientists working with HR-MS systems, where high resolution gives the advantages of differentiating isobaric masses and extracting analytes in the subtracted chromatogram with much better sensitivity. In addition, BgS algorithms with the typical feature of RT tolerance have been developed. In this case, differences of retention periods of blank peaks in control versus analyte files are ignored by pre-defining the RT windows. This leads to a cleaner subtracted chromatogram [103,114].

Lately, Zhu et al. introduced a noise-reduction algorithm along with the refined BgS discussed above [78]. The combined algorithm helped to level down general noise, and it also assisted in removing specific background peaks in the analyte file, like those due to reagents. This approach was very effective in yielding post-run chromatograms where the peaks of drugs and metabolites were free from noise and interference. In this way, the metabolites present in complex biological matrices (e.g., urine and bile) were deduced effectively despite the domination of background regions in TIC even after using BgS. This technique is anticipated to be very useful tool in metabolite detection, identification and quantitation, particularly during first-in-human and chronic toxicology studies, where the non-radiolabeled compound is administered and data are considered critical to meet current regulatory requirements [32,78,114].

Table 8. Analytical approaches in metabolite quantification [16,19,31] Technique

Principle

Advantages

Remarks/Limitations

LC-CLND

Quantitative response from CLND is proportional to total nitrogen concentration of analyte

It can provide absolute ratio of drug and metabolite present in biological matrices; less robust quantitative tool

LC-ELSD

Light-scattering based detection

Broad applicability

LC-CORONA CAD

Corona discharge of analyte particles

LC-ICP-MS

Detection of elements (e.g., metals, halogens, sulfur and phosphorus)

Broad applicability; better sensitivity and dynamic range than ELSD Detector response is independent of molecular structure; good sensitivity and dynamic range

19

Signal corresponding to

Analyte should contain nitrogen; number of nitrogens should be known; non-selective method; chances of interference are more because endogenous compounds are generally nitrogen containing; necessity of nitrogen-free mobile phase and gases Poor sensitivity and dynamic range; inconsistent inter-analyte response; incompatibility with mobile-phase gradient; significant interferences from biological matrices Significant interferences from biological matrices Limited to the analysis of compounds containing elements (e.g., metals, halogens, sulfur and phosphorus); number of atoms of element in question should be known Limited applicability; low sensitivity

F-NMR

LC-ACR and LC-MSC

AMS

19

F in NMR

These radiochemical-detection systems utilize the stop-flow technique to increase resident time of radiolabeled analyte to increase sensitivity. In LC-MSC, the samples are withdrawn in 96-well plates Used predominantly for the detection of 14C ions. The approach is based on converting sample to graphite pellet followed by ionization, which is ultimately detected as a positivelycharged species

Simple and robust method; very selective approach 10–20-fold more sensitive than conventional flow-through radiodetection methods

Significant improved intensity over LC-ACR

For detection, the metabolites should contain the intact radiolabeled position in the structure

Labor-intensive method; low throughput; high cost

Key: CLND = Chemiluminescence Nitrogen Detector, ELSD = Evaporating Light Scattering Detection, CORONA CAD = Charge Aerosol Detection, ICP-MS = Inductively Coupled Plasma Mass Spectrometry, 19F-NMR = Nuclear Magnetic Resonance, ACR = Accurate Radioisotope Counting, MSC = Microplate Scintillation Counters, and AMS = Accelerated Mass Spectrometry.

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Table 9. Objectives, nature of samples handled, applicable approaches and expected outputs of high-throughput Met ID during different stages of drug discovery and development Stage

Objective

Sample

Applicable approach

Code

Expected output

Stage I: Pre-synthetic

Prediction

Virtual structures

In-silico tools

A

Fast prediction of sites, structures and mass of metabolites

Stage II: Early to Late Discovery

Fragmentation profile of the drug

Pure compound

Direct mass studies: Accurate mass, MSn, isotopic pattern matching and HDE-MS

B

Comprehensive fragmentation pattern. Prediction of constant neutral loss and constant fragment ion

Virtual structures

In-silico prediction of drug fragments and mechanism of their formation (as a support to B) Generalized approaches List-dependent metabolite detection Retention time (RT) tolerant background subtraction algorithm Noise-reduction algorithm MDF and MMDF Isotope pattern filter Alternative approaches UV-VIS detection Radio detection Polarity switching Semi-targeted approaches Constant neutral loss scan Precursor ion scan Fully-targeted approaches Post-run EIC Predicted MRM/SRM Chemical derivatization to improve detection All the above listed detection approaches

C

Detection of the stable metabolites

In-vitro incubates containing microsomes, hepatocytes, etc. + trapping agent, like GSH, NAc, etc.

D E

To detect all metabolites (trace, minor and major) Removal of false positives

F G H I J K L M N O P D-P

To detect adducts of reactive metabolites/ compounds with trapping agents

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Detection of reactive metabolite

In-vitro incubates containing microsomes and hepatocytes

Stage

Objective

In vitro incubates with additives as given above

Applicable approach Generalized approaches HR-MS: Molecular formula, ring plus double bond, metabolic generation Isotopic pattern matching of natural or fabricated compound Generalized fragmentation using MSE approach MS/MS studies in DDA mode Polarity switching On-line HDE-MS in HR-MS mode Complementary approaches RRT shifts UV spectra

Stage III: Pre-clinical to Clinical Development

Identification of adducts of reactive metabolites, in vivo Detection and identification of new metabolites Confirmation of the presence and structure of metabolites identified in in vitro samples

In vivo biofluids, like urine, plasma, faeces, bile, tissues, etc.

Isolated/synthesized metabolites

Targeted detection approaches

Code

Expected output

Q

Identification of simple and major metabolites

H0

Maximum structure information in single LC-MS injection Prediction of metabolites that are likely to be formed in vivo

R S K0

T U V L-O

Confirmation of reactive metabolite formation

All above detection and preliminary identification approaches

D-V

Identification of uncommon and low-level metabolites

On-flow MSn studies in DDA fashion Targeted MSn studies On-line HDE-MS in HR-MS mode Chemical derivatization or hydrolysis followed by LC-MS Isolation of metabolites followed by structural analysis Synthesis of possible metabolite and spectra, and RRT matching with the actual metabolite

S0

Confirmation of metabolite structure

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Preliminary identification

Sample

W T P0 Y Z

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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

6. 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 ξ

Metabolite Prediction

A

Stage I

Stage II

Stage III

- In silico

- In vitro

- In vitro - In vivo

Direct MS Pure drug

B

In vitro sample

LC-MS

D

E

MS fragmentation pattern of drug

C

F

G

N

H

I

Metabolite detection complete?

J

K

Optimize MS parameters Yes New peaks detected?

P Yes

CNL possible?

No

Yes

Constant fragment possible?

Yes

L

Predict MRM transitions

M O

Remove false positives List of all metabolites with RRT and m/z values

List stable metabolites

Metabolite detection

List reactive metabolites H’

Q

R

S

U

V

K’

Predict metabolic change by accurate mass shifts

Predict metabolite generation and list accordingly T

Postulate structure of remaining metabolites from the list by comparison of their accurate mass values and fragmentation pattern with the drug and previous generation metabolite(s), supported by RT and UV spectra studies

Postulate structure of first generation metabolites based on comparison of fragmentation patterns of the drug and its metabolite, supported by RT and UV spectra study

Preliminary metabolite identification

In vivo sample

LC-MS

Repeat all Stage II detection and identification studies

Confirmation of the presence of metabolites identified in the in vitro samples

L

M

N

O

Detection of the adducts of reactive metabolites

Detection and preliminary identification of new metabolites S’ Sufficient information?

No

W

Yes Compare mass fragmentation of the metabolite with drug and/ or previous generation metabolite(s)

Structure confirmed?

No

Confirmation possible through HDE-MS studies Derivatization required?

Yes

P’

Yes

T’

Structure confirmed?

Yes

Yes Confirmed metabolite structure

No Y

Isolation possible? Yes No Z

Figure 7. Strategy for high-throughput Met ID during drug discovery and development based on HM approach. Key: Stage I, II and III, and alphabets are the same as those shown in Table 9. Final outcome is shown in bold letters in oval boxes; input and output are presented by bold (or purple) and dotted (or red) filled arrows, respectively. Dotted boxes and dotted line arrows show complementary approaches. The placement of steps, B, C, D, E, etc., in groups does not necessarily mean that all are essential. They need to be included in the study as necessary.

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metabolite standards are available. Most quantitative metabolism studies therefore employ radiolabeled compounds [16,19], which are administered to a test system and the radioactivity is followed over time. This enables the compound and its metabolites to be distinguished from the myriad of endogenous substances. Unfortunately, radiolabeled compounds are not always available, so many other approaches have been explored for metabolite quantitation. A comprehensive discussion on these tools is outside the scope of this review because details are available elsewhere in the literature [16,19]. However, Table 8 gives a summary of the techniques, their principles, including advantages and limitations.

7. 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, highthroughput 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 comprehensive practical strategy has been developed (as shown in Fig. 7). The proposed strategy is based on the ‘‘HM’’ principle (i.e., High-quality throughput using Minimum resources). According to the proposed strategy, the first step in the pre-synthetic phase involves subjecting of virtual compounds to in silico metabolite prediction (A), yielding information on the structures of metabolites, their mass values and sites of metabolism based on the common biotransformation reactions. Once the synthetic compound(s) are available, a comprehensive mass fragmentation pattern (B) is established by direct-injection MS studies. Also, at this stage the possibility of constant neutral loss(es) or the presence of unique fragment(s) (C) can be assessed for the predicted metabolites. Subsequently, in vitro studies are initiated to generate metabolism samples using hepatocytes, microsomes and other liver fractions, which are subjected to LC-MS after sample preparation. Once the data are acquired, post-run metabolite detection is carried out using multiple tools {e.g., list-dependent metabolite detection (D), RT-tolerant BgS algorithm (E), noise-reduction algorithm (F), MDF and MMDF (G), isotope pattern filter (H), and/or post-run EIC (N)}. This is followed by exploration of CNL or appearance of unique constant mass fragment ion through CNLS (L) and PIS (M) studies, respectively. Further, predicted transition-based MRM analyses (O) are also performed on a need basis. The optional approaches {e.g., UV-PDA detection (I), radio detection (J), polarity switching (K), and chemical

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derivatization followed by LC-MS detection (P)} are adopted as and when required, based on feasibility. If new metabolites are detected using these approaches, their mass parameters are optimized for identification in the next stage. All the detected metabolites are then listed in a table, along with their masses, RRT values and intensities (semi-quantitative). It may be useful here to segregate the tabulated information into stable and reactive metabolites, if trapping studies have been completed independent of the Met ID efforts described above. Once the detection phase is over, the studies move to preliminary Met ID. For this, molecular formula, ring plus double bonds, number of nitrogens and isotopic abundance (H 0 ) are derived from HR-MS data (Q). It may also be useful at this stage to determine the metabolic change based on accurate mass shifts, and to identify the metabolic generation. The predicted firstgeneration metabolites are characterized by study of their fragmentation patterns (R, S) and comparing those with that of the parent compound. Here, complementary information {i.e., polarity of compound (RRT values) (U) and PDA spectra (V)} is critically assessed with respect to proposed metabolic changes (Table 5). Polarity switching (K 0 ) is a good approach to collect fragmentation data in both positive ionization and negative ionization modes in a single run. In addition, HDE-MS studies (T) may be carried out on case-by-case basis, if felt necessary from critical evaluation of the data collected before this stage. The structures of subsequent generation metabolites are also determined by following the same protocol, except that their accurate mass values and fragmentation profiles are compared with both the parent compound and first-generation metabolites. Subsequently, emphasis shifts to a more exhaustive Met ID exercise involving in vivo experiments, which are normally conducted on selected compounds that reach the pre-clinical stage. The purposes here are to: (1) confirm the presence and the nature of metabolites identified in the in vitro samples; (2) detect and identify new metabolites, if any; and, (3) detect reactive metabolites formed in vivo. First, in vivo samples are subjected to detection using the same approaches (D–O), as discussed above for in vitro samples. If any new metabolite peak is detected by overlaying LC-MS chromatograms of in vitro and in vivo samples, the preliminary Met ID protocol H 0 -T is implemented. In parallel, reactive metabolites are detected/identified using the targeted approaches {e.g., CNLS, PIS, EIC and MRM (L, M, N and O, respectively)}. The metabolite structures are finally confirmed through multiple steps S 0 -Z, in which first data dependent on-flow MSn (S 0 ) are exploited, if the fragmentation information obtained in MS2 is insufficient for structure elucidation. http://www.elsevier.com/locate/trac

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In case even this is insufficient to yield the total information, targeted MSn studies (W) may be carried out in the next step. Otherwise, if data are sufficient, the advice would be to compare mass fragment profiles of the metabolite to the drug or the previous generation metabolite so as to confirm its structure. If the confirmation is still not there, two complementary pathways can be explored. One is confirmation through on-line HDE-MS study (T 0 ), which applies when multiple structures are possible with different numbers of exchangeable hydrogens. The second involves confirmation by utilizing chemical derivatization or hydrolysis followed by LC-MS studies (P 0 ). If there are still doubts about the structure, one needs to confirm whether it is possible to isolate the metabolite from the matrix. In case it can be done, the metabolite should be isolated and the structure confirmed through spectral characterization in the off-flow mode (Y). In the absence of isolation, the only way left is synthesis of the postulated metabolite, which is then subjected to spectral and RRT matching with the metabolite in the sample (Z). 8. 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 developments in Q-TOF instruments, these have potential to become powerful tools in Met ID laboratories in the future [37]. It is desirable that various postacquisition data treatment modules, currently being developed independently by different vendors, are integrated in a manner that biotransformation scientists easily get unambiguous elucidation of metabolite structures by following the proposed strategy. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

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