Xenobiotic Metabolism: A View through the...

Xenobiotic Metabolism: A View through the Metabolometer

Andrew D. Patterson,† Frank J. Gonzalez,† and Jeffrey R. Idle*,†,‡

Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health,Bethesda, Maryland 20892, and Institute of Pharmacology, First Faculty of Medicine, Charles UniVersity,

Praha, Czech Republic

ReceiVed January 25, 2010

The combination of advanced ultraperformance liquid chromatography coupled with mass spectrometry,chemometrics, and genetically modified mice provide an attractive raft of technologies with which toexamine the metabolism of xenobiotics. Here, a reexamination of the metabolism of the food mutagenPhIP (2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine), the suspect carcinogen areca alkaloids (arecoline,arecaidine, and arecoline 1-oxide), the hormone supplement melatonin, and the metabolism of theexperimental cancer therapeutic agent aminoflavone is presented. In all cases, the metabolic maps of thexenobiotics were considerably enlarged, providing new insights into their toxicology. The inclusion oftransgenic mice permitted unequivocal attribution of individual and often novel metabolic pathways toparticular enzymes. Last, a future perspective for xenobiotic metabolomics is discussed and its impacton the metabolome is described. The studies reviewed here are not specific to the mouse and can beadapted to study xenobiotic metabolism in any animal species, including humans. The view through themetabolometer is unique and visualizes a metabolic space that contains both established and unknownmetabolites of a xenobiotic, thereby enhancing knowledge of their modes of toxic action.

Contents

Introduction 851

Metabolism of Xenobiotics Using Metabolomics 852

Heterocyclic Amine Food Mutagen 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)

852

Areca Alkaloids Arecoline, Arecaidine, andArecoline 1-oxide

853

Melatonin 855

Aminoflavone (NSC 686288) 856

Conclusions and Future Perspectives 858

Introduction

Xenobiotic is a term used to describe chemical substancesthat are foreign to animal life and thus includes such examplesas plant constituents, drugs, pesticides, cosmetics, flavorings,fragrances, food additives, industrial chemicals and environ-mental pollutants. It has been estimated that humans are exposedto 1-3 million xenobiotics in their lifetimes (1). Most of thesechemicals that gain access to the body via the diet, air, drinkingwater, drug administration, and lifestyle choices undergo a broadrange of processes of detoxication that in general render themless toxic, more polar, and readily excretable. Detoxicationreactions in lower animals may also impact human health.Incomplete detoxication in organisms in the food chain maydetermine the extent of human exposure to environmental toxins,for example, hepatotoxic microcystins from algal blooms (2)and mycotoxins ingested by agricultural species (3). While thegeneral principles of xenobiotic detoxication were delineatedlong ago (4), it was also recognized that xenobiotic metabolism

could modify the pharmacological properties of drugs (4, 5) oreven activate inert chemicals into biologically reactive species(6). Moreover, the cancer chemotherapeutic drugs stilboestroldiphosphate (7) and cyclophosphamide (8) were the first to bespecifically designed to undergo xenobiotic metabolism andactivation in the human body to generate pharmacologicallyactive species. Clearly, it is important to understand both thequalitative and quantitative aspects of xenobiotic metabolismin both humans and lower species to gain insights intomechanistic aspects of toxicity profiles.

The study of xenobiotic metabolism over the decades hashinged on technical developments in the field of analyticalchemistry. Xenobiotic metabolism has its origins in the mid-1800s, and for almost a century, its practice involved isolation,purification, and simple chemical investigation of urinaryconstituents. The advent of UV/visible and fluorescence spec-troscopy, radiolabeled compounds, and partition chromatographycatalyzed a major expansion of activities in the field. However,the biggest single advance of benefit to the study of xenobioticmetabolism was the development of biomedical mass spec-trometry, at first GC-MS1 and subsequently the range of LC-MS and NMR technologies that are ubiquitous today. Drugmetabolism may now be conducted by what may be called drugmetabolomics. During its short history, metabolomics hasgenerally been thought of as a tool for either discovering cellularresponses to external stimuli such as toxicant administration(9, 10) or cataloging metabolic pathways in health and disease(11, 12). In addition to examining endogenous molecularchanges in response to stimuli, metabolomics can equally be

* Corresponding author. Institute of Pharmacology, First Faculty ofMedicine, Charles University, Albertov 4, 128 00 Praha 2, Czech Republic.E-mail: [email protected].

† National Institutes of Health.‡ Charles University.

1 Abbreviations: CYP, cytochrome P450; LC-MS, liquid chromatogra-phy-coupled mass spectrometry; NMR, nuclear magnetic resonance; GC-MS, gas chromatography-coupled mass spectrometry; UPLC, ultraperfor-mance liquid chromatography; ESI, electrospray ionization; QTOFMS,quadrupole time-of-flight mass spectrometry; OPLS, orthogonal projectionto latent structures; PCA, principal components analysis; PhIP, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine; PLS-DA, projection to latentstructures-discriminant analysis.

Chem. Res. Toxicol. 2010, 23, 851–860 851

10.1021/tx100020p 2010 American Chemical SocietyPublished on Web 03/17/2010

applied to the examination of xenobiotic metabolites (1, 13, 14),the footprints of cellular metabolism that are left on xenobioticmolecules.

In this review, we will examine the advances in xenobioticmetabolism that have been made using metabolomics, a sciencethat combines advanced analytical chemistry with chemometricanalyses such as multivariate data analysis. Because of the dosesemployed, when xenobiotics are administered to experimentalanimals, their metabolites occur generally at concentrations inexcess of most endogenous metabolites, i.e., the majority ofthe metabolome. This alone makes them relatively easy to findusing metabolomic protocols (summarized in Figure 1). Ad-ditionally, the chemometric methods (15, 16) that comparecontrol animals with dosed animals or fluids collected prior toxenobiotic dosing versus post greatly assist in the identificationof metabolites deriving from biotransformation of the admin-istered xenobiotic, as opposed to those that comprise theunperturbed metabolome (Table 1). Finally, the use of massspectrometry with accurate mass determination yields a restrictednumber of empirical formulas for each xenobiotic-related ionbecause it is known that each of these ions must have derivedfrom the metabolism of the administered xenobiotic. Further-more, there are also software packages that can assist ingenerating a table of metabolites directly from the mass spectraldata matrix (17, 18).

Metabolism of Xenobiotics Using Metabolomics

More than 52 million organic and inorganic substances havebeen synthesized of which over 39 million are commerciallyavailable (19) and thus represent potential human exposures.The proportion of these xenobiotics whose metabolism has beenestablished in humans and laboratory animals is trivial. It iswell established that xenobiotics can elicit toxic reactionsthrough mechanisms involving their biotransformation to reac-tive chemical species (20). To understand their toxicologicpotential, it is vital to determine the detailed metabolic pathwaysof xenobiotics that have known human exposures.

The studies revisited below follow a similar experimentaldesign. Simply stated, urine from vehicle- and xenobiotic-treatedmice was analyzed using ultraperformance liquid chromatog-raphy coupled with mass spectrometry and compared usingadvanced chemometrics. This approach was first introduced byPlumb and colleagues (21) and, recently, has been reviewed(13). The metabolomic approach is not to be confused withmetabolite profiling in which, for example, drug and drugmetabolites are identified using methods such as mass defectfiltering (17, 22) or by scanning (LC/MS/MS) for known drugmetabolites such as glutathione conjugates or glucuronides (23).In other words, in a true metabolomics study no emphasis isplaced on measuring a particular metabolite or group ofmetabolites, but rather on measuring as many metabolites aspossible and then using chemometrics to identify xenobioticmetabolites. While the studies presented below represent, to thebest of our knowledge, the first successful applications of thexenobiotic metabolomic approach, other recent examples includethe identification of novel tolcapone (a catechol-O-methyltransferase inhibitor) metabolites in rats (24) and new metabo-lites of fenofibrate in Cynomolgus monkeys (25). In both cases,the metabolic maps of tolcapone (6 novel metabolites identified)and fenofibrate (5 novel metabolites identified) were expanded.

Heterocyclic Amine Food Mutagen 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP). PhIP is the most abundantheterocyclic amine formed during the cooking of meat and fish(26). Only food that is boiled or cooked below 200 °C isrelatively free of PhIP and related heterocyclic amines. Meta-

Figure 1. Multivariate data analysis approach to identifying potentialdrug metabolites. Beginning with an S-Plot (center), suspected drugmetabolites can be identified using (1) the trend plot feature inSIMCA-P to demonstrate the absence of the suspected drug metabolitein vehicle-treated animals; (2) comparing extracted ion chromatogramsfor the presence (drug-treated) and absence (vehicle-treated) of thesuspected drug metabolite; and (3) tandem MS fragmentography ofthe suspected drug metabolite and an authentic compound (synthesizedor obtained from commercial sources) to confirm the identity of thedrug metabolite. Other methods to identify drug metabolites includingisotope analysis may be informative for xenobiotics containing chlorine(isotope ratio ) 3:1 assuming a single chlorine), for example.

Table 1. Popular Chemometric Approaches to XenobioticMetabolism

description

principalcomponentsanalysis (PCA)

PCA is a typical first-pass visual approach to extractinformation from high-dimensional metabolomic datasets. The first principal component represents themaximal variation in the data set. Each subsequentprincipal component explains the remaining variation.PCA is unsupervised, that is, no class information isprovided. Sample grouping and outliers are displayedin the scores, while the variables influencing theobserved groupings are displayed in the loadings.

projection tolatent structuresdiscriminantanalysis(PLS-DA)

PLS-DA models are created by rotating the PCAmodel to maximize class separation. Thus, PLS-DAmodels are supervised since information regardingclass membership (e.g., vehicle and treated)is provided by the user. Like PCA, information isdisplayed in the scores and loadings. Additionalsteps may be required to validate the modelssuch as cross-validation.

orthogonalprojectionto latentstructures(OPLS)

OPLS simplifies data interpretation byconcentrating related variation into the firstcomponent while placing unrelated (orthogonal)variation into the second component. Like PLS-DA,OPLS models are supervised and may requireadditional validation steps to assist interpretation.

852 Chem. Res. Toxicol., Vol. 23, No. 5, 2010 Patterson et al.

bolic activation of PhIP to N2-acetoxy-PhIP and N2-sulfonyloxy-PhIP largely involves N-hydroxylation by CYP1A2 followedby conjugation of the hydroxylamine by N-acetyltransferase andsulfotransferase, respectively. Interspecies differences in thepatterns of PhIP metabolism have been reported, making itdifficult to extrapolate laboratory data to human risk assessmentfor PhIP exposure. In essence, rodent P450 enzymes predomi-nantly carry out a detoxication reaction (4′-hydroxylation), whilethe human counterparts produce the N-hydroxy metabolite thatleads to esterification and production of the active electrophilicmetabolite. There were a total of nine known metabolites, sixarising from the detoxication reactions of N-glucuronidation,4′-hydroxylation, and subsequent sulfate conjugation. In contrast,three activated metabolites were reported in the literature, N2-hydroxy-PhIP and its O-acetyl and sulfate conjugates (Figure2). Because of the rodent-human metabolic differences, trans-genic mice humanized for the CYP1A2 gene were employed tounderstand better the metabolism and toxicology of PhIP in micewith a human pattern of PhIP metabolism (27). It should benoted that this class of heterocyclic amines is among the mostmutagenic group of substances yet tested (26), and there is areal concern for cancer risk to humans from their dietaryexposure (28).

The metabolomics of PhIP metabolism in the mouse has beenundertaken from a number of different standpoints. First, it wasclear that other metabolites of PhIP might exist. Second, byapplying metabolomic methods in different transgenic mouselines, the role of various enzymes in the detoxication andactivation of PhIP and also in the formation of DNA adductsshould become clearer. PhIP (10 mg/kg p.o.) was administeredto wild-type (mCyp1a2+/+), Cyp1a2-null (mCyp1a2-/-), andCYP1A2-humanized (hCYP1A2+/+, mCyp1a2-/-) male 129/SvJ strain mice and 0-24 h urine collected and subjected tometabolomic analysis using ultraperformance liquid chromatog-raphy-coupled electrospray ionization quadrupole time-of-flightmass spectrometry (UPLC-ESI-QTOFMS) and principal com-ponents analysis (PCA) (29). This dose, while many timesgreater than the dose a typical human would receive from

cooked meat (30), was used to maximize drug metabolitediscovery. The mass spectrometry data best fitted a three-component model in the PCA analysis, with the control(untreated), wild-type, null, and humanized animals all clusteringin the scores space (Figure 3A). Interestingly, control, wild-type, and humanized mice clustered on a plane defined bycomponents 1 and 2 in the three-dimensional scores space, withthe null animals lying far above in component 3. Both knownand unknown metabolites of PhIP were visible in the three-dimensional loadings space (Figure 3B), including PhIP itself(1) and PhIP N2-glucuronide (12), a known metabolite (Figure2) that is not formed by CYP1A2, and two unknowns that wereidentified by tandem mass spectrometry experiments as N2-methyl-PhIP (2) and 4′-hydroxy-N2-methyl-PhIP (6). The othermetabolites labeled in Figure 3B appear to be distributed closeto or on the surface of the plane defined by the control, wild-type, and humanized mice. These metabolites of PhIP aretherefore likely to be formed by mouse and/or human CYP1A2and include the known metabolites (Figure 2) 4′-hydroxy-PhIP(3), 5-hydroxy-PhIP (5), 4′-hydroxy-PhIP 4′-O-sulfate (7), PhIPN3-glucuronide (11), N2-hydroxy-PhIP N2-glucuronide (16), andN2-hydroxy-PhIP N3-glucuronide (17). Other novel metabolitesconfirmed by mass fragmentography were 5-hydroxy-PhIP 5-O-sulfate (8), N2,4′-dihydroxy-PhIP 4′-O-sulfate (9), 4′,5-dihy-droxy-PhIP 4′-O-sulfate (10), 5-hydroxy-PhIP 5-O-�-D-glucu-ronide (14), N2,4′-dihydroxy-PhIP 4′-O-�-D-glucuronide (18),and 4′-hydroxy-PhIP N2-�-D-glucuronide (19). Thus, of the 19excretory products of PhIP in the mouse, 8 were novelmetabolites discovered by metabolomics (29). Finally, consistentwith the distribution of scores and loadings in the three-dimensional space (Figure 3), the relative urinary excretion ofmetabolites in the three mouse lines was wild-type, 1 ≈ 3 ≈17 . 12 (6 not detected); Cyp1a2-null, 1 > 12 > 6 > 17 > 3;CYP1A2-humanized, 17 . 1 > 3 ≈ 12 > 6 (29). These insightsassist in understanding the genotoxicity of PhIP to target organssuch as colon and breast (29). They also shed insight into thevalue of genetically modified mice in human risk assessmentfor carcinogen exposure and suggest that in the case of PhIP,CYP1A2-humanized mice might be more predictive.

Areca Alkaloids Arecoline, Arecaidine, and Arecoline1-oxide. It has been estimated that some 600 million peopleare exposed to areca alkaloids arecoline, arecaidine, guvacine,and guvacoline through the habit of areca nut chewing (31),the fourth most popular habituated chemical group after tobacco,alcohol, and caffeine (32). Areca nut chewing has beenassociated with oral cancer in India, Pakistan, Taiwan, and China(33). The prevailing theory attributes this to the formation ofnitrosamines from areca alkaloids in the mouth, in particularguvacine and guvacoline. However, it is not known what rolethe predominant alkaloids arecoline and arecaidine might playin this process. The metabolism of the principal alkaloids waslargely unknown despite their toxicological importance to aboutone-sixth of the world’s adult population. Figure 4A shows themetabolic map of arecoline and arecaidine that was availableto the International Agency for Research on Cancer (IARC) atthe time of its evaluation of the carcinogenic risk of arecaalkaloids to humans (33). As tertiary amines, these metabolitesare unlikely to form nitrosamines in the mouth. No other obviouscancer causing mechanism emerges from perusal of the chem-istry of these compounds.

In order to better understand the biotransformation of theareca alkaloids, two metabolomic studies have been undertakenin the mouse, one with arecoline and arecaidine (34) and theother with the principal metabolite of arecoline, arecoline

Figure 2. Known metabolism of PhIP prior to metabolomic analysis.Nine metabolites were known, six detoxication products (green segment)and three active metabolites that lead to DNA adducts (pink segment).

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1-oxide (Figure 4) (35). Groups of male strain FVB mice wereadministered arecoline hydrobromide (20 mg/kg, both p.o. andi.p.) and others arecaidine (20 mg/kg, both p.o. and i.p.). Urineswere collected for 0-12 h, both prior to dosing and immediately

postdose and subjected to UPLC-ESI-QTOFMS analysis inpositive ion mode and by PLS-DA (projection to latentstructures-discriminant analysis). The two-component PLS-DAscores plots showed clustering of the treated and control animalswith a clear separation, principally in component 1 (34). Figure5 displays the PLS-DA loadings plot for arecoline-treated (p.o.)versus control mice. The mouse urinary metabolome in positiveion mode corresponds to approximately 5,000 ions, which derivefrom considerably less chemical constituents, the differencebeing due to in-source fragment ions, dimers, isotopes, andadducts with Na+, NH4

+, etc. These phenomena have beendiscussed in greater detail elsewhere (36). The constellation ofions comprising the mouse metabolome can be clearly seen inthe PLS-DA loadings plot (Figure 5). Administration ofarecoline perturbed this distribution of ions in the loadings spaceby adding a significant number of new ions that were deviatedin both component 1 (abscissa) and component 2 (ordinate) ofthe variance. Two far removed ions are shown that representions deriving from the two most abundant arecoline metabolitesin urine, 1-methyl-nipecotic acid (X) and arecoline 1-oxide (Y).It is worth noting that the former had not been previouslyreported. Other novel metabolites (Figure 4B), together withall the previously reported metabolites (Figure 4A), emerged

Figure 3. Metabolomics of PhIP metabolism in the mouse. (Panel A) Clustering of wild-type, CYP1A2-humanzed (hCYP1A2), Cyp1a2-null, andcontrol mouse urines in a three-dimensional scores space. (Panel B) Distribution of ions in a three-dimensional metabolic space defined by a PCAloadings plot. Numbers represent metabolite IDs. Not all 18 metabolites discovered are visible. The chemical structures of all eight novel PhIPmetabolites are shown underneath. Adapted from ref 29. Copyright 2007 American Chemical Society.

Figure 4. Metabolomics of the metabolism of the areca alkaloidsarecoline, arecaidine, and arecoline 1-oxide in the mouse. (Panel A(pink)) The known metabolites prior to metabolomic investigation.(Panel B (blue)) Novel metabolites of arecoline and arecaidine in themouse discovered through metabolomics. (Panel C (green)) Novelmetabolites of arecoline 1-oxide in the mouse discovered throughmetabolomics.


from analysis of the ions shown in the shaded ellipse in Figure5. The studies with arecaidine also yielded the carboxylic acidmetabolites shown in Figure 4B (34), while those with (()-arecoline 1-oxide administration yielded the novel metabolitesshown in Figure 4C (35). Overall, the four known metabolitesof these areca alkaloids were confirmed and nine novelmetabolites described (34, 35). The phenomenon of apparentdouble-bond reduction per se to yield nipecotic acid derivativesis without precedent. It was considered that the nine saturatedmetabolites (i.e., without the double bond) all arose due toglutathione conjugation and further degradation of the resultantmercapturic acids (34, 35). Were any of these reactions to occurin the tissues of the oral cavity, they might place a burden onthe antioxidant defenses that depend upon reduced glutathioneand other cellular thiols that are not abundantly produced inthis tissue. This might, therefore, open new avenues of researchinto potential toxicologic processes associated with the commonexposure to areca nut alkaloids.

Melatonin. Although melatonin is an endogenous hormoneproduced in microgram quantities per day in humans by thepineal gland, retina, and the gut (37), it is now widely sold asa dietary supplement to be self-administered at pharmacologicaldoses, about 2 orders of magnitude above physiological amounts(38). Under these conditions, melatonin should be treated as axenobiotic. One of the principal rationales for the widespreaduse of melatonin is the evidence that it can act as an antioxidant(39). Therefore, melatonin has been subjected to research inanimal models of human disease, such as hepatic, renal, andbrain ischemia (39). Most interest, however, has centered aroundthe potential of melatonin as an antioxidant to prevent cancer,particularly breast and endometrial cancer. Available datasuggest that melatonin is not only an antioxidant but also hasantimitotic and antiangiogenic activity (40). It may also enhancethe immune system in the elderly by stimulating the productionof progenitor cells for granulocytes and macrophages, byinvigorating the production of natural killer cells and CD4+cells, while inhibiting CD8+ cells. Moreover, melatoninstimulates natural killer cells and T helper lymphocytes torelease various cytokines (41). There are, therefore, manyreasons to believe that physiological depletion of melatonin maylead to breast, endometrial, and colorectal cancer and thatadministration of melatonin may have cancer preventative effects(42). The situation is more complex due to the potentialcontributions of melatonin metabolites. The major metabolite6-hydroxymelatonin has been reported to be genotoxic under

in vitro conditions (43). Contrary reports state that 6-hy-droxymelatonin shares antioxidant properties with melatonin(44, 45). Certain metabolites of melatonin, such as AMK andAFMK, that are detected in vivo are thought to arise from thereaction of melatonin with reactive oxygen species (ROS) and,in a strict sense, are not metabolites but rather antioxidantproducts (39). Given the uncertainties regarding the role ofmelatonin metabolites in cancer prevention, a detailed metabo-lomic investigation of melatonin metabolism was deemednecessary.

A complex metabolomic study of melatonin was undertakenthat asked a range of questions and employed C57BL/6, CBA,and 129/SvJ strain mice, together with the transgenic Cyp1a2-null and CYP1A2-humanized mouse lines (38). For the produc-tion of the metabolic map of melatonin, mice were administeredmelatonin (4 mg/kg i.p.) and 0-24 h urine collected. This doseis 60-times the average human (70 kg) would expect to receivefrom a 2 mg melatonin supplement. Control urines from thesame mice were collected 2 days previously. These urines weresubjected to UPLC-ESI-QTOFMS analysis in positive ion modeand the data obtained analyzed by PCA and by OPLS(orthogonal projection to latent structures). PCA showed separateclustering of the treated and untreated animals in a two-dimensional scores space with the separation occurring exclu-sively in component 1 (38). Further analysis with OPLS yieldeda loadings S-plot (Figure 6), which is a form of data presentationthat can give insight into the relative abundance (w[2]) ofindividual metabolites (strictly, ions derived from them) andhow well they correlated to the OPLS model (p(corr)[1]).Clearly, administration of xenobiotics should produce metabolitesignals that are highly correlated to the model that involves acomparison with urines from control untreated animals. Theshaded ellipse in Figure 6 represents ions that are upregulatedafter melatonin administration and that have a correlation tothe OPLS model of 0.85 to 1.0. Additional experimentsinvolving tandem mass spectrometry confirmed the presenceof known melatonin metabolites in mouse urine, but alsorevealed a number of novel metabolites. Figure 7 shows thenew metabolic map of melatonin in the mouse with six of theseven novel metabolites shown in the shaded boxes. The seventhnovel metabolite was the glucuronide of dihydroxymelatonin(6), whose exact structure could not be determined and wasreasoned to be 4,6-, 6,7-, or 2,6-dihydroxymelatonin (38). Thenovel metabolites shown are melatonin N-glucuronide (4), cyclicmelatonin (14), cyclic N-acetylserotonin 5-O-glucuronide (13),cyclic 6-hydroxymelatonin (12), 5-hydroxyindole acetaldehyde(15), and the unidentified dihydroxymelatonin (6). As statedearlier, compounds 16 (AFMK) and 17 (AMK) are not me-tabolites but were products of the reaction of melatonin withROS. It is noteworthy that neither AFMK nor AMK wereobserved in this study, despite earlier reports that they eachcontributed 0.01% of the administered dose in mice (39) andthat 50% of the administered dose of radioactive melatonin wasrecovered in rat urine as AMF and AFMK combined (46).Perhaps there exists an unreported major species difference inmelatonin disposition between the rat and the mouse. One furthercontroversy that was settled by this metabolomics study con-cerned the conjugation of the primary major metabolite ofmelatonin in the mouse, 6-hydroxymelatonin. This metaboliteis conjugated with sulfate in humans and rats, but two groupshad reported starkly differing findings, one that the mouseconjugated 6-hydroxymelatonin also with sulfate and the otherwith glucuronic acid. This metabolomic study demonstratedunequivocally that 6-hydroxymelatonin is conjugated almost

Figure 5. Metabolomics of arecoline metabolism in the mouse. Themouse urinary metabolome can be clearly seen as a large cloud of ionscentered on the 0,0 intersection of the PLS-DA loadings space. Theshaded ellipse represents the metabolic space for arecoline, from whichboth the known and novel metabolites were identified. Adapted fromref 34. Copyright 2006 American Chemical Society.


exclusively with glucuronic acid in the mouse (38). Thus, themetabolomic investigation of melatonin metabolism producednew data that may help better understand the physiology,biochemistry, endocrinology, toxicology, and cancer preventa-tive effects of melatonin.

Aminoflavone (NSC 686288). During the search for novelbreast cancer chemotherapeutic agents, 5,4′-diaminoflavone andsome of its congeners demonstrated high activity against thebreast cancer cell line MCF-7 that is known to possess theestrogen receptor (ER) and be estrogen-responsive (47). Interestfocused on 5-amino-2-(4′-amino-3′-fluorophenyl)-6,8-difluoro-7-methyl-4H-1-benzopyran-4-one (DAF; NSC 686288; ami-noflavone), and it was reported that rats dosed with aminofla-vone had a single metabolite in their blood, which was shownto be the 4′-acetylamino compound (48). Aminoflavone wasextensively metabolized to a number of metabolites by CYP1A1and CYP1A2 in rat and human liver microsomes, one of whichwas reported to be a potentially reactive hydroxylamine (49).In addition, induction of CYP1A1 and CYP1A2 in cell linesincreased the DNA binding and cytotoxicity of aminoflavone.Furthermore, it was shown that aminoflavone was itself aCYP1A inducer and could thus induce its own metabolism andenhance its cytotoxicity (49). Given the role of metabolism inpharmacology and toxicology of this promising experimentaldrug and the uncertainties surrounding its metabolism in vivo,a metabolomic investigation of aminoflavone metabolism in themouse was undertaken.

Because of the reported role of CYP1A isozymes in the invitro metabolism and activation of aminoflavone, a complexprotocol was evolved that employed wild-type 129/SvJ mice,Cyp1a2-null mice, and CYP1A2-humanized mice (50). Ami-noflavone was administered (50 mg/kg p.o. in corn oil) and urinecollected for 0-24 h. Prior control urines were collected foreach mouse. Urines were subjected to UPLC-ESI-QTOFMSanalysis in positive ion mode and the resultant data matrix ofions analyzed by PCA. As shown in Figure 8A, control andaminoflavone treated mouse urines clustered and separated inthe PCA scores space, largely along component 1. Accordingly,a metabolic space was created in the PCA loadings plot that

Figure 6. Metabolomics of melatonin metabolism in the mouse. Theshaded ellipse represents the metabolic space for melatonin defined asthat segment of the OPLS loadings S-plot that contains ions thatcorrelated highly (0.85 to 1.0) to the OPLS model. Arrows indicatemodel fit (Y-axis) and abundance (X-axis). Adapted from ref 38.

Figure 7. Metabolic map for melatonin in the mouse showing six of the seven novel metabolites (blue segments). The seventh novel metabolitewas a glucuronide of dihydroxymelatonin (6) whose exact structure was not determined. Adapted from ref 38.


defined the aminoflavone metabolites (Figure 8B). As with thecase of arecoline (Figure 5), there were ions derived fromxenobiotic metabolites that were clearly displaced from themouse urinary metabolome. These ions were subjected to massfragmentography by tandem mass spectrometry to identify theirchemical structures (13, 50). The resultant metabolic map foraminoflavone is shown in Figure 9. The only aminoflavonemetabolite that was known to be produced in vivo, N4′-acetyl-aminoflavone (48), was not present in mouse urine (50). Allthe other metabolites discovered were novel, although the exactnature of some of the glucuronic acid and sulfate conjugateswas not established. Nevertheless, the amount and quality ofnew metabolic information emerging from this metabolomicsurvey was considerable and established that aminoflavone isprincipally N-hydroxylated at N5 and that this hydroxylamine,presumably the same metabolite that was detected with humanand rat liver microsomes but never identified (49), is furtherconjugated with glucuronic acid (Figure 9). Clear evidence ofboth activation and detoxication of aminoflavone was found andthe role of CYP1A2 in these pathways established through theuse of genetically modified mice combined with metabolomics(50). Subsequent to this work, it was reported that activationof aminoflavone by sulfotransferase was necessary for itsgenotoxicity and antiproliferative effects (51). Aminoflavoneis now in clinical trials, and much more has been establishedregarding its molecular mechanisms of action (52). LC-MSbased methods similar to those used in the metabolomics studyare now employed to determine its concentration in humanplasma (53).

Figure 8. Metabolomics of aminoflavone metabolism in the mouse. (PanelA) Clustering of the control and aminoflavone-treated mice in a PCA scoresplot. (Panel B) Visualization of the aminoflavone metabolic space (redcircle) in a PCA loadings plot. Note that the mouse urinary metabolomeis clearly shown as a large cloud of ions centered on the 0,0 intersectionof the PCA loadings space. Adapted from ref 13.

Figure 9. Metabolomics of aminoflavone metabolism in the mouse. Only one in vivo metabolite of aminoflavone had been reported, N4′-acetyl-aminoflavone in rat plasma (pink segment). This metabolite was not found in this study. However, a large number of novel metabolitesof aminoflavone were discovered in the mouse, including hydroxylation at the N5-, N4′-, and C5 positions, together with various glucuronideand sulfate conjugates.


Conclusions and Future Perspectives

As has been expertly addressed (54), it is without doubt thatxenobiotic metabolism directly contributes, in both number (e.g.,PhIP exposure results in 19 detectable urinary metabolites) andrelative abundance (e.g., xenobiotics are some of the most highlyconcentrated metabolites) to the metabolome (Figure 10). It isfor these reasons that the metabolomic approaches discussedin this review have been successful. Xenobiotic metabolism canalso influence endogenous metabolism, and while the focus ofthis review has been primarily on xenobiotic metabolism,additional insight into changes in host metabolism can begleaned by purging drug and drug-related metabolites from thedata set and then performing a new chemometric analysis. Thisapproach has been recently demonstrated to understand fenofi-brate effects in humans (55). The influence of xenobioticmetabolism on the host metabolome also raises importantquestions about attempts to catalog or even define the metabo-lome. For example, should the metabolome be quarantined fromother “omes,” existing simply as a collection of endogenouschemicals involved in processes such as glycolysis, the citricacid cycle, or fatty acid �-oxidation pathway, or would it bemore appropriate to consider the metabolome as an amalgamof compounds comprising the products of all endogenous andxenobiotic metabolism given that complete isolation from theexposure to compounds found in food, pesticides, cosmetics,environmental pollutants, or drugs is seemingly unavoidable inany human study? While some may dismiss this as semantics,this discussion has important implications for any in vivometabolomics (unbiased, not targeted) study, where, mostcertainly, xenobiotics and their metabolites will be interspersedand inseparable from the endogenous biochemicals. In ouropinion, it would be premature, particularly if one envisions aclinical setting where limited patient history might available,to state that the endogenous metabolome would provide moreinformation than, say, the xenobiome and vice versa. Technically

speaking, high-throughput measurement of a wide-range ofcompounds would be relatively simple and inexpensive giventhe sophistication of current technologies such as mass spec-trometry. Furthermore, having a global, unbiased perspectiveof the complex interplay between endogenous and xenobioticmetabolism would add substantially to our understanding ofbiochemical, pharmacological, and toxicological processes andmight eventually identify useful biomarkers for assessing riskand exposure.

As a step in the right direction, particularly for xenobioticmetabolomics and toxicology, the Human Metabolome Project(http://www.metabolomics.ca/) has extended their catalogingefforts far beyond endogenous compounds and now includedetailed information on xenobiotics commonly found in food(FooDB), those taken as drugs (DrugBank), or those receivedthrough exposure (t3db) such as pollutants and pesticides(56–60). However, metabolite information, especially toxicmetabolites such as those from acetaminophen including N-acetyl-p-benzoquinone imine, remains limited. A centralizedrepository for drug metabolite data, much like how FooDB,DrugBank, and t3db are organized, would be a valuable andconvenient resource for drug (and other xenobiotics) metabolisminvestigations of existing or candidate drugs.

It has been reported here that application of advanced massspectrometry coupled with multivariate data analyses is able toprovide a new perspective on xenobiotic metabolism andtherefore toxicology. This view through the metabolometerprovides an unprecedented level of metabolic detail for drugsand chemicals that would not be seen through other methodolo-gies. This technology is by no means restricted to the study ofthe mouse and has been used, for example, to re-examine themetabolism of fenofibrate in monkeys (25). However, in thecase of the mouse, there exists a very powerful combination oftechnologies, genetically modified mice combined with me-tabolomics, uniquely suited to delineate the role of specific genesand their proteins in discrete metabolic pathways. Theseobservations can ultimately be extended to heterogeneous humanpopulations where other factors such as age, gender, and raceare likely to impact and thus complicate metabolomic studies(61). Further discussion of this subject falls beyond the scopeof this review. Through the metabolometer, visualization of ametabolic space (see Figures 3, 5, 6, and 8) in a loadings plotof ions derived from the analysis of biofluids from xenobiotic-treated animals gives immediate insights into both qualitativeand quantitative components of xenobiotic transformation.Providing that investigations are carefully designed, controlled,and executed, the approach presented here is possible for anyorganic molecule in any animal species.

Acknowledgment. We thank Drs. Chi Chen, Sarbani Giri,and Xiaochao Ma for their extensive contributions to thexenobiotic metabolomics paradigm. This work was funded bythe National Cancer Institute, Intramural Research Program,Center for Cancer Research. A.D.P. was supported by aPharmacology Research Associate in Training Fellowship fromthe National Institute of General Medical Sciences. J.R.I. isgrateful to U.S. Smokeless Tobacco Company for a grant forcollaborative research.

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Figure 10. Xenobiotic (and gut microbiota) metabolism including thatfrom food, pesticides, cosmetics, pollutants, and drugs can add, in somecases, substantially, both in number and abundance, to the metabolome.


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