Metabolomics in Toxicology: Preclinical and Clinical ...

TOXICOLOGICAL SCIENCES 120(S1), S146–S170 (2011)

doi:10.1093/toxsci/kfq358

Advance Access publication December 2, 2010

Metabolomics in Toxicology: Preclinical and Clinical Applications

Donald G. Robertson,*,1 Paul B. Watkins,†,‡ and Michael D. Reily*

*Applied and Investigative Metabolomics, Bristol-Myers Squibb Co. Princeton, New Jersey 08543; †Hamner-University of North Carolina Institute for Drug

Safety Sciences, Research Triangle Park, North Carolina, 27709; and ‡Department of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NorthCarolina 27599

1To whom correspondence should be addressed at Applied and Investigative Metabolomics, Bristol-Myers Squibb Co., Route 206 & Province Line Road,

Princeton, NJ 08543. Fax: (609) 252-7156. E-mail: [email protected].

Received October 21, 2010; accepted November 19, 2010

Key Words: metabolomics; metabonomics; metabolic profiling;

biomarkers; mechanisms of toxicity.

HISTORICAL PERSPECTIVE

Metabolomics or Metabonomics: What is in a Name?

Metabolomics first burst into the toxicology arena approx-

imately 10–12 years ago, experiencing rapid growth as

a science in the intervening period (Fig. 1). Though

metabolomics still trails the publication rates of its sister

technologies, genomics and proteomics, it is closing the gap in

terms of both the numbers of publications and the quality of

those publications. One continuing source of confusion is

differentiating the terms metabolomics, metabonomics, and

metabolic profiling. Although metabonomics was the first term

formally defined (Nicholson et al., 1999), the term metab-

olomics came into usage shortly thereafter (Fiehn, 2002).

Metabolic profiling is used by some as a generic term to avoid

confusion of the two aforementioned ‘‘omic’’ terms, but that

phrase itself can be confused with the comprehensive

metabolite analysis of xenobiotics, so it is debatable whether

its use adds any clarity to the situation. Whatever the case, it is

now clear that metabolomics is the term preferred by most

practitioners (Fig. 1). Therefore, metabolomics will be used in

this review with the understanding that it represents all three

terms. For most people, the names can be used interchange-

ably, but the reader is still advised to utilize all three terms

when performing literature searches.

Now that we have determined what to call it, we need to

define what it is. The literature is replete with fine-tuned

definitions, but the most succinct definition still appears to be

most appropriate, and that is that metabolomics is ‘‘the

comprehensive and quantitative analysis of all metabolites’’

(Fiehn, 2001). Every word in that phrase could be subject to

debate as no analytical technique can measure ‘‘all’’ metabo-

lites and what exactly qualifies as a metabolite? Do dietary-

derived metabolites qualify, what about gut flora–derived

metabolites? Rather than wade into this academic argument, we

will leave it to the reader to define metabolomics as he or she

chooses. For the toxicologist, the important point is that

a metabolomics approach has the potential to reveal novel

biochemical sequelae of toxicant administration that can lead to

mechanistic insights and identification of biomarkers of cause

and/or effect. Furthermore, the technology has the potential to

characterize models or disease states to provide biochemical

bases for observed interactions with xenobiotics.

Given the explosion of recent literature, this review will not

revisit the origins of metabolomics to any extent but will focus

on recent advances in the field. Although botanical, environ-

mental, and nutritional applications of the technology have

expanded with the field, there is now such a wealth of literature

this review will not be able to cover those burgeoning areas of

metabolomic research. After providing some historical perspec-

tive, this review focuses on the biomedical applications of

metabolomic technology with emphasis on new analytical

approaches, preclinical, and clinical applications of the technol-

ogy, particularly those areas relevant to practicing toxicologists.

Repositioning the Field

Toxicology played a significant role in the early develop-

ment of the technology (Robertson, 2005); yet, the relative

contribution of toxicology applications (in broadest definition

of that term) to the field has been decreasing steadily since

2000 (Fig. 2). Rather than signaling an apparent decline in

interest by toxicologists, this trend simply reflects the fact that

� The Author 2010. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.For permissions, please email: [email protected]

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the expansion of the technology in nontoxicology-related

endeavors has far exceeded the rate of expansion for toxicology

applications. Additionally, although studies profiling metabolic

responses to various toxins elicited a lot of interest by

toxicologists early on, the field has moved beyond these

descriptive studies to more focused mechanistic and biomarker

identification/validation studies. These studies tend to be more

complex and difficult to conduct and interpret. The result has

been fewer, though in our opinion, more important publica-

tions. This said, the continuing importance of the technology in

the field is emphasized by the fact that in 2009, the relative rate

of toxicology-associated metabolomic publications exceeded

the relative rate of toxicology-associated genomics or proteo-

mic publications by two- to threefold (Fig. 2).

A Change of Players

A tidal change in metabolomic technology in toxicology

over the past several years has been the apparent shift in the

user base from industry (including contract research organ-

izations, instrument vendors, and software developers) to

academic, governmental, and medical organizations. An

examination of first author affiliations of toxicology-associ-

ated metabolomics publications over time reveals that

approximately 46% (12/26) of first author affiliations were

industrial in 2002, 36% (24/67) in 2005, and 13% (17/136) in

2009, representing decreases in both relative and absolute

terms. What does this mean? First, it must be considered that

publication by industry scientists is frequently hampered by

intellectual property concerns so that the publication rate is

not absolutely reflective of industry research, as much

industrial research remains unpublished. Still, assuming that

barriers to publication by industrial scientists in 2009 are

similar to what they were in 2002, the drop in publication rate

is disconcerting. There are probably several reasons for this,

but economic considerations are probably the main cause.

The last several years have been extremely trying for industry

with downsizing and outsourcing much more prevalent than

in the first half of the decade. Although metabolomics groups

were seen as ‘‘nice to have’’ within industrial toxicology

departments, they were not considered by many to be ‘‘need

to have’’; therefore, when budget or headcount cuts were

mandated, many dedicated metabolomics groups were

reconfigured, down sized, or eliminated entirely. While trying

not to sound too self-serving, we believe this to be a strategic

mistake as will hopefully become clear in this review. In

addition, early applications of metabolomics to toxicological

problems focused on enhanced descriptions of biochemical

sequelae to administration of xenobiotics. Although that was

a useful exercise, it did little to aid decision making processes

within toxicology departments—the real ‘‘bottom line’’ for

any new technology in an industrial setting. With new

metabolomic research identifying specific mechanisms and

novel biomarkers in both the preclinical and clinical settings,

it is evident that metabolomics is moving beyond descriptive

science into the arena of a value-added activity.

One thing that has not changed in recent years is that the

majority of toxicology-associated metabolomic work is

FIG. 1. Number of publications/year including metabolomics, metabo-

nomics, or metabolic profiling in the title, keywords, or abstract as

determined by Scopus (Anonymous 2010b). Note that papers using all

three terms would be counted in each segment so the total number of

publications may be actually somewhat lower than indicated, particularly in

more recent years. Unambiguous total paper counts are indicated above the

bars in Figure 2.

FIG. 2. Number of publications/year including metabolomics, metabo-

nomics, or metabolic profiling in the title, keywords, or abstract that also

contain the root word ‘‘. . .toxic. . .’’ (e.g., toxicology, toxicokinetic, hepato-

toxicity, etc.). The number above each bar represents total publications for the

year (excludes duplicate term counting). A similar analysis conducted using

genomics, transcriptomics, and proteomics as search terms for the year 2009 is

presented for comparison.

METABOLOMICS IN TOXICOLOGY S147

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conducted outside the United States. This was evidenced by

69% (94/136) of first author affiliations being based outside the

United States (primarily Europe, China, and Japan) in 2009 as

compared with 61% (41/67) in 2005.

ANALYTICAL TRENDS

Measuring the Metabolome

The concept of the metabolome as the ‘‘total complement of

metabolites in a cell’’ (Tweeddale et al., 1998) has since been

broadened to include not only endogenous small molecules but

also those introduced and modified by diet, medication,

environmental exposure, and coexisting organisms (Dunn,

2008). From an analytical (and pharmaceutical) perspective,

this broader definition of the metabolome is the most useful and

most closely fits the concept under which the term ‘‘metabo-

nomics’’ was defined (Nicholson et al., 1999). Regardless of

the exact definition, the analytical goal of metabolomics is to

achieve a comprehensive measurement of the metabolome and

how it changes in response to stressors, with the biological

payoff being illumination of the relationship between the

perturbations and effected biochemical pathways. The system-

atic practice of correlating analytical observables in biofluids to

human health dates back to Hippocrates (Kouba et al., 2007),

and the first truly modern vestiges of metabolomics date back

nearly 40 years to Linus Pauling and his idea of ‘‘orthomo-

lecular medicine’’ (Pauling et al., 1971). Hippocrates had the

instruments of his own senses (sight, smell, and taste), which

were superseded in Pauling’s era by high-resolution gas

chromatography (GC) systems coupled with various types of

detectors, including mass spectrometers (Jellum et al., 1971).

Modern metabolomics has benefited and contributed to the

development of ever more sophisticated instruments that can

measure hundreds of components simultaneously along with

the methods for analyzing and quantifying them.

Metabolomics Approaches

Within the field of metabolomics, there is a continuum of

approaches that range from more spectroscopy-centric finger-

printing to nontargeted profiling to targeted analysis.

A common denominator among them is a high-information

content data set that directly reflects the biochemical state of an

organism’s metabolome at a given point in time.

Metabolomic fingerprinting is typically conducted on

minimally prepared peripheral biofluids, and sample differen-

tiation is based on spectroscopic or chromatographic data. These

‘‘analytical fingerprints’’ can contain thousands of individual

data points that relate to the composition of the sample, which

may or may not be annotated as to their molecular origin. Such

a data set can be used as input into multivariate statistical tools

such as principal component analysis (PCA), described more

below. The advantage of this approach is that it avoids the

frequently rate-limiting step of data annotation and therefore

may be rapidly applied for establishing differentiation between

individuals or groups, with applications in in vivo screening of

similar drug candidates (Dieterle et al., 2006; Robertson et al.,2007; Robosky et al., 2002), prescreening animals prior to

extensive toxicological evaluations, and building of toxicolog-

ical classification models (Lindon et al., 2005).

Nontargeted metabolomics goes a step beyond fingerprinting

and seeks to assign as many individual chromatographic or

spectroscopic peaks as possible (Vinayavekhin and Saghatelian,

2010). This approach produces a comprehensive list of metab-

olites with absolute or relative quantification and has been referred

to as metabolomics (Fiehn et al., 2001) or metabolic profiling

(Brown et al., 2005). The changes in components can be mapped

to specific pathways and provide biomarkers and/or mechanistic

information. It should be clear that nontargeted metabolomics

always provides an analytical fingerprint, but the inverse is true

only if the analytical data are readily annotated. Vibrational

spectroscopies such as infrared (IR) can provide a highly

reproducible and detailed spectrum that reflects the molecular

composition of a complex mixture, but assignment of IR bands to

individual molecular species is not normally possible. This makes

IR a good metabolomic fingerprinting method but not useful in

a nontargeted approach. The requirement of unambiguous

annotation essentially limits the nontargeted metabolomic

analytical techniques to nuclear magnetic resonance (NMR)

spectroscopy and mass spectrometry (MS)–based approaches.

Targeted metabolomics differs from those discussed above in

that measured analytes have been selected a priori, usually to

address certain specific biological questions within a study.

Because the exact analytes are known, it is possible and

desirable to measure their absolute concentrations with

appropriate use of internal standards. The lines between targeted

metabolomics and traditional multiplexed assay development

are arguably ill defined but usually center on the number of

metabolites measured simultaneously. For example, an assay

developed to measure serum acetate is not targeted metabolo-

mics, but one to measure 13 or 14 organic acids in a urine sample

probably qualifies. Another distinction between targeted

metabolomics and traditional multianalyte assays is that the

former should preserve the ability to also identify things that are

not necessarily on the predefined list. Often, targeted approaches

can be developed for specific compound classes such as lipids

(Wolf and Quinn, 2008), bile acids (Want et al., 2010a), or

amino acids (Kimura et al., 2009; Mayboroda et al., 2007) that

are pertinent to a particular study. Targeted metabolomic assays

often result from the desire to more accurately quantify specific

sets of molecules observed in an untargeted study.

In summary, the concepts discussed above actually represent

a continuum of approaches that go from largely qualitative

fingerprinting to more quantitative targeted approaches. Most

metabolomic studies seek to provide a little from all three

concepts—a rapid qualitative read on what the data are

S148 ROBERTSON, WATKINS, AND REILY

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indicating combined with as much quantitative information as

possible while ideally preserving the ability to discover the

unexpected.

Analytical Instrumentation

The ideal characteristics of analytical data that will be most

useful in the approaches described above include information

content, reproducibility, and amenability of the data to facile

annotation. Although there are many analytical techniques that

meet one or two of these requirements, only two approaches

stand out as meeting all three: MS and NMR. The vast majority

of metabolomic publications in the past decade use one or

both of these techniques, which are highly complementary

(Robertson et al., 2007). It is interesting to note that the

majority of publications utilize one method, probably reflecting

the analytical strengths of the reporting laboratory. In recent

years, the rapid growth of MS-based publications has been

noteworthy (Fig. 3). On the other hand, the growth of reports

that utilize both platforms has been keeping pace with the

general trend, and it is difficult to argue against the improved

confidence in annotation and breath of coverage that a multi-

platform approach affords (Gowda et al., 2009; Lenz and

Wilson, 2007; Pan and Raftery, 2007).

The earliest literature surrounding the use of metabolomics for

toxicity assessment was based on NMR spectroscopy of

biofluids that began in the early 1980’s and has been extensively

summarized (Ala-Korpela, 2008; Daykin and Wulfert, 2006;

Lindon et al., 1999; Maher et al., 2009; Reily and Lindon, 2005).

Although there continues to be significant developments in the

field of NMR and its application to metabolomics, one could

argue that it has reached a certain level of maturity. The reason

that NMR will continue to be a useful metabolomics tool lies in

its unique ability to report quantitatively on the chemical and

dynamic environment of individual atoms within an isolated

molecule by itself or in the presence of other molecules in

solution with a linear dynamic range of> 105. Thus, NMR can be

used as a selective and quantitative tool, which is suited for a key

analytical requirement of toxicological metabolomics: the

quantitative measurement of many molecules in a complex

mixture. The interlaboratory and longitudinal reproducibility of

the method has also been well established (Keun et al., 2002b).

NMR’s principal liability is its low sensitivity, and as a result, the

majority of developments in NMR hardware have focused on

improving this parameter. Developments such as ultralow-

temperature detection circuits (so-called cryoprobes) (Keun

et al., 2002a; Robosky et al., 2007) and low-volume probes

(Martin, 2005; Mukhopadhyay, 2007) have pushed the limits of

detection for modern NMR spectrometers into the 10�5M range,

requiring only low microgram quantities of typical endogenous

metabolites. Also because of the inherent sensitivity problem, the

bulk of NMR work in the field relies on observing the most

ubiquitous and sensitive nucleus, the proton. Fortunately, the

proton lies at the heart of every hydrogen atom and thus almost

every molecule of interest is festooned with numerous NMR

spectroscopic probes for exploitation. Although NMR-based

metabolomic research has and will focus on proton spectroscopy,

recent trends in the NMR metabolomics literature have exploited

other nuclides, such as13

C at natural abundance (Shaykhutdinov

et al., 2009) or isotope-enriched (Fan et al., 2009),31

P (DeSilva

et al., 2009), and multidimensional methods (Ludwig and Viant,

2010; Van et al., 2008), and such nonhydrogen NMR work

provides exciting prospects for the future.

Unlike proton NMR, MS is a collection of very diverse

platforms and methodologies, which have significant potential

as metabolomic tools, many of which have been reviewed

(Koal and Deigner; Lu et al., 2008b; Scalbert et al., 2009;

Villas-Boas et al., 2005; Want et al., 2007). Broadly, MS-

based metabolomics fall into two categories: those that rely on

physical separation of complex mixtures prior to mass analysis

(hyphenated methods, such as liquid chromatography [LC]-MS

or GC-MS) and those that measure mixtures directly (direct

injection (DI) methods). Perhaps, the most significant de-

velopment in the past 5þ years that affects almost all areas of

MS research is the increasing availability of high-resolution

accurate mass (HRAM) detectors. These enable the measure-

ment of mass-to-charge ratio of ions with low ppm accuracy;

enough to narrow down the number of possible atomic

compositions of an ion and in some cases assign it a unique

molecular formula, greatly enhancing the ability to annotate

peaks (Hu et al., 2005). This is particularly useful in untargeted

approaches where the need to identify unknowns that change

can greatly benefit from this additional information. Many

nominal mass (non-HRAM) MS-based techniques lend them-

selves to a targeted approach. Multiple reaction monitoring

(MRM) using tandem quadrupole MS allows highly selective

monitoring of precursor ions and product ions of each

FIG. 3. Number of publications/year containing metabonomics or

metabolomics in the title, abstract, or keywords that also contain MS

exclusively (red); NMR exclusively (green), or both (blue).


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metabolite and enables high sensitivity over a broad dynamic

range without the need for isotope-labeled standards. The

MRM approach is preferred when a limited number of analytes

(< 50) are to be measured, whereas a full scan, high-resolving

power HRAM system has advantages with larger target analyte

lists (Lu et al., 2008a).

From a data generation standpoint, the biggest challenge in

MS of complex mixtures is the simultaneous ionization and

detection of multiple analytes. Placing a charge on a molecule is

prerequisite to MS measurement, and for most isolated analytes,

this is usually readily achievable. This must be done with

enough energy to induce ionization, but not enough to destroy

the molecule, thereby preserving the molecular ion (M) or its

þ/� hydrogen form (M þ H/M � H). This is most commonly

accomplished using electrospray ionization (ESI), wherein

a charge is applied to a droplet and transferred to the analytes

contained therein as the droplet evaporates under vacuum, or by

atmospheric chemical ionization, where charged reagent

molecules combine with analytes through collision to confer

charge. Thus, when trying to apply a charge to many

components at once, competition for the applied energy can

favor one or a few particular components, and difficult-

to-ionize analytes may go completely undetected. The most

common way to minimize this ion suppression phenomenon is

to physically separate the mixture prior to ionization. The most

recent developments in chromatography-hyphenated MS meth-

ods have been the improvements of LC separation technologies,

the rediscovery of GC-MS, and the development of capillary

electrophoresis (CE) as a front-end separation platform (Garcia-

Perez et al., 2008; Monton and Soga, 2007). Significant

narrowing of LC peaks by small particle (~1 lm) stationary

phases combined with ultrahigh-pressure (uHP) pumps that

overcome the high backpressure (15 K psi) associated with such

columns, help produce LC peaks that are only a few seconds

wide. Such ultrahigh-pressure liquid chromatography

(uHPLC)-MS systems greatly minimize ion competition and

improve the ability to identify and quantify many analytes in

a sample, making uHPLC-MS a staple for metabolomics

(Wilson et al., 2005). Indeed, most MS instrument manufac-

tures have begun offering higher pressure LC systems to

complement their MS detectors in the past few years.

Long a favorite method for analyzing mixtures in environ-

mental and plant science, GC is making a resurgence into

biomedical metabolomics applications (Fiehn, 2008; Pasikanti

et al., 2008a,b). Unlike most LC-MS approaches that seek to

preserve the molecular ion, the majority of GC-MS studies rely

on electron impact (EI) ionization, which breaks apart each

analyte into many ionized fragments and produces a diagnostic

‘‘fragmentation pattern’’ which can be compared with the

patterns from known metabolites for identification purposes.

Although this is ideal for some applications, it also presents

challenges when analyzing complex mixtures containing

molecules that have very similar fragmentation, such as may

be encountered in toxicological metabolomics. New approaches

that differentiate GC-MS–based metabolomics from its more

traditional roles include the development of more comprehen-

sive mammalian-centric EI fragmentation libraries (Kind et al.,2009; Kopka et al., 2005; Tautenhahn et al., 2008) and chemical

ionization approaches combined with HRAM detectors that,

unlike EI, can preserve the intact molecular information

(Carrasco-Pancorbo et al., 2009). One drawback to GC-MS is

the need to render all analytes more volatile, generally though

derivatization chemistry, introducing one additional source of

analytical variability that one must account for.

It is true that the utility of the MS is greatly enhanced by

predetector separation of individual analytes, but the chroma-

tography itself introduces additional analytical variability.

Realizing this, approaches have been developed that attempt

to avoid chromatography by introducing the sample into the

mass spectrometer via DI (Beckmann et al., 2008; Hansen and

Smedsgaard, 2007). An example of a metabolomic fingerprint-

ing approach is described in the study of yeast cell extracts for

the investigation of cadmium toxicity utilizing DI-MS that

revealed that about 400 out of several thousand measured

ions correlated with increased glutathione (GSH) synthesis

(Madalinski et al., 2008). Although this approach is simple and

rapid, the detrimental effects of salts and charge competition

and the inability to distinguish isobaric compounds need to be

considered. Miniaturization partially mitigates these problems,

and increased salt tolerance has been demonstrated with chip-

based nanoelectrospray compared with regular flow electro-

spray (Boernsen et al., 2005). A relatively new method that

avoids chromatography and requires virtually no sample

preparation is desorption electrospray ionization (DESI), which

ionizes samples under open atmospheric conditions using

a stream of ionizing gas to desorb, ionize, and transfer the

analytes into the MS instrument. DESI data from urine samples

blotted directly onto a paper strip were able to differentiate

several different inborn errors of metabolism (Pan et al., 2007).

Finally, some of the ionization artifacts of DI-MS have been

overcome using stable isotope-labeled (SIL) metabolomes

(Giavalisco et al., 2008) or SIL internal standards (Altmaier

et al., 2008) to provide quantitative calibration standards for

native metabolites. These approaches have yet to have the

impact that uHPLC-MS and GC-MS have had, but eliminating

the need for chromatography is so compelling that they will

continue to be a worthy target for advances in analytical

metabolomics.

Data Evaluation and Interpretation

Once the metabolomic data are collected, the data have to be

processed. Each analytical approach and platform obviously

has specific requirements that can only be briefly touched on in

this review. Fingerprinting methods generally rely on the

application of multivariate statistical analysis to raw or

minimally reduced data. In the case of NMR, this has

traditionally been done by parsing the spectra into frequency


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bins, or buckets, that are small enough to contain a limited

number of metabolites and large enough to smooth out the

sample-to-sample variation caused by small differences in

sample pH (Holmes et al., 1994). In practice, bins of 0.01–

0.04 ppm meet these criteria. These are then normalized and

integrated, thereby reducing each 16K–32K data point NMR

spectrum by a factor of ~102. These so-called ‘‘bucketed’’

spectra each contain 200–500 data points, with each point

representing the sum of contributions of all the measured nuclei

within each frequency bucket. Many such spectra can then be

subjected to various types of supervised and unsupervised

multivariate analyses (Holmes and Antti, 2002; Holmes et al.,1994; Robertson et al., 2005, 2007). New spectral alignment

approaches are rapidly making binning passe, and utilizing the

entire NMR spectrum for multivariate analysis is becoming

established (Staab et al.; Stoyanova et al., 2004). For hyphenated

MS-based fingerprinting, data features represented as retention

time (RT) and mass/charge (m/z) pairs, along with measured

intensity, have similarly been used as multivariate input.

Normalization strategies are required to account for biological

variability and depend on the method and matrix. For blood

fluids and tissues, volume or weight normalization generally

suffices because these matrices tend to be homeostatic. On the

other hand, urine concentration depends on factors such as food

and water intake and diurnal variation, and normalizing to the

total signal of all endogenous components is the norm (Warrack

et al., 2009). In the case of proton NMR, this measure can be

thought of as a surrogate for the concentration of total dissolved

organic content because most polar organic compounds contain

at least one hydrogen. In our experience, urinary total NMR

signal correlates strongly with urinary creatinine concentration,

the standard normalizing factor for most urine clinical chemistry

tests, unless there are perturbations to the urine creatinine itself. If

complete 24-h collections are made, one can, in principle, use

urine volume to normalize the data. Although these chemometric

procedures are invaluable for identification of outliers, classifi-

cation, and statistical evaluation of buckets (which can later be

annotated), care must be taken to avoid misinterpretation and

overfitting of the data (Robertson et al., 2007).

As useful as pattern recognition and multivariate analysis

are, when the dust settles, the ultimate users of metabolomics

results are biologists. Experience has shown that when data

analysis ends with colorful PCA or partial least squares plots,

the real impact of a metabolomics study is not realized.

Ultimately, it is not sufficient to tell the customer that you can

elegantly differentiate dosed animals from controls or females

from males—the biologist or toxicologist already knows that!

The real value of a metabolomics study is a detailed knowledge

of what molecules are changing and by how much. Indeed,

multivariate analysis can help you get to this information, but it

is not an end unto itself. On the surface, the task of getting from

raw LC-MS, GC-MS, or NMR data to a quantitative metabolite

table seems straightforward, but there are numerous obstacles

that are encountered depending on the platform. These barriers

include annotation of the data (assigning the peaks to specific

molecules) and quantitation of the analytes.

Because NMR integrals are directly proportional to the

concentration of the corresponding nucleus (atom), the process

of preparing a quantitation table is relatively straightforward.

One simply needs to find an isolated peak that represents

a nucleus in an analyte and integrate it to obtain a relative

concentration. Comparison with the integral of an external or

internal standard of known concentration yields absolute

concentration. Unfortunately, it is rare to find a peak that is

not overlapping with another component in a typical biofluid

NMR spectrum (Fig. 4). Thus, one of the biggest problems

facing NMR-based metabolomics is one of deconvolution of

overlapping peaks. Recent work that promises to address these

issues includes peak fitting to library spectra (Wishart, 2008),

advanced mathematical deconvolution analysis (Rubtsov et al.,2010), two dimensional (2D) NMR (Van et al., 2008; Xia

et al., 2008), pseudo 2D NMR (Ludwig and Viant, 2010;

Ludwig et al., 2009; Parsons et al., 2009), statistical

correlations (Sands et al., 2009), or a combination of these

methods. Annotation of the spectral peaks is normally done by

comparing with authentic spectra and ultimately by co-addition

of authentic standards. Several commercial (SBASE, Bruker

Biospin, Inc.; NMR Suite, Chenomx Inc.) and publicly

available databases (Cui et al., 2008; Wishart et al., 2009)

exist that contain many 1D and 2D spectra of endogenous

metabolites.

For MS, the issues are a bit different. Because modern

spectrometers allow selective filtering of the data through very

high-resolution mass measurement, when combined with high-

resolution chromatography, overlap can be minimized (al-

though not entirely eliminated). Nonetheless, quantification of

MS data suffers from two major problems. First, through

isotopic distribution, molecular fragmentation, and adduct

formation, each molecule produces many ions, even under

the most gentle ionization conditions. As a result, a typical ESI

LC-MS data set from a biofluid sample may contain tens of

thousands of peaks (millions if one considers noise) that

represent only hundreds of compounds. Thus, an important

theme in the application of MS to metabolomics is to relate

these individual ions to molecular species (Fig. 5). Various

software approaches have been proposed for this massive data

reduction problem, but no one solution has emerged as a best

practice (Katajamaa and Oresic, 2007; Reily et al., 2011).

Second, unlike the quantum mechanical transitions measured in

NMR, the ionization of an analyte in a mass spectrometer is

highly dependent on the nature of the molecule itself and the

other analytes that might simultaneously be competing for

ionization energy, as discussed above. Hence, absolute

quantitation is only possible with calibration against multiple

concentrations of authentic standards in the relevant matrix—a

tedious and time-consuming process. Thus, such analyses are

rarely conducted in a nontargeted fashion. More commonly,

quantitation is relegated to subsequent targeted analysis. For


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nontargeted analysis, the assumption can be made that a given

analyte has a similar ionization response across many samples,

and therefore, relative changes can be determined. However,

even in that instance, it must first be determined that detector

saturation is not occurring (Reily et al., 2011). Highly

multiplexed DI-MS methods that utilize SIL standards of

representative compounds represent a compromise between

rigorous quantitation and high throughput (Altmaier et al.,2008). One potential approach that has not been extensively

tested would be to utilize an auxiliary detector that has a more

predictable concentration response such as UV or flame

ionization for LC-MS and GC-MS, respectively. As with

NMR, annotation relies on comparing RTs and/or m/z values

with those of known standards run under identical conditions.

For GC-MS, the US National Institute of Science and

Technology database (Anonymous, 2010a) has been the gold

standard for decades and has recently been augmented by more

metabolomics-relevant incarnations (Hummel et al., 2007;

Kind et al., 2009). The METLIN database (Sana et al., 2008;

Smith et al., 2005) is the most extensive pubic repository for

LC-MS data and includes fragmentation libraries for many

molecules measured under standard conditions. Although the

applicability of this library as conditions and instrumentation

stray from those used to build it has not been thoroughly tested,

its establishment is an important scientific achievement, and the

proliferation of useful commonly available libraries is an

important goal for this field.

PRECLINICAL APPLICATIONS

Model Characterization

Routinely, metabolomics evaluation is nontargeted enabling

users to gain a comprehensive evaluation of the systemic

response of the subject (preclinical or clinical) to pathophys-

iological stimuli or genetic modification (Nicholson et al.,1999). In other words, it represents a powerful tool for

evaluation of systems biology (Goodacre et al., 2004).

Metabolomics has been used to demonstrate biochemical

sequelae of strain differences (Gavaghan et al., 2000;

Plumb et al., 2003), gender differences (Hodson et al., 2007;

Kochhar et al., 2006), diurnal variation (Bollard et al., 2001;

Minami et al., 2009; Slupsky et al., 2007), and age (Gu et al.,2009; Robertson et al., 2000; Williams et al., 2005). This

ability to discern the biochemical underpinnings of many

common animal model variables (Bollard et al., 2005)

demonstrates the power of the technology to characterize

animal models (Griffin, 2004, 2006). Phenotyping of geneti-

cally modified mouse models is a powerful tool for discovering

and understanding the downstream ramifications of target gene

(mal)function (Rull et al., 2009). Over the past 5 years,

metabolomic evaluations of these mouse models have

frequently been reported (Table 1). Beyond genetically

modified models, other in vivo models investigated using

metabolomic technology include various dietary models of

FIG. 4. Six hundred megahertz proton NMR spectrum of human urine from a healthy male subject. The inset shows an expansion of a 0.15-ppm frequency bin

of the aromatic region, highlighting the problem of spectral overlap. Each peak represents a unique hydrogen nucleus on a molecule in the sample and the

integrated peak area is proportional to its concentration.


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various aspects of metabolic syndrome in rats (An et al., 2004;

Kim et al., 2009b; Koves et al., 2008; Li et al., 2008, 2010a),

mice (Connor et al., 2010; Dumas et al., 2006; Kim et al.,2005; Toye et al., 2007), and primates (Cox et al., 2009).

Metabolomics evaluations in lactating (Rudolph et al., 2007)

and developing mouse models (Shimizu et al., 2009) have also

been reported.

The ability to discern global biochemical responses to

single-gene alterations or focused physiological or pharmaco-

logical interventions is one of the great strengths of

metabolomic technology, and it forces practitioners to see all

the systemic ramifications of experimental design and model

manipulation, some of which they might later wish they never

started to investigate (Robertson, 2008). For example, several

years ago while in the process of running a number of

metabolomic studies in rats, we noted that control rats from

several studies had remarkably altered urine metabolic spectra

compared with the majority of other studies we had conducted

(Fig. 6). Further investigation revealed that these unusual

samples came from a single room (colony) of rats at the

Charles River Raleigh facility, whereas animals in the room

right next to it had a phenotype consistent with our historical

controls (Robosky et al., 2005, 2006). We were able to

attribute the cause of this phenotypic difference to altered gut

flora most probably because of a failure to acquire a normal gut

flora because of stringent animal room hygiene and the fact that

new rat colonies are typically originated from a foundation

colony with limited gut flora (Rohde et al., 2007). Since that

time, we have had at least two other instances (as yet

unpublished) where similar findings were noted in control

C57BL/6 mice suggesting this phenomenon may be more

common than might be realized.

The above examples highlight that a key area of metabolomic

research over the past 10 years has been with regard to the role

gut microflora plays with systemic biochemistry (Calvani et al.,2010; Dumas et al., 2006; Fava et al., 2006; Martin et al., 2007;

Nicholson, 2008; Nicholson and Wilson, 2003; Nicholson et al.,2005; Wikoff et al., 2009). One interesting study combining

both gut flora research and model evaluation using metabolo-

mics was reported by Martin et al. (2008) in which

a metabolomic evaluation of the effects of probiotic treatment

of germ-free mice colonized with human gut flora was

monitored. The probiotics altered mciroflora content with

subsequent effects on lipid metabolism, lowered plasma

lipoproteins, and stimulated glycolysis as well as disrupting

a number of other biochemical pathways. These results

demonstrate the key role gut flora can play in various central

metabolic processes and also highlight the fact that the

interaction between gut flora and toxic responses is seldom

evaluated and poorly understood by practicing toxicologists.

A key advantage of nontargeted metabolomics is that it forces

the scientific practitioner to look at all aspects of metabolic

response, not just those of preselected target organs. It is this

facet of the technology that has revealed that we have been

FIG. 5. Depiction of the massive data reduction required for typical

metabolomics HRAM LC-MS data.


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missing a key ‘‘target organ’’ evaluation—that of the intestinal

flora which, in humans, weigh approximately 1.5 kg and contain

a genome with 100-fold more genes than our own (Kinross

et al., 2008). In the past, short of frank gastrointestinal effects

(e.g., stool changes or histopathology), evaluating effects

on gut flora was a difficult task. Metabolomic analyses simplify

that task to the point where gut flora as a potential target should

no longer be routinely ignored in toxicology studies.

Toxicity Screening

On of the earliest promises of metabolomic technology was

with regard to the potential for rapid throughput in vivotoxicity screening particularly within the pharmaceutical

industry (Harrigan and Yates, 2006; Robertson et al., 2000,

2002). It was speculated that relatively rapid analytical

assessment of biofluids followed by extensive chemometric

analysis of the spectra would reveal both mechanisms and

biomarkers of toxicity. Furthermore, the nature of the

chemometric analyses would obviate the necessity for the

time-consuming process of peak (metabolite) annotation. One

of the goals for the COMET I consortium was to generate

predictive toxicology models based on over 100 in vivo

studies in both rats and mice (Lindon et al., 2003, 2005).

Although the consortium did deliver predictive models

(Ebbels et al., 2007), these models have gained little traction

in the industry as it was unclear what exactly was being

modeled. Was it cause (mechanism) or effect (both direct and

indirect)? For example, metabolomic analysis of urine or

serum from animals given a compound inducing a hepatic

toxicity because of oxidative stress may reveal metabolites

directly indicative of that stress (e.g., changes in the reduced

and oxidized glutathione ratio), the resultant hepatic pathology

(indicating hepatic dysfunction), metabolites associated in-

directly with the toxicity (e.g., weight loss), or a metabolic

profile reflective of some combination of these effects.

Furthermore, the metabolite profile may reflect effects on

minor or unrecognized target organs that nevertheless produce

disproportionate metabolic derangements. In recent years, it

has become clear that not only is metabolite annotation critical

for proper toxicological evaluation but also a background in

pathway biochemistry is necessary to gain a thorough un-

derstanding of the role various endogenous metabolites play.

Recent studies conducted in the Bristol-Myers Squibb

laboratories demonstrate the power of metabolomics in early

in vivo safety screening (prior to good laboratory practice

toxicology evaluation). Some shrewd metabolomic detective

work on samples from early in vivo studies led to the

identification of ascorbate and gulonic acids as preclinical

markers of microsomal induction (Aranibar et al., 2009). Since

that time, several screening studies have shown increases in

both acids indicative of microsomal induction. Although it

could be argued that the fact that the compounds in question

were inducers would have eventually been identified by

traditional methods, we first noted it in vivo using noninvasive

metabolomic technology. The work further provides a nice

example of an integrated metabolomic/transcriptomic approach

to biomarker discovery. A transcriptomic and metabolomic

analysis was conducted on samples collected from rats treated

with various known inducers (Fig. 7). Increased urine ascorbic

and gulonic acids were noted after treatment with phenobar-

bital, diallyl sulfide, and DMP904 (another microsomal

inducer; Wong et al., 2005) but not b-naphthoflavone.

Importantly, transcript analysis of treatments increasing

urinary gulonic and ascorbic acids revealed upregulated

hepatic aldo-keto reductase 1A1 (glucuronate reductase)—an

enzyme that catalyzes the formation of gulonic acid from

glucuronate, with concurrent decreased expression of both

regucalcin (gulonolactonase), the enzyme responsible for

conversion of gulonic acid to gulono-1, 4-lactone, and

gulonolactone oxidase, the rate-limiting enzyme in ascorbate

biosynthesis. Furthermore, increased hepatic expression of

enzymes regulating ascorbic acid reutilization including

glutaredoxin reductase and thioredoxin reductase was noted.

These data demonstrate the use of metabolomic data to aid in

transcriptomic interpretation of subtle changes that might have

been missed or dismissed if analyzed in isolation.

TABLE 1

Selected Genetically Modified Mouse Models Evaluated Using

Metabolomics Technology

Model Disease being modeled Reference

MJD1 Spinocerebellar ataxia Griffin et al. (2004)

PKd�/� Ischemia-reperfusion

injury

Mayr et al. (2004)

Cln3 Batten disease Pears et al. (2005)

R6/2 Huntington’s disease Tsang et al. (2006)

peroxisome

PPARa�/�Metabolic syndrome Atherton et al. (2006),

Makowski et al. (2009),

and Zhen et al. (2007)

HD-N171-N82Q Huntington’s disease Underwood et al. (2006)

Plscr�/� Obesity-induced

inflammation

Mutch et al. (2007)

Interleukin 10�/� Inflammatory

bowel disease

Lin et al. (2009),

Martin et al. (2009),

and Murdoch et al. (2008)

apoE�/� Atherosclerosis Leo and Darrow (2009)

and Mayr et al. (2008)

TRAMP Prostate cancer Teichert et al. (2008)

Apc(Min/þ) gastrointestinal

tumorigenesis

Backshall et al. (2009)

Pxr�/�

and Fxr�/�Transactivation Cho et al. (2009, 2010)

CRND8 Alzheimer’s disease Salek et al. (2010)

LDLr�/� Fatty liver disease Vinaixa et al. (2010)

Sec61a1 Diabetes Lloyd et al. (2010)

Note. PPAR, peroxisome proliferator activated receptor; TRAMP, transgenic

adenocarcinoma of the mouse prostate.


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In another example, a compound under development was

demonstrated to produce significant and dose-related increases

in rat serum cholesterol by routine clinical pathology

assessment. Metabolomics analysis revealed that not only

was serum cholesterol increased but several measured

phytosterols also increased in nearly an identical fashion. This

strongly suggested that the increased cholesterol was because

of an effect on sterol absorption at the level of the gut that was

not specific to cholesterol. Serum bile acid profiles (which were

part of the metabolomic screening analysis) were unaffected,

suggesting that the changes in cholesterol were not because of

altered bile acid kinetics. Combined, these data brought a level

of mechanistic understanding to the finding of increased

cholesterol that was not apparent using traditional toxicology

endpoints.

It is difficult to assess how many companies may now be

utilizing metabolomics in such a fashion in early discovery,

though some certainly are (Dieterle et al., 2006). However, in

many cases, intellectual property concerns may prevent

publication of such examples at least until compounds are

discontinued and more likely until the entire chemical series or

therapeutic program is discontinued. Although anonomized

examples (like those above) demonstrate the point, they are

intellectually far from satisfying for the typical toxicologist or

typical journal editor.

In Vitro Applications

The growth of MS-oriented metabolomic approaches has

greatly expanded the potential for utilization of the technology

for in vitro applications, though NMR approaches to in vitroapplications are still being pursued (Duarte et al., 2009;

Gottschalk et al., 2008). The field was recently reviewed by

Cuperlovic-Culf et al. (2010) and is rapidly growing. The

FIG. 7. Combined transcriptomic/metabolomic analysis of a rat model of

cytochrome P450 induction. Significantly unregulated transcripts are indicated

in red, and significantly downregulated transcripts are indicated in green. Both

urinary gulonate and ascorbate were significantly increased by treatment with

phenobarbital, diallyl sulfide, and DMP904 (a proprietary known microsomal

inducer) but not b-naphthoflavone. Adapted from Aranibar et al. (2009).

FIG. 6. Representative NMR spectra from two rats obtained from Rooms 9 and 10 in the Charles River Raleigh facility in 2004 demonstrating pronounced

phenotypic differences. HIP samples (having relatively high levels of hippurate) represent more typical spectra relative to historic controls, whereas CA samples

are from an unusual phenotype with low levels of hippurate and high levels of chlorogenic acid–derived metabolites. Inset: Entire shipments of animals could be

phenotypically distinguished based on their levels of urinary hippurate and chlorogenic acid. Taken from Robosky et al. (2005).


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single-cell focus of most in vitro approaches greatly simplifies

evaluation of metabolomic data as nonspecific metabolic

changes are greatly reduced compared with in vivo assessment

of biofluids. Not only can the target cell (or cell extract) be

analyzed but the flux of metabolic changes in the media is also

a great source of biochemical insight (Nagy et al., 2008). The

disadvantage of in vitro approaches is no different for

metabolomics than for any other application—relevance to

in vivo response.

One technique particularly suited for in vitro applications is

the evaluation of single metabolite dynamics. An interesting

review by Wheatley (2005) reports on the role of arginine in

both normal and tumor cell lines as assessed by metabolomic

technology. Assessment of metabolic flux is also much more

readily investigated on the single-cell level (Wang and

Bodovitz, 2010), and metabolomics is a valuable approach

for concurrently monitoring numerous metabolites (Lee et al.,2006; Rabinowitz, 2007).

Beyond pure biochemical applications, in vitro evaluation of

various aspects of reproductive biology is one area where

metabolomics has made significant inroads over the past

5 years (Assou et al., 2010; Botros et al., 2008; Bromer and

Seli, 2008; Revelli et al., 2009; Seli et al., 2008). Within the

toxicology community, in vitro metabolomic approaches have

been used to assess the toxicity of cell-penetrating peptides in

CHO cells (Kilk et al., 2009), the toxicity of cigarette smoke on

lung epithelial cells (Vulimiri et al., 2009), the neurotoxicity of

mercury and caffeine in primary reaggregating brain cell

cultures (van Vliet et al., 2008), and evaluation of de-

velopmental toxicity potential in human stem cells (West

et al., 2010).

Mechanisms and Biomarkers

It can be argued that if you define the mechanism of efficacy

or toxicity of a xenobiotic, you can identify an appropriate

biomarker. Of course, whether that biomarker is practical or

analytically feasible is another question. Theoretically, one can

identify a biomarker without understanding the mechanism, but

that is far from ideal. Early on in the development of the

technology, mechanisms and biomarkers were recognized as

key deliverables of metabolomic technology (Bailey and

Ulrich, 2004; Colatsky and Sumner, 2003; Dunckley et al.,2005; Holmes and Shockcor, 2000; Kleno et al., 2004;

Mortishire-Smith et al., 2004; Robertson, 2005; Roy et al.,2004). Early attempts at biomarker discovery associated

observed metabolic disruptions with disease or toxicity and

voila a biomarker was born. The early literature is replete with

examples of ‘‘the usual suspects’’ (Robertson, 2005) (e.g.,

citrate, 2-oxoglutarate, creatine, trimethylamine, etc.) as being

biomarkers of just about everything. To be fair, as these

molecules do change in many toxicities and disease states, they

are indeed biomarkers, at least by some definition. The problem

of course is that they are nonspecific enough to be essentially

useless as diagnostic biomarkers. Although there may be noble

attempts to make these nonspecific biomarkers mechanistically

relevant, it is a bit like ‘‘six-degrees of molecular separa-

tion’’—no molecule is separated from any mechanism by any

more than six steps on a metabolic pathway chart . . .. Despite

these growing pains, metabolomics technology has begun to

deliver on the promise of biomarkers of disease and toxicity.

Metabolomics has been promulgated as an approach for

mechanistic understanding and biomarker discovery for

neuroscience applications (Dunckley et al., 2005; Pendyala

et al., 2007; Quinones and Kaddurah-Daouk, 2009), oncology

(Claudino et al., 2007), cardiovascular disease (Vasan, 2006),

tuberculosis (Parida and Kaufmann, 2010), cystic fibrosis

(Wetmore et al., 2010), and metabolic syndrome (Oresic, 2010)

to name a few applications. Within the toxicology community,

metabolomics has not been ignored (Clarke and Haselden,

2008), with the technology proposed as a tool for discovering

biomarkers of both renal (Sieber et al., 2009) and hepatic

toxicity (Antoine et al., 2009; McBurney et al., 2009).

Beyond these reviews, specific biomarkers have been

identified. Select biomarkers attributed to various toxicities,

disease states, or physiological changes are summarized in

Table 2, with several discussed in more detail below. In an

important paper by Soga et al. (2006), a metabolomics

investigation utilizing a CE-time of flight-MS approach was

employed to evaluate an acetaminophen model of hepatic

oxidative stress in the mouse. The authors identified ophthal-

mic acid as a potential biomarker of oxidative stress associated

with decreased GSH levels in both the liver and the serum.

Their proposed mechanism is summarized in Figure 8. The

synthesis of ophthalmic acid from 2-aminobutyrate uses the

same enzymatic machinery used to synthesize GSH from

cysteine. The key point is that GSH produces feedback

regulation of c-glutamylcysteine synthase (GCS). As GSH is

depleted because of the rigors of oxidative stress, the feedback

inhibition of GCS is reduced leading to increased biosynthesis

of GSH, and because ophthalmic acid is synthesized using the

same machinery, it is also increased. However, unlike GSH,

ophthalmic acid is not depleted by oxidative stress leading to

its accumulation. Importantly, this work went beyond a simple

survey of changed metabolites. Extensive mechanistic work to

validate the finding was reported enabling other researchers to

follow-up on their conclusions (Kombu et al., 2009).

Though often criticized as a ‘‘fishing expedition,’’ recent

work in our laboratory highlights how use of metabolomic

technology can lead to the discovery of novel biomarkers

(Aranibar et al., 2010). As part of a mechanistic study of

myotoxins, a metabolomic analysis was conducted on urine

samples from rats treated with up to 1 mg/kg/day cerivastatin

for 14 days. Detailed statistical analysis of the resultant NMR

spectra revealed two resonance peaks contributing to the

PCA separation of samples from control and treated animals.

Further analytical work eventually identified these peaks as


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N-acetylated 1- and 3-methylhistidine. Although urinary 3-

methylhisitidine has been used as a biomarker of muscle

protein turnover for many years (Young et al., 1973), neither

isomer has been used as a biomarker of myotoxicity in rats.

Continuing work in our laboratory suggests that these

biomarkers are also useful in serum and are as sensitive and

specific as any competing biomarker for statin myopathy with

the added advantage that these markers increase with muscle

atrophy and decrease with muscle hypertrophy (Waterfield

et al., 1995), a diagnostic capacity not currently available with

other biomarkers of myotoxicity.

Exciting developments in clinical biomarker discovery

utilizing metabolomic technology include the identification of

sarcosine as a mechanistically linked biomarker of prostate

cancer progression (Sreekumar et al., 2009). Sarcosine was

increased in the tissue and urine of patients with prostate cancer

and in prostate cancer cell lines. Importantly, knockdown of

glycine n-methyl transferase (responsible for sarcosine synthe-

sis from glycine) decreased cancer invasiveness, whereas

supplementation with sarcosine induced an invasive phenotype

in benign prostate epithelial cells. Elegant work by Newgard

et al. (2009) proposed a branched chain amino acid (BCAA)

‘‘signature’’ of obesity and insulin resistance in human

subjects. The changes in BCAAs were accompanied by

specific increases in C3 and C5 acylcarnitines and several

other intermediate metabolites. They attributed a rise in

circulating BCAAs in obese subjects to low insulin-like

growth factor 1 levels. Their hypothesis is that increased

catabolism of BCAAs leads to activation of the mammalian

target of rapamycin/S6K1 kinase pathway and phosphorylation

of insulin receptor substrate 1 on multiple serines, contributing

to insulin resistance. Independently, Gall et al. (2010) proposed

a-hydroxybutyrate as an early biomarker of insulin resistance

and glucose intolerance in a nondiabetic population. The

authors concluded that the increase in a-hydroxybutyrate may

be because of two mechanisms including elevation of hepatic

oxidative stress resulting in an increased demand for GSH

production and by elevation of the reduced and oxidized

nicotinamide adenine dinucleotide ratio because of increased

lipid oxidation and observed that they saw some evidence of

both mechanisms in their study. This group noted similar

metabolic changes as those described by Newgard (Newgard

TABLE 2

Selected Metabolomics-Derived Biomarkers

Biomarker Disease/toxicity/physiological change Species Reference

5-Oxoproline Brombenzene toxicity Rat Waters et al. (2006)

Acetylfelinine Farnesyl pathway inhibition Rat Dieterle et al. (2007)

Bile acid profile Mechanisms of hepatic toxicity Rat Want et al. (2010a) and Yang et al. (2008)

Gulonate/ascorbate Cyp induction Rat Aranibar et al. (2009)

1 and 3 Methylhistidine Myotoxicity Rat Aranibar et al. (2010)

Galactoglycerol 3-Chloro-1,2 propanediol exposure Rat Li et al. (2010b)

Ophthalmic acid Oxidative stress (GSH depletion) Mouse Soga et al. (2006)

Indole-3-lactic acid Alcoholic liver disease ppar�/� mouse Manna et al. (2010)

Fatty acid profiling Type II diabetes Human Yi et al. (2006)

Propionyl carnitine Methylmalonic and propionic acidemias Human Wikoff et al. (2007)

Lysophosphatidylcholines (16:0, 18:0,

18:1, 18:2)

Metformin activity Human Cai et al. (2009)

BCAA Insulin resistance Human Newgard et al. (2009)

Sarcosine Prostate cancer Human Sreekumar et al. (2009)

a-Hydroxybutyrate Insulin resistance Human Gall et al. (2010)

Medium chain acylcarnitines Moderate exercise Human Lehmann et al. (2010)

BCAA/histidine ratio Knee osteoarthritis Human Zhai et al. (2010)

FIG. 8. Biochemical basis for ophthalmic acid as a biomarker for oxidative

stress. As GSH is depleted in oxidative stress, feedback inhibition of GCS is

reduced leading to increased synthesis of c-glutamyl-2-aminobutyrate (as well as

c-glutamylcysteine), leading to increased ophthalmate (and GSH). Whereas the

derived GSH is depleted by oxidative stress, ophthalmate is not—leading to its

accumulation under conditions of oxidative stress. Adapted from Soga et al. (2006).


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et al., 2009) but concluded that this single biomarker may

prove more useful. It will be interesting to see which approach

(if any) gains more traction in clinical practice. Clearly,

metabolomics technology is moving beyond the stage of

‘‘showing promise’’ to actual delivery of specific biomarkers.

In addition to biomarker discovery, metabolomics has been

utilized as a tool to reexamine classic toxicity models for novel

mechanistic insights. Galactosamine has been utilized as

a hepatotoxicity model for many years (Coen et al., 2009;

Keppler et al., 1968). Early mechanistic studies identified

uridine nucleotide depletion as a probable mechanism re-

sponsible for the hepatic pathology as UTP supplementation

ameliorated the toxicity (Keppler et al., 1969). One confounding

finding was the observation that glycine supplementation also

ameliorated the toxicity, which was attributed to an alternative

mechanistic explanation involving Kupffer cells (Stachlewitz

et al., 1999). Metabolomic evaluation of a galactosamine model

revealed the novel observation that glycine increased hepatic

uridine, which was most likely responsible for the ameliorating

effects in the model (Coen et al., 2007) nicely demonstrating

how metabolomics can bring new insights to ‘‘settled science.’’

CLINICAL APPLICATIONS

Overview

Measurements of specific endogenous metabolites have long

been a part of the practice of medicine from the measurement of

urea, creatinine, glucose, and cholesterol that are part of most

clinical blood test panels to measurements routinely made in the

blood of newborns to exclude treatable diseases such as

phenylketonuria. There is a growing enthusiasm for metabolo-

mics among clinicians, which in part reflects disappointment in

the clinical application of genetics to personalized medicine

(Limdi and Veenstra, 2010). Factors such as epigenetics, disease,

aging, environmental exposures, and diet can all significantly

impact susceptibility to a disease, the natural history of that

disease, as well as the beneficial and adverse responses to any

treatment option. Metabolomics holds the promise of character-

izing the phenotype of the patient at any given moment,

potentially providing the physician with the ultimate information

to personalize care. Moreover, metabolomics can be performed

on bodily fluids readily accessible to the physician, including

urine, blood, saliva, breath condensate, sweat, ascites, amniotic,

and cerebrospinal fluid. Although it is likely that the metabolome

in each of these fluids can fluctuate as a function of time of day,

environmental factors, and diet (Kochhar et al., 2006; Lenz

et al., 2004; Slupsky et al., 2007; Stella et al., 2006), where

studied, it appears that large portions of the metabolome remain

relatively invariant within an individual (Assfalg et al., 2008;

Bernini et al., 2009; Holmes et al., 2008) and can therefore

provide low ‘‘noise’’ levels for detection of potential biomarkers.

This has allowed application of metabolomics approaches to

broad areas of medicine, including, cardiovascular disease

(Goonewardena et al., 2010), hypertension (Holmes et al.,2008), neuropsychiatric diseases (Quinones and Kaddurah-

Daouk, 2009), inflammatory bowel disease (Bezabeh et al.,2009), infectious diseases (Slupsky, 2010), respiratory diseases

(Basanta et al., 2010), autoimmune diseases (Seeger, 2009), pre-

and neonatal diseases (Atzori et al., 2009; Graca et al., 2009),

male infertility (Deepinder et al., 2007), and even eye diseases

(Young and Wallace, 2009). Additional general reviews of the

potential role of metabolomics in the clinic have been recently

published (Goldsmith et al., 2010; Gowda et al., 2008; Madsen

et al., 2010; Oresic, 2009; Vinayavekhin et al., 2010). This

review will focus on metabolomic approaches to cancer and

adverse drug reactions (ADRs).

Cancer

Metabolomics has had its biggest clinical impact in the field

of oncology, and this has already generated several reviews

(Chung and Griffiths, 2007; Kim et al., 2008; Lane et al., 2008,

2009; Serkova et al., 2007; Spratlin et al., 2009). Oncology is

fertile ground for metabolomics for several reasons. First,

the concept of personalized therapy is a comfortable one to the

oncologist. Patient-specific parameters, such as body surface

area, clinical laboratory assessments of kidney and liver

function, and more recently host genetic factors, are routinely

used in the selection of therapy and individualization of dose

(Walko and McLeod, 2009). The oncologist often selects the

treatment options based on cancer tissue histology and,

increasingly, on molecular phenotyping such as selecting

herceptin treatment only from those patients whose breast

cancer expresses Her 2 neu (Gonzalez-Angulo et al., 2010). In

addition, the oncologist is comfortable with following serum-

based biomarkers of response to treatment, such as serial

measurements of prostate-specific antigen to gauge response to

treatment for prostate cancer (Payne and Cornford, 2010).

Moreover, oncologists are especially eager to try new

approaches to optimize therapy for their patients because in

spite of their best efforts, treatments often fail or cause

toxicities with tragic consequences.

An advantage of the field of oncology is the routine surgical

removal of tumor tissue that allows extensive metabolic

characterization in the laboratory. This can be performed in

whole tissue with ‘‘magic angle’’ NMR techniques (Sitter et al.,2010) or in tissue extracts with either MS or NMR techniques.

Tissue studies, together with transcriptomic and proteomic

characterizations, have revealed that cancer cells tend to have

metabolic phenotypes that are distinct from that of healthy cells,

and this property may be useful to characterize various cancers

in terms of prognosis and response to treatment (Bathen et al.,2007; Oakman et al., 2010; Sitter et al., 2006). A variety of

clinical studies have attempted to identify cancer-specific

changes in the metabolome that would be reflected in readily

accessible bodily fluids (Table 3). Development of such


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biomarkers could facilitate wider cancer screening in the

population and might provide information on tumor response

to treatment.

Both MS and magnetic resonance imaging (MRI)

approaches have been used in cancer biomarker discovery

(Table 3). One advantage of using MRI-based metabolomic

approaches is that once specific discriminating metabolites are

identified, it may be possible to use MR technology to visualize

and accurately locate tumors in the body. For example, cancer

cells typically contain elevated phospholipid levels character-

ized by an elevation in choline-containing lipids (Glunde and

Serkova, 2006). This property has led to a clinical role for

magnetic resonance spectroscopy imaging (MRSI) in the

detection and management of breast (Bartella and Huang,

2007), prostate (Sciarra et al., 2010), and brain (Dowling et al.,2001) cancers. One recent study suggested that MRSI imaging

of choline had a 100% sensitivity for detected malignant versus

benign breast tumors and suggested that this approach could

eliminate the need for over 50% of breast biopsies (Bartella

et al., 2007). MRSI imaging to detect breast and prostate

cancer is no longer considered experimental, and the costs for

these tests are reimbursed by major health insurance plans. This

has provided an attractive path for metabolomic biomarker

discovery and clinical translation in oncology.

Based on the successes, there has been substantial interest

within the oncology community to expand research on the use

of metabolomics in the detection and management of cancers

(Evelhoch et al., 2005).

Adverse Drug Reactions

ADRs remain a major problem in the clinic despite the best

efforts of the pharmaceutical industry to design and screen for

these liabilities. The most problematic toxicities are those that

are ‘‘idiosyncratic,’’ meaning they are not clearly dose related,

often occur late in therapy, and are relatively rare. The liability

for idiosyncratic toxicities might not be evident during

preclinical and clinical development of a drug and only

become apparent postmarketing when a large population of

patients is exposed. There is an urgent need for improved

biomarkers that could identify drugs in development with the

potential for serious ADRs. There is also need for biomarkers

that could identify the onset of an ADR before the patient

becomes ill and ideally identify the susceptible patient before

they actually receive the drug. Such biomarkers would improve

the safety of existing drugs and provide a path to approval for

drugs found to have ADR liabilities during development.

The organs most frequently affected by serious ADRs are the

liver and kidney. Currently, available biomarkers do not

reliably detect or quantitate risk for ADRs involving these

organs, nor do they predict which patients will develop these

injuries before they occur. Blood urea nitrogen and crea-

tine are the blood tests most commonly used in the clinic to

detect kidney dysfunction, but these become abnormal only

after severe kidney damage has occurred. Available urine tests

are neither sensitive nor specific for many types of kidney

ADRs. To identify liver injury at the early, presymptomatic

stage, it is often recommended that the patients receiving

potentially hepatotoxic drugs undergo frequent monitoring of

serum alanine aminotransferase (ALT) levels. However, there

are no data to support the effectiveness of this practice (Senior,

2009). Moreover, it is not necessary to stop treatment in all

patients experiencing asymptomatic elevations in serum ALT

because these elevations will frequently resolve despite

continuing drug treatment (Senior, 2009; Watkins, 2009;

Watkins et al., 2008). This phenomenon of ‘‘adaptation’’ does

TABLE 3

Examples of the Use of Metabolomics to Improve Detection of Cancers

Fluid Cancer Technique Reference

Serum Breast GC-MS Wang et al. (2009)

Kidney GC/LC-MS Gao et al. (2008)

Prostate LC-MS Osl et al. (2008)

Ovarian NMR,LC/MS Guan et al. (2009) and Odunsi et al. (2005)

Liver NMR Gao et al. (2009)

Oral NMR Tiziani et al. (2009)

Plasma Pancreas LC-MS Urayama et al. (2010)

Saliva Breast, oral, pancreatic CE-TOF-MS Sugimoto et al. (2010)

Oral LC-MS Yan et al. (2008)

Urine Kidney LC/GC-MS Kim et al. (2009a)

Bladder GC-MS Issaq et al. (2008)

Breast GC-MS Nam et al. (2009)

Breath Lung GC-MS Phillips et al. (2007) and Poli et al. (2005)

Breast GC-MS Phillips et al. (2006)

Feces Colon NMR Monleon et al. (2009)

Prostatic fluid Prostate NMR Cheng et al. (2001)

Bile Biliary NMR Wen et al. (2010)


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not occur in all patients, and liver injury may become

irreversible in these patients even if drug therapy is

discontinued (Senior, 2009; Watkins, 2009; Watkins et al.,2008). It is not currently possible to distinguish patients with

benign and reversible ALT elevations from those in whom the

liver injury will progress. The combination of elevations in

serum ALT and bilirubin has been proposed as a reliable sign

of serious liver toxicity, but this denotes serious and potentially

irreversible liver injury (Watkins et al., 2008).

Liver and kidney ADRs are attractive targets for metab-

olomic biomarker discovery (Beger et al., 2010)—the liver

because it is the major organ in the body that generates the

metabolome in blood and the kidney because it largely

influences the urinary metabolome. With kidney toxicity, most

metabolomics studies to date have been performed in rodents

(Boudonck et al., 2009) and few human studies have been

reported. The paucity of human research may reflect current

enthusiasm for newly discovered protein markers of renal

injury that can be measured in urine (Dieterle et al., 2010).

However, in a recent study in 13 healthy volunteers (Klawitter

et al., 2010a), changes in urine metabolome were observed

after a single dose of the potentially nephrotoxic drug

cyclosporin A and these changes were similar to those

observed in rats experiencing nephrotoxicity because of this

same drug (Klawitter et al., 2010b).

The potential of metabolomics to predict liver toxicity was

first suggested by Clayton et al. (2006). In this study, Sprague-

Dawley rats were given a single toxic threshold dose of

acetaminophen (APAP). NMR-based metabolomic profiles of

urine samples collected prior to dosing were found to

significantly correlate with the extent of liver injury observed

subsequently in each rat. The use of predose metabolomic

measurements to predict postdose outcomes has been termed

‘‘pharmaco-metabonomics,’’ defined as ‘‘the prediction of the

outcome of a drug or xenobiotic intervention in an individual

based on a mathematical model of pre-intervention metabolite

signatures’’ (Clayton et al., 2006).

Acetaminophen causes liver injury by conversion to a re-

active metabolite N-acetyl-p-benzo-quinone (NAPQI) in the

liver, and reactive metabolites are believed to underlie most

drug-induced liver injury (Srivastava et al., 2010). The ability

of the pretreatment metabolome to predict individual suscep-

tibility to acetaminophen hepatotoxicity may be explained if

portions of the metabolome correlate with the liver’s ability to

produce and accumulate the NAPQI. This is plausible because

many of the enzymes and transporters involved in the

disposition of drugs are also known to be involved in the

disposition and metabolism of endogenous substances

(Amacher, 2010). In this case, the high level of an enzyme

or transporter in a person could directly result in parallel

increases in the production of endogenous metabolites and the

reactive acetaminophen metabolite. Alternatively, or in addi-

tion, endogenous metabolites might compete with acetamino-

phen for certain nontoxic metabolic pathways, influencing the

fraction of the consumed dose that is converted to the reactive

metabolite. In this case, a low concentration of an endogenous

metabolite would be correlated with a high concentration of the

reactive metabolite.

There is experimental support for these hypotheses. In the rat

study referenced above (Clayton et al., 2006), the urine

metabolome not only predicted individual susceptibility to

acetaminophen toxicity but also the extent of production of the

major metabolite (glucuronide) of acetaminophen excreted in

urine. In a study of healthy human volunteers given a single oral

dose of acetaminophen, those with high predose levels of p-

cresol-sulfate in their urine tended to have reduced urinary

elimination of the sulfate metabolite (Clayton et al., 2009).

p-Cresol is known to be produced from by the gut bacteria. The

authors concluded that the p-cresol coming from the gut via

enterohepatic circulation competed with the acetaminophen in

the liver for sulfation. Further support for the endogenous

metabolome being able to predict drug disposition in man has

come from a recent study in healthy volunteers who received

a single oral dose of the immunsuppressant drug tacrolimus

(Phapale et al., 2010). Using an unbiased LC/MS profile and

partial least squares modeling, it was found that the level of just

four endogenous metabolites yielded a significant prediction of

the systemic exposure to tacrolimus (assessed as the serum area

under the concentration curve). Because tacrolimus has a narrow

therapeutic index and a high degree of individual variation in

oral clearance (Venkataramanan et al., 1995), it was proposed

that quantitation of these four metabolites in urine might be

useful in the clinic to guide personalized dosing of tacrolimus.

Following up on the observations of Clayton et al. (2006) in

rats, serial characterization of the urine metabolome was

performed in 58 healthy volunteers (Winnike et al., 2010) who

received recurrent treatment with acetaminophen that produces

mild liver injury (elevations in ALT, aspartate aminotransfer-

ase, and alpha glutathione-S-transferase) in about one-third of

subjects (Watkins et al., 2006). Unlike what was observed in

the rat studies, NMR profiles of 24-h urine collections obtained

before dosing could not significantly separate those subjects

who would develop ALT elevations after commencing

acetaminophen dosing from those who would not. However,

this separation was achieved in urine obtained shortly after

dosing but before the onset of ALT elevations. The application

of metabolomics to predict adverse events early in the dosing,

but prior to the occurrence of phenotypic change, was termed

‘‘early intervention pharmaco-metabonomics’’ (Winnike et al.,2010). An interesting finding of this study was that the

acetaminophen mercapturate and cysteine conjugates, which

are the urinary excretion products of NAPQI, tended to be

higher in responders. However, the urinary levels of these

metabolites were not predictive by themselves but only became

predictive when changes in the endogenous metabolome were

also considered. This demonstrates the potential power of

combined analysis of endogenous and exogenous metabolites

in predicting outcomes of drug therapy.


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Another recent study has supported the ability of pretreatment

metabolome to predict patients who are most likely to develop

liver injury caused by the oral anticoagulant, ximelagatran

(Andersson et al., 2009; Keisu and Andersson, 2010). This drug

was withdrawn from worldwide markets because of rare

incidence of severe hepatic injury (Lee et al., 2005). To

identify potential biomarkers that could be used to identify

susceptible patients, unbiased NMR- and MS-based metabolo-

mics analysis was conducted on plasma samples obtained from

patients before they were treated with ximagatran in a large

clinical trial (Olsson, 2003). It was observed that patients who

would develop liver injury tended to have lower pretreatment

levels of serum pyruvate than did those patients who would not

develop elevations. A limitation of this study is that the blood

samples analyzed in subjects with and without ALT elevations

were not obtained at the same intervals prior to treatment.

The above studies suggest that pharmaco-metabonomics,

perhaps combined with genetic testing, may have a future role

in personalized medicine.

CURRENT NEEDS/FUTURE DIRECTIONS

Clearly, metabolomics has grown and changed over the past

several years evolving into a technology with wide application

across the spectrum of biological sciences. Although toxicol-

ogy is no longer at the center of the metabolomics universe, the

technology has demonstrated tremendous potential for toxico-

logical applications. Current needs for the technology include

some sort of standardization in MS approaches so that different

laboratories running different instrumentation can still generate

comparable data on the same set of samples. Currently, this is

not the case as different MS configurations and separation

strategies can give very different biochemical pictures of

a biofluid or tissue extract. The current atmosphere has a bit of

a ‘‘Wild West’’ feel to it with most investigators using their

own approaches that tend to vary from laboratory to laboratory.

Although this is probably a necessary stage of development

of the technology, as no single approach or strategy can yet

claim superiority, it is inhibiting to those wishing to pursue

regulatory applications of the technology. Although some

attempts have been made to standardize MS approaches and

their reporting (Jones et al., 2007; Kanani et al., 2008; Sumner

et al., 2007; Want et al., 2010b), we still have a long way to go.

Reviews by Gomase (Gomase et al., 2008) and Go (2010)

provide nice summaries of current database resources for

metabolomic analysis. Although these databases are increas-

ingly valuable, they are dependent on the approaches used to

generate them so that unless identical methods are employed,

they can become difficult to utilize. A fundamental tenet of

scientific research is that experiments be repeatable. Unless and

until metabolomics can routinely (and easily) meet that

requirement questions as to its applicability as a decision

making tool will remain.

In general, NMR and MS data sets, even when they are

acquired on the same samples, are interpreted separately, and

the molecular information is then collated and explained using

‘‘gray matter.’’ Clearly, both of these approaches are just tools

for extracting the information of the metabolome, and future

developments will be aimed at making these tools more

interactive and seamless (Lindon and Nicholson, 2008). In

the end, we want a thorough and accurately annotated as-

sessment of the metabolome, and the tools we use to arrive at

that are inconsequential.

Apart from analytical issues is the ever-present problem of

information overload. Even the simplest of experiments can

generate hundreds or even thousands of metabolic differences.

Assuming that all these metabolic changes could be correctly

annotated, making sense of all that data is a herculean task.

Software solutions are evolving, particularly those that integrate

metabolomics data with transcriptomic data, but they are not yet

so user friendly that one needs to simply enter data and out pops

a definitive diagnosis or mechanism. A significant gray matter

investment is still required. This is probably the area where

those that adopted the technology early, only to drop it shortly

thereafter, veered farthest of course. No matter how sophisti-

cated our analytical techniques and software applications

become, they will not eliminate the need for talented bio-

chemists and toxicologists to interpret the data. Metabolomics

should not be viewed as a technology to do our thinking for us

but rather as a tool to unveil hidden aspects of biochemistry and

pathophysiology that have previously proven elusive.

One clear advantage metabolomics has over transcriptomics

is that the former directly conveys phenotype and can be

obtained from peripheral samples. This makes the technology

very attractive from a translation standpoint. As the routine

collection of biopsy samples is unattractive from a clinical

standpoint, identification of preclinical biomarkers in urine,

serum, or other accessible biofluid is easily transferred to the

clinical setting. The question remains about how predictive

preclinical biochemistry is of clinical biochemistry, but there is

no reason to suspect that metabolomic endpoints are any less

predictive than currently employed markers of efficacy or

toxicity across species. We currently routinely use clinical

chemistry assessment of metabolic biomarkers (e.g., glucose,

cholesterol, bilirubin, creatinine, etc.) with great confidence in

the biological significance of these markers in both the

preclinical and clinical settings. It is not difficult to imagine

that 1 day we will have the same confidence in dozens if not

hundreds of additional metabolic markers made accessible by

metabolomic technology. As preclinical and metabolomic

research gains traction, characterization of these ‘‘novel’’

biomarkers will naturally follow. Proteomics could theoreti-

cally fulfill a similar role as metabolomics; however, sample

throughput and data turnaround time for proteomics

approaches need to be markedly improved. This translational

aspect of the metabolomic technology is one of the most

exciting prospects of metabolomics research.


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What does the future hold? It is clear that metabolomics has

now established itself as a distinct field with applications across

the spectrum of biological sciences from single-cell experi-

ments to complex clinical studies. As our tools get more

sophisticated, integrated omic approaches will begin to merge

these scientific areas, such that the commonalities of disease

mechanisms and toxic mechanisms will become apparent and

the line where physiological response drifts into toxic response

will be more sharply drawn. Within the toxicology community,

mechanisms and biomarkers are where the technology is

currently beginning to payoff and where the greatest gains will

come in the near future.

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