Metabonomics

5.43 Metabonomics I D Wilson, AstraZeneca, Macclesfield, UK J K Nicholson, Imperial College, London, UK & 2007 Elsevier Ltd. All Rights Reserved. 5.43.1

Introduction

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5.43.2

Analytical Platforms for Metabonomics

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5.43.2.1

Sample Types and Sampling

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5.43.2.2

Nuclear Magnetic Resonance (NMR) Spectroscopy

991 991 992

5.43.2.2.1 5.43.2.2.2

5.43.2.3

Liquid samples Solid and semisolid samples

Mass Spectrometry

5.43.2.3.1 5.43.2.3.2 5.43.2.3.3

Gas chromatography-mass spectrometry High-performance liquid chromatography-mass spectrometry Capillary electrophoresis-mass spectrometry

5.43.2.4

Data Processing and Model Building

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5.43.2.5

Metabolite Identification

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5.43.2.5.1

Solid phase extraction chromatography (SPEC) nuclear magnetic resonance spectroscopy High-performance liquid chromatography-nuclear magnetic resonance (HPLCNMR) spectroscopy and high-performance liquid chromatography-mass spectrometry-based methods of identification

5.43.2.5.2

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5.43.3

Applications of Metabonomics

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5.43.3.1

Metabotyping

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5.43.3.2

Metabonomics and the Study of Toxicity

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5.43.3.3

The Investigation of Disease Models

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5.43.3.4

Metabonomic Studies in Humans

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5.43.4

Outlook

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References

5.43.1

992 993 994 996

1004

Introduction

Genomics, proteomics, and metabonomics represent a triumvirate of approaches that may aid our understanding of biological processes in cells, organs, and the whole organism at the biomolecular level. Recent advances in analytical technologies, coupled with the increasing availability of genome sequences, have led to intense interest in the application of these ‘omics’ technologies to obtain a greater understanding of the biological events leading to the onset and development of disease. Equally important to those engaged in pharmacological research is the response, both pharmacological and toxicological, of animals or human subjects to the therapeutic agents designed to combat or prevent these diseases. Outside this area there is also a need to assess the effects of the so-called functional foods as well as pesticides and other chemicals where there is likely to be environmental or industrial exposure to human populations. A better understanding of the effects of xenobiotics, including drugs, on any biological system requires information on events and processes occurring at all levels of biomolecular organization within the organism, including genes, proteins, and metabolism. So while knowledge at the genome level will be of great value in studying disease or toxicity, it must be recognized that there are many environmental factors that will modulate the potential outcomes encoded in the genome. These factors include things such as diet, age, gender, strain, lifestyle, and even the gut microflora that live symbiotically within higher organisms but whose presence and contribution cannot be deduced from the genome sequence of the host. All of these, in combination, will affect the phenotype, and can have a very significant influence on outcomes. As result of this complexity the complementary ‘omics’ platforms of proteomics and metabonomics are also

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Metabonomics

becoming widely employed (either singly or in combination) in so-called systems biology approaches to the study of disease and toxicity. Metabonomics, defined as ‘‘the quantitative measurement of the dynamic multiparametric response of a living system to pathophysiological stimuli or genetic modification,’’1,2 thus determines changes in the organisms complement of low-molecular-weight organic metabolites in biofluids and organs (in the related field of metabolomics, the aim is to determine the total small-molecule complement of the cell1). As such, metabonomics provides information on the endpoints of the various processes going on within the organism in response to change. Like all ‘omics’ approaches, metabonomics is a ‘hypothesis-free’ means of investigating metabolic systems and discovering biomarkers (insofar as there is a hypothesis, it is that ‘‘something metabolic may have changed; let’s see if we can find out what!’’). These biomarkers may then be used to monitor the responses of the system under examination and, perhaps more importantly, for the development of hypotheses that can advance mechanistic understanding.

5.43.2

Analytical Platforms for Metabonomics

The ideal characteristics of analytical techniques for metabonomic studies are that they should provide as comprehensive a metabolic fingerprint as possible in a reasonably short analysis time (so as to enable moderate to high throughput). Such an ideal technique would be unbiased toward particular classes of metabolites and be equally sensitive to all of the components in mixture. It should, in addition, possess a wide dynamic range, in order to cover the whole range of concentrations of endogenous molecules that might be present, and be able to provide quantitative data on all of the components present. Equally important, the technique should provide sufficient structural data to enable the investigator rapidly and unambiguously to identify the marker, or markers, detected. Not surprisingly, no such technique is currently available that can fulfill all of these criteria, and it is rather difficult to envisage any single methodology that could match up to this analytical challenge. Currently, the two major analytical methods used for obtaining metabolic profiles data are based on either high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR)3–5 or, more recently, high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS),6 with a few examples of the use of gas chromatography (GC-MS)7and capillary electrophoresis-MS (CE-MS).8,9 As well as these methodologies, others have advocated the use of less discriminatory techniques, such as HPLC-ultraviolet (UV),10 HPLC-electrochemical detection (EC),11 or infrared-spectroscopy,12 but it is difficult to support the application of these approaches as they provide only a limited coverage of the molecules likely to be present in any biological sample, with minimal chances of full characterization and identification. So, while not entirely fulfilling the characteristics of the ideal detection and identification system described above, the NMR and separation MS-based methods provide the best compromise currently available and are described in more detail below.

5.43.2.1

Sample Types and Sampling

No matter how sophisticated the analytical technique used in the study, the key to any metabonomics investigation is the sample, and careful sample collection and treatment are essential if good results are to be obtained. The sample types that can be analyzed by the current techniques encompass all of those that might be required for biomedical or toxicological analysis, including urine, bile, blood plasma, intact tissues, and tissue extracts. When combined with chemometric techniques such as principal component analysis (PCA), particular metabolites, or groups of metabolites that provide specific markers for a particular condition (e.g., toxicity, disease, physiological variation) can be identified. In many ways urine provides an ideal method for the noninvasive study of the effects of such conditions on endogenous metabolic pathways. Samples can be taken over the duration of the study and provide a time course of effects that can be used to pinpoint onset and severity of toxicity, and determine the best times for other more invasive investigations. In addition, unless small rodents such as mice are involved, there are usually few restrictions on the size of the sample obtained. Blood plasma provides a more direct ‘window’ on the organism under study, but clearly requires more invasive procedures. There are also well-defined limits on the amounts of sample (and the number of sampling times) that can be taken in any given study. Tissue samples obtained from target organs clearly require surgical intervention, which in animal studies are usually only obtained on autopsy. In the case of humans the removal of, e.g., tumors or diseased organs as part of therapy affords the possibility of the direct study of these tissues. However, when considering sampling, great care must be taken in all metabonomic studies to ensure both integrity and validity of the study design as there are numerous pitfalls for the unwary. Many factors can result in changes to sample composition and, for good results to be obtained, these must be controlled. Perhaps the most obvious is that biofluid samples provide ideal growth media for bacteria and, unless steps are taken, for example, to preserve urine being collected from animals in metabolism cages, the metabolic profile observed may be more indicative of fermentation than a response to an experimental treatment. More subtle factors such as the time of day of collection,

Metabonomics

and the gender, age, strain, and diet of the animals (or humans), as well as stress, exercise, and physical activity, can have very significant effects on global metabolite profiles.13–17 If not carefully controlled there will be apparent studyrelated changes in metabolite profiles that are simply artefacts due to poor experimental design and no amount of advanced analytical technology can compensate for a badly designed study.

5.43.2.2

Nuclear Magnetic Resonance (NMR) Spectroscopy

5.43.2.2.1 Liquid samples NMR spectroscopy has many of the ideal characteristics required for the nontargeted analysis of liquid samples for endogenous metabolites. There is thus no need for the preselection of the analytical conditions in the case of biofluid samples such as plasma, urine, and bile, as these can be analyzed without the need for any form of sample pretreatment (other than adding c. 10% by volume of D2O to act as a field frequency lock for the spectrometer and buffering to minimize chemical shift variation). Currently, analysis at 600 MHz (for 1H NMR) is the most popular method, requiring about 600 mL of sample in total, and taking c. 5–10 min per sample depending upon the sample and technique used. Higher-field-strength spectrometers are available, and probes with smaller sample requirements can be used. More recently the so-called cryoprobes have been introduced which combine high sensitivity with even more modest sample requirements. The resulting NMR spectra have very high information content, enabling the rapid detection and identification of analytes present in the sample. Another favorable feature of NMR spectroscopy is that it is nondestructive, permitting the subsequent reanalysis of the sample by other methods (e.g., HPLC-MS, GC-MS). A unique feature of NMR is that it readily allows equilibria between molecules in the sample to be observed. Against NMR spectroscopy as a bioanalytical tool for metabonomics is the criticism that it is relatively insensitive compared to mass spectrometry, for example. In answer to this criticism there is the argument that this disadvantage is more than compensated for by the fact that, unlike techniques such as MS and UV spectroscopy, NMR is equally sensitive for all proton-containing analytes. Also, as indicated above, the sensitivity of NMR spectrometers is constantly improving as a result of advances in field strength and probe design and currently lies in the ng range. The subject of the practice of NMR spectroscopy for metabonomics has been reviewed.3–5 Typical spectra for control human plasma, bile, and urine samples are shown in Figure 1.

Plasma

Bile

Urine

4.0

3.5

3.0

2.5

2.0

1.5

1.0

1H

Figure 1 Partial 1H NMR spectra (600 MHz) of control human plasma, bile, and urine.

δ

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Metabonomics

5.43.2.2.2 Solid and semisolid samples NMR spectroscopy can, in addition to biofluid samples, also be used to obtain metabolic profiles of solid and semisolid tissue samples by employing the technique known as magic angle spinning (MAS). High-resolution (HR) MAS has been used for the investigation of a range of tissue types, including kidney,18 lymph node,19 and prostate20 tumors, as well as liver tissue,21 and has recently been reviewed.22 HRMAS provides a complementary method for the analysis of tissues compared to making extracts, with the advantage that intracellular compartmentation, which is lost during tissue homogenization and solvent extraction, is maintained. In a study of the toxicity of acetaminophen (paracetamol), liver samples from mice were analyzed both as extracts using conventional solution 1H NMR spectroscopy and intact via HR 1H MAS spectroscopy.22,34 The MAS results clearly demonstrated significant perturbations in the profile of the liver tissue, with a rapid loss of glucose and glycogen combined with increased lipid content. These studies were complemented by transcriptomic24 and proteomic25 studies and the combination revealed a picture showing that the drug caused a global energy failure in the livers of mice receiving a toxic dose. More recent studies26 on the hepatotoxin methapyrilene, administered to the rat at 0, 50, and 150 mg kg  1 day  1 show a similar pattern of changes in the liver, as illustrated in the HRMAS NMR spectra shown in Figure 2. HRMAS NMR spectroscopy is not limited to tissues but can also be used to study the effects of various treatments on isolated organelles such as mitochondria.27

5.43.2.3

Mass Spectrometry

As discussed above, NMR spectroscopy can be performed directly on biofluid samples, but this approach is much less practicable for MS-based systems. This is because of problems due to ion suppression, which result in irreproducible and highly variable responses for analytes, while often ignored, currently present insurmountable difficulties for complex

Control

(CH2)n

Methap (50 mg kg−1)

0.09

Methap (150 mg kg−1)

0.08

0.07

Normalized intensity

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0.05

=CH−CH2−CH= CH2CH2CO

Glucose and glycogen

0.04

CH=CHCH2

TMAO

R−CH3

0.03 CH2CH2CO 0.02

0.01

0 6

5.5

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1.5

1

0.5

δ 1H (ppm) Figure 2 Average standard 1H HRMAS NMR spectra of liver samples obtained from male rats dosed with 0, 50, and 150 mg kg  1 day  1 of methapyrilene, showing dose-related changes in fatty acids, trimethylamine N-oxide (TMAO), glucose, and glycogen levels as a result of hepatotoxicity.26

Metabonomics

samples such as urine and bile. This view is based on our unpublished studies comparing data obtained by the direct infusion of urine into the ion source of the MS versus HPLC-MS, which showed advantages for the latter. In our opinion, therefore, approaches employing MS for metabonomics that use a combination of MS with a separation technique, such as HPLC, GC, or CE, will give the best results by spreading out the components of a biological sample mixture, thus reducing the potential for ion suppression. These ‘hyphenated techniques’ are described in more detail below.

5.43.2.3.1 Gas chromatography-mass spectrometry Although GC-MS, with HR capillary columns, has been widely applied to the metabolomic analysis of microorganisms and plants,7 there are fewer published applications in mammalian systems. There is, however, considerable potential for GC-MS in this area, and it would be very surprising if many more examples did not appear in the future. An illustration of the potential of GC-MS for metabonomic analysis is shown by the example of the analysis of plasma from Zucker fa/fa and normal Wistar-derived animals given in Figure 3. The total ion current (TIC) traces shown are for both GC-MS with electron impact ionization (EI) (Figure 3) and chemical ionization (CI) (Figure 4). EI techniques provide mass spectra that contain diagnostic fragments that can enable much structural identification to be carried out (especially when combined with the extensive searchable databases). CI, on the other hand, gives mostly molecular ion information, enabling the confirmation of the molecular mass of the unknown. GC provides a highly developed, stable, selective, sensitive, and HR separation system. This capability is continually being enhanced and, with the introduction of GC-GC separations, combined with ever more powerful MS detectors, including time-of-flight (ToF) and ToF-ToF instruments, the comprehensive analysis of very complex samples should be possible. The use of ToF enables accurate masses to be obtained, with the benefit that atomic compositions can be deduced, providing further useful information for structure determination. The major disadvantage of TOF MS EI+ TIC 5.57e4 16.59

4.60 4.88

100

9.90

5.17

14.97 %

AZ_EI_007

7.04

14.00 13.72

6.01 6.30

8.36

9.61

10.08 10.93 12.13 17.40

12.98

0 4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

Time (a)

4.61

100

TOF MS EI+ TIC 5.53e4

4.88

16.61 7.04

16.62

14.97 14.01

%

AZ_EI_009

5.18

13.72 6.01

9.61

6.30

9.91

10.93

12.13

10.04 11.93

14.85 17.04 17.40

12.98

0 4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

Time (b) Figure 3 Typical TICs obtained from GC-EIMS analysis of plasma obtained from 20-week-old male animals corresponding to (a) Wistar-derived Alderley Park and (b) Zucker fa/fa rats.106 (Reproduced by permission of the Royal Society of Chemistry.)

993

Metabonomics

100

TOF MS CI+ TIC 5.47e4

4.64

4.89 %

AZ_CI_207

9.96

10.09

7.06

10.98 0 4.00

6.00

8.00

10.00

(a)

12.00

16.70 13.79

15.05

14.00

16.00

18.00

4.63 TOF MS CI+ TIC 5.34e4

%

4.89 16.70

5.42 0 4.00 (b)

12.19

Time

100

AZ_CI_209

994

6.00

7.06

9.66 9.96 8.00

10.98

10.00

12.19

12.00

13.78

15.04

14.00

16.00

18.00

Time

Figure 4 Typical TICs obtained from GC-CIMS analysis of plasma obtained from 20-week-old male animals corresponding to (a) Wistar-derived (AP) and (b) Zucker ( þ / þ ) rats.106 (Reproduced by permission of the Royal Society of Chemistry.)

GC-based techniques is the need for a fair amount of sample processing prior to analysis. Thus, the direct injection of biofluids samples on to capillary GC columns is not technically viable and, even if it were, the bulk of the components in most biological fluids is relatively involatile. It is normal to analyze extracts of the sample after derivatization to provide volatile analytes.7,28 The degree of sample preparation required, together with the relatively long run times currently associated with GC, means that the technique is relatively low-throughput compared to some other technologies.

5.43.2.3.2 High-performance liquid chromatography-mass spectrometry HPLC-MS has begun to be employed in metabonomic studies only relatively recently.6 However, given the very widespread availability of such systems in laboratories, and the compatibility of reversed-phase separations with many of the components in biofluids, a rapid increase in applications can be anticipated. Studies published to date cover areas such as rodent toxicology,29–35 metabotyping (metabolic phenotyping) such as the study of strain, gender, and diurnal variation in mice,14 and disease models.36 A particular advantage of HPLC-MS over GC-based methods is that, for samples such as urine, it requires relatively little sample preparation, other than the removal of particulates, prior to analysis. For samples such as plasma or serum, proteins must be removed if the integrity of the HPLC column is to be protected, but this is easily done by precipitation with 2 or 3 volumes of acetonitrile. In general, gradient reversedphase HPLC separations have been performed in preference to isocratic ones as these allow the separation of the maximum number of components in a given analysis time. The samples can be analyzed using a variety of ionization methods (e.g., electrospray ionization (ESI) or CI), with the current practice being to perform HPLC with ESI using both positive and negative ionization modes (usually in separate analytical runs).7 In Figure 5, typical TICs are shown for the gradient HPLC of mouse urine in both positive and negative ionization mode. As alluded to above for GC-MS, using HPLC in combination with a ToF instrument to obtain accurate mass data from which atomic compositions can be determined greatly enhances the utility of the technique for the identification of unknowns. As well as HPLC-MS on conventional systems using 4.6 or 3.0 mm internal diameter (i.d.) columns packed with 3–5 mm packing materials, alternatives such as narrow-bore c. 2 mm i.d.), microbore (0.5, 1.0 mm i.d.) and capillary HPLC column formats can be employed.7 More recently chromatography on 1.7 mm columns has been introduced in the form of ultra-performance

100

1: TOF MS ES+ TIC 2.55e3

AM_Male_Black1 H030305_CS_ES+_021

4.09 0.64

4.07 6.10

% 2.72 0.92 1.12

4.42

3.83

2.95 3.30 3.10

4.49 5.49

4.56

3.67 3.62

2.63

4.91

6.30

5.22

6.39

2.51 7.44 6.94

8.84

7 0.50

1.00

1.50

2.00

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(a)

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H030306_CS_ES-_021

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8.50

Time

3.40

5.60 5.57

3.99 3.94 4.11

0.70 0.88

4.15

1: TOF MS ESTIC 1.34e3

4.73 5.20

0.98 1.16

%

4.21 4.60 4.38

6.48

5.33

7.43 1.23

3.69

7.15

1.30 2.54

1.50

7.57 7.78

6.97

2.98 3.14

8.41 8.75 8.20

1.83 2.08 0 1.00

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Time (b) Figure 5 Reversed-phase gradient HPLC-MS analysis of a sample urine obtained from a male black mouse using (a) positive and (b) negative ESI modes.

8.50

Metabonomics

0.50

995

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Metabonomics

LC (UPLC)-MS,33,78 offering a substantial improvement in separation performance over conventional HPLC for complex mixture analysis. A typical example of the UPLC-MS of mouse urine is shown in the TIC in Figure 6. The improved resolution and increased number of peaks detected in UPLC-MS compared to conventional HPLC-MS are clear when these results are compared to those shown in Figure 5.

5.43.2.3.3 Capillary electrophoresis-mass spectrometry The other major separation technique currently used to obtain metabolite profiles is capillary zone electrophoresis (CZE or, more commonly, CE), usually coupled with MS. To date the bulk of the applications in this area have been to bacterial samples using ‘targeted’ analyses against a panel of up to c. 1700 metabolite standards.8,9 Like HPLC, CE methods have the advantage of requiring little or no sample preparation for samples such as urine. As an electrophoretic method it provides a different separation mechanism to HPLC or GC, based on charge, and CE-MS enables the HR separation and detection of metabolites. As a microbore separation technique CE requires only very small amounts of sample.

5.43.2.4

Data Processing and Model Building

The various analytical techniques described above all have one thing in common. They generate vast amounts of data, with typical metabolite profiles containing hundreds to thousands of components. While manual examination of such data sets is possible in a few cases, it is extremely time-consuming and cannot be advocated as a strategy. Instead, various multiparametic statistical approaches have been developed, of which the most widely applied is PCA. This is a well-known ‘unsupervised’ approach whereby the data from the various groups in the study are examined for differences without the introduction of bias by the investigator.39 A typical example of a PCA scores and loadings plot for the 1H NMR spectroscopic data obtained from normal and Zucker rats is given in Figure 7. The scores plot reveals the differences between the two experimental groups while the signals responsible for these differences can be found in the loadings plot. Any initial examination of the data generated by a metabolic profiling investigation should be based on such an unsupervised approach in the first instance. Supervised methods, where the investigator uses prior knowledge, such as which animals or subjects were controls and which dosed/diseased, can be used to construct models for the prediction of the class to which a sample belongs. One such supervised approach is ‘projection to latent structures’ by means of partial least squares (PLS), which can be described as the regression extension of PCA.40,41 With PLS, unlike PCA, instead of describing the maximum variation in the data (X), what is attempted is to derive latent variables, which are analogous to principal components, that maximize the co-variation between the measured data (X) and the response variable (Y) regressed against. PLS discriminant analysis (PLS-DA) applies the PLS algorithm to classification of the data using a ‘dummy’ Y matrix that comprises an orthogonal unit vector for each class.42 Other methods of supervised data analysis, such as soft independent modeling of class analogies (SIMCA),43 have also been widely used for determining class in metabonomics studies. The various chemometric methods used for metabonomics have been reviewed.44,45

5.43.2.5

Metabolite Identification

Once detected, the biomarker, or biomarkers, have to be identified and strategies for this type of investigation have been described.46 In the case of NMR-detected metabolites the simplest, and least time-consuming, method for accomplishing this is to use the structural information contained in the spectrum of the biofluid sample itself. Most of the common metabolites found in urine, for example, are now known, and many have characteristic 1H NMR spectra.47 Confirmation of identity can then be performed by overspiking with an authentic standard (this is particularly important where resonances can be affected by small variations in sample pH). However, where it is not possible to assign unknowns directly from the standard one-dimensional (1D) 1H NMR spectrum, then two-dimensional (2D) spectra can be obtained to aid the identification. Total correlation spectroscopy (TOCSY) is widely used for this, as protons within a spin system, especially when there are overlapping multiplets or there is extensive second-order coupling, are readily observed as off-diagonal peaks in the TOCSY spectrum. TOCSY gives both long-range and short-range correlations and is especially useful when coupling constants are small. Clearly all of the other standard NMR-based structure determination techniques can also be used. The use of HPLC-MS for metabolite profiling is still in a state of rapid development and exhaustive databases of metabolites and retention time data are still being produced: similar comments can be made about CE-MS. In the case of GC-MS, extensive databases are currently available, although the coverage of analytes likely to be detected in metabonomics experiments is by no means complete. Typically the retention time and mass spectral data obtained for the unknown marker can be compared with those of known metabolites and, as with NMR spectroscopy, overspiking can be used to confirm identity. For complete unknowns a detailed examination of the mass spectral data and, where accurate

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0.47

0.73

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2.36 2.44

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% 1.81 2.19

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2.70 4.35

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5.69 5.76 5.32

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6.14 5.65 5.44

7.23 7.31 7.02

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Time Figure 6 Reversed-phase UPLC-MS (positive ESI) of urine obtained from a sample of urine obtained from a male black mouse.

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t[2]

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3.22

1.34

3.86 0.94 3.38 2.06 1.5 0.98 2.780.86 2.42 2.18 3.06 5.1 5.06 3.02 2.38 5.02 1.94 3.7 5.34 3.66 1.9 12.7 .72.66 2.58 1.74 2.14 2.3 5.14 3.18 1.62 2.94 5.182.82 4.1 7.22 2.1 7.34 3.5 4.34 2.98 6.94 1.06 7.46 3.14 6.9 2.5 7.38 7.42 7.02 4.02 7.06 6.1 6.82 7.78 7.3 1.86 7.1 6.34 6.14 7.14 6.74 6.18 6.02 7.18 6.86 6.78 6.7 6.42 6.22 6.06 6.62 6.66 6.46 6.54 6.38 7.82 7.86 7.9 7.54 7.58 7.62 8.26 8.14 8.02 7.94 7.66 7.7 7.74 8.66 8.7 8.3 8.34 9.22 9.26 8.74 8.38 8.18 9.3 8.9 8.98 8.78 8.54 8.42 8.22 8.06 7.98 9.34 9.46 9.62 9.94 2.54 0.26 7.26 6.5 8.5 9.02 9.06 9.1 8.82 8.58 9.14 8.86 8.94 8.62 8.46 8.1 9.78 9.82 9.38 9.66 9.18 9.86 9.54 9.42 9.7 9.58 9.9 9.98 9.5 9.74 0.34 0.38 0.3 0.22 0.42 0.46 1.82 4.42 6.58 7.5 0.5 0.54 6.98 3.58 1.02 3.11.78 4.38 4.46 4.3 5.5 6.3 0.58 4.5 0.62 4.22 6.26 2.74 0.66 3.46 5.46 0.7 0.78 0.825.22 1.1 2.62 1.22 0.74 3.94 2.34 1.66 2.26 1.98 1.545.38 1.38 3.78 1.461.141.18 3.3 3.9 4.26 2.86 4.06 2.9 5.42 3.62 2.46 2.02 3.82 3.98 4.18 1.58 1.42 3.54 3.34 5.32.22 1.26

0.9

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−0.20 (b)

5.26

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Figure 7 (a) Scores and (b) loading plots obtained following PCA of the NMR spectra obtained for the plasma of 20-week-old male Wistar-derived (AP, open squares) and Zucker fa/fa (ZU, solid squares) rats.106 (Reproduced by permission of the Royal Society of Chemistry.)

mass data are acquired, the probable atomic composition, may provide clues to the identity of the compound that can be confirmed if an authentic standard can be procured. If derivatization was required in order to obtain the GC-MS data in the first place (i.e., the unknown was not volatile), then the chemical modifications introduced during derivatization can provide valuable clues as to the functional groups present on the unknown, which can further aid identification. If direct identification of the metabolite(s) of interest is not possible, then its isolation in a purified form will probably be required to enable detailed spectroscopic information to be obtained. A variety of methods are available to this.

5.43.2.5.1 Solid phase extraction chromatography (SPEC) nuclear magnetic resonance spectroscopy Probably the least demanding method for the extraction and concentration of analytes from biofluids is solid phase extraction (SPE), where samples are passed through a chromatographic stationary phase contained within a syringe barrel (an SPE ‘cartridge’). This results in the extraction of the analyte (together with many other components) which can then be recovered by elution with an organic solvent such as methanol. Using SPE several milliliters of sample (depending upon the amount of sorbent used) may be rapidly extracted, desalted, and eluted in a few hundred microliters of a volatile solvent. Such a procedure is sufficient for obtaining a concentrate but, if the extraction is followed by stepwise gradient elution with water/solvent mixtures ranging in proportion from 0 through 20, 40, 60, 80, and 100% organic solvent, a low-resolution chromatographic separation is obtained from which a fraction enriched in the target analyte can be garnered. Indeed, it has proved possible to isolate spectroscopically pure metabolites in this way.48–51 Even if SPEC does not provide sufficiently pure analytes by itself, a feel is obtained for the chromatographic properties of the compounds under study that can help to guide further workup. Once the fractions have been obtained, analysis can be performed on them by NMR and MS (if appropriate) or by HPLC-MS in order to locate the fraction(s) containing the metabolite of interest. When present in sufficient purity and quantity the identification is usually straightforward. An illustration of the use of this simple SPEC-based approach to isolation and identification is provided by the case of 5-oxoproline present in rats in urine following the

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administration of acetaminophen at 1% by weight of the diet.49 Based on the chemical shifts and relative intensities of the signals detected in the urine it was possible to deduce the presence of a methylene group, adjacent to a carbonyl function, coupled to two other, strongly coupled highly nonequivalent methylene protons. These protons were also coupled to a single methine proton that had a chemical shift similar to that of an a-CH proton of an amino acid that formed the X of an ABX spin system. Despite these data the compound remained unidentified. However, SPEC,49 followed by 1H NMR to monitor the fractions, and then fast atom bombardment (FAB)-MS obtained the essential information that the unknown had a molecular mass of 129 Da and therefore contained, in addition to two methylene and a methine, at least 1 nitrogen. Based on these findings the unknown was tentatively identified as 5-oxoproline, which was then confirmed by comparison with an authentic standard. Similarly, SPEC was used on samples of urine from rats for the identification of 3-hydroxyphenylpropionic acid (3-HPPA),50 derived from dietary chlorogenic acid via the gut microflora. Once again the 3-HPPA was partially characterized from the urinary 1H NMR spectrum which showed four aromatic multiplets between 7.3 and 6.7 ppm and two triplets at 2.84 and 2.48 ppm integrating to two methylene and four aromatic protons respectively. This was consistent with a 1,3-disubstitution pattern on the aromatic ring, while the chemical shifts suggested the presence of a phenolic OH and the chemical shifts for the methylene groups suggested the presence of a carboxylic acid. SPEC was used to obtain a purified concentrate for 13C NMR, which indicated that the unknown contained nine carbon atoms. These data led to a putative structure which was then confirmed as 3-HPPA by comparison to a standard.

5.43.2.5.2 High-performance liquid chromatography-nuclear magnetic resonance (HPLC-NMR) spectroscopy and high-performance liquid chromatography-mass spectrometry-based methods of identification When low-resolution techniques prove to be unsuited to providing sufficiently pure material, then either preparative chromatographic isolation followed by spectroscopy or online methodologies such as HPLC-NMR and HPLC-MS must be performed.46 Although an efficient use of resources, HPLC-NMR is relatively insensitive and, where low-concentration analytes are encountered, the identification strategy must be formulated accordingly. Currently, a number of different modes of HPLC-NMR can be employed depending upon the problem. Where the sample (or extract) is concentrated, analysis can be performed on-flow, with the required spectra obtained as the sample elutes through the probe. If the analytes are present at lower concentrations, then stopped-flow techniques can be used where the peak corresponding to the analyte (observed using UV, MS, or some other conventional HPLC detector) is held stationary in an NMR-flow probe until a suitable spectrum is obtained. We have found this to be quite efficient in practice and shown that stopped-flow HPLC-NMR can be performed on several peaks in a single run without degrading the separation. As the analyte is stationary in the flow probe quite complex (and time-consuming) NMR experiments can be performed in addition to simple 1D NMR spectroscopy, including 2D experiments such as TOCSY and correlation spectroscopy (COSY). Even if the peak contains several incompletely separated metabolites, techniques such as ‘time slicing’ can be used to obtain good-quality spectra. Here flow is restored for a few seconds to edge the peak a little further through the flow probe and this is then followed by the acquisition of a further spectrum. By taking a number of such spectra across the peak it is often possible to obtain spectra of the individual components. An extremely powerful approach, though somewhat time-consuming, is when the whole separation is subject to examination using the timeslicing method (alternatively, very low flow rates can be used). As well as stopped-flow HPLC-NMR, the peaks of interest can be collected as they elute from the column in sample loops (‘peak parking’ or ‘peak picking’), after which they can then be transferred from the sample collection loops into the NMR flow probe for spectroscopy. It is also possible to collect peaks via an online SPE where, if required, several runs can be performed, collecting and combining several samples of the unknown to provide a concentrate. The analyte may then be recovered using a fully deuterated solvent and directed into the NMR flow probe for spectroscopy. An example where HPLC-NMR has been used in metabonomics studies concerns the identification of certain aromatic metabolites present in rat urine that separated two separate populations of the same strain.51 Reversed-phase gradient HPLC combined with 1H NMR spectroscopy allowed the identification of three of these peaks as hippuric acid, 3-HPPA, and 3-hydroxycinnamic acid. This enabled the conclusion to be reached that the observed differences between the two groups of rats resulted from differences in the respective populations of gut flora and this affected the metabolism of dietary aromatics such as chlorogenic acid. Similarly, HPLC-MS-based approaches can be very useful in the identification of markers and, for example, xanthurenic and kynurenic acid (both metabolites of tryptophan) were detected as being correlated with nephrotoxicity in the rat.32 Following detection by HPLC-Orthogonal acceleration (oa)ToF-MS a tentative identification was made based on the use of accurate mass and atomic composition data which was confirmed by the use of authentic standards.

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5.43.3

Applications of Metabonomics

The applications of metabonomics have covered a very wide range of investigations, from basic studies in biology through more specific applications such as the investigation of disease models, toxicology, clinical investigations, and epidemiology. Typical examples of these studies are described below.

5.43.3.1

Metabotyping

The ‘metabotype’15 is a term coined to describe the metabolic phenotype presented by samples obtained from a particular population. An example of this might be the differences observed between the metabonomic profiles of, e.g., urine samples of different stains of rodents, or resulting from diurnal variation, diet, age, and gut microflora. Such differences are often by no means trivial, and if not properly understood can represent a confounding factor in metabonomic (or indeed other ‘omic’) investigations as changes may be seen in the profile that have little to do with the experimental treatment and everything to do with normal biology. Thus, using either 1H NMR spectroscopy or HPLC-MS, it is relatively easy to distinguish between male and female rats or indeed mice of the same strain,13–15,52 based simply on urine. Similarly, in the case of rodents, the age of an animal significantly modifies the urinary profile,53,54 and this can be true over even relatively short periods. No doubt similar effects will be seen with other species. In Figure 8 the effect of age on the profile of male Wistar-derived rats for a 16-week period, from 4 to 20 weeks of age, is shown.54 The time of day when samples are collected is also important and if 24-h collections of urine are not obtained then the importance of carefully controlling sampling time to ensure that the same period is used for collection can not be overemphasized, as sample composition can change dramatically.13–15 Similarly, diet can affect the urinary metabolite profile, as can the composition of the gut microflora, which can change during the study.50,55–57 Thus every effort should be made to use a single batch of diet for the duration of a study, something which may not be easy if dosing over several years is contemplated. Clearly, before undertaking any series of metabonomic experiments it is important to try to establish the baseline conditions for the system under study and obtain an idea of the inherent biological variability in the system. However, as these studies show, metabonomics can provide useful and informative means of investigating the influence of normal physiological processes on the composition of biofluids.58

5.43.3.2

Metabonomics and the Study of Toxicity

A major area for the exploitation of metabonomics has been the study of toxicity and this area has been the subject of many publications and reviews (see 59–62 for some recent examples). An example of this type of application is provided by the work of the consortium for metabonomic toxicology (COMET), where a number of pharmaceutical companies and Imperial College have combined to look at a wide range of toxins in rodents.63 This work was performed with the

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aim of building metabolic databases of responses to well-characterized ‘typical’ toxins to which the effects of new chemical entities could be compared to better predict their organ-specific toxicity and mechanism of action. In rodent studies, largely using 1H-NMR-based sample analysis of urine or plasma, metabonomics has been demonstrated to be capable of classifying organ-specific toxicity in animals in tissues such as kidney,64–80 liver23,24,81–92 (including peroxisome proliferation93,94), testes,95–97 phospholipidosis,30,98,99 and vasculitis.100,101 Of course, while it is possible to detect toxicity that is directed specifically to a particular organ, or indeed to a specific region within it, it is often the case that more than one organ can be affected,92 and that the focus of the toxicity can change with time, so results from such studies need careful interpretation. In addition, as toxicity progresses, feeding may be reduced and this will also affect the metabolite profile.102 While the bulk of the metabonomics studies in toxicology have been conducted using NMR spectroscopic methods, more recently HPLC-MS-based analysis, either alone or together with NMR spectroscopy, has begun to be performed.29–38 The combination of NMR and HPLC-MS techniques is particularly powerful as the different sensitivities and specificities of the two spectrometers enable a more complete metabolic profile to be generated. Indeed, a series of studies on a number of model nephrotoxins has shown the complementary nature of NMR and HPLC-MS.32–34 In the first of these studies the effect of the administration of a single dose of mercuric chloride to male Wistar-derived rats on the urinary metabolite profiles of a range of endogenous metabolites was studied.32 Urine was collected for the 9 days of the study and both HPLC-oa-TOF/MS and 1H NMR spectroscopy revealed marked changes in the pattern of endogenous metabolites as a result of the HgCl2-induced nephrotoxicity. The greatest disturbance in the urinary metabolite profiles was detected at 3 days postdose, after which the metabolite profile gradually returned to a more normal composition. The urinary markers of toxicity detected using 1H NMR spectroscopy included increases in lactate, alanine, acetate, succinate, trimethylamine, and glucose, together with reductions in the amounts of citrate and a-ketoglutarate. In contrast, the HPLC-MS-detected markers (in positive ESI) included decreased kynurenic acid, xanthurenic acid, pantothenic acid, and 7-methylguanine concentrations, while an ion at m/z 188, possibly 3-amino-2-naphthoic acid, was observed to increase. In addition, unidentified ions at m/z 297 and 267 also decreased after dosing. Negative ESI revealed a number of sulfated compounds such as phenol sulfate and benzene diol sulfate, both of which appeared to decrease in concentration in response to dosing, together with an unidentified glucuronide (m/z 326). One conclusion from this study was that both NMR and HPLC-MS (positive and negative ESI) gave similar time courses for the onset of toxicity and recovery. However, the markers seen were quite different for each technique, clearly suggesting a role for both types of analysis. Similar conclusions about the complementary nature of NMR and HPLC-MS were confirmed in an investigation of the nephrotoxicity of the immunosuppressant cyclosporin A.33 In this instance HPLC-MS analysis was complicated by the presence of ions derived from cyclosporin, its metabolites, and the dosing vehicle which had to be eliminated from the data prior to analysis by PCA. There was, however, once again, excellent concordance between the observed time course of toxicity whichever technique was used. However, as with the mercuric chloride example given above, the markers were different depending upon whether NMR or HPLC-MS was examined. A similar conclusion was reached when the effects of gentamicin on urinary metabolite profiles were examined by HPLC-MS and NMR spectroscopy.35 The complementary data provided by the combination of 1H NMR and HPLC-MS suggest that, wherever possible, both techniques should be used to analyze samples. Similarly, a future role for GC-MS metabonomic studies of toxicity seems highly likely.

5.43.3.3

The Investigation of Disease Models

As well as metabotyping and providing organ-specific biomarkers of toxicity, metabonomics has great potential to aid in the characterization of animal disease models. This characterization is important as the extent to which the model actually mirrors human disease is, self-evidently, critical to the success of the drug discovery process. In addition, if the animal model does have some concordance with human disease, a detailed metabolic analysis may result in the detection of novel biomarkers that can be extended to the clinic to monitor efficacy. An example of such studies examining disease models is provided by investigations of the urinary and plasma103–106 metabolite profiles of Zucker (fa/fa) obese rats, which are used as a model of type 2 diabetes. Some of this work involved the collection of samples of urine from male rats at fortnightly intervals from weaning (4 weeks of age) until the animals were 20 weeks of age. These samples were compared with those of a control group of Wistar-derived animals. The urinary NMR and HPLC-MS profiles showed obvious age-related differences between conventional 20-week-old Wistar-derived and Zucker rats. In the 1H NMR spectra these differences were evident as, e.g., decreased taurine and increased urinary glucose in the urine of Zucker compared to Wistar-derived rats. These differences are illustrated in Figure 9 for 20-week-old animls. While the appearance of glucose in the urine of diabetic rats might easily have been

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predicted, the meaning of the reduction in taurine excretion is not at all clear. The HPLC-MS data also revealed a similar progression with time. A mean trajectory for these animals, comparing the Zucker (fa/fa) obese and the control group, is shown in Figure 10. The two groups begin to diverge at c. 10 weeks of age, and by 20 weeks they occupy quite different areas of metabolic space.

5.43.3.4

Metabonomic Studies in Humans 1

The use of H NMR spectroscopy for obtaining metabolic profiles for the diagnosis of metabolic diseases and inborn errors of metabolism is a long-standing application of the technique, with examples including such diseases as ‘maple syrup urine’ and 5-oxoprolinuria.107–111 1H NMR spectroscopy has also been used to monitor toxicity in the case of drugs such as the anticancer agent ifosphamide. In this study the maximum kidney toxicity effects of ifosphamide were observed by the fourth treatment cycle.112 1H NMR has also been used to examine the effects of phenol poisoning.113 However, studies undertaken to aid clinical development in human subjects, either to monitor treatment or discover new biomarkers, pose much greater difficulties compared to similar investigations in inbred strains of rodents. In particular, humans are much more diverse, and live in much less well-controlled environments than the laboratory animals used for toxicological studies. Thus the animals in metabonomic studies are housed in uniform, and carefully controlled, environmental conditions, and are similar genetically, the same age, usually the same gender, in the same weight range, and eat a controlled diet. In comparison humans, especially patients, in addition to not being housed in uniform environmental conditions are, by their very nature, often ill and subject to considerable variability in virtually all of the things that are carefully controlled in animal studies, and may also be on a range of medications. Even when such factors are controlled to some extent by performing the study on a small number of volunteers of the same gender in a clinical trials unit, there is large interindividual variability.114 The effects of diet can be very significant and we have found unexpected dietary components (ethanol and ethyl glucoside) associated with the probable use of rice wine in cooking (or saki consumption) appearing in the urine.115 Similarly, we have detected differences in betaine concentrations in urine between Swedish and British subjects that were sufficient to separate the two groups. This difference was most likely due to a higher consumption of fish by the Swedish population rather than any underlying genetic difference.116 In the same study we noted dramatic effects on the urinary metabolite profile as a result of dietary changes by a female subject. Two samples were supplied by this individual, some months apart, the first of which was obtained when the subject was following a high-meat Atkins-style diet. This diet resulted in the excretion of large quantities of the amino acid taurine (often seen as a result of hepatotoxicity in rodents). However, when the second sample was analyzed the subject had reverted to a ‘normal’ pattern of food consumption, with the consequent absence of large amounts of taurine (and instead evidence of ethanol consumption).

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Such variables in human studies can result in a large amount of ‘metabolic noise,’ and these factors need to be recognized and, in clinical trials, careful collection of data on diet and medication is needed in order to be able to make confident interpretations of the metabolic data. As well as monitoring toxic effects, clinical applications of metabonomics for diagnosis have been illustrated by a number of studies. In the case of cardiovascular disease, 1H NMR spectroscopy of blood plasma was able to assess accurately the level of coronary atherosclerosis,117 which is currently only possible with invasive techniques such as angiography. This represents an important potential application as coronary heart disease (CHD) is a major disease in developed nations, where as many as 1 in 3 individuals may develop the condition before the age of 70. In this study serum or plasma samples from subjects with well-characterized disease were analyzed by 1H NMR and the data were then subjected to pattern recognition techniques (in particular the orthogonal signal correction (OSC) data filter). Simple visual comparison of the spectra of human sera from patients with severe triple-vessel CHD patients and normal coronary artery (NCA) subjects showed few systematic differences. However, PCA showed that, while there was overlap between the CHD and normal subjects, some clustering was evident, due mainly to lipids, particularly verylow-density lipoprotein, low-density lipoprotein, and choline. The class separation was then optimized using OSC to improve subsequent multivariate pattern recognition. This enabled the triple-vessel disease and NCA groups to be well separated in the scores plot of principal component 1 (PC1) and PC2. A PLS-DA model was then constructed using data from c. 80% of the samples, selected at random, to predict the class membership of the remaining 20% of samples. The resulting model was then used to predict the class membership of the samples not included in the training set, successfully predicting CHD with a sensitivity of 92% and a specificity of 93%, based on a 99% confidence limit for class membership. Indeed, 1H NMR-based metabonomic analysis could also distinguish the severity of the CHD, enabling those with single-, double-, and triple-vessel disease to be distinguished, while the conventional risk factors (including age, blood pressure, low-density lipoprotein, and high-density lipoprotein cholesterol, total cholesterol, total triglyceride, fibrinogen, plasminogen activator inhibitor 1 (PAI-1), white blood cell count, creatinine, or history of cigarette smoking) were unable to do this. 1H-NMR-based metabonomics was therefore substantially better at distinguishing the severity of CHD using a single blood sample than any of the conventional risk factors yet identified.

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Other areas where metabonomics has been applied to human disease include the use of 1H NMR spectroscopy for the analysis of plasma from subjects with epithelial ovarian cancer, where it was possible to use 1H NMR-based analysis of serum and multivariate statistical analysis to detect a characteristic metabolic fingerprint of the disease.118 As well as biofluids, the technique of HRMAS has been used in the investigation of tumor biochemistry, for example with studies on prostate cancer-derived samples20 (and more recently a larger study with c. 200 subjects also investigated this tumor type119). Breast tumor extracts have also been studied.120 These studies demonstrated that it was not only possible to show large metabolic differences between malignant and healthy tissues, but also between different grades of tumor (e.g., more and less aggressive tumors) and it seems clear that metabonomics will have a significant part to play in the diagnosis and mechanistic understanding of tumor biology in the future.

5.43.4

Outlook

Metabonomics, via the multicomponent analysis of biological fluids and tissues, provides a well-established methodology for addressing the differences in global metabolite profiles that characterize different physiological states. As such, metabonomics has much to offer drug discovery as part of a strategy for understanding the test system, investigating human disease, detecting and identifying novel biomarkers, and monitoring therapy to evaluate efficacy or toxicity. In the future, as part of efforts to speed drug discovery and development, the drive will be to increase the throughput of samples, both via improvements in the analytical methodology, and via advances in chemometric techniques to tease out the metabolic differences that characterize samples. It is also possible to predict that there will be a much greater application of metabonomics to the study of human disease. In addition there will undoubtedly be increased emphasis on combining metabonomics data with that obtained from the other ‘omics’ technologies (genomics/transcriptomics and proteomics) in order to try to obtain a deeper understanding of the systems under investigation. Combining such different data sets in a meaningful way requires new statistical tools and methods for data visualization.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.

Nicholson, J. K.; Lindon, J. C.; Holmes, E. Xenobiotica 1999, 29, 1181–1189. Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Disc. 2002, 1, 253–258. Nicholson, K.; Wilson, I. D. Prog. NMR Spectrosc. 1989, 21, 449–501. Lindon, J. C.; Holmes, E.; Nicholson, J. K. Prog. NMR Spectrosc. 2004, 45, 109–143. Lindon, J. C.; Nicholson, J. K.; Holmes, E. NMR Spectroscopy of Biofluids. In Metabonomics in Toxicity Assessment; Robertson, D. G., Lindon, J. C., Nicholson, J. K., Holmes, E., Eds.; CRC Press: Boca Raton, 2005, pp 105–172. Wilson, I. D.; Plumb, R.; Granger, J.; Major, H.; Williams, R.; Lenz, E. M. J. Chrom. B. 2005, 817, 67–76. Fiehn, O.; Kopka, J.; Trethewey, R. N.; Willmitzer, L. Anal. Chem. 2000, 72, 3573–3580. Soga, T.; Ohashi, Y.; Ueno, Y.; Naraoka, H.; Tomita, M.; Nishioka, T. J. Proteome Res. 2003, 2, 488–494. Jia, L.; Terabe, S. Capillary Electrophoresis and Its Application in Metabolome Analysis. In Metabolome Analysis; Vaidyanathan, S., Harrigan, G. G., Goodacre, R., Eds.; Springer: New York, 2005, pp 83–101. Pham-Tuan, H.; Kaskavelis, L.; Daykin, C. A.; Janssen, H.-G. J. Chrom. B 2003, 789, 283–301. Meyer, E. C.; Gamache, P. H.; Acworth, I. N. The Application of Electrochemistry to Metabolic Profiling. In Metabolome Analysis; Vaidyanathan, S., Harrigan, G. G., Goodacre, R., Eds.; Springer: New York, 2005, pp 119–135. Harrigan, G. G.; Laplante, R. H.; Cosma, G. N.; Cockerell, G.; Goodacre, R.; Maddox, J. F.; Luyendyk, J. P.; Ganey, P. E.; Roth, R. A. Tox. Lett. 2004, 146, 197–205. Bollard, M. E.; Holmes, E.; Lindon, J. C.; Mitchell, S. C.; Branstetter, D.; Zhang, W.; Nicholson, J. K. Anal. Biochem. 2001, 295, 194–202. Plumb, R. S.; Granger, J.; Stumpf, C.; Wilson, I. D.; Evans, J. A.; Lenz, E. M. Analyst 2003, 128, 819–823. Gavaghan, C. L.; Holmes, E.; Lenz, E.; Wilson, I. D.; Nicholson, J. K. FEBS Lett. 2000, 484, 169–174. Gavaghan, C. L.; Wilson, I. D.; Nicholson, J. K. FEBS Lett. 2002, 530, 191–196. Lenz, E. M.; Bright, J.; Knight, R.; Wilson, I. D.; Major, H. J. Pharm. Biomed. Anal. 2004, 36, 841–849. Moka, D.; Vorreuther, R.; Schicha, H.; Spraul, M.; Humpfer, E.; Lipinski, M.; Foxhall, P. J. D.; Nicholson, J. K.; Lindon, J. C. J. Pharm. Biomed. Anal. 1998, 17, 125–132. Cheng, L. L.; Lean, C. L.; Bigdanova, A.; Wright, J. R.; Ackerman, J. L.; Brady, T. J.; Garrido, L. Magn. Reson. Med. 1996, 36, 653–658. Tomlins, A.; Foxall, P. J. D.; Lindon, J. C.; Lynch, M. J.; Spral, M.; Everett, J. R.; Nicholson, J. K. Anal. Commun. 1998, 35, 113–115. Waters, N. J.; Farrant, R. D.; Haselden, J. N.; Connor, S. C.; Connelly, J.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Anal. Biochem. 2000, 282, 16–23. Griffin, J. L.; Nicholson, J. K.; Holmes, E.; Lindon, J. C. 1H Magic Angle Spinning NMR Spectroscopy of Tissues. In Metabonomics in Toxicity Assessment; Robertson, D. G., Lindon, J. C., Nicholson, J. K., Holmes, E., Eds.; CRC Press: Boca Raton, 2005, pp 173–194. Coen, M.; Lenz, E. M.; Nicholson, J. K.; Wilson, I. D.; Pognan, F.; Lindon, C. J. Chem. Res. Toxicol. 2003, 16, 295–303. Coen, M.; Nicholson, J. K.; Lindon, J. C.; Lenz, E. M.; Wilson, I. D.; Ruepp, S. U.; Pognan, F. J. Pharm. Biomed. Anal. 2004, 35, 93–105. Ruepp, S. U.; Tonge, R.; Shaw, J.; Wallis, N.; Pognan, F. Toxicol. Sci. 2002, 65, 135–150. Craig, A.; Sidaway, J.; Holmes, E.; Orton, T.; Jackson, D.; Rowlinson, R.; Nickson, J.; Tonge, R.; Wilson, I.; Nicholson, J. J. Proteome Res., in press. Bollard, M. E.; Murray, A. J.; Clarke, K.; Nicholson, J. K.; Griffin, J. L. FEBS Lett. 2003, 533, 73–78. Halket, J. M.; Waterman, D.; Przyborowska, A. M.; Patel, R. K. P.; Fraser, P. D.; Bramley, P. M. J. Exp. Botany 2005, 56, 219–243.

Metabonomics

29. Plumb, R. S.; Stumpf, C. L.; Gorenstein, M. V.; Castro-Perez, J. M.; Dear, G. J.; Anthony, M.; Sweatman, B. C.; Connor, S. C.; Haselden, J. N. Rapid Commun. Mass Spectrom. 2002, 16, 1991–1996. 30. Idborg-Bjorkman, H.; Edlund, P.-O.; Kvalheim, O. M.; Schuppe-Koistinen, I.; Jacobsson, S. P. Anal. Chem. 2003, 75, 4784–4792. 31. Lafaye, A.; Junot, C.; Ramounet-le Gall, B.; Fritsch, P.; Tabet, J.-C.; Ezan, E. Rapid Commun. Mass Spectrom. 2003, 17, 2541–2549. 32. Lenz, E. M.; Bright, J.; Knight, R.; Wilson, I. D.; Major, H. Analyst 2004, 129, 535–541. 33. Lenz, E. M.; Bright, J.; Knight, R.; Wilson, I. D.; Major, H. J. Pharm. Biomed. Anal. 2004, 35, 599–608. 34. Lenz, E. M.; Bright, J.; Knight, R.; Westwood, F. R.; Davies, D.; Major, H.; Wilson, I. D. Biomarkers 2005, 10, 173–187. 35. Williams, R. E.; Major, H.; Lock, E. A.; Lenz, E. M.; Wilson, I. D. Toxicology 2005, 207, 179–190. 36. Williams, R. E.; Lenz, E. M.; Evans, J. A.; Wilson, I. D.; Granger, J. H.; Plumb, R.; Stumpf, C. J. Pharm. Biomed. Anal. 2005, 38, 465–471. 37. Wilson, I. D.; Nicholson, J. K.; Castro-Perez, J.; Granger, J. H.; Johnson, K.; Smith, B. W.; Plumb, R. S. J. Proteome Res. 2005, 4, 591–598. 38. Plumb, R. S.; Granger, J. H.; Stumpf, C. L.; Johnson, K.; Smith, B. W.; Gaulitz, S.; Wilson, I. D.; Castro-Perez, J. Analyst 2005, 130, 844–849. 39. Beebe, K. R.; Pell, R. J.; Seasholt, M. B. In Chemometrics; A Practical Guide; Wiley: New York, 1998, pp 81–110. 40. Jackson, J. E. A User’s Guide to Principal Components; Wiley: New York, 1991. 41. Eriksson, L.; Johansson, E.; Kettaneh-Wold, H.; Wold, S. Introduction to Multi and Megavariate Analysis using Projection Methods (PCA & PLS); Umetrics: Umea, 1999. 42. Jellum, E.; Bjornson, J.; Nesbakken, R.; Johansson, E.; Wold, S. J. Chromatogr. 1981, 217, 231–237. 43. Wold, S.; Albano, C.; Dunn, W. J.; Edlund, U. I.; Esbensen, K.; Gelaadi, P.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjostrom, M. Multivariate Data Analysis in Chemistry. In Chemometrics: Mathematics and Statistics in Chemistry; Kowalski, B. R., Ed.; Reidel: Dordrecht, 1984, pp 17–95. 44. Lindon, J. C.; Holmes, E.; Nicholson, J. K. Progr. NMR Spectrosc. 2001, 39, 1–40. 45. Eriksson, L.; Johansson, E.; Antti, H.; Holmes, E. Multi and Megavariate Data Analysis: Finding and Using Regularities in Metabonomics Data. In Metabonomics in Toxicity Assessment; Robertson, D. G., Lindon, J. C., Nicholson, J. K., Holmes, E., Eds.; CRC Press: Boca Raton, 2005, pp 263–336. 46. Shockor, J.; Wilson, I. D. Strategies and Techniques for the Identification of Endogenous and Xenobiotic Metabolites Detected in Metabonomic Studies. In Metabonomics in Toxicity Assessment; Robertson, D. G., Lindon, J. C., Nicholson, J. K., Holmes, E., Eds.; CRC Press: Boca Raton, 2005, pp 225–262. 47. Wilson, I. D.; Nicholson, J. K. Biological Fluids. In Encyclopeadia of Analalytical Science; Townshend, A., Worsfold, J., Haswell, S. J., Macrae, R., Werner, H. W., Wilson, I. D., Eds.; Academic Press: Umea, 1995, pp 3518–3531. 48. Wilson, I. D.; Nicholson, J. K. Anal. Chem. 1987, 59, 2830–2832. 49. Ghauri, F. Y. K.; McLean, A. E. M.; Beales, D.; Wilson, I. D.; Nicholson, J. K. Biochem. Pharmacol. 1993, 46, 953–957. 50. Phipps, A. N.; Wright, B.; Stewart, J.; Wilson, I. D. Pharm. Sci. 1997, 3, 143–146. 51. Gavaghan, C. L.; Nicholson, J. K.; Connor, S. C.; Wilson, I. D.; Wright, B.; Holmes, E. Anal. Biochem. 2001, 291, 2245–2252. 52. Stanley, E. G.; Bailey, N. J. C.; Bollard, M. E.; Haselden, J. N.; Waterfield, C. J.; Holmes, E. J.; Nicholson, J. K. Anal. Biochem. 2005, 343, 195–202. 53. Bell, J. D.; Sadler, P. J.; Morris, V. C.; Levander, O. A. Magn. Res. Med. 1991, 17, 414–422. 54. Williams, R. E.; Lenz, E. M.; Lowden, J.; Rantalainen, M.; Wilson, I. D. Mol. Biosyst. 2005, 1, 166–175. 55. Phipps, A. N.; Wright, B.; Stewart, J.; Wilson, I. D. Xenobiotica 1998, 28, 527–537. 56. Williams, R. E.; Eyton-Jones, H. W.; Farnworth, M. J.; Gallagher, R.; Provan, W. M. Xenobiotica 2002, 32, 783–794. 57. Nicholls, A. W.; Mortishire-Smith, R. J.; Nicholson, J. K. Chem. Res Toxicol. 2003, 16, 1395–1404. 58. Bollard, M. E.; Stanley, E. G.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. NMR Biomed. 2004, 9, 1–31. 59. Robertson, D. G.; Reily, M. D.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. In Comprehensive Toxicology; Vanden Heuvel, J. P., Perdew, G. J., Mattes, W. B., Greenlee, W. F., Eds.; Elsevier Science: Amsterdam, 2002; Vol. 14, pp 583–610. 60. Lindon, J. C.; Holmes, E.; Nicholson, J. K. Progr. Nuclear Magn. Res. Spectrosc. 2004, 45, 109–143. 61. Robertson, D. G. Toxicol. Sci. 2005, 85, 809–822. 62. Robertson, D. G.; Lindon, J. C.; Nicholson, J. K.; Holmes, E.; Metabonomics in Toxicity Assessment; CRC Press: Boca Raton, 2005. 63. Lindon, J. C.; Nicholson, J. K.; Holmes, E.; Antti, H.; Bollard, M. E.; Keun, H.; Beckonert, O.; Ebbels, T. M.; Reily, M. D.; Robertson, D. et al. Toxicol. Appl. Pharmacol. 2003, 187, 137–146. 64. Murgatroyd, L. B.; Pickford, R. J.; Smith, I. K.; Wilson, I. D.; Middleton, B. J. Hum. Exp. Toxicol. 1992, 11, 35–41. 65. Anthony, M.; Gartland, K. P. R.; Beddell, C. R.; Lindon, J. C.; Nicholson, J. K. Arch. Toxicol. 1992, 66, 525–537. 66. Murgatroyd, L. B.; Pickford, R. J.; Smith, I. K.; Wilson, I. D. Biochem. Pharmacol. 1991, 41, 2045–2049. 67. Anthony, M. L.; Gartland, K. P. R.; Lindon, J. C.; Beddell, C. R. J. C.; Nicholson, J. K. Arch. Toxicol. 1994, 68, 43–53. 68. Anthony, M. L.; Rose, V. S.; Nicholson, J. K.; Lindon, J. C. J. Pharm. Biomed. Anal. 1995, 13, 205–211. 69. Anthony, M. L.; Sweatman, B. C.; Beddell, C. R.; Lindon, J. C.; Nicholson, J. K. Mol. Pharmacol. 1994, 46, 199–211. 70. Foxall, P. J. D.; Lenz, E. M.; Lindon, J. C.; Neild, G. H.; Wilson, I. D.; Nicholson, J. K. Ther. Drug Monit. 1996, 18, 498–505. 71. Gartland, K. P. R.; Beddell, C. R.; Lindon, J. C.; Nicholson, J. K. J. Pharm. Biomed. Anal. 1990, 8, 963–968. 72. Gartland, K. P. R.; Bonner, F.; Nicholson, J. K. Mol. Pharmacol. 1989, 35, 242–251. 73. Gartland, K. P. R.; Bonner, F. W.; Timbrell, J. A.; Nicholson, J. K. Arch. Toxicol. 1989, 63, 97–106. 74. Holmes, E.; Bonner, F. W.; Nicholson, J. K. Arch. Toxicol. 1995, 70, 89–95. 75. Holmes, E.; Bonner, F.; Sweatman, B. C.; Lindon, J. C.; Beddell, C. R.; Rahr, E.; Nicholson, J. C. Mol. Pharmacol. 1992, 42, 922–930. 76. Holmes, E.; Nicholson, J. K.; Nicholls, A. W.; Lindon, J. C.; Connor, S. C.; Polley, S.; Connelly, J. J. Chemomet. Intel. Lab. Systems 1998, 44, 245–255. 77. Nicholson, J. K.; Timbrell, J. A.; Sadler, P. J. Mol. Pharmacol. 1985, 27, 644–651. 78. Halligan, S.; Byard, S. J.; Spencer, A. J.; Gray, T. J.; Harpur, E. S.; Bonner, F. W. Toxicol. Lett. 1995, 81, 15–21. 79. Robertson, D. G.; Reily, M. D.; Sigler, R. E.; Wells, D. F.; Paterson, D. A.; Braden, T. K. Toxicol. Sci. 2000, 57, 326–337. 80. Sanins, S.; Nicholson, J. K.; Elcombe, C.; Timbrell, J. A. Toxicol. Sci. 1990, 64, 407–411. 81. Sanins, S.; Nicholson, J. K.; Elcombe, C.; Timbrell, J. A. Arch. Toxicol. 1992, 66, 489–495. 82. Beckwith-Hall, B. M.; Nicholson, J. K.; Nichols, A. W.; Foxall, P. J.; Lindon, J. C.; Connor, S. C.; Abdi, M.; Connelly, J.; Holmes, E. Chem. Res. Toxicol. 1998, 11, 260–272. 83. Sequira, S.; So, P. W.; Everett, J. R.; Elcombe, C. R.; Kelvin, A. S.; Nicholson, J. K. J. Pharm. Biomed. Anal. 1990, 8, 945–952. 84. Waters, N. J.; Holmes, E.; Williams, A.; Waterfield, C. J.; Farrant, R. D.; Nicholson, J. K. Chem. Res. Toxicol. 2001, 14, 1401–1412. 85. Waters, N. J.; Holmes, E.; Waterfield, C. J.; Farrant, R. D.; Nicholson, J. K. Biochem. Pharmacol. 2002, 64, 67–77.

1005

1006

Metabonomics

86. Clayton, T. A.; Lindon, J. C.; Everett, J. R.; Charuel, C.; Hanton, G.; Le Net, J. L.; Provost, J. P.; Nicholson, J. K. Arch. Toxicol. 2003, 77, 208–217. 87. Clayton, T. A.; Lindon, J. C.; Everett, J. R.; Charuel, C.; Hanton, G.; Le Net, J. L.; Provost, J. P.; Nicholson, J. K. Arch. Toxicol. 2004, 78, 86–96. 88. Nicholls, A. W.; Holmes, E.; Lindon, J. C.; Shockor, J. P.; Farrant, R. D.; Haseleden, J. N.; Damment, S. J.; Waterfield, C. J.; Nicholson, J. K. Chem. Res. Toxicol. 2001, 14, 975–987. 89. Kleno, T. G.; Kiehr, B.; Baunsgaard, D.; Sidelmann, U. G. Biomarkers 2004, 9, 116–138. 90. Mortishire-Smith, R. J.; Skiles, G. L.; Lawrence, J. W.; Spence, S.; Nicholls, A. W.; Johnson, B. A.; Nicholson, J. K. Chem. Res. Toxicol. 2004, 17, 165–173. 91. Waters, N. J.; Waterfield, C. J.; Farrant, R. D.; Holmes, E.; Nicholson, J. K. Chem. Res. Toxicol. 2005, 18, 639–654. 92. Ringeissen, S.; Connopr, S. C.; Brown, H. R.; Swetaman, B. C.; Hodson, M. P.; Kenny, S. P.; Haworth, R.; McGill, P.; Price, M. A.; Aylott, M. C. M. R. et al. Biomarkers 2003, 8, 240–271. 93. Connor, S. C.; Hodson, M. P.; Ringeissen, S.; Sweatman, B. C.; McGill, P. G.; Waterfield, C. J.; Haselden, J. N. Biomarkers 2004, 9, 364–385. 94. Nicholson, J. K.; Higham, D. P.; Timbrell, J. A.; Sadler, P. J. Mol. Pathol. 1989, 36, 398–404. 95. Gray, J.; Nicholson, J. K.; Creasy, D. M.; Nicholson, J. K. Arch. Toxicol. 1990, 64, 443–450. 96. Lenz, E. M.; Wilson, I. D.; Timbrell, J. A.; Nicholson, J. K. Biomarkers 2000, 5, 424–435. 97. Nicholls, A. W.; Nicholson, J. K.; Haselden, J. N.; Waterfield, J. C. Biomarkers 2000, 5, 410–423. 98. Delaney, J.; Neville, W. A.; Swain, A.; Miles, A.; Leonard, M. S.; Waterfield, C. J. Biomarkers 2004, 9, 271–290. 99. Espina, J. R.; Shockcor, J. P.; Herron, W. J.; Car, B. D.; Contel, N. R.; Ciaccio, P. J.; Lindon, J. C.; Nicholson, J. K. Magn. Resn. Chem. 2001, 39, 559–565. 100. Roberstson, D. G.; Reily, D.; Albassam, M.; Dethloff, L. A. Cardiovasc. Toxicol. 2001, 1, 7–19. 101. Slim, R. M.; Roberstson, D. G.; Reily, D.; Albassam, M.; Reily, M. D.; Robosky, L.; Dethloff, L. A. Toxicol. Appl. Pharmacol. 2002, 183, 108–115. 102. Connor, S. C.; Wu, W.; Sweatman, B.; Manini, J.; Haselden, J. N.; Crowther, D. J.; Waterfield, C. J. Biomarkers 2004, 9, 156–179. 103. Williams, R. E.; Lenz, E. M.; Evans, J. A. E.; Wilson, I. D.; Granger, J. H.; Plumb, R.; Stumpf, C. J. Pharm. Biomed. Anal. 2005, 38, 465–471. 104. Granger, J.; Plumb, R.; Castro-Perez, J.; Wilson, I. D. Chromatographia 2005, 61, 375–380. 105. Williams, R.; Lenz, E. M.; Wilson, A. J.; Granger, J.; Wilson, I. D.; Major, H.; Plumb, R. Mol. Biosyst. 2006, 2, 174–183. 106. Williams, R. E.; Lenz, E. M.; Rantalainen, M.; Wilson, I. D. Mol. Biosyst. 2006, 2, 193–202. 107. Iles, R. A.; Chalmers, R. A. Clin. Sci. 1988, 74, 1–10. 108. Iles, R. A. Clinical Biofluids. In Encyclopedia of Analalytical Science; Townshend, A., Worsfold, J., Haswell, S. J., Macrae, R., Werner, H. W., Eds.; Academic Press: London, 1995, pp 3532–3544. 109. Ghauri, F. Y. K.; Parkes, H. G.; Nicholson, J. K.; Wilson, I. D. Clin. Chem. 1993, 30, 1341. 110. Holmes, E.; Foxall, P. J. D.; Nicholson, J. K.; Neild, G. H.; Brown, S. M.; Beddell, C. R.; Sweatman, B. C.; Rahr, E.; Lindon, J. C.; Spraul, M.; Neidig, P. Anal. Biochem. 1994, 22, 284–296. 111. Lindon, J. C.; Holmes, E.; Bollard, M. E.; Stanley, E. G.; Nicholson, J. K. Biomarkers 2004, 9, 1–31. 112. Foxall, P. D.; Singer, J. M.; Hartley, J. M.; Nield, G. H.; Lapsley, M.; Nicholson, J. K.; Souhami, R. L. Clin. Cancer Res. 1997, 3, 1507–1518. 113. Foxall, P. J. D.; Bending, M. R.; Gartland, K. P. R.; Nicholson, J. K. Hum. Toxicol. 1989, 8, 491–498. 114. Lenz, E. M.; Bright, J.; Wilson, I. D.; Morgan, S. R.; Nash, A. F. P. J. Pharm. Biomed. Anal. 2003, 33, 1103–1115. 115. Teague, C.; Holmes, E.; Maibaum, E.; Nicholson, J.; Tanf, H.; Chan, Q.; Elliot, P.; Wilson, I. D. Analyst 2004, 129, 259–264. 116. Lenz, E. M.; Bright, J.; Wilson, I. D.; Hughes, A.; Morrisson, J.; Lindberg, H.; Locton, A. J. Pharm. Biomed. Anal. 2004, 36, 841–849. 117. Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.; Bethell, H. W. L.; Clarke, S.; Schofield, P. M.; McKilligin, E.; Mosedale, D. E. et al. Nat. Med. 2002, 8, 1439–1444. 118. Odunsi, K.; Wollman, R. M.; Ambrosne, C. B.; Hutson, A.; McCann, S. E.; Tammela, J.; Geisler, J. P.; Muiller, G.; Seller, T.; Cliby, W. et al. Int. J. Cancer 2004, 113, 782–788. 119. Cheng, L. L.; Burns, M. A.; Taylor, J. L.; He, W.; Alpern, E. F.; McDougal, W. S.; Wu, C.-L. Cancer Res. 2005, 65, 3030–3034. 120. Beckonert, O.; Monnerjhan, J.; Bonk, U.; Liebfritz, D. NMR Biomed. 2003, 16, 1–11.

Biographies

Ian D Wilson, BSc, MSc, PhD, DSc, CChem, FRSC, EurChem FChrom Soc, FRES, is a Senior Principal Scientist in the Department of Drug Metabolism and Pharmacokinetics at the AstraZeneca Research site at Alderley Park in Cheshire, UK. He trained as a biochemist at the University of Manchester Institute of Science and Technology, from

Metabonomics

where he received both BSc (1974) and MSc (1975) degrees. Subsequently, he obtained a PhD (1978) and more recently a DSc (1998) from Keele University. He is the author or co-author of some 300 papers or reviews, and has received a number of awards in separation and analytical science from the Royal Society of Chemistry, including the SAC Silver Medal for Analytical Chemistry (1990), the Analytical Separations Medal (1996), the Analysis and Instrumentation medal (2002) and the SAC Gold Medal for Analytical Chemistry (2006). He has also received the Jubilee Medal of the Chromatographic Society (1994) and gave the inaugural Desty memorial lecture for innovation in separation science (1996). As well as being on the editorial boards of a number of journals, he is Editor of the Journal of Pharmaceutical and Biomedical Analysis and Editor in Chief of the Encyclopaedia of Separation Science. He is currently visiting professor at the Universities of York, Keele, Sheffield Hallam, Manchester, and Imperial College. His research is presently directed toward the further development of hyphenated techniques in chromatography and their application to problems in drug metabolism and metabonomics.

Jeremy K Nicholson, BSc, PhD, CBiol, FIBiol, FRSA, FRC Path CChem, FRSC, obtained his BSc from Liverpool University (1977) and his PhD from London University (1980) in biochemistry working on the application of analytical electron microscopy and the applications of energy dispersive x-ray microanalysis in molecular toxicology and inorganic biochemistry. He was appointed Lecturer in Chemistry (Birkbeck College, London University, 1981–83) and Lecturer in Experimental Pathology at the London School of Pharmacy (1983–85) returning to Birkbeck as a Senior Lecturer in Chemistry, then Reader (1989) and Professor of Biological Chemistry (1992). Since 1998 he has been Professor and Head of Biological Chemistry at Imperial College London. Professor Nicholson is the author of over 500 scientific papers, patents and articles on the development and application of novel spectroscopic and chemometric approaches to the investigation of disturbed metabolic and physicochemical processes in cells and biofluids and their relationship to disease processes. His work has been recognized by the award of several scientific prizes, including: the 1992 Royal Society of Chemistry SAC Medal for Analytical Science; the 1997 Royal Society of Chemistry Gold Medal for Analytical Chemistry; the 1994 Chromatographic Society Jubilee Silver Medal; the 2002 Pfizer Prize for Chemical and Medicinal Technologies and the 2003 Royal Society of Chemistry Medal for Chemical Biology. These awards cover various aspects of the development of NMR, LC-NMR, and LC-NMR-MS approaches for biofluid analysis, drug metabolism studies, and for the development and application of metabonomic technologies for toxicological and clinical diagnostics. Research interests include: biological NMR spectroscopy, novel LC-MS and chemometric approaches to bioanalysis, metabolic modeling and studies leading to understanding the molecular basis of disease and toxic processes. Professor Nicholson holds visiting and honorary professorships in several countries and is on the editorial board of 10 international science journals, including Consulting Editor of the Journal of Proteome Research. He is also an honorary professor at six universities and a consultant to many pharmaceutical companies in the UK, continental Europe, and the US and is a founder director of Metabometrix, an Imperial College spin-off company specializing in molecular phenotyping, clinical diagnostics, and toxicological screening via metabonomics.

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Comprehensive Medicinal Chemistry II ISBN (set): 0-08-044513-6 ISBN (Volume 5) 0-08-044518-7; pp. 989–1007

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