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responses. Metabolic changes are real-worldend points, whereas gene expression changesare not; they merely indicate the potential foran end-point change. As such, metabonomicsprovides a useful connection between the‘omics’ platforms and actual tissue histology.

Post-genomic analytical technologiesTo investigate the complex metabolic conse-quences of disease processes, toxic reactionsand genetic manipulation, non-selective, butspecific, ‘information-rich’ analyticalapproaches are required (BOX 1). Several spec-troscopic methods in addition to NMR canproduce metabolic signatures of biomateri-als, including mass spectrometry (MS)19,20,gas chromatography/mass spectrometry(GC/MS)21, high-performance liquid chro-matography (HPLC)22,23 and optical spectro-scopic techniques24. Bioanalytically, NMRand MS are powerful means of generatingmultivariate metabolic data. NMR has theadvantages of being non-destructive, applic-able to intact biomaterials, and intrinsicallymore information rich with respect to thedetermination of molecular structures, espe-cially in complex-mixture analyses. Further-more, a technique known as magic anglespinning (MAS)-NMR can be used to carryout biochemical studies on intact tissues andcells, which, if carefully conducted, can pre-serve the samples for other studies and allowabnormal molecular compartmentation andinteractions to be studied in intact tissues. MSis analytically more sensitive than NMR, butdifferential ionization suppression can makepattern quantification difficult, and extractionand derivatization might be necessary. Thechoice between NMR and MS approaches isultimately matrix or problem dependent. Bothtechnologies require further development,especially of high-throughput and data-pro-cessing methods, to optimize their use in com-plex metabolism studies. However, 1H-NMRspectroscopy can be efficiently applied to

The later that a molecule or molecular classis lost from the drug development pipeline,the higher the financial cost. Minimizingattrition is therefore one of the mostimportant aims of a pharmaceuticaldiscovery programme. Novel technologiesthat increase the probability of making theright choice early save resources, andpromote safety, efficacy and profitability.Metabonomics is a systems approach forstudying in vivo metabolic profiles, whichpromises to provide information on drugtoxicity, disease processes and genefunction at several stages in the discovery-and-development process.

The ‘old’ testing paradigms often fail to preventmany ‘doomed’ molecules from enteringdevelopment. There is a need for methodolo-gies that can describe altered gene expressionand cellular protein profiles in terms of theirearly metabolic consequences, and relatethese to developing, established or regressingpathology. Studying the effects of drugs onwhole organisms by metabonomics relies onmultiparametric measurement of alterationsin metabolism over time in response to astressor or intervention1–10. This approach canalso be readily adapted to investigate the func-tional consequences of genetic variation andtransgenesis3,11–13, which is potentially of greatimportance in the creation and validation ofnew models of human disease and efficacy.There is considerable scope for the applica-tion of metabonomic approaches in the phar-maceutical industry, from discovery throughto clinical development and beyond. In thediscovery phase, these include early in vivotoxicological testing, lead compound selec-tion and pre-lead prioritization, and in vivoefficacy screening in animal models. In thedevelopment phase, applications includefinding new preclinical safety biomarkers andmechanisms, metabotyping and the valida-tion of animal models against human diseaseprofiles, and the discovery of new clinicalsafety and efficacy biomarkers.

In this article, we will explore the relation-ships between the findings of the new ‘omics’sciences, describe analytical technologies for

measuring multiparametric metabolicresponses, with particular emphasis on NMR-based approaches, discuss multivariate statis-tical methods that optimize informationrecovery from multivariate NMR data sets,and consider the application of metabonomicsto drug discovery and development, partic-ularly in minimizing attrition.

Metabonomics in pathophysiology The realization that obtaining the genomesequence of humans or other species does notin itself explain the fundamental nature ofmany disease processes has triggered amarked increase in interest in approaches thatrelate gene expression to phenotypicoutcome1,2,14–16. Several technologies are beingdeveloped to achieve this end, namely:genomics and transcriptomics, which examinegenetic complement and gene expression,respectively; proteomics, which involves theanalysis of protein synthesis and cell signalling;metabolomics, which investigates metabolicregulation and fluxes in individual cells or celltypes; and metabonomics — the determina-tion of systemic biochemical profiles andregulation of function in whole organisms byanalysing biofluids and tissues.

In complex organisms, these levels of bio-molecular organization and control are inter-dependent, and are affected by environmentalevents and stresses throughout life. Theircharacterization, by appropriate analyticalmethods, describes changes in biologicalactivity by using complex multivariate datasets that can be analysed using variouschemometric and bioinformatic tools15,17,18.The aim of such procedures is to extract latentbiochemical information that is of diagnosticor prognostic value, and which reflects ‘actual’biological events rather than the ‘potential’ fordisease or toxicity that is offered by the collec-tion of gene expression and proteomic dataafter exposure to a drug or stressor. It is there-fore necessary to relate real-world or end-point observations to the measurements thatare provided by the ‘omics’ technologies (FIG.

1). This allows an understanding of the rela-tionships between the inputs that change‘omics’ responses and the outputs of those

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Metabonomics: a platform for studyingdrug toxicity and gene function

Jeremy K. Nicholson, John Connelly, John C. Lindon and Elaine Holmes

I N N O VAT I O N

Real world

Inputs:Noxious agent/

stressor

Outputs:Biological end points;

pathology

'Omics' world

Gene expression

Protein profile

Metabolic profile

Time

Time

Time

Time

Figure 1 | Relating the real world to the ‘omics’world. Relationships between real-world inputs(stressors) and outputs, and their time-displacedconnections with the measured ‘omics’ responses.Note time differentials at all interaction stages.

154 | FEBRUARY 2002 | VOLUME 1 www.nature.com/reviews/drugdisc

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An event must therefore be evaluated in rela-tion to time at each level of biomolecularorganization if molecular responses are to beaccurately associated with their macroscopicconsequences in an organism. So, in meta-bolic studies, it is of paramount importance tomeasure time-dependent patterns of changein response to stimuli, because metabolicfluxes occur very rapidly, even in normalhomeostasis, and consideration of the‘metabolite content’ at only a fixed point intime can be misleading. The same considera-tions also apply to proteomic and transcrip-tomic data. Levels of messenger RNA andassociated protein products might correlatepoorly, even in carefully conducted studies on

also a powerful tool for investigating pheno-typic abnormalities in mutant animals13 andhuman diseases32,43-48, and in modelling phys-iological variation in experimental animalsand man42,49.

Biochemical changes over timeIn an integrated biosystem, it is axiomatic thatthe initiation of functionally connected geneexpression events, cell signalling, protein-synthesis changes and metabolic responses toa stressor must be essentially sequential.Maturation and persistence of changes in geneexpression, protein synthesis and post-transla-tional modification, and subsequent effects onmetabolic processes, also differ significantly.

measure the metabolite profiles of biofluidsand tissues. NMR measurements, coupled withmultivariate statistical, chemometric methodsfor the purpose of latent-information extrac-tion and sample classification, offer a powerfulnew approach to whole-system diagnosticsand metabolic function. The efficient applica-tion of NMR-based metabonomics in toxico-logical and clinical investigations has beenshown1,2,25–40, and we have recently shown itsfundamental value in characterizing themetabolic consequences of genetic variationin mammalian systems, and in identifying the‘metabotypes’ or metabolic phenotypes thatresult from a combination of genetic and envi-ronmental factors3,11,12,41,42. Metabonomics is

Box 1 | Important techniques and procedures in metabonomics

NMR spectroscopy. Some atomic nuclei possess a non-zero magnetic moment. This property is quantized, and leads to discrete energy states in amagnetic field. Nuclei such as 1H, 13C, 15N, 19F and 31P can undergo transitions between these states when radio-frequency pulses of appropriateenergy are applied. The exact frequency of a transition depends on the type of nucleus and its electronic environment in a molecule. For example,1H nuclei in a molecule give NMR peaks at frequencies (chemical shifts) that are characteristic of their chemical environment. NMR spectroscopy isused extensively as a structural tool, and information on isomers and molecular conformations can be obtained by interpretation of the chemicalshifts as well as the splitting patterns due to indirect nuclear interactions (J-couplings). In metabonomics, the patterns that occur with time whenmany biochemical entities are detected simultaneously in the mixture by NMR are interpreted63,82.

Pattern-recognition methods. Pattern recognition and related multivariate statistical approaches can be used to discern significant patterns incomplex data sets, and are particularly appropriate in situations in which there are more variables than samples in the data set. The general aim ofpattern recognition is to classify objects — in this case, 1H-NMR spectra — or to predict the origin of objects, by identifying inherent patterns in aset of indirect measurements. Pattern-recognition methods can reduce the dimensionality of complex data sets by means of two- or three-dimensional mapping procedures, thereby facilitating the visualization of inherent patterns in the data.

Supervised and unsupervised techniques. Methods such as principal components analysis (PCA) are termed ‘unsupervised’ techniques, in that noa priori knowledge of the class of the samples is required, and they are based on the calculation of latent variables. Principal components are linearcombinations of the original descriptors, such that they are uncorrelated, and describe decreasing amounts of data variance (that is, PC1>PC2>PC3and so on). Use of PCA enables the ‘best’ representation, in terms of biochemical variation in the data set, to be shown in two or three dimensions.In addition, multiparametric data can be modelled, so that the class of a sample from an independent data set can be predicted on the basis of aseries of mathematical models that are derived from the original data or ‘training’ set. These methods are known as ‘supervised’ methods, and useclass information to maximize the separation between classes. Supervised methods, such as soft independent modelling of classification analogy(SIMCA), partial least squares (PLS) analysis and PLS discriminant analysis (PLS-DA), can be used to predict objects that are unknown to thesystem on the basis of their NMR spectral properties, and are therefore valuable for generating models for predicting drug toxicity83–86.

Strategy for metabonomic analysis. An NMR data-analysis procedure is shown in the figure. After spectra are accumulated and processed (panel a),a primary data reduction is carried out that digitizes the one-dimensional spectrum into a series of typically 250–1,000 integrated regions (panel b)36,59. After removal of redundant signals and appropriate scaling, primary data analysis is used to map the samples according to theirbiochemical composition, using methods such as PCA. Samples that are generated from animals that are in a similar pathophysiological state aregenerally intrinsically similar in composition, and therefore occupy neighbouring positions in the PC space (panel c). Each class of samples is thenmodelled separately, and class boundaries and confidence limits are calculated to construct a model for the prediction of independent data (panel d)11.

NMR spectra

Primary data reduction121110987654321

5

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94

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Unsupervised mapping of data in three-dimensional space

Supervised classification and calculation of confidence limits

a b c d

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protein and lipoprotein signals dominate sim-ple one-dimensional 1H-NMR spectra, withsmall-molecule fingerprints superimposed onthem65. Spin-echo experiments minimize thebroad signal contributions from proteins,other macromolecules and micelles53,65.

An important challenge posed by NMRspectroscopy of biofluids is how to efficientlyrecover metabolic information that allowsdiagnosis or classification of disease or toxicity.The information that is needed for useful class-ification of spectra from biological samplescan often be obtained without detailed struc-tural chemical (spectroscopic) analyses. Moresophisticated interrogation involves the identi-fication of the molecular species that differen-tiate ‘pathological’ from ‘normal’ states, and astatistical description of the biomarkers thataid understanding of disease or toxic mecha-nisms. So, NMR spectra of biofluids serve intwo distinct, but closely related, modes; that is,as quantitative metabolic-fingerprinting tools,and as a means of determining metabolite(biomarker) structure63.

The detailed structural assignment of abiofluid NMR spectrum can be a complexprocedure, involving the application of several

unicellular organisms, such as yeast50 andbacteria51, or in higher organisms52. In fact, itmight not be appropriate to try to identifysimple correlations between transcriptomicand proteomic data that are collected at singletime points after exposure to a stressor, asthese might not exist at all because of the non-linearity of many gene expression and proteinsynthesis relationships.

In multicellular organisms, these time dif-ferentials will probably vary unpredictablywith gene and tissue, and modulation byother external stressors in addition to, forexample, a drug, is possible. It is important,therefore, to choose appropriate windows oftime to study transcriptomic and proteomicresponses to exposure to drug candidates. Animportant potential role for metabonomics isto direct the use and timing of proteomic andgenomic analyses in order to maximize theprobability of observing biological transitionsthat predict functional outcomes; this prin-ciple applies to human, animal and microbialsystems. In the case of a single exposure to atoxic drug, there will be a response that takestime to complete, and the patterns that areobserved in gene expression, proteins andmetabolites will therefore vary according towhen the measurements are made (FIG. 2a). Ifthe measurements are made long after dos-ing, it is possible that the only profilechanges will be due to biomarkers of recoveryor cellular repair. In the case of a multi-dosestudy (FIG. 2b), the second and subsequentdoses of a compound might arrive before theeffects of the first dose are cleared, complicat-ing the profiles further. As the doses continue,there might be a rising curve of toxicity (mea-sured by whatever means), and there couldthen be superimposed profile changes due tocell death and regeneration.

1H-NMR spectroscopy of biomatrices1H-NMR spectroscopy of biofluids such asurine and plasma has been successfullyapplied to investigate numerous diseases andtoxic processes53–62. Because biofluids fulfildiverse biological purposes, their metaboliccomposition varies with their role and thefunctional integrity of the organ systems thatare communicating with them, and ultimatelywith the physiological status of the wholeorganism. So, each biofluid yields a character-istic 1H-NMR spectroscopic fingerprint inwhich the spectral intensity distribution isdetermined by the relative concentrations ofsolutes, and in some cases by their intermolec-ular interactions61,63. High-frequency 1H-NMRspectroscopy is particularly useful in biochem-ical investigations, in that it is sensitive (low-nanogram detection limits are possible with

appropriate instrumentation), and nearly allmetabolic intermediates have unique 1H-NMRsignatures61,63. Simple one-dimensional spectratypically take only a few minutes to acquire,with no sample preparation other thanbuffering and addition of D

2O to provide a

reference frequency. The large interfering sig-nal that arises from water in all biofluids iseasily eliminated using appropriate solvent-suppression methods61.

Metabolic profiles of biofluids such asplasma, cerebrospinal fluid and urine reflectboth normal variation and the pathophysio-logical impact of toxicity or disease on single ormultiple organ systems53,55,64,65. Urine andplasma are obtained in a non- or minimallyinvasive manner, and are therefore appropriatefor clinical-trial monitoring and disease diag-nosis. Even a one-dimensional high-frequency1H-NMR spectra (600 MHz or greater) ofurine typically contains many thousands ofsharp lines from hundreds or potentially thou-sands of metabolites63. 1H-NMR spectra ofurine are dominated by low-molecular-weightcompounds, whereas plasma contains bothlow- and high-molecular-weight components,which give a wide range of signal line widths:

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Dam

age

or to

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Dose 2 Dose 3

Cell degeneration and death increase with time

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Primary biomarkers Regeneration markers

Dose 1 Sample points Time

a

Figure 2 | Effect of time in toxicological studies. Theoretical relationships between dosing and ‘omics’responses in a | single-dose and b | multi-dose situations. The observed changes in gene expression,proteomic or metabolic profiles are dependent on when samples are taken, and will vary in a complex wayaccording to the severity and time taken to develop maximal pathology (for example, cell damage asdetermined by histology) — shown for simplicity as a bell-curve in part a and as a rising curve in part b.The horizontal bars reflect the ranges of times over which up- and downregulation of responses canoccur. In a single-dose study, measurement points before maximal damage reflect primary biomarkers oftoxicity. Those measured after maximal damage must also contain biomarkers that relate to cellularregeneration. In a multi-dose study, second and subsequent doses change the biomarker responses, asthe system is not at equilibrium before the next dose is given. Effects might also change due to enzyme-induction processes that change the drug-metabolite patterns. As cell death increases, the ‘omics’responses increasingly reflect end-stage changes that carry little mechanistic information.

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spectrum of rat urine, with regions labelled bythe biological or functional changes in theanimal, is shown in FIG. 3, and contrasts withthe conventional way of labelling an NMRspectrum according to molecular structure.This diagram shows a series of ‘biomarkerwindows’, in which there are combinations ofpattern and intensity changes according to thesite and mechanism of organ dysfunction.Many such windows exist in NMR spectra ofbiofluids, and diagnostic information onhundreds of different types of disease or toxicprocess can potentially be derived from asimple NMR measurement, as shown in FIG. 4

using examples of liver and kidney toxicity.Although toxins can affect gene regulation

or expression directly, significant responsesmight be completely unrelated to geneswitching (for example, enzyme inhibitors).In such cases, transcriptomic and proteomicmethods are likely to be unrevealing.However, many drug-induced effects involvedisturbed endogenous metabolite concentra-tion fluxes or ratios that result from directchemical reactions, altered binding to macro-molecules, modified control mechanisms,and induction or inhibition of enzymes, toname but a few. If these disturbances over-whelm compensatory or adaptive mecha-nisms, consequences that are recognized astoxicity occur. As metabolite concentrationsin several key body fluids relate to cell andtissue processes, so toxin- or disease-induceddisequilibria are reflected in those fluids.

Using pattern-recognition methods, NMRspectra can be used to: classify the sample asbeing normal or abnormal (this is useful inthe control of spectrometer automation usingflow injection); classify target-organ toxicityand the site and mechanism of action withinthe organ; identify biomarkers of toxic effect;and evaluate the time course of the effect;for example, the onset, evolution and regres-sion of toxicity. The information that isderived from databases of NMR spectra canbe maximized using appropriate chemo-metric and multivariate analytical strategies.Preliminary analysis involves the applicationof ‘unsupervised’ pattern-recognition meth-ods, such as principal components analysis(PCA) or cluster algorithms that assume noprevious knowledge of sample class. Specifictoxic challenges can be characterized byPCA trajectories, in which clustering ofsample coordinates reflects an intrinsic sim-ilarity in biochemical composition. Such tra-jectories are shown in BOX 2 for a model livertoxin and its metabolites. Having establishedthe presence of site- or mechanism-relatedmetabolic responses, more sophisticatedsupervised algorithms can be applied to the

Metabonomics in drug toxicologyAlthough transcriptomic/genomic and pro-teomic measurements respond to theadministration of toxic agents, it is difficultto relate findings to classical toxicologicalend points, and hence influence the drugattrition rate. Metabonomics offers a com-plementary approach that gives informationon whole-organism functional integrity overtime after drug exposure. Target tissues orprocesses, and biomarkers, can be identifiedby characteristic changes in the pattern ofconcentrations of endogenous metabolitesin biofluids that relate to the site and mecha-nism of toxicity.

1H-NMR analysis of biofluids has uncov-ered novel metabolic markers of organ-specifictoxicity in the laboratory rodent, and this‘exploratory’ role is one in which biofluidNMR spectroscopy excels. For example, acombination of changes in the urinary levelsof trimethylamine-N-oxide, N,N-dimethyl-glycine, dimethylamine and succinate,together indicate renal papillary damage, forwhich no biochemical markers existed pre-viously36,38. The biomarker information thatis present in the NMR spectra of biofluids ispotentially very rich, as hundreds of com-pounds that represent a variety of metabolicpathways are measured simultaneously61,63.The NMR spectrum of a biofluid can be con-veniently thought of as a series of ‘biomarkerwindows’, which are spectral regions that con-tain signals from metabolites that are associ-ated with specific targets for toxicity. An NMR

NMR techniques, including 1H–1H and1H–13C two-dimensional experiments65.Diffusion-ordered and diffusion-edited NMRspectra can also be of value, especially whensolutes cover a wide range of molecularweights, as with blood plasma66. Directlycoupled chromatography–NMR spectro-scopic methods can be used, especially fordetermining the structure of drug metabolites— the most powerful of these ‘hyphenated’approaches is HPLC–NMR–MS67. Completeassignment of NMR spectra of biofluids isextremely difficult, because NMR-detectablemetabolite concentrations (excluding water)vary over six orders of magnitude. This causesdifficulties in the interpretation of two-dimen-sional spectra of minor components in thepresence of peaks from compounds at muchhigher concentrations. Furthermore, unlikesingle proteins, which also give complex spec-tra, it is not possible to uniformly enrichbiofluid metabolite mixtures with 13C or 15Nlabels, limiting the range of heteronuclear-correlation NMR methods that can be used.In practice, however, neither issue markedlylimits the usefulness of 1H-NMR spectroscopyof biofluids as a diagnostic and biomarker-identification tool. Biofluid NMR is useful forrapid screening, especially when carried outusing flow-injection methods68. However,target-organ toxicity or disease fingerprintscan also be investigated by using MAS-NMR ofintact tissues69–76, and this can give deep insightinto toxic mechanisms, such as cadmium-induced nephrotoxicity26,77.

Ppm

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Testis Mitochondrialand RTA

Liver(cholestasis)

Renal cortex S1(Pancreas)

MitochondrialAcylCoADH

Renal cortex S2/3Medulla

Figure 3 | A functional NMR spectrum of rat urine. A 600-MHz 1H-NMR spectrum of rat urine isshown, which is colour-coded to indicate spectral biomarker windows that are diagnostic for a subset ofdiverse pathophysiological conditions. One NMR spectrum can carry information on a wide range ofpathological or toxic processes (potentially hundreds of disease classes in a single spectral measurement).AcylCoADH, acyl coenzyme A dehydrogenase dysfunction; RTA, renal tubular acidosis.

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recovery of biological information. In thecase of toxicity assessment, it is possible togenerate false-positive data in situations inwhich the compound of interest causes sig-nificant metabolic changes without associ-ated toxicity, because of marked physiological

data, in which the sample class can be usedto optimize the differentiation betweenclasses. Information that relates to biomarkersof toxicity or recovery can be extracted fromthe analysis with a view to furthering ourunderstanding of the mechanisms of toxicity78.

Time-resolved metabonomic experiments canalso be used to deconvolve the overlapping bio-chemical effects of drugs and their metabolites,which might have different toxicities.

As with all ‘omics’ platforms, metabon-omics has certain limitations in terms of the

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Liver (steatosis)

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Glomerulus

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Hepatic arteryHepatic vein

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Gallbladder

Bile duct

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Figure 4 | Metabonomic detection of liver and kidney toxicity. Stack plot of NMR spectra showing characteristic metabolic fingerprints of tissue-specifictoxicity produced by different site-selective xenobiotics given in single doses to rats in relation to an untreated control. Each 600-MHz spectrum represents onetime point after dosing for each toxic compound. The xenobiotics affect specific regions within the organs (depicted in the figure by shaded boxes): puromycinaffects the renal glomeruli; uranyl nitrate affects the lower regions of the proximal tubules; 2-bromoethanamine affects the renal medulla, including the loop ofHenle and the collecting ducts; and hydrazine affects the hepatic parenchymal cells.

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There are obvious limitations in terms ofchoice of biofluid; for instance, urine mightnot be as appropriate as cerebrospinal fluidfor studying neuropathology. There is also thepotential for confusion with mixed-toxicitydrugs that, for example, affect both liver andkidney, as the biomarkers of toxicity will be acomplex combination that relates to bothsites and possibly to multiple mechanisms.However, this is offset by the fact that mixedtoxicities often have different timescales, and

ment does not occur until there is significanttissue damage. In the case of low-potencycompounds, there might be particular diffi-culties in separating toxicological from physi-ological effects. However, in previousdose–response studies, NMR-based metabo-nomic methods were at least as sensitive asconventional methods for detecting lesions atthe ‘threshold-dose’ level26,27,56,61, and evenminor physiological changes were detected innormal animals41,42,49.

or pharmacological effects. For example,acetazolamide is a renal carbonic anhydraseinhibitor that massively reduces the excretionof intermediates in the citric-acid cycle.Misinterpretation can, however, be mini-mized by using supervised methods thatinclude models of such effects — such modelsare now under construction in our labora-tory. Conversely, certain pathologies, such asliver fibrosis, are associated with negligibleeffects on biofluids, as metabolic derange-

Box 2 | Convolution of biochemical and molecular events caused by drug metabolism

Most drugs are extensively biotransformed tometabolites with markedly different toxicologicalproperties. Such biotransformations take variabletimes in different tissues and in different species,convolving the cellular toxicological effects.Drug/metabolite proportions also change with time asthe parent drug becomes absorbed, distributed andcleared by metabolism and excretion (panel a). This isan important problem, as snapshots of net toxic effectusing any ‘omics’ approach cannot be readilyinterpreted, and this is illustrated with respect to thehepatotoxin α-naphthylisothiocyanate, whichundergoes the changes shown in panel b.

When administered separately to rats, thesecompounds produce markedly different metabolictrajectories, as they act by different mechanisms36.Trajectory plots map the metabolic response, show theextent and type of the lesion, and indicate whetherthere is functional recovery36,59. A three-dimensionalprincipal components analysis (PCA) stereo pair plot,which consists of two images that are designed to beviewed with stereo glasses to give a three-dimensionaleffect, shows trajectories for each compound (panel c).Each connected point represents the mean metabolicposition of five animals at given time points(numbered 1–9 in the diagram) after dosing. The α-naphthylisothiocyanate (ANIT) trajectoryrepresents the aggregate biochemical effects of themetabolic flux (ANIT to α-naphthylisocyanate(ANIC) to α-naphthylamine (ANA)). There is nofunctional recovery of the ANIT-induced lesion overthe seven-day course of the experiment (the trajectorydoes not return to origin). The ANIC trajectory issimpler, and is convolved only by its primarymetabolite, ANA — there is functional recovery. Thefirst metabolic direction of the ANA-dosed animals isin the third principal component (PC3). For ANIC, thesecond direction is in PC3, as it takes time for ANA tobe produced. For ANIT, the PC3 direction changetakes longer, as ANA is formed after ANIC. Thisapproach, termed ‘metabolic trajectory deconvolution’,allows effects of drugs and their metabolites to beseparated for mechanistic purposes. Endogenousmetabolic changes that are caused by the effects ofsuccessive drug metabolites must also mirror complextime-related changes in the transcriptomic andproteomic patterns, thus limiting the value of single-time-point measurements of genes and proteins.

α-naphthylisothiocyanate (ANIT) α-naphthylisocyanate (ANIC) α-naphthylamine (ANA)

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Endogenous metabolicpathway activity

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NC S

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indication for the use of metabonomics inthe phenotyping of mutant or transgenicanimals and the investigation of the conse-quences of transgenesis. In a recent study, wehave shown that the transfection process itselfcan cause marked metabolic differences inhepatoma cell lines due to the disruption ofhost cell membranes 81. It is important to dif-ferentiate such unintended consequences ofthe genetic engineering process from theintended result, as these can potentially con-fuse the interpretation of the function ofparticular genes or gene classes when the cellsystem or organism is examined for the physi-cal effects of the intervention. This is impor-tant to pharmaceutical companies that aretrying to genetically engineer new animalmodels of disease using biochemically inva-sive transfection procedures. Furthermore,metabonomic approaches can give deepinsight into the metabolic similarities or dif-ferences between mutant or transgenic ani-mals, and the human disease processes thatthey are actually intended to simulate. If theveracity of an animal model can be estab-lished using metabolic criteria — that is, bio-markers of the disease process — then itmight also be possible to monitor the efficacyof novel therapeutic agents (normalization ofthe biochemical profile) using metabonomiccriteria. Such approaches might be of greatfuture value to the pharmaceutical industry inthe quest for discovering safe and efficaciousnew drugs.

ConclusionsMetabonomics is now recognized as anindependent and widely used technique forevaluating the toxicity of drug-candidatecompounds, and has been adopted by severalpharmaceutical companies into their drugdevelopment protocols. It is possible to identifythe target organ of the toxicity, derive the bio-chemical mechanism of the toxicity, anddetermine the combination of biochemicalbiomarkers for the onset, progression andregression of the lesion. Furthermore, the

such effects can therefore be deconvolved bymaking repeated sequential measurements inindividual animals. The distinction betweenadaptive and toxic effects remains a challengewith all the ‘omics’ platforms. This applies tothe variables that change due to the initialadverse (mechanism-related) interactions, thehomeostatic response to the cellular derange-ment (which could reflect entirely positivereactions of a healthy cell or tissue), and thechanges due to cell death. Although it is possi-ble that these responses might all occur at thesame time in a complex organism, there willbe a progressive change in their contributionsover time, which could hold the key to theirfuture deconvolution.

MAS-NMR spectroscopic measurementson intact tissues allow the direct correlation oftissue biomarkers with histological change.The development of a renal papillary lesion,as observed by MAS-NMR analysis of intactrenal papilla samples after the administrationof 2-bromoethanamine to rats79, revealed aconsistent response over time, with the excep-tion of a few rats that were deemed to beeither ‘fast’ or ‘slow’ responders to the treat-ment79 (FIG. 5). Indeed, the metabonomicdetermination of individual differences inresponse to drug therapies offers great poten-tial. We have recently shown the value ofobtaining several NMR data sets frombiofluid samples and tissues of the same ani-

mals collected at different time points. Thisprocedure is termed ‘integrated metabo-nomics’80, and can be used to describe thechanges in metabolic chemistry in differentbody compartments that are caused by expo-sure to toxic drugs. Such timed profiles inmultiple compartments are themselves char-acteristic of particular types and mechanismsof toxicity, and can be used to give a morecomplete description of the consequences oftoxicity than can be obtained from one fluid ortissue alone. The use of integrated metabo-nomics in relation to the conventional screen-ing procedures that are now used in drug dis-covery is illustrated in FIG. 6.

Metabonomics in functional genomics Metabonomics can be used to separateclasses of experimental animals, such as miceand rats, according to their strain on the basisof the endogenous metabolite patterns intheir biofluids3,11–13. This is possible becausedifferences in ‘silent-gene’ function betweenstrains can influence the fluxes of metabolitesthrough many key intermediary pathways,resulting in distinct animal ‘metabotypes’11,12.Such differences might help to explain thedifferential toxicity of drugs between strainsin which the metabolic fate and receptorpopulations, and hence potential toxicity ofthe drug itself, is linked to the activity ofendogenous pathways. There is also a strong

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PC

2

PC1

-100-200 0 200 400 600

-50

0

50

100

150

FastresponderSlow

responder

Degenerative phase: osmolyte metabolism

Mitochondrial dysfunction phase:dicarboxylate metabolism

Control

2 h p.d.

4 h p.d.

6 h p.d.

24 h p.d.

Figure 5 | Principal components analysis (PCA) of rat renal papillary damage. The figure depicts aPCA map that shows the trajectory of biochemical changes in the intact rat renal papilla (detected usingmagic angle spinning (MAS)-NMR) after the administration of 2-bromoethanamine. Some animals respondto the intoxication faster than others, even though they are of uniform age and sex and were raised underthe same conditions. This is a typical type of response, with ‘slow’ and ‘fast’ responders beingcharacteristic of many drugs and toxins. The coloured boxes refer to the sampling time point aftertreatment. PC, principal component; p.d., post-dosing.

“…integration of data types will also pave the way to understanding therelationships between genefunction and metaboliccontrol in health anddisease.”

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11. Holmes, E. et al. Chemometric models for toxicity basedon NMR spectra of biofluids. Chem. Res. Toxicol. 13,471–478 (2000).

12. Gavaghan, C. L., Holmes, E., Lenz, E., Wilson, I. D. &Nicholson, J. K. An NMR-based metabonomic approachto investigate the biochemical consequences of geneticstrain differences; application to the C57BL10J andAlpk:ApfCD mouse. FEBS Lett. 484, 169–174 (2000).

13. Griffin, J. L. et al. Metabolic profiling of genetic disorders:a multi-tissue 1H NMR spectroscopic and patternrecognition study into dystrophic tissue. Anal. Biochem.293, 16–21 (2001).

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15. Delneri, D., Brancia, F. L. & Oliver, S. G. Towards a trulyintegrative biology through the functional genomics ofyeast. Curr. Opin. Biotechnol. 12, 87–91 (2001).

16. Raamsdonk, L. M. et al. A functional genomics strategythat uses metabolome data to reveal the phenotype ofsilent mutations. Nature Biotechnol. 19, 45–50 (2001).

17. Kettaneh, N., Berglund, A. & Wold, S. in Proceedings ofthe PLS ‘01 International Symposium (eds Vinzi, V. E.,Lauro, C., Morineau, A. & Tenenhaus, M.) 1 (CICIACeresta, Paris, 2001).

18. Johnston, M. The yeast genome: on the road to theGolden Age. Curr. Opin. Genet. Dev. 10, 617–623 (2000).

19. Goodacre, R., Rooney, P. J. & Kell, D. B. Discriminationbetween methicillin-resistant and methicillin-susceptibleStaphylococcus aureus using pyrolysis massspectrometry and artificial neural networks. J. Antimicrob.Chemother. 41, 27–34 (1998).

20. Freeman, R. et al. Rapid identification of species withinthe Mycobacterium tuberculosis complex by artificialneural network analysis of pyrolysis mass spectra. J. Med. Microbiol. 40, 170–173 (1994).

21. Halket, J. M. et al. Deconvolution of gaschromatography/mass spectrometry of urinary organicacids — potential for pattern recognition and automatedidentification of metabolic disorders. Rapid Commun.Mass Spectrom. 13, 279–284 (1999).

22. Ramos, L. S. Characterisation of Mycobacteria speciesby HPLC and pattern recognition. J. Chromatogr. Sci. 32,219–227 (1994).

23. Vigneau-Calahan, K. E., Shestopalov, A. I., Milbury, P. E.,Matson, W. R. & Kristal, B. S. Characterisation of diet-dependent metabolic serotypes: analytical and biologicalvariability issues in rats. J. Nutr. 131, S924–S932 (2001).

24. Winson, M. K. et al. Diffuse reflectance absorbancespectroscopy taking in chemometrics (DRASTIC). Ahyperspectral FT-IR-based approach to rapid screeningfor metabolite overproduction. Anal. Chim. Acta 348,273–282 (1997).

25. Holmes, E. et al. Development of a model forclassification of toxin-induced lesions using 1H NMRspectroscopy of urine combined with pattern recognition.NMR Biomed. 11, 1–10 (1998).

26. Griffin, J. L. et al. The initial pathogenesis of cadmiuminduced renal toxicity. FEBS Lett. 478, 147–150 (2000).

27. Nicholls, A., Nicholson, J. K., Haselden, J. N. &Waterfield, C. J. A metabonomic approach to theinvestigation of drug-induced phospholipidosis.Biomarkers 5, 410–423 (2000).

28. Espina, J. R. et al. Detection of in vivo biomarkers ofphospholipidosis using NMR-based metabonomicapproaches. Magn. Reson. Chem. 39, 559–565 (2001).

29. Howells, S. L., Maxwell, R. J., Peet, A. C. & Griffiths, J. R.An investigation of tumour 1H nuclear magneticresonance spectra by the application of chemometrictechniques. Magn. Reson. Med. 28, 214–236 (1992).

30. Maxwell, R. J. et al. Pattern recognition analysis of 1H NMR spectra from perchloric avid extracts of humanbrain biopsies. Magn. Reson. Med. 39, 869–877 (1998).

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32. Tomlins, A. M. et al. High resolution 1H NMRspectroscopic studies on dynamic biochemicalprocesses in incubated human seminal fluid samples.Biochim. Biophys. Acta 1379, 367–380 (1998).

33. Griffin, J. L., Walker, L. A., Shore, R. F. & Nicholson, J. K.Metabolic profiling of chronic cadmium exposure in therat. Chem. Res. Toxicol. 14, 1428–1434 (2001).

34. Foxall, P. J. D. & Nicholson, J. K. Nuclear magneticresonance spectroscopy: a non-invasive probe of kidneymetabolism and function. Exp. Nephrol. 6, 409–414(1998).

35. Bairaktari, E., Katopodis, K., Siamopoulos, K. C. &Tsolas, O. Paraquat-induced renal injury studied by 1H NMR spectroscopy of urine. Clin. Chem. 44,1256–1261 (1998).

1. Nicholson, J. K., Lindon, J. C. & Holmes, E.‘Metabonomics’: understanding the metabolic responsesof living systems to pathophysiological stimuli viamultivariate statistical analysis of biological NMR data.Xenobiotica 29, 1181–1189 (1999).

2. Lindon, J. C., Nicholson, J. K., Holmes, E. & Everett, J. R.Metabonomics: metabolic processes studied by NMRspectroscopy of biofluids. Concepts Magn. Reson. 12,289–320 (2000).

3. Holmes, E., Nicholson, J. K. & Tranter, G. Metabonomicclassification of genetic variations in toxicological andmetabolic responses using probabalistic neural networks.Chem. Res. Toxicol. 48, 182–191 (2001).

4. Robertson, D. G. et al. Metabonomics: evaluation ofnuclear magnetic resonance (NMR) and patternrecognition technology for rapid in vivo screening ofliver and kidney toxicants. Toxicol. Sci. 57, 326–337(2000).

5. Gartland, K. P. R. et al. Pattern recognition analysis ofhigh resolution 1H NMR spectra of urine: a nonlinearmapping approach to the classification of toxicologicaldata. NMR Biomed. 3, 166–172 (1990).

6. Anthony, M. L., Beddell, C. R., Lindon, J. C. & Nicholson,J. K. Studies on the comparative toxicity of S-(1,2-dichlorovinyl)-L-cysteine, S-(1,2-dichlorovinyl)-homocysteineand 1,1,2-trichloro-3,3,3-trifluoro-1-propene in theFischer 344 rat. Arch. Toxicol. 69, 99–110 (1994).

7. Nicholson, J. K., Higham, D., Timbrell, J. A. & Sadler, P. J.Quantitative 1H NMR urinalysis studies on thebiochemical effects of acute cadmium exposure in therat. Mol. Pharmacol. 36, 398–404 (1989).

8. Griffin, J. L., Williams, H. J., Sang, E. & Nicholson, J. K.Abnormal lipid profile of dystrophic cardiac tissue asdemonstrated by one- and two-dimensional magic anglespinning NMR spectroscopy. Magn. Reson. Med. 46,249–255 (2001).

9. Phipps, A. N., Stewart, J., Wright, B. & Wilson, I. D. Effectof diet on the urinary excretion of hippuric acid and otherdietary derived aromatics in the rat. Xenobiotica 28,527–537 (1998).

10. Holmes, E. et al. Identification of novel biomarkers ofrenal toxicity using automatic data reduction techniquesand PCA of proton NMR spectra of urine. Chemometr.Intell. Lab. Syst. 44, 251–261 (1998).

future is an understanding of the reasons forthe rare human ‘idiosyncratic’ toxicity of cer-tain drugs that are already on the market orare in late-stage development. It is possible toforesee future situations in which metabo-nomic, gene expression and proteomic dataare interrogated using multivariate approachesto provide a holistic picture of complexorganisms that are undergoing physiologicalstress, using optimized biomarker combina-tions from all three platforms. Such integra-tion of data types will also pave the way tounderstanding the relationships betweengene function and metabolic control inhealth and disease.

Jeremy K. Nicholson, John Connelly, John C. Lindonand Elaine Holmes are at the Biological Chemistry

Section, Biomedical Sciences Division, Faculty ofMedicine, Imperial College of Science, Technology

and Medicine, South Kensington, London SW7 2AZ, UK. Correspondence to J.K.N.

e-mail: j.nicholson@ic.ac.uk

DOI: 10.1038/nrd728

technique can provide a metabolic fingerprintof an organism (metabotyping) — a key tofunctional genomics — and hence has appli-cations in the design of drug clinical trials andthe evaluation of genetically modified animalsas disease models. Finally, using metabo-nomics, it has proved possible to derive newbiochemically based assays for disease diag-nosis, and to identify combination biomark-ers for disease, which can then be used tomonitor the efficacy of drugs in clinical trials.Clearly, metabonomics is not a panacea for allfuture drug-safety studies and clinical investi-gations, and will complement, rather thanentirely supplant, conventional methods,particularly bearing in mind the needs ofregulatory bodies. There are certainly partic-ular areas within drug discovery for whichmetabonomics will probably not prove useful,because of sensitivity or specificity issues, andthese limitations require further exploration.One potential area of importance in the

Preliminary activity screening

Data reduction and mapping

Conventional/alternative screening procedures

Normal/abnormal? Toxicity classificationBiomarker identification

Compound libraries

Pre-development pharm/tox profiling

Expert-system-based decision processes

Tissues (MAS-NMR)

Rodent dosing and biofluid NMR

Figure 6 | Integrated metabonomics. Incorporation of metabonomics-based expert systems for toxicityclassification (presence of abnormality, type of abnormality and biomarkers of abnormality) in relation to otherdrug discovery procedures.

P E R S P E C T I V E S

prostatic hyperplastic and tumour tissues. Anal.Commun. 35, 113–115 (1998).

73. Garrod, S. L. et al. High resolution MAS-1H NMRspectroscopic studies on intact rat renal cortex andmedulla. Magn. Reson. Med. 41, 1108–1118 (1999).

74. Waters, N. J. et al. High resolution magic angle spinningNMR spectroscopy of intact liver and kidney: optimisationof sample preparation procedures and biochemicalstability of tissue during spectral acquisition. Anal.Biochem. 282, 16–23 (2000).

75. Bollard. M., Holmes, E., Lindon, J. C. & Nicholson J. K.MAS-NMR studies on rat liver: assignment of glycogenresonances. Magn. Reson. Med. 44, 201–207 (2000).

76. Millis, K., Maas, E., Cory, D. G. & Singer, S. Gradientenhanced high resolution magic angle spinning nuclearmagnetic resonance spectroscopy of human adipocytetissue. Magn. Reson. Med. 38, 399–403 (1997).

77. Griffin, J. L. et al. The biochemical profile of rat testiculartissue as measured by magic angle spinning NMRspectroscopy. FEBS Lett. 486, 225–229 (2000).

78. Nicholls, A. W. et al. Metabonomic investigations intohydrazine toxicity in the rat. Chem. Res. Toxicol. 14,975–987 (2001).

79. Garrod, S. et al. High resolution 1H NMR and magic anglespinning NMR spectroscopic investigation of thebiochemical effects of 2-bromoethanamine in intact renaland hepatic tissue. Magn. Reson. Med. 45, 781–790(2001).

80. Waters, N. J. et al. NMR and pattern recognition studies on the time-related metabolic effects of α-naphthylisothiocyanate on liver, urine, and plasma inthe rat: an integrative metabonomic approach. Chem.Res. Toxicol. 14, 1401–1412 (2001).

81. Griffin, J. L., Mann, C. J., Scott, J., Shoulders, C. C. &Nicholson, J. K. Choline containing metabolites duringcell transfection: an insight into magnetic resonancespectroscopy detectable changes. FEBS Lett. 509,263–266 (2001).

82. Evans, J. N. S. Biomolecular NMR Spectroscopy (OxfordUniv. Press, Oxford, 1996).

83. Manley, B. F. J. Multivariate Statistical Methods: a Primer(Chapman & Hall, London, 1986).

84. Beebe, K. R., Pell, R. J. & Seahsholt, M. B.Chemometrics: a Practical Guide (John Wiley & Sons,New York, 1998).

85. Jurs, P. C. Pattern recognition used to investigatemultivariate data in analytical chemistry. Science 232,1219–1224 (1986).

86. Eriksson, L., Johansson, E., Kettanah-Wold, N. & Wold, S.Introduction to Multi- and Megavariate Data AnalysisUsing Projection Methods (PCA and PLS) (UMETRICS,Sweden, 1999).

AcknowledgementsWe thank I. Wilson, (AstraZeneca Pharmaceuticals, UK), J.Everett, (Pfizer Global Research, UK) and D. Robertson (PfizerGlobal Research, USA) for helpful discussions on the philosophyof metabonomics. We are grateful to M. Spraul and BrukerAnalytik GmbH, Germany, for help with many metabonomics pro-jects. Particular thanks are due to H. Antti, A. Nicholls, C.Gavaghan and J. Bundy for data collection and help with dataprocessing. We thank J. Azmi for her help with the α-naph-thylisothiocyanate data and analysis. We also thank TheEngineering and Physical Sciences Research Council, the MedicalResearch Council, the Natural Environment Research Council andthe Biotechnology and Biological Sciences Research Council forfunding this and related work over many years.

Online links

FURTHER INFORMATIONBiological Chemistry Section:http://www.med.ic.ac.uk/divisions/1/home.aspMetabometrix: http://www.metabometrix.com/Access to this interactive links box is free online.

36. Beckwith-Hall, B. M. et al. Nuclear magnetic resonancespectroscopic and principal components analysisinvestigations into biochemical effects of three modelhepatotoxins. Chem. Res. Toxicol. 11, 260–272 (1998).

37. Ghauri, F., McLean, A., Beales, D., Wilson, I. D. &Nicholson, J. K. Induction of 5-oxoprolinuria in the ratfollowing chronic feeding with N-acetyl 4-aminophenol(paracetamol). Biochem. Pharmacol. 46, 953–957(1993).

38. Holmes, E., Bonner, F. & Nicholson, J. K. Comparativestudies on the nephrotoxicity of 2-bromoethanaminehydrobromide in the Fisher 344 rat and themultimammate desert mouse (Mastomys natalensis).Arch. Toxicol. 70, 89–95 (1995).

39. Holmes, E., Bonner, F. & Nicholson, J. K. Comparativebiochemical effects of low doses of mercury II chloride inthe F344 rat and the multimammate mouse (Mastomysnatalensis). Comp. Biochem. Physiol. C 114, 7–15(1996).

40. Holmes, E., Bonner, F. & Nicholson, J. K. 1H NMRspectroscopic and histopathological studies onpropylene imine induced renal papillary necrosis in the ratand the multimammate desert mouse (Mastomysnatalensis). Comp. Biochem. Physiol. C 116, 125–134(1997).

41. Bollard, M. E. et al. Investigations into biochemicalchanges due to diurnal variation and estrus cycle infemale rats using high resolution 1H NMR spectroscopyof urine and pattern recognition. Anal. Biochem. 295,194–202 (2001).

42. Gavaghan, C. L. et al. HPLC–NMR spectroscopic andchemometric studies on metabolic variation in SpragueDawley rats. Anal. Biochem. 291, 245–252 (2001).

43. Moolenaar, S. H. et al. β-Ureidopropionase deficiency: anovel inborn error of metabolism discovered using NMRspectroscopy on urine. Magn. Reson. Med. 46,1014–1017 (2001).

44. Moolenaar, S. H. et al. In vivo and in vitro NMRspectroscopy reveal a putative novel inborn errorinvolving polyol metabolism. NMR Biomed. 14, 167–176(2001).

45. Burns, S. P., Holmes, H. C., Chalmers, R. A., Johnson, A.& Iles, R. A. Proton NMR spectroscopic analysis ofmultiple acyl-CoA dehydrogenase deficiency — capacityof the choline oxidation pathway for methylation in vivo.Biochim. Biophys. Acta 1406, 274–282 (1998).

46. Neild, G. H., Foxall, P. J. D., Lindon, J. C., Holmes, E. &Nicholson, J. K. Uroscopy in the 21st century: high fieldNMR spectroscopy. Nephrol. Dial. Transplant. 12,404–417 (1997).

47. Foxall, P. J. D., Bewley. S., Neild, G., Rodeck, C. H. &Nicholson, J. K. Analysis of fetal and neonatal urine usingproton NMR spectroscopy. Arch. Dis. Child. 73,F153–F157 (1995).

48. Foxall, P. J. D. et al. Proton nuclear magnetic resonancestudies of early ifosfamide-induced nephrotoxicity in man:ifosfamide-induced encephalopathy and glutaric aciduria.Clin. Cancer Res. 3, 1507–1518 (1997).

49. Holmes, E. et al. Automatic data reduction and patternrecognition methods for analysis of 1H nuclear magneticresonance spectra of human urine from normal andpathological states. Anal. Biochem. 220, 284–296(1994).

50. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R.Correlation between protein and mRNA abundance inyeast. Mol. Cell. Biol. 19, 1720–1730 (1999).

51. Tweeddale, H., Notley-McRobb, L. & Ferenci, T. Effect ofslow growth on metabolism of Escherichia coli, asrevealed by global metabolite pool (‘metabolome’)analysis. J. Bacteriol. 180, 5109–5116 (1988).

52. Cutler, P. et al. An integrated proteomic approach tostudying glomerular nephrotoxicity. Electrophoresis 20,3647–3658 (1999).

53. Nicholson, J. K., Buckingham, M. J. & Sadler, P. J. High resolution proton NMR studies of vertebrate bloodand plasma. Biochem. J. 211, 605–615 (1983).

54. Bales, J. R., Higham, D. P., Howe, I., Nicholson, J. K. &Sadler, P. J. Use of high resolution proton nuclear magneticresonance spectroscopy for rapid multi-componentanalysis of urine. Clin. Chem. 30, 426–432 (1984).

55. Nicholson, J. K. et al. Proton NMR studies of serum,plasma and urine from fasting normal, and diabeticsubjects. Biochem. J. 217, 365–375 (1984).

56. Nicholson, J. K., Timbrell, J. A., & Sadler, P. J. ProtonNMR spectra of urine as indicators of renal damage:mercury nephrotoxicity in rats. Mol. Pharmacol. 27,644–651 (1985).

57. Halligan, S. et al. A study of the nephrotoxicity of threecephalosporins in rabbits using 1H NMR spectroscopy.Toxicol. Lett. 81, 15–21 (1995).

58. Gartland, K. P. R., Beddell, C., Lindon, J. C. &Nicholson, J. K. The application of pattern recognitionmethods to the analysis and classification of toxicologicaldata derived from NMR spectroscopy of urine. Mol. Pharmacol. 39, 629–642 (1991).

59. Holmes, E. et al. NMR spectroscopy and patternrecognition analysis of the biochemical processesassociated with the progression and recovery fromnephrotoxic lesions in the rat induced by mercury (II)chloride and 2-bromoethanamine. Mol. Pharmacol. 42,922–930 (1992).

60. Ghauri, F., Wilson, I. D., Parkes, H. G. & Nicholson, J. K.Asymptomatic 5-oxoprolinuria in man detected by protonNMR spectroscopy of urine. Clin. Chem. 39, 1341(1993).

61. Nicholson, J. K. & Wilson, I. D. High resolution protonNMR spectroscopy of biological fluids. Prog. Nucl. Magn.Reson. Spectrosc. 21, 449–501 (1989).

62. Lynch, M. J. & Nicholson, J. K. Proton MRS of humanprostatic fluid: correlations between citrate, spermine andmyo-inositol and changes with disease. Prostate 30,248–255 (1997).

63. Lindon, J. C., Holmes, E. & Nicholson, J. K. Patternrecognition methods and applications in biomedicalmagnetic resonance. Prog. Nucl. Magn. Reson.Spectrosc. 39, 1–40 (2001).

64. Ghauri, F. Y. K. et al. NMR spectrosocopy of human postmortem cerebrospinal fluid: distinction of Alzheimer’sdisease from control using pattern recognition andstatistics. NMR Biomed. 6, 163–167 (1993).

65. Nicholson, J. K., Foxall, P., Spraul, M., Farrant, R. D. &Lindon, J. C. 750 MHz 1H and 1H–13C NMRspectroscopy of human blood plasma. Anal. Chem. 67,793–811 (1995).

66. Liu, M., Nicholson, J. K., Parkinson, J. A. & Lindon, J. C.Measurement of biomolecular diffusion coefficients inblood plasma using two-dimensional 1H–1H diffusion-edited total correlation spectroscopy. Anal. Chem. 69,1504–1509 (1997).

67. Shockcor, J. et al. Combined hyphenation of HPLC,NMR spectroscopy and ion-trap mass spectrometry(HPLC–NMR–MS) with application to the detection andcharacterization of xenobiotic and endogenousmetabolites in human urine. Anal. Chem. 68,4431–4435 (1996).

68. Spraul, M. et al. Flow injection NMR spectroscopy ofbiofluids for rapid structural studies and high throughputbiochemical screening. Anal. Commun. 34, 339–341(1997).

69. Moka, D. et al. Magic angle spinning proton nuclearmagnetic resonance spectroscopic analysis of intactkidney tissue samples. Anal. Commun. 34, 107–109(1997).

70. Cheng, L. L. et al. Enhanced resolution of proton NMRspectra of malignant lymph nodes using magic anglespinning. Magn. Reson. Med. 36, 653–658 (1996).

71. Cheng, L. L. et al. Quantitative neuropathology by highresolution magic angle spinning proton magneticresonance spectroscopy. Proc. Natl Acad. Sci. USA 94,6408–6413 (1997).

72. Tomlins, A. et al. High resolution magic angle spinning1H nuclear magnetic resonance analysis of intact

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O N L I N E O N L Y N I C H O L S O N

Jeremy Nicholson is Professor and Head ofBiological Chemistry in the Faculty ofMedicine at Imperial College, London, wherehe leads one of the world’s largest researchgroups in metabolic science. He is the authorof more than 350 scientific papers and arti-cles, and has won the Royal Society ofChemistry Gold and Silver Medals forAnalytical Science, and the ChromatographicSociety Jubilee Silver Medal for his work ondrug metabolism, analytical toxicology andclinical biochemistry. He is a Fellow of theRoyal Society of Chemistry and the RoyalCollege of Pathologists.

Elaine Holmes is a lecturer in biologicalchemistry at Imperial College, London. Sheobtained her B.Sc. in Biology from LondonUniversity, and a Ph.D. in Chemistry fromBirkbeck College, London Univeristy, onNMR spectroscopy of biofluids and pattern-recognition analysis. She and her group areworking on new approaches to enhancedinformation recovery from complex biologi-cal spectroscopic data, and novel NMR meth-ods in clinical diagnosis.

John Connelly obtained his B.Sc. and Ph.D.from Surrey University, United Kingdom, intoxicology. He was subsequently elected as aFellow of the Institute of Biomedical Sciences.For several years, he was Contracts Managerat the Robens Institute, Surrey University, andsubsequently at Beecham, SmithKlineBeecham and GlaxoSmithKline, where heheld positions as Head of Clinical Pathology,Director of Drug Development Sciences,Director of Investigative Toxicology andGroup Director of the Safety AssessmentExecutive Committee, with internationalresponsibilities. He formed the Biofluid NMRGroup at SmithKline Beecham in 1995. He isnow Director of Biology at Metabometrix Ltd(an Imperial College spin-off company), anda visiting researcher in biological chemistry atImperial College.

John Lindon holds B.Sc., Ph.D. and D.Sc.degrees in chemistry from BirminghamUniversity, United Kingdom, and is a Fellowof the Royal Society of Chemistry. He has anextensive research background in NMR spec-troscopy of complex systems, including liquidcrystals, biofluids and tissues, and has pub-lished more than 300 research papers andarticles. He has many years of experience inpharmaceutical research and developmentwith the Wellcome Foundation, Beckenham,United Kingdom. His main research interestsare NMR spectroscopy, applications of spec-troscopy in general to biomedical research,

computational chemistry and chemometrics.He is a senior research investigator and pro-fessor at Imperial College in the BiologicalChemistry section.