In this minireview, the main technologies used in metabonomics are summarized, brief details of the types of samples used are given, and the current phar-maceutical applications of metab
Trang 1Metabonomics in pharmaceutical R & D
John C Lindon, Elaine Holmes and Jeremy K Nicholson
Biomolecular Medicine, Faculty of Medicine, Sir Alexander Fleming Building, Imperial College London, UK
Introduction
Metabonomics has been formally defined [1] and can
be understood as the comprehensive and simultaneous
systematic determination of metabolite levels in whole
organisms and their changes over time as a
conse-quence of stimuli such as diet, lifestyle, environment,
genetic effects, and pharmaceutical interventions, both
beneficial and adverse For mammalian systems, this
involves the analysis of biofluids and tissues, and the
complex datasets are usually interpreted using
chemo-metric techniques [1,2] The approach builds on
meta-bolic analyses using NMR spectroscopy [3,4] and mass
spectrometry [5] first reported around 20 years ago,
and indeed it goes back to the concept suggested by Pauling et al in 1971 [6]
In this minireview, the main technologies used in metabonomics are summarized, brief details of the types of samples used are given, and the current phar-maceutical applications of metabonomics are des-cribed, but the important aspect of the measurement
of metabolic fluxes using stable isotope labeling is not covered here Some prospects for the future are dis-cussed later
Metabonomics studies of pharmaceutical relevance generally use biofluids, or cell or tissue extracts Urine and plasma are easily obtained, essentially
noninvasive-ly, and hence can be readily used for disease diagnosis
Keywords
biomarkers; diagnostics; drug safety;
metabonomics; spectroscopy
Correspondence
J C Lindon, Biomolecular Medicine, Faculty
of Medicine, Biomedical Sciences Division,
Imperial College London, Sir Alexander
Fleming Building, South Kensington,
London, SW7 2AZ, UK
Fax: +44 20 75 943 066
Tel: +44 20 75 943 194
E-mail: j.lindon@imperial.ac.uk
(Received 20 October 2006, revised 20
November 2006, accepted 30 November
2006)
doi:10.1111/j.1742-4658.2007.05673.x
This minireview is based on a lecture given at the First Maga Circe Confer-ence on metabolomics held at Sabaudia, Italy, in March 2006 in which the analytical and statistical techniques used in metabonomics, efforts at stan-dardization and some of the major applications to pharmaceutical research and development are reviewed Metabonomics involves the determination
of multiple metabolites simultaneously in biofluids, tissues and tissue extracts Applications to preclinical drug safety studies are illustrated by the Consortium for Metabonomic Toxicology, a collaboration involving several major pharmaceutical companies This consortium was able, through the measurement of a dataset of NMR spectra of rodent urine and serum samples, to build a predictive expert system for liver and kidney toxicity A secondary benefit was the elucidation of the endogenous bio-chemicals responsible for the classification The use of metabonomics in disease diagnosis and therapy monitoring is discussed with an exemplifica-tion from coronary artery disease, and the concept of pharmaco-meta-bonomics as a way of predicting an individual’s response to treatment is exemplified Finally, some advantages and perceived difficulties of the metabonomics approach are summarized
Abbreviations
CE, capillary electrophoresis; CLOUDS, classification of unknowns by density superposition; COSY, correlation spectroscopy; CPMG, Carr–Purcell–Meiboom–Gill; CSF, cerebrospinal fluid; DA, discriminant analysis; FT, Fourier transform; IBS, irritable bowel syndrome; LC-PUFA, long chain polyunsaturated fatty acid; MAS, magic angle spinning; OSC, orthogonal signal correction; PCA, principal component analysis; PLS, partial least squares; QC, quality control; SHY, statistical hetero-spectroscopy; STOCSY, statistical total correlation
spectroscopy; TOF, time of flight; TSP, trimethylsilylpropionic acid sodium salt; UPLC, ultra performance liquid chromatography.
Trang 2and in a clinical trials setting for monitoring drug
ther-apy However, there is a wide range of fluids that has
been studied, including seminal fluid, amniotic fluid,
cerebrospinal fluid, synovial fluid, digestive fluids,
blis-ter and cyst fluids, lung aspirates and dialysis fluids In
addition, a number of metabonomics studies have used
analysis of intact tissue biopsy samples and their lipid
and aqueous extracts [7] This particular approach can
also be used to characterize in vitro cell systems such as
tumor cells [8] and tissue spheroids [9]
Metabonomics analytical technologies
The principal analytical techniques that are employed
for metabonomic studies are based on NMR
spectro-scopy and mass spectrometry (MS) MS requires a
separation of the metabolic components using either
gas chromatography (GC) after chemical
derivatiza-tion, or liquid chromatography (LC), with the newer
method of ultra performance
increasingly Some users have advocated direct
injec-tion MS especially with the use of Fourier transform
mass spectrometers The use of capillary
electrophor-esis (CE) coupled to MS has also shown some
prom-ise Other more specialized techniques such as Fourier
transform infra-red (FTIR) spectroscopy and arrayed
electrochemical detection have been used in some cases
[10,11] The main limitation of these is the low level of
detailed molecular identification that can be achieved
However, the combination of retention time and redox
properties can serve as a basis for database searching
of libraries of standard compounds and the separation
output can also be directed to a mass spectrometer for
additional identification experiments
All metabonomics studies result in complex
multiva-riate datasets that require visualization software and
chemometric and bioinformatic methods for
interpret-ation The aim of these procedures is to produce
bio-chemically based fingerprints that are of diagnostic or
other classification value A second stage, crucial in
such studies, is to identify the substances causing the
diagnosis or classification, and these become the
com-bination of biomarkers that define the biological or
clinical context
NMR spectroscopy
Standard commercial NMR spectrometers can be
used for metabonomics, and for large scale
pharma-ceutical studies, automatic sample preparation is often
employed This can involve addition of buffer to
sta-bilize the pH, and D2O as a magnetic field lock signal
for the spectrometer NMR spectra typically take only
around 5 min to acquire using robotic flow-injection methods For large scale studies, barcoded vials con-taining the biofluid can be used and the contents of these can be transferred and prepared for analysis using robotic liquid handling technology into 96-well plates under laboratory information management sys-tem control Alternatively, for more precious samples
or for those of limited volume, conventional 5 mm or capillary NMR tubes are usually used, either individu-ally or using a commercial sample tube changer and automatic data acquisition
The large interfering NMR signal arising from water
in all biofluids is eliminated by use of standard NMR solvent suppression pulse sequences The reference compound used in aqueous media is usually the sodium salt of 3-trimethylsilylpropionic acid (TSP), with the methylene groups deuterated to avoid giving rise to peaks in the 1H NMR spectrum Absolute con-centrations can be obtained if the sample contains an added internal standard of known concentration, or if
a standard addition of the analyte of interest is added
to the sample, or if the concentration of a substance is known by independent means (e.g., many metabolites can be quantified by conventional biochemical assays) Whilst a1H NMR spectrum of urine contains thou-sands of sharp lines from predominantly low molecular mass metabolites, blood plasma and serum contain both low and high molecular mass components, and these give a wide range of signal line widths Broad bands from protein and lipoprotein signals contribute strongly to the 1H NMR spectra, with sharp peaks from small molecules superimposed on them [12] Standard NMR pulse sequences, where the observed peak intensities are edited on the basis of molecular diffusion coefficients or on NMR relaxation times, can
be used to select only the contributions from macro-molecules, or alternatively to select only the signals from the small molecule metabolites, respectively It is also possible to use these approaches to investigate molecular mobility and flexibility, and to study inter-molecular interactions such as the reversible binding between small molecules and proteins
The development of high resolution1H magic angle spinning (MAS) NMR spectroscopy has allowed the acquisition of high resolution NMR data on small pieces of intact tissues with no pretreatment [7,13] Rapid spinning of the sample (typically at 4–6 kHz)
at an angle of 54.7 relative to the applied magnetic field serves to reduce the loss of information caused by line broadening effects seen in nonliquid samples such
as tissues MAS NMR spectroscopy has straightfor-ward, but manual, sample preparation NMR spectro-scopy on a tissue sample in an MAS experiment is the
Trang 3same as solution state NMR and all common pulse
techniques can be employed in order to study
meta-bolic changes and to perform molecular structure
elu-cidation and molecular dynamics studies
Some typical 1H NMR spectra are given in Fig 1
showing the different profiles from rat liver tissue
(using MAS NMR spectroscopy), urine and blood
plasma
Two-dimensional NMR spectroscopy can be useful
for increasing signal dispersion and for elucidating
the connectivities between signals, thereby aiding
bio-marker identification Those of principal use include
1H-1H 2D J-resolved spectroscopy, which attenuates
the peaks from macromolecules and yields information
on the multiplicity and coupling patterns of resonances
and 1H-1H spin connectivity experiments known as
correlation spectroscopy (COSY) and total correlation
spectroscopy (TOCSY), giving information on which
hydrogens in a molecule are close in chemical bond
terms Use of other types of nuclei, such as naturally
abundant 13C or 15N, or where present 31P, through
heteronuclear correlation experiments, can be import-ant to help assign NMR peaks These experiments benefit from the use of so-called inverse detection, where the lower sensitivity or less abundant nucleus NMR spectrum (such as 13C) is detected indirectly using the more sensitive⁄ abundant nucleus (1H) The commercialization of cryogenic probes where the detector coil and preamplifier (but not the samples) are cooled to around 20K is already proving useful for metabonomics studies This has provided an improve-ment in spectral signal⁄ noise ratios of up to a factor of five by reducing the thermal noise in the electronics of the spectrometer Conversely, a reduction in data acquisition times by up to a factor of 25 become poss-ible for the same amount of sample NMR spectro-scopy of biofluids detecting the much less sensitive13C nuclei which also only have a natural abundance (1.1%) also becomes possible because of the increase
in signal-to-noise ratio [14] This technology also makes the use of tissue-specific microdialysis samples more feasible [15]
A
B
7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 δ 1H
5.5 6.0 8.2
C
Fig 1 (A) 600 MHz standard solvent sup-pression pulse 1 H NMR spectrum of rat urine (B) 600 MHz Carr–Purcell–Meiboom– Gill (CPMG) 1 H NMR spectrum of rat blood plasma (C) 600 MHz CPMG 1 H MAS NMR spectrum of the left lateral lobe of a rat liver.
Trang 4Mass spectrometry
Mass spectrometry coupled to a chromatographic
separ-ation has also been widely used in metabolic
fingerprint-ing and metabolite identification Although most studies
to date have been on plant extracts and model cell
sys-tem extracts [16], its application to mammalian studies
is increasing MS is inherently considerably more
sensi-tive than NMR spectroscopy provided the metabolite
ionizes, but it is generally necessary to employ different
separation techniques for different classes of substances
Analyte quantitation by MS in complex mixtures of
highly variable composition can be impaired by variable
ionization and ion suppression effects For plant
meta-bolic studies, most investigations have used chemical
derivatization to ensure volatility and analytical
repro-ducibility, followed by GC-MS analysis Some
approa-ches using MS rely on more targeted studies, for
example by detailed analysis of lipids [17]
For metabonomics applications on biofluids, an
HPLC chromatogram is generated with MS detection,
usually using electrospray ionization, and both positive
and negative ion chromatograms can be measured At
each sampling point in the chromatogram there is a
full mass spectrum and so the data is three-dimen-sional in nature, i.e., retention time, mass and inten-sity Given this very high resolution it is possible to cut out any mass peaks from interfering substances such as drug metabolites, essentially without affecting the structure of the dataset
The problem of ion suppression is minimized by improving the efficiency of the chromatography and this has been achieved using UPLC This is a combina-tion of a 1.7 lm reversed-phase packing material, and
a chromatographic system, operating at around 827.4 bar
2 UPLC provides around a 10-fold increase in speed and a three- to five-fold increase in sensitivity compared to a conventional stationary phase
UPLC-MS has already been used for metabolic profiling of urines in a number of rodent studies [18] A compar-ison of data generated using both HPLC-MS and UPLC-MS is given in Fig 2
CE coupled to mass spectrometry has also been explored as a possible technology for metabonomics studies [19] Metabolites are first separated by CE based on their charge and size and then selectively detected using MS, and the technique has been applied
to studies of bacterial growth
HPLC
UPLC
6 8 7.5 10
150 300 450
600 750 m/z 150 300 450 600 750 m/z
9000
7500
6000
4500
3000
1500
0 cm
1750
1500
1000 1250
750
500
250
0 cm
Fig 2 Three-dimensional plots of retention time, m ⁄ z and intensity from control white male mouse urine using (left) HPLC-MS with a 2.1 cm · 100 mm Waters
5 Symmetry 3.5 lm C18 column (Milford, MA, USA), eluted with 0–95% linear gradient of water with 0.1% (v ⁄ v) formic acid:acetonitrile with 0.1% (v ⁄ v) formic acid over 10 min at a flow rate of 0.6 mLÆmin)1and (right) UPLC-MS with 2.1 cm · 100 mm Waters ACQUITY 1.7 lm C18 column, eluted with the same solvents at a flow rate of 0.5 mLÆmin)1 In both cases, the column eluent was monitored by ESI orthogonal acceleration
6 -TOF-MS from 50 to 850 m ⁄ z in positive ion mode Reproduced with permission from Wilson
et al [18].
Trang 5For biomarker identification, it is also possible to
separate out substances of interest on a larger scale
from a complex biofluid sample using techniques such
as solid phase extraction or HPLC For metabolite
identification, directly coupled
chromatography-spectro-scopy methods can also be used The most general of
these ‘hyphenated’ approaches is HPLC-NMR-MS [20]
in which the eluting HPLC peak is split, with parallel
analysis by directly coupled NMR and MS techniques
This can be operated in on-flow, stopped-flow and
loop-storage modes, and thus can provide the full
array of NMR and MS-based molecular identification
tools These include 2D NMR spectroscopy as well as
MS-MS for identification of fragment ions and Fourier
transform (FT)-MS or time of flight (TOF)-MS for
accurate mass measurement and hence derivation of
molecular empirical formulae
Chemometrics methods
One common objective in metabonomics is to classify a
sample based on identification of inherent patterns of
peaks in a dataset (usually a spectrum) and secondly to
identify those spectral features responsible for the
clas-sification The approach can also be used for reducing
the dimensionality of complex datasets, for example by
2D or 3D mapping procedures, to enable easy
visual-ization of any clustering or similarity of the various
samples Alternatively, in what are known as
‘super-vised’ methods, multiparametric datasets can be
mod-elled so that the class of separate samples (a ‘validation
set’) can be predicted based on a series of mathematical
models derived from the original data or ‘training set’
One popular technique that has been used
exten-sively in metabonomics is principal components
analy-sis (PCA) Each PC is a linear combination of the
original data parameters (e.g., intensity values for a
range of ion m⁄ z-values from MS) and each successive
PC explains the maximum amount of variance
poss-ible, not accounted for by the previous PCs Each PC
is orthogonal and therefore independent of the other
PCs and so the variation in the spectral set is usually
described by many fewer PCs than comprise the
num-ber of original data point values, because the less
important PCs describe the noise variation in the
spec-tra Conversion of the data to PCs results in two
mat-rices known as scores and loadings Scores, the linear
combinations of the original variables, can be regarded
as the new variables, and in a scores plot each point
represents a single sample spectrum The PC loadings,
where each point represents a different spectral
inten-sity, define the way in which the old spectral variables
are linearly combined to form the new variables and
show those variables carrying the greatest weight in determining the positions of the points in the scores plot In addition, there are many other visualization (or unsupervised) methods such as nonlinear mapping and hierarchical cluster analysis
To illustrate PCA, Fig 3 shows the scores and loa-dings plots for PC1 versus PC2 for data from a series
of1H NMR spectra of rat urine in a toxicity study In
Fig 3 Results of a principal components analysis based on NMR spectra of urine from rats treated with control dosing vehicle, or one of the two liver toxins, a-naphthylthioisocyanate (ANIT) or hydrazine (A) PC scores plot (PC1 versus PC2) where each point corresponds to a single urine sample, showing clear clustering of the samples from control urine and from the toxin-treated animals The liver toxins form separate clusters because they have different biochemical mechanisms of action and hence different biochemical profiles in the urine (B) The corresponding PC loadings plot where each point corresponds to a specific NMR spectral region, leading
to the possibility of identifying biomarkers of the toxicological effect For example, in the scores plot, the points corresponding to urines from hydrazine-treated animals appear in the lower left quad-rant and in the corresponding loadings plot, this region indicated that NMR peaks from 2-AA (2-aminoadipate) were important, and thus this is a biomarker of the hydrazine-induced toxicity.
Trang 6the scores plot, each point represents a single NMR
spectrum and the clustering of points shows the
differ-ent biochemical effects of the two differdiffer-ent toxins
rel-ative to the control group In cases where samples are
collected over time, onset and recovery trajectories can
be observed The loadings plot indicates which regions
of the NMR spectra are responsible for the clustering
If a predictive model is required, one widely used
supervised method (i.e using a training set of data with
known outcomes) is projection on latent structures
(PLS) This is a method that relates a data matrix
con-taining variables from samples, such as spectral
inten-sity values (an X matrix), to a matrix containing
outcome variables (e.g., measurements of response, such
as toxicity scores) for those samples (a Y matrix) PLS
can also be used to examine the influence of time on a
dataset, which is particularly useful for biofluid NMR
data collected from samples taken over a time course of
the progression of a pathological effect PLS can also be
combined with discriminant analysis (DA) to establish
the optimal position to place a discriminant surface that
best separates classes It is important to build and test
such chemometric models using independent training
data and validation datasets Extensions of this
approach allow the evaluation of those descriptors that
are completely independent (orthogonal) to the Y
mat-rix of end-point data This orthogonal signal correction
(OSC) can be used to remove irrelevant and confusing
parameters and has been integrated into the PLS
algo-rithm [21] If the Y matrix contains continuous data,
then PLS regression is a very useful approach
There is a variety of other methods that use nonlinear
combinations of the data variables and these include
genetic algorithms, machine learning, Bayesian
mode-ling and artificial neural networks In these, a training
set of data is used to develop algorithms, which ‘learn’
the structure of the data and can cope with complex
functions For example, probabilistic neural networks
have shown promise for predicting toxicity from
NMR-based metabonomics data [22]
It should be emphasized that both unsupervised and
supervised approaches can be useful in metabonomics
Unsupervised methods provide information on the
nat-ural structure of the data, whilst for supervised
meth-ods it is vital to carry out proper validation of the
models generated, involving training datasets and blind
validation and test sets of data
Statistical spectroscopy for biomarker
identification
Recently, a new method for identifying multiple
NMR peaks from the same molecule in a complex
mixture, hence providing a new approach to molecu-lar identification, has been introduced This is based
on the concept of statistical total correlation spectro-scopy and has been termed STOCSY [23] This takes advantage of the colinearity of many of the intensity variables in a set of spectra (e.g., 1H NMR spectra)
so that a pseudo-2D NMR spectrum can be calcula-ted that displays the correlation among the intensities
of the various peaks across the whole sample This method is not limited to the usual connectivities that are deducible from more standard 2D NMR spectro-scopic methods, such as TOCSY Added information
is available by examining lower correlation coeffi-cients or even negative correlations because this leads
to connection between two or more molecules involved in the same biochemical pathway In an extension of the method, the combination of STO-CSY with supervised chemometrics methods offers a new framework for analysis of metabonomic data In
a first step, a supervised multivariate discriminant analysis can be used to extract the parts of NMR spectra related to discrimination between two sample classes This information is then combined with the STOCSY results to help identify the molecules responsible for the metabolic variation The applica-bility of the method is illustrated in Fig 4, where a spin system of two triplets can be noticed at d 2.91 and d 2.51 This spin system is strongly correlated to others resonances in the aromatic region of the spec-trum, although not spin-coupled Computing only the correlation between one of the data points represent-ing the maximum of one of the triplets, and all the other variables leads to a single vector, which has the size of the number of variables used Then, by selecting the spectrum with the maximum value of this selected variable, it is possible to plot it with a colour code corresponding to the correlation between the selected resonance and all the other points of the spectra Correlations can be observed between reso-nances with no NMR-based spin-coupling connectiv-ity Thus, in the aromatic region, shown in Fig 4, it
is possible to recognize the resonances of a meta-substituted benzene ring (one triplet, two doublets, and one singlet) Thus, this molecule can be identi-fied as a derivative of a meta-substituted phenylprop-anoic acid and is probably 3-hydroxyphenylpropionic acid
The approach is not limited to NMR spectra alone and has been extended to other forms of data It has recently been applied to coanalysis of both NMR and mass spectra from a metabonomic toxicity study [24] This allowed better assignment of biomarkers of the toxin effect by using the correlated but complementary
Trang 7information available from the NMR and mass spectra
taken on a whole sample cohort
Standards and reporting needs in metabonomics
Several initiatives have been under way to investigate
the reporting needs and standardization of reporting
arrangements for metabonomics studies The Standard
Metabolic Reporting Structures group (http://
www.smrsgroup.org) has produced a draft policy
document covering all of those aspects of a metabolic
study that are recommended for recording, from the
origin of a biological sample, the analysis of material
from that sample, and chemometric and statistical
approaches to retrieve information from the sample
data, and a summary publication has appeared [25]
The various levels and consequent detail for reporting
needs, including journal submissions, public databases
and regulatory submissions have also been addressed
In parallel, a scheme called ArMet for capturing data
and metadata from metabolic studies has been
pro-posed and developed [26] These activities were
fol-lowed up in August 2005 with a workshop and
discussion meeting sponsored by the US National
Institutes of Health, from which plans are being
devel-oped to define standards in a number of areas relevant
to metabonomics, including characterization of
sam-ple-related metadata, technical standards and related
data, metadata and quality control matters for the
analytical instrumentation, data transfer methodologies
and schema for implementation of such activities, and
development of standard vocabularies to enable
trans-parent exchange of data [27] For details of the current
activity in this area the reader is referred to the Meta-bolomics Standards Initiative (http://msi-workgroups sourceforge.net/)
Pharmaceutical R & D metabonomics applications
Physiological and gut microfloral effects
A good understanding of normal biochemical profiles
is a prerequisite for evaluation of metabolic changes caused by xenobiotics or disease Thus metabonomics has been used to identify metabolic differences, in experimental animals such as mice and rats, caused by
a range of inherent and external factors [28] These dif-ferences may help explain differential toxicity of drugs between strains and interanimal variation within a study Many effects can be distinguished, including male⁄ female differences, age-related changes, estrus cycle effects in females, diet, diurnal effects, differenti-ation of wildtype and genetically modified animals, and interspecies differences and similarities using both NMR- and MS-based approaches
The importance of the symbiotic relationship between mammals and their gut microfloral popula-tions has been recognized [29] and highlighted in sev-eral studies These include a study in which axenic (germ free) rats were allowed to recolonize their gut microflora in normal laboratory conditions with their urine biochemical profiles being monitored for 21 days using 1H NMR spectroscopy [30], and the combined effects of gut bacteria and gut parasites on metabolic profiles [31]
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7.4 7.3 7.2
HO
HO
O
1
0.95
0.85
0.75
0.65 0.7 0.8 0.9
δ 1 H (p.p.m.)
r 2
δ 1 H (p.p.m.)
2.8 2.7 2.6 2.5
Fig 4 One-dimensional STOCSY analysis to identify peaks correlated to that at the chemical shift, d 2.51 The degree of correla-tion across the spectrum has been colour-coded and projected on the spectrum (A) Full spectrum; (B) partial spectrum between d 7.1–7.5; (C) partial spectrum between d 2.4–3.0 The STOCSY procedure enabled the assignment of this metabolite
as 3-hydroxyphenylpropionic acid Adapted from Cloarec et al [23]
Trang 8The metabolic consequences of early life maternal
separation stress have been investigated in rats, either
alone or in combination with secondary acute water
avoidance stress [32] The effect of a long chain
poly-unsaturated fatty acid (LC-PUFA) enriched dietary
intervention (postulated to be beneficial) in stressed
animals was also assessed Systematic changes in
meta-bolic biochemistry were evaluated using 1H NMR
spectroscopy of blood plasma and multivariate pattern
recognition techniques The biochemical response to
stress was characterized by decreased levels of total
lipoproteins and increased levels of amino acids,
glucose, lactate, creatine and citrate Secondary acute
water avoidance stress also caused elevated levels of
O-acetyl glycoproteins in blood plasma LC-PUFA
dietary enrichment did not alter the metabolic response
to stress but did result in a modified lipoprotein
pro-file This work indicated that the different stressor
types resulted in some common systemic metabolic
responses that involve changes in energy and muscle
metabolism, but that they are not reversible by dietary
intervention
Irritable bowel syndrome (IBS) is a common
multi-factorial intestinal disorder for which the aetiology
remains largely undefined, and recently, using a
Trichi-nella spiralis-induced model of postinfective IBS, the
effects of probiotic bacteria on gut dysfunction have
been investigated using metabonomics [33] Mice were
divided into four groups: an uninfected control group
and three T spiralis-infected groups, one as infected
control and the two other groups subsequently treated
with either Lactobacillus paracasei or L paracasei-free
medium Plasma, jejunal wall and longitudinal
myen-teric muscle samples were collected at day 21
postinfec-tion, and NMR spectroscopy was used to characterize
these and plasma metabolic profiles T spiralis-infected
mice showed an increased energy metabolism, fat
mobilization and a disruption of amino acid
meta-bolism due to increased protein breakdown, which
were related to the intestinal hypercontractility
Increased levels of taurine, creatine and
glycerophos-phorylcholine in the jejunal muscles were associated
with the muscular hypertrophy and disrupted jejunal
functions L paracasei treatment normalized the
mus-cular activity and the disturbed energy metabolism as
evidenced by decreased glycogenesis and elevated lipid
breakdown in comparison with untreated T
spiralis-infected mice Changes in the levels of plasma
metabo-lites (glutamine, lysine, methionine) that might relate
to a modulation of immunological responses were also
observed in the presence of the probiotic treatment
The work suggested that probiotics may be beneficial
in patients with IBS
Pre-clinical drug candidate safety assessment
In vivo preclinical drug safety assessment remains one
of the main bottle-necks in pharmaceutical R & D and
is a prime target for improving efficiency in drug development
Having defined the metabolic hyperspace occupied
by normal animals, metabonomics can be used for rapid classification of a biofluid sample as normal or abnormal Classification of the target organ or region
of toxicity, the biochemical mechanism of a toxin, the identification of combination biomarkers of toxic effect and evaluation of the time course of the effect, e.g., the onset, evolution and regression of toxicity, can all
be determined There have been many studies using
1H NMR spectroscopy of biofluids to characterize drug toxicity going back to the 1980s [3], and the role
of metabonomics in particular, and magnetic reson-ance in general in toxicological evaluation of drugs has been comprehensively reviewed [34] The situation is now changing with the introduction of the combined use of NMR spectroscopy and HPLC-MS for toxicity studies
The usefulness of NMR-based metabonomics for the evaluation of xenobiotic toxicity effects has recently been comprehensively explored by the successful Con-sortium for Metabonomic Toxicology This was con-ducted by five pharmaceutical companies and Imperial College, London, UK [35], and its aim was to develop methodologies for the acquisition of metabonomic data using 1H NMR spectroscopy of urine and blood serum from rats and mice for preclinical toxicological screening of candidate drugs, to build databases of spectra and to develop an expert system for predicting target organ toxicity
To assess the levels of analytical and biological vari-ation that could arise through the use of metabonom-ics on a multisite basis, a feasibility study was carried out at the start of the project, using the same detailed protocol and using the same model toxin, across all company sites The biological variability was evaluated
by a detailed comparison of the ability of the compan-ies to provide consistent urine and serum samples for
an in-life study of the same toxin There was a high degree of consistency between samples from the various companies and dose-related effects could be distinguished from intersite variation An intersite NMR analytical reproducibility study also revealed a high degree of robustness giving a multivariate coeffi-cient of regression between paired samples of only about 1.6% [36]
To achieve the project goals, new methodologies for analyzing and classifying the complex datasets were
Trang 9developed The predictive expert system that was
developed takes into account the metabolic trajectory
over time, and so a new way of comparing and scaling
these multivariate trajectories was developed [37] A
novel classification method for identifying the class of
toxicity based on all of the NMR data for a given
study was also developed This has been termed
‘Clas-sification Of Unknowns by Density Superposition
(CLOUDS)’ [38] and is a novel non-neural
implemen-tation of a classification technique developed from
probabilistic neural networks
This consortium showed that it is possible to
con-struct predictive and informative models of toxicity
using NMR-based metabonomic data, delineating the
whole time course of toxicity Curated databases of
spectral ( 35 000 NMR spectra) and conventional
(clinical chemistry, histopathology, etc.) results for 147
model toxins and treatments that served as the basis
for computer-based expert systems for toxicity
predic-tion were also produced All of the project goals
inclu-ding provision of multivariate statistical models (expert
systems) for prediction of toxicity, initially for liver
and kidney toxicity in the rat and mouse were
achieved, and the predictive systems and databases
were transferred to the sponsoring companies [39]
Clinical pharmaceutical applications
Many examples exist in the literature on the use of
NMR-based metabolic profiling to aid human disease
diagnosis, such as the investigation of diabetes using
plasma and urine, neurological conditions such as
Alzheimer’s disease using cerebrospinal fluid, arthritis
using synovial fluid and male infertility using seminal
fluid In addition, analysis of urine has been used in
the investigation of drug overdose, renal
transplanta-tion and various renal diseases NMR spectroscopy
of urine and plasma has been used extensively for
the diagnosis of inborn errors of metabolism in
chil-dren [40] Most of the earlier studies using NMR
spectroscopy have been reviewed previously [41], but
more recent studies include cerebrospinal fluid sample
analysis using NMR spectroscopy to distinguish
var-ious types of meningitis infection (bacterial, viral and
fungal) [42] and to investigate subarachnoid
haemor-rhage [43] Human serum samples have been analysed
using NMR spectroscopy to develop a diagnostic
method for coronary artery disease [44] One area of
disease where progress is being made using
NMR-based metabonomics studies of biofluids is cancer
This is highlighted by a publication on the diagnosis
of epithelial ovarian cancer based on analysis of
serum [45]
Pharmacometabonomics For personalized healthcare, an individual’s drug treat-ments must be tailored so as to achieve maximal effic-acy and avoid adverse drug reactions One of the approaches has been to understand the genetic
make-up of different individuals (pharmacogenomics) and to relate these to their varying abilities to handle pharma-ceuticals both for their beneficial effects and for identi-fying adverse effects Very recently, an alternative approach to understanding such intersubject variability has been developed using metabonomics, and used to predict the metabolism and toxicity of a dosed sub-stance, based solely on the analysis and modeling of a predose metabolic profile [46] Unlike pharmaco-genomics, this approach, which has been termed ‘phar-macometabonomics’, is sensitive to both the genetic and modifying environmental influences that determine the metabolic fingerprint of an individual This new approach has been illustrated with studies of the toxic-ity and metabolism of compounds with very different modes of action (allyl alcohol, galactosamine and acet-aminophen) administered to rats
Integration of -omics results
The value of obtaining multiple NMR spectroscopic and⁄ or LC-MS datasets from various biofluid samples and tissues of the same animals collected at different time points has been demonstrated This procedure has been termed ‘integrated metabonomics’ [2] and can be used to describe the changes in metabolism in different body compartments affected by exposure to, for exam-ple, toxic xenobiotics If profiles are obtained over time, they provide extra information and are character-istic of particular types and mechanisms of pathology Samples from multiple sources give a more complete description of the biochemical consequences than can
be obtained from one fluid or tissue alone
Although this review concentrates on metabolic ana-lyses, there is a requirement to integrate information at the transcriptomic, proteomic and metabonomic levels, despite these different levels of biological control showing very different time scales of change This is because some time courses can be very rapid, such as gene switching, some require much longer time scales, e.g protein synthesis, or in the case of metabolic chan-ges, can encompass enormous ranges of time scales Biochemical changes do not always occur in the order, transcriptomic, proteomic, metabolic, because, for example, pharmacological or toxicological effects at the metabolic level can induce subsequent adaptation effects at the proteomic or transcriptomic levels One
Trang 10important potential role for high-throughput and
highly automated metabonomics methods therefore
could be to direct the timing of more expensive or
labour-intensive proteomic and transcriptomic analyses
in order to maximize the probability of observing
meaningful and relevant biochemical changes using
those techniques
In addition, overlaid with this temporal complexity,
is the fact that environmental and lifestyle effects have
a large effect at all levels of molecular biology Gene
and protein expression effects and metabolite levels
can be altered by such factors, and this variation has
to be incorporated into any analysis as part of
inter-sample and interindividual variation Even healthy
ani-mals and man can be considered as ‘super-organisms’,
with an internal ecosystem of diverse symbiotic gut
microflora that have metabolic processes that interact
with the host and for which, in many cases, the
gen-ome is not known The complexity of mammalian
bio-logical systems and the diverse features that need to be
measured to allow ‘-omics’ data to be fully interpreted
have been reviewed recently [47] and it has been
argued that novel approaches will continue to be
required to measure and model metabolic processes in
various compartments from such global systems with
different interacting cell types, and with various
geno-mes, connected by cometabolic processes
Integration of metabonomics data with that from
other multivariate techniques in molecular biology
such as from gene array experiments or proteomics is
now becoming a reality Pharmaceutically related
examples include phenotypic differences [48] and
toxic-ity studies of acetaminophen [49], bromobenzene
[50,51], a-naphthylisothiocyanate [52], hydrazine [53]
and methapyriline [54]
Future promise
In summary, it is clear that metabonomics will have an
impact in pharmaceutical R & D but some potential
disadvantages of the approach include the use of
mul-tiple analytical technologies with different sensitivities,
dynamic ranges and metabolite detection abilities and
the complexity of the resulting datasets Through the
inappropriate application of chemometrics, it is
poss-ible to over-interpret the data, but this is easily
avoi-ded by correct statistical rigour There remains a need
for the regulatory agencies to be trained in the
inter-pretation of the data and for the availability of more
well trained practitioners generally
However, on the other hand, the analytical
proce-dures used are stable and robust, and have a high
degree of reproducibility, and although advances will
obviously be made in the future, current data will always be readable and interpretable In contrast to other -omics, metabonomics enjoys a good level of biological reproducibility and the cost per sample and per analyte is relatively low It has the advantage of not having to preselect analytes, and generally it is minimally invasive with hypothesis generation studies being easily possible Metabolic biomarkers are closely identifiable with real biological endpoints and provide
a global systems interpretation of biological effects, including the interactions between multiple genomes such as humans and their gut microflora One major potential strength of metabonomics is the possibility that metabolic biomarkers will be more easily used across species than transcriptomic or proteomic bio-markers and this should be important for pharmaceuti-cal studies
For complex disease and drug effect evaluation, combinations of biomarkers are likely to be necessary and thus there will be many opportunities for metabo-nomics that are as yet under-explored, such as its use
in environmental toxicity studies, its use in directing the timing of transcriptomic and proteomic experi-ments, and its use for deriving theranostic biomarkers
It will surely be an integral part of any multiomics study where all the datasets are combined in order to derive an optimum set of biomarkers
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