Metabonomics in Pharmaceutical Discovery and ... - ACS Publications

Metabonomics in Pharmaceutical Discovery and Development Donald G. Robertson,* Michael D. Reily, and J. David Baker Metabonomics Evaluation Group, Pfizer Global Research and Development, Ann Arbor, Michigan 48105 Received October 11, 2006

Metabonomics has emerged as a key technology in pharmaceutical discovery and development, evolving as the small molecule counterpart of transcriptomics and proteomics. In drug discovery laboratories, metabonomics aids in target identification, phenotyping, and the understanding of the biochemical basis of disease and toxicity. This review focuses on three areas where metabonomics is used in the industry: (1) analytical considerations, (2) chemometric and statistical concerns, and (3) biological aspects and applications. Keywords: metabonomics • NMR spectroscopy • mass spectrometry • chemometrics • pharmaceutical development

1. Introduction Metabonomics is a relatively recent scientific development, and through the technology’s short history, pharmaceutical applications have played an important part in its development.1 Even before the term was officially coined,2 pharmaceutically relevant applications of metabonomic technology were making their way into the literature.3-11 Indeed, from the late 1990s to approximately 2005, the early adopters of the technology for biomedical applications were primarily pharmaceutical companies. This distribution of metabonomics practitioners began to significantly change with the publication of the NIH road map initiative and the call for funding proposals,12-15 and it can be anticipated that in the near future academic and commercial exploration of the technology will expand dramatically. However, there still remains the question of why metabonomics was so readily adopted by the pharmaceutical industry? Was it prescience or panic? In the mid 1990s, several factors were converging within the industry that made the promise of metabonomics technology very appealing. The advent of combinatorial chemistry and high-throughput screening greatly increased the diameter of the early drug discovery pipeline, leaving a profound bottleneck as the potential new drugs began to be evaluated in vivo using traditional multiweek animals studies. Additionally, the industry was rapidly running out of simple novel (and patentable) targets (e.g., enzyme inhibitors), opting for more fundamental targets such as kinases, growth factors, and transcription regulation. While these targets are potentially much more powerful, they can be exceedingly complex, leading to a great need to understand fundamental biology from both an efficacy and safety perspective. Combined, these factors led to faster influx of novel chemical entities into preclinical development (pharmacology, ADME, and safety departments) while presenting with more complex biologic profiles. Metabonomics was seen as one * Corresponding author. Dr. Donald G. Robertson, Molecular Profiling, Pfizer Global Research and Development, 2800 Plymouth Rd., Ann Arbor, Michigan 48105. Phone: (734) 622-7534. Fax: (734) 622-2562. E-mail: [email protected].

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approach to understand the many biologic sequelae of compound administration in vivo while minimizing both time and compound requirements necessary to get answers. While the technology has impacted all aspects of preclinical drug development, the emphasis of this review will be on safety assessment-related applications, as this is where the authors have the greatest experience and this is what has been the focus of the majority of preclinical applications to date.

2. Metabonomics Analytical Methods At the heart of any successful metabonomics study is a highquality data set that produces a biochemical snapshot reflecting the temporal state of an organism through its endogenous small molecules or “metabolites”. The interpretation that metabonomics seeks to achieve can be thought of at two levels. The first of these is basically a spectral or chromatographic result that reflects the overall changes in metabolite concentration without necessarily identifying the actual components that are changing. This type of data can be thought of as a metabolic fingerprint16 and is simply a numerical representation of an analytical response arising from the milieu of components that comprise the sample. Such data from many samples are readily amenable to multivariate statistical analysis and generation of classification models as described later in this review. Virtually any analytical method can provide a metabolic fingerprint, but those that are most useful should be both highly reproducible and provide a high level of detail.17 Reproducibility should be manifested not only within a given sample set, but preferably over long time periods and on different platforms, allowing comparison of data sets acquired in different laboratories months or years apart. Inasmuch as reproducibility is affected by analytical and sample variability, efforts to simplify sample handling and processing will ensure that metabonomics observations are due to changes in the biological state of the organism and not due to some small variation in reagents, protocol, or laboratory environment.17 The second and more powerful level of interpretation is a comprehensive and quantitative list of metabolites that can be mapped to specific 10.1021/pr060535c CCC: $37.00

 2007 American Chemical Society

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Metabonomics in the Pharmaceutical Industry

pathways and hence provide biomarkers and/or mechanistic information about a process, an approach which as been referred to as metabolomics16 or metabolic profiling.18 There are relatively few techniques that are capable of providing this level of detail, and preeminent among these are analytical methods based on nuclear magnetic resonance (NMR) and various incarnations of mass spectrometry (MS). This is in no small part due to the power of both NMR and MS to generate reproducible and useful spectral patterns and to directly identify molecular components within complex biological samples. A comparison of the benefits of NMR and MS techniques and various equipment configurations relevant for metabonomics has been recently reviewed,17 as have general analytical approaches for metabonomics.19 2.1. NMR Spectroscopy. Proton NMR spectroscopy provides a reproducible20 and linear analytical response at the atomic level with high dynamic range, positioning it as an excellent tool for both fingerprinting and profiling. NMR enjoys a rather universal detection of metabolites, providing they contain a hydrogen atom, and there are no extinction coefficients or variable ionization problems that trouble other analytical techniques. On the downside, NMR is relatively insensitive, and the pH dependence of the chemical shift of protons near ionizable groups can be a limitation for NMR as a fingerprinting tool. The latter situation is primarily a problem in analysis of urine, which can vary widely in pH, with the range of 5.2-7 not uncommon in normal rats. Recently, we have described an internal pH indicator that is used to measure pH in biological fluids that allows in situ determination of exact pH in biofluid samples.21 NMR is also well-suited for identification of unknowns, and numerous NMR experiments have been designed to exploit NMR parameters toward the identification and quantification of species in complex mixtures such as biofluids.1,22,23 There are numerous reviews on NMR-based metabonomics that cover general aspects1,17,19,22-26 and applications including toxicology,27,28 preclinical toxicity screening,29,30 ecotoxicology,31 physiology,32 endocrinology,33 reverse pharmacology,34 cancer,35 inborn errors of metabolism,36 neurological disorders,37 and kidney transplant rejection.38,39 One of the distinct advantages of NMR-based approaches is that resolution of individual components is achieved spectroscopically, and therefore, biofluids can be analyzed directly with minimal sample preparation. Recent technological developments in NMR spectroscopy, such as cryogenically cooled flow probes40,41 optimized for salty samples42 and microcoil probes,43 will help address (but not eliminate) poor sensitivity. In summary, despite its Achilles’ heel of low sensitivity, proton NMR spectroscopic approaches have become, and will continue to be, a front line analytical technique for metabonomics, capable of providing a matrix-independent, highly reproducible, and detailed metabolic fingerprint and profile with limited sample manipulation. 2.2. Mass Spectrometry. Mass spectrometry has been used for plant metabolism for many years, but only recently has begun finding a niche in pharmaceutical metabonomics applications. Unlike NMR, which is primarily used to look at whole biofluids, MS is normally employed as a highly sensitive and selective detector used in conjunction with separation techniques. The most widely used hyphenated MS systems employ either gas chromatography (GC), high-performance liquid chromatography (HPLC), or capillary electrophoresis (CE) as a front-end separation technique. General MS methodologies for metabonomics have been reviewed44,45 as have the specific

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GC-MS,48 and CEhyphenated techniques of LC-MS, 49-51 MS. Despite the inherent variability (both long and short term) that chromatography can introduce, useful patterns from which predictive models have been derived have come from HPLC-MS52-54 and GC-MS.55 Direct infusion of biological samples into the mass spectrometer has the advantage of bypassing the chromatography altogether and therefore has the potential of being higher throughput and more reproducible. Recent approaches for direct18 or chip-based56 infusion approaches suggest that it may be possible to introduce biofluid or prepared biofluid samples directly into a mass spectrometer to obtain molecular fingerprint and/or profile information. Reports on specific MS-based metabonomics applications relevant to pharmaceutical industry include diabetes,57 rheumatoid arthritis,58 genetic modification,59 clinical hepatitis,60 neonatal asphyxiation,61 and preclinical toxicology.62,63 Building on a long and successful history of metabolic analysis in plant science, mass spectrometry is now playing an increasingly important role in pharmaceutical applications of metabonomics and brings with it ultrahigh sensitivity and powerful molecular identification capabilities. 2.3. Other Analytical Methods. Although MS and NMR dominate the pharmaceutical metabonomics arena due to the advantages already mentioned, other approaches have been put to bear on this technology as well. Notable among these are vibrational spectroscopies such as Raman and Fourier transform infrared (FT-IR) approaches, which have been evaluated as metabolic fingerprinting tools both in clinical39,64 and preclinical65 settings. The array of tools applicable to metabonomics continues to grow, fueled by the need for a broader and more detailed understanding of the metabolome. No single technique can lay claim to be the universal detector, and as a result, multiple analytical methodologies will always be the best approach for a thorough understanding of metabolic changes. Combined analytical approaches48,66,67 are becoming more commonplace and will no doubt eventually become the rule rather than the exception.

3. Chemometrics 3.1. Overview. Data analysis strategy is as important as the analytical technique employed for obtaining useful metabonomics results. Data analysis usually requires an initial step of reducing the data to a form amenable for subsequent statistical analyses. Both univariate and multivariate statistics follow the data reduction step, and both supervised and unsupervised strategies are employed for multivariate statistics and model building for prediction and classification of outcomes. 3.2. Data Processing. Within the pharmaceutical industry, NMR and MS approaches are the methods most commonly used for metabonomic investigations. As collected, NMR and LC/GC-MS data need additional processing, standardization, normalization, and reduction prior to analysis. Standard NMR processing includes referencing, phasing, window functions,68 digital filtering to remove solvent resonances,69 baseline correction, and normalization70 prior to subsequent reduction and analysis. For NMR, chemical species are usually detected as multiple resonances, the area-under-the-curve of which are proportional to the concentration of the chemical. NMR measurements are very robust between instruments, making it particularly attractive for model building.20 MS data are less robust between instruments and is additionally dependent on hardware parameters, ionization method, solvent systems, and Journal of Proteome Research • Vol. 6, No. 2, 2007 527

reviews matrix conditions. It is, therefore, common for MS analysis to add multiple internal standards of known concentration appropriate for various chemical classes of analysis. If temporal quantitative stability is desired (e.g., model building), an array of standards for each component of interest needs to be used frequently to develop and maintain a calibration response for each component. This can be time-consuming for groups in which this type of analysis is not their main focus. Consequentially, commercial labs are beginning to provide this type of service. Beyond base processing, additional normalization is applied to the data prior to analysis. For NMR of urine, the most common normalization is to set the total spectral area (less regions of solvent, drug metabolites, and reference standards) to 100%. This normalization is a snapshot of the “state” of the system. The normalized spectrum has the interpretation of a probability distribution, indicating the relative concentrations of the observed components. It is intuitive that a stressed system due to disease or a drug candidate will be in transition to, or in an abnormal state, which would be represented by a change in relative metabolite concentrations. Similar normalizations can apply to MS via normalization to the total ion current. For urine, this normalization minimizes the difference in concentrations due to varying urine volume. Another popular normalization is to measure spectral bands relative to a known endogenous standard. For flux measurements, data can be normalized to be proportional to total excreted product over a given period of time. 3.3. Data Reduction. The most useful reduction of the data would be to a table of annotated metabolites with concentrations (or relative concentrations) for each sample. Standard statistical methods could follow. In practice, however, this goal may be resource constrictive and not absolutely necessary depending on the questions being asked. The problem with NMR is that metabolites have multiple resonances, and many of these are congested with resonances of other metabolites. It seems intuitive to develop libraries of known metabolites and use these as a basis set to fit experimental spectra for subsequent concentrations. Peak positions are frequently dependent on matrix pH, and for ionized compounds, the nature of the counterion can have an effect, making library building much more complex. The library must be designed to take these variations into account. The ultimate library would include representative spectra across a variety of matrix conditions. While difficult, this approach is an active area of research.71,72 For MS data, metabolite identifications are aided by chromatographic separation and by the fact that mass spectra can either be a fingerprint to be matched to a library of known entities or represent a mass whose accuracy is sufficient to assign a molecular formula to be queried against known metabolites that have the same formula. Libraries are being developed to aid in MS identifications.73 Regardless of the ionization type, mass spectra are complicated by the fact that multiple masses may be present for each metabolite and may include multiple ion adducts, dimers, multiple-charged species, fragment ions, and so forth. While it would be logical to combine each ion associated with a metabolite into a single ion current, this is also difficult in practice. Ions from a given metabolite are usually highly convoluted with other species. This is easier to accomplish routinely with high-resolution techniques such as GC-MS or newer high-pressure LC-MS systems. For NMR-based metabonomics, it is common practice to account for small peak movements by dividing spectra into 528

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contiguous segments wide enough to account for such variation, but small enough not to include too many peaks from different metabolites. The most common segment width is 0.04 ppm resulting in around 250 segments often referred to as bins. These bins are treated as pseudo variables, and if a bin is determined to be interesting for a given study, then follow-up analysis to determine which metabolites are contributing to the bin can be accomplished. The binning process is a compromise, however, in that the bin boundaries are arbitrary and peaks do often occur in varying amounts between adjacent bins; therefore there are efforts attempting to set bin boundaries rationally.74 Mass spectra are usually reduced to retention time (RT)-ion pairs with associated integrated ion areas. Alignment is based on matching these pairs to a given tolerance. In the absence of fully annotated components versus samples, tables of binned NMR data versus samples and MS RT-ion data versus samples are the most common substrate for subsequent analysis. There are also methods that allow for reducing the effect of feature movement in supervised model building on full spectral data that will be discussed below.75 3.4. Statistical Analysis. With tables and sample annotations, it is possible to compute standard p-tests to screen for variables that have significantly different changes. There are two common mistakes made with this kind of analysis. The first is that, when measuring multiple variables, accepting a given p-value (e.g., 0.05) as significant will introduce a large proportion of false positives. If 1000 variables are measured and a cutoff of 0.05 is used, 50 variables will, on average, be discovered as significant. There are many strategies for controlling false discovery rates (FDR) that can be employed.76,77 The Benjamini and Hochberg procedure is simple to deploy in Microsoft Excel and is very effective at controlling FDR. The second most common mistake is using a univariate filter for subsequent multivariate analyses. The univariate filtering usually employs all samples and, therefore, invalidates subsequent cross-validation in multivariate analyses. In addition, the univariate filtering prior to multivariate analysis will almost always make the multivariate analyses seem to work. In fact, with random data and subsequent univariate false discoveries, multivariate analyses will still generate models that seem to look good. Multivariate methods can discover significant patterns of variables for class separation or modeling, none of which individually has any statistical significance. Univariate filtering will most likely not allow these significant couplings to be discovered. When dealing with “omics” data, in general, most variables will be coupled with other variables. This is to be expected from knowledge of biological pathways and known biochemical reactions. With many variable selective strategies, valuable insight into these couplings may be lost. For example, for a normal state, two variables may always be positively correlated. In a diseased or stressed state, this correlation may be lost or even reversed. Elimination of one of these as redundant would miss classification of the later condition. In practice, it is this coupling of variables that drives the most popular multivariate methods discussed in the next sections. 3.4.1. Unsupervised Pattern Recognition. Unsupervised pattern recognition attempts to discover, perhaps blinded to the annotations, natural clusters of samples driven by patterns of variables intensities. As already mentioned, the coupling of variables can be the most significant aspect of differentiating between groups. As such, covariance-based methods are particularly effective, and the most common procedure is


principal component analysis (PCA). Multivariate distance metrics can also be employed such as hierarchical cluster analysis (HCA) and k-nearest neighbors (k-NN),78 but covariance methods are more natural for omics data where coupling has a natural interpretation and distance may not. There is a small but significant difference between PCA with spectral data and tables of variables. With fields such as expression profiling, each measured entity is potentially an independent variable and is treated as such. With spectroscopicbased data, the true variables are the endogenous chemicals and their concentrations. Recall that there seldom is a reliable distillation to this level and often binned or raw data is used. With the case of spectroscopy, calling PCA a dimensionality reduction method is only partly true. A raw NMR spectrum typically has 32k points. When resolution-matching algorithms are used, this number could be adjusted from 4k to infinity and everything in between, and the information content of the raw data would be the same. Obviously these points are not variables. This is because these points are discrete samplings of a continuum. The variables are the integrated band areas, which are not dependent on sampling intervals (assuming sufficient sampling to describe the peaks). The maximum dimensionality of the data is the number of independent components that can be detected. Using binned areas is also dimension indeterminate prior to factor analysis. A spectrum with a single peak would have finite area across the whole spectrum and cannot be absolutely linked to a single bin but would still be a single factor. A more accurate description of the PCA procedure in this case would be dimension determination often referred to as the rank of the system. PCA will find the actual degrees of freedom in the data, which would be the true dimensionality, which is neither a function of the original sampling intervals nor the number of integrated bins. This misunderstanding is very common in the literature. All PCA analyses use the score plots to determine if there are any natural clusters. These clusters can indicate the presence of toxicity, disease, or other stressors that may make the variational structure in one group different from another. Often, if time course data is available from pretest through dosing and recovery, a natural trajectory through the data can be observed as the treated group moves away from a control group and then back during recovery. These trajectories can be very much mechanism-dependent and useful in classifying compound action. Some PCA analyses look at the loadings plots to see which variables are most contributing to cluster separation along a particular component. This can be a very useful procedure, but a common mistake is to interpret the loadings as if they have univariate significance. The loading values have to be taken in the context of all the significant changes in a particular factor and not independently. Less common but very powerful is to look at the residuals for each sample, sometimes referred to as the DModX.79,80 Recall that the PCA procedure is stopped before all variation is described. This means that there is some “residual” variation left for each sample. The magnitude of this variation can be a very significant statistic to discover samples with idiosyncratic variation. This can be used for both quality control (discovery of sample outliers) and for discovery of samples that have unexplained variation due to disease, or other stressor. In practice, this is more sensitive to change of state for a group than score plots and will be described in more detail below. PCA continues to be the most used technique for metabonomics analysis within the pharmaceutical industry and is often a precursor for subsequent methodology.

reviews 3.4.2. Supervised Pattern Recognition. Unsupervised methods work well when the variational differences between classes are larger than the variational differences within classes. Natural clusters are apparent, and conclusions can be drawn. However, the variational structures of classes are modeled together. Each sample from each class contributes to the modeled components. If one sample is taken out or added in, the components will change, since the balance of class variability contributing to the components will also change. If class sizes are highly unequal, then the larger class will dominate the derived components. If class separation is marginal, it may be difficult to identify clusters and derive conclusions. For these cases, supervised methods are needed. Supervised methods use known information to leverage structure in the data that can be used to model outcomes or classes. Outcomes and classes can have discrete membership or be continuous or stepwise variables. Supervised methods must be used with care to avoid overfitting, particularly when the number of samples is much smaller than the number of variables. A natural extension from PCA to a supervised method is to divide the sample data into subgroups by class outcome prior to model building. This method is known as soft independent modeling by class analogy (SIMCA).2,81 A separate PCA model is developed for each class. In this way, only the variation that is natural for a given class model is present in any one PCA model. This also makes each model less sensitive to addition of more samples of the same class to the model. An unknown sample can be queried against each class PCA model and two statistics calculated. The generated scores for the unknown can be used to calculate a multivariate standard deviation. This measurement is known as the Mahalanobis distance.78 The second measurement would be the magnitude of the residual. This metric is the most sensitive for class membership. An unknown can be compared to multiple PCA classes and the residual and Mahalanobis distance used to determine most probable class membership. Another method related to PCA is called partial least-squares (PLS). PLS is similar to PCA in that the data is decomposed into factors and scores, but the factors are biased by a desired outcome that can be either continuous or binary. In this way, the scores can be used for regression analysis with minimal components determined. Subtle differences that correlate with an outcome that may require a large number of principal components will be captured in lower components in the PLS procedure. The variant of PLS that uses binary or classification as an outcome is known as PLS discriminant analysis (PLSDA).82-84 The model predictions will not be binary, but class assignments can be attributed to the prediction value closest to the binary outcome target values. Through the use of external validation sets and cross-validation procedures, care must be taken with any supervised method that overfitting does not occur. In the past few years, the PLS procedure has been modified to project systematic variation out of a data set that does not correlate with a given outcome. One early method is known as orthogonal signal correction (OSC),85 and subsequent methods have been called orthogonal-PLS methods.86 While standard PLS biases the factors to correlate with the outcome desired, it is balanced with the PCA driver of describing large systematic variation. It is frequently the case that much of the systematic variation does not correlate with the outcome, making interpretation of the resulting factors with respect to Journal of Proteome Research • Vol. 6, No. 2, 2007 529

reviews biological drivers difficult. The OSC procedure has been used as a pre-PLS filter to remove large systematic variation from a data set that does not correlate with an outcome followed by normal PLS calculations. In reality, the OSC procedure is not technically a filter. It in itself is a supervised method that uses outcome values to find the orthogonal components. If interpreted as a filter and careful attention is not made to have external test sets not used in the OSC procedure, the subsequent PLS procedure will be extremely subject to overfitting, and the normal associated PLS diagnostics will not catch the problem. Subsequent o-PLS methods have imbedded the orthogonalization procedure inside the PLS algorithm and not as a prefilter. While, in principle, o-PLS procedures should not give better predictive models, this method will be less prone to overfitting than OSC-PLS, and the result will be models that are much more interpretable than standard PLS procedures. In particular, this method has been shown to minimize the effect of peak movement in NMR spectra. If the underlying cause of peak movement (e.g., pH) is not a factor in a classification problem, then peak movement is a source of variation orthogonal to the class outcome desires, and the o-PLS procedure applied to full resolution or minimally spectra has been demonstrated with success.75 While not prevalent in the metabonomics literature, efforts to use Neural Networks have also been published.87,88 3.4.3. Longitudinal Data Analysis. Because of the frequent use of peripheral samples, such as urine in pharmaceutical applications, it is possible to obtain multiple samples from the same animal over the course of a disease model or with treatment and recovery. This is a powerful advantage over other common approaches that require separate groups of animals to be sacrificed for histological scoring or for gathering tissues for further analysis. Variations on PLS modeling has been used to model time course directly such as modeling time as an independent variable to find factors that correlate with time and subsequently unfolding the scores of each time point for an animal as an extended set of descriptors. This can be subjected to further supervised or unsupervised data analysis. This twofold approach has been described as batch-PLS modeling89,90 due to its derivation from modeling batchoriented production. Another variant is to simply unfold the raw time data into one supervector of all time points.91 Subsequent modeling treats the entire time course with a single model. This can be a very powerful approach to differentiating response in both time and class of action.

4. Biological Aspects Although a diverse collection of metabonomics data has been collected from species as varied as bacteria,92 earthworms,93,94 bank voles,95 and steer,96 preclinical pharmaceutical interests lie primarily with rodents (typically rats and mice), rabbits, dogs, and monkeys. Even across this limited number of species, there are a host of issues that must be considered and understood in order to design, conduct, and interpret metabonomics studies. As these issues vary tremendously from species to species, this report will focus on rat and mouse studies, as these species represent the subject of the vast majority of literature reports of pharmaceutical applications of metabonomics. To many, the sensitivity of metabonomics to metabolic disruption by factors other than intended may be dismaying. This has lead some to conclude that the technology is simply “too sensitive” for routine analysis. However, the argument about sensitivity would only be valid if the technology were 530


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amplifying noise not signal. This is not the case with metabonomics; the measured metabolites are real and therefore represent real physiological impact, whether understood or not. Perhaps a better way of thinking about metabonomics is that it reveals previously unknown or misunderstood factors that may impact study interpretation, almost always a good thing. 4.1. Husbandry. It has been recognized for some time that gut florae play a significant role in maintaining the normal homeostatic biochemistry of mammals97-99 contributing to both metabolism100-102 and detoxification103-106 of xenobiotics, as well as potentially playing a role in disease processes.107 Metabonomic analyses readily identify subpopulations of rats within colonies that exhibit distinct systemic metabolic variations according to their gut flora.108-112 While the potential for gut flora-induced metabolic variation was an interesting observation, it has recently been demonstrated that this is more than just a transient and capricious event.67 Metabolic phenotype variants were identified from a well-known animal supplier, suggesting that this type of variation is probably more common than realized. The phenotypes were stable for over 1 year and remained so while housed separately from animals with a historical phenotype. The metabolic differences were attributed to gut flora variations, probably dependent on how the animal colonies were initiated.113 While this observation may be dismissed as anecdotal, the metabolic differences between the phenotypes were not trivial, with up to 15-fold differences in some metabolites. This finding represents a source of variation in studies that is not often determined, yet readily identified, by simple pretest metabonomic assessment of urine. Beyond animal suppliers, other husbandry factors that can significantly affect metabolic predisposition in a study setting include strain,32,66,84,87 sex (estrus),32,114 age,115,116 diet,1,32,117,118 stress,119 and diurnal cycle,32,114 all producing measurable effects on metabolic profile. Fortunately for pharmaceutical companies, the regulated nature of the business means that most, if not all, of these factors are routinely monitored and recorded so that reconstructing possible sources of metabolic variation is more straightforward than it might otherwise be. 4.2. Experimental Design. Within the pharmaceutical context, one of the great advantages of metabonomics technology, as compared to other omic approaches, is the ease with which it can be incorporated into existing study design. This is important, as it reduces bulk drug requirements, animal usage, and most importantly time in development programs necessitated by add-on studies. Urine is not routinely collected in most preclinical studies, yet, if it is, there is usually more than enough to perform studies. Therfore, its collection does not interfere with other aspects of study conduct. The uses of urine as an appropriate biofluid for metabonomic analyses has been described elsewhere,30 therefore it will not be dealt with here, but suffice it to say, it frequently is as informative as or more informative than serum or plasma metabonomic analyses with regard to biochemical effects of candidate therapeutics. The one glaring exception is lipid analyses. Figure 1 presents some previously published data30 on temporal effects of CCl4 administration in rats. This example demonstrates two of the most powerful factors metabonomics brings to preclinical evaluation of pharmaceuticals. In the example, 4 rats were dosed with 0.5 mL/kg CCl4 and urine metabolic profiles assessed daily for 96 h post-dose. It was quite evident that, even with a group as small as 4 rats, 3 different metabolic trajectories were evident. In this example, all 4 rats were affected by the compound, but a standard toxicological

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Figure 1. (a) Principal component scores plot of urine samples collected from rats treated with a single dose of 0.5 mg/kg CCl4. Different symbols represent different time periods with the letter inside each symbol representing an individual animal. Dotted trajectory lines are from two similarly responding animals (b and c), and solid trajectory lines are from two animals with disparate responses (a and d). Numbers above symbols represent concurrently determined serum ALT activities (IU/L). Mean ALT levels are indicated in the legend box. (b) Individual animal serumALT levels over time. Rats a-d are the same for both figures. Reprinted with permission from ref 30. Copyright 2002 Elsevier Science.

endpoint measurement (e.g., serum ALT) at any one time point would not have indicated this. First, this example demonstrates that the ability to monitor individual animals over time, from pretest through peak effect to reversal, brings back interpretive power to individual animal assessment. Data from any single time point would have been variable, and a typical method for dealing with such variability is to increase “N” so that at any one time an “average response” is measured with the belief that the average sample response would be more indicative of the population response. While this may be true, metabonomics allows the ability to understand the variability (temporal displacement in this example) enabling study design with smaller “n” size, always a good thing when considering bulk drug requirements and the desire to reduce animal usage. A related point this example demonstrates is that temporal understanding of individual animal response is critical for data interpretation. To illustrate this, one needs only think about

applying transcript analysis to this data set. If that were to be done, at what time should samples have been collected? After all, we typically design our experiments by timed endpoints not by animal response. It is clear that transcript analysis by time would have been misleading, as there were three temporal responses to the compound. Metabonomics enables the scientist to interpret data by maximal (or minimal) response on an individual animal basis, providing the capability to normalize what might otherwise seem to be highly variable data.

5. Applications 5.1. Model Characterization. Even with only a cursory familiarity with metabonomics, it should be apparent that one area where the technology can have impact is in better descriptions of our experimental models. This can mean characterization of the disease processes that are targets of intervention,120,121 or it can mean better descriptions of the Journal of Proteome Research • Vol. 6, No. 2, 2007 531

reviews models themselves such as atypical strains, pharmacological manipulation, or transgenic animals.122 While these cannot be covered in detail, metabonomic target description/discovery work has been conducted in models of apoptosis,123 obesity (with Zucker rats),48,124 lipopolysaccharide models of idiosyncratic toxicity,125 and genetically modified cell lines.126 5.2. Chemotype Differentiation. With the advent of combinatorial synthesis technology and high-throughput screening, one of the most pressing needs in early drug discovery is differentiating chemotypes that have similar efficacy at the target. This can sometimes be a bit of a coin flipping exercise, as teams choose compounds to move forward into development based on insignificant differences in IC50 or equally meaningless differences in pharmacokinetic or pharmaceutics properties. The ability to differentiate chemotypes in vivo can be very beneficial for prioritizing compounds to go forward or to diversify the chemical portfolio, thus, maximizing chances for success. Figure 2 provides an example of such an application. In this example, two chemical series were evaluated by metabonomics in vivo very early in the discovery and development process. It was quite clear that even though both series had similar IC50 and in vivo efficacy, they were quite distinct in their metabolic impact on the animal. This suggests that one series had either off-target effects or in vivo kinetics (also off target) quite different from the other. Without having to identify a single metabolite, useful information was provided to the development team about these chemical series. While this experiment did not demonstrate which compound was better to take forward because it was safer or more efficacious, it did provide information that, all other things being equal, provided a classification of the chemical series with a recommendation that representatives from both classes move forward, rather than all of one or the other, providing meaningful diversity in the portfolio. Further understanding of the metabolic significance of the differential effects may have provided efficacy or safety differentiation as well, but that would require more work which may or may not be worth it at this early stage of drug discovery and development. 5.3. Safety Screening. One of the earliest proposed uses of metabonomics technology was in the arena of screening.116 Though the initial support for metabonomics as the end-all for screening has cooled,30 the technology is still being used for that purpose.127 In particular, efforts like the COMET consortium demonstrated the difficulties with generating predictive screening models.128,129 Despite the encountered difficulties, one metabonomics-based screening approach that is still being pursued is in the area of targeted screening for which there are no current useful biomarkers (whether they are “validated” or not). 5.4. Mechanisms and Biomarkers. If one defines the mechanism of action of a drug (or the mechanism of toxicity for that matter), it follows that there exist potential biomarkers. The problem, of course, is that, while these biomarkers may be definable on a metabolic pathway chart, whether or not the putative biomarkers fill analytical and biologic criteria necessary for a clinically useful biomarker (e.g., sensitivity and selectivity) is another question. However, as mechanisms and biomarkers are frequently closely linked (though not always), they are discussed here together. The quest for biomarkers in the pharmaceutical industry is now at a fever pitch and is driven by both safety and economic consideration. The safety implications are obvious. Any biomarker that can either exclude patients a priori (e.g., pharma532


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Figure 2. Principal component scores plot from urine samples collected from rats treated with the same dose of compound from two chemical series (A and B) that target the same receptor and have roughly equivalent in vitro activity. Open circles, controls; closed circles, treated. The numbers next to the circles represent the sample collection time (24-h samples). Physiologic end points suggested both compounds had similar efficacy in vivo. However, the metabolic profiles of the compounds are quite different, suggesting either off-target effects or off-target distribution of the compound from Series A. Reprinted with permission from 17. Copyright2005 Informa Healthcare.

cometabonomics130) or enable early removal of patients before any significant untoward effect is evidenced is clearly highly desirable. The economic ramifications of biomarkers are somewhat less obvious, but still well-understood in the industry. For example a valid peripheral biomarker (surrogate marker) of atherosclerosis that would enable the rapid clinical assessment of atheroma progression would significantly reduce the costs (in both terms of time and money) of clinical trials currently requiring either invasive imaging procedures and/or 5 year morbidity and mortality studies. While metabonomics has not delivered a valid clinical biomarker to date, it can be envisioned that this will occur in the not too distant future. If the literature is any indication, metabonomics has added significant value to mechanistic toxicity research, more so than mechanism of action (efficacy) research. This is primarily because preclinical toxicity is where much of the early work was conducted. There are probably several reasons for this,

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including just plain luck of the draw. Traditional morphologic and clinical chemistry endpoints are gold standards any new technology must compare itself to early on in the value determination process. As pathology groups (both microscopic and clinical) are typically aligned with safety assessment departments in the pharmaceutical industry, it makes logistical sense that much of the early proof-of-concept work be conducted there. There is also the pragmatic issue of publication as well. Most pharmaceutical companies are hesitant to publish early discovery pharmacology work for competitive and intellectual property reasons. As toxicity all too frequently heralds the end of a program, publication of data on discontinued programs or compounds tends to be a little more straightforward, though still a challenge. The exact opposite is true with regard to publication of metabonomics data from active programs and compounds. Early metabonomics work frequently focused on toxicity research, though little of it was relevant to pharmaceutical research.131-137 However, work with pharmaceutically irrelevant compounds can sometimes lead to novel insights about mechanistic biomarkers.138 Recently though, pharmaceutical-relevant mechanistic toxicity and biomarker research has been published on problematic topics including drug-induced vascular injury,139,140 phospholipidosis,141,142 and peroxisome proliferation.143-145 The power of metabonomics in mechanistic research is demonstrated by the example presented in Figure 3. Kinase inhibitors are common and problematic targets in drug development. The MEK-mitogen-activated protein kinase (MAPK) signal transduction pathway is involved with numerous cellular processes including cell growth and differentiation. Phosphorylation of MAPK (pMAPK) by MEK results in activation of this pathway. These pathways are being explored as targets for drug intervention in a number of therapeutic areas. A MEK-inhibitor developed as a potential anti-cancer agent was found to produce hepatic and GI toxicity in the mouse.146 As part of a mechanistic exploration, the compound (PD 254552) was compared to a structurally similar, but inactive, analogue (PD 320125-2) in a 7-day metabonomics study conducted in mice (Figure 3a). PCA data suggested biochemical effects with the active compound but not the inactive compound (Figure 3b) with significant metabolic differentiation within 24 h of the first dose and a change in metabolic trajectory by day 5 (Figure 3c). Furthermore, a profound difference in levels of an unidentified drug metabolite was noted with the active but not inactive compound (Figure 3d). Taken together, these data demonstrate several key advantages of the metabonomics approach. First, the study was conducted in mice, and the use of urine as an analyte negated the need for interim sacrifices to obtain blood or tissues at early time points. This dramatically minimized drug requirements to conduct the study as compared to what might be required for a rat transcriptomic study, for example. Second, within the same animal (always difficult in a mouse), the progress of biochemical changes could be monitored with the change in metabolic trajectory, suggesting a temporal difference in the toxicity. However, it could not be determined from the metabonomic data alone if the change was due to an exacerbation of or recovery from the lesion. While it is clear that metabonomics studies are not an end of themselves, further work will always need to be conducted to confirm mechanisms and validate biomarkers, frequently using techniques other than metabonomics. However, the technology can provide a rapid way to get started, sometimes revealing previously unknown or unsuspected mechanisms

which can save a lot of time in what is usually a laborious research effort. 5.5. Clinical Applications. While the use of metabonomics in the preclinical setting is of great interest, a significant milestone in drug development is initiation of human clinical studies. While most pharmaceutical companies with metabonomics capability are currently conducting or planning clinical studies using the technology, there is a dearth of literature on the subject. To be sure, while clinical applications of the technology (nonpharmaceutical) are well-documented, the pharmaceutical-sponsored clinical literature is minimal. Most publications to date have dealt with considerations in conducting human clinical trials147-149 rather than the effects of compounds in the clinical setting. There are probably several reasons for this. First, the recent adoption of the technology by the industry and the typically long time scale of clinical studies might be somewhat responsible. Additionally, intellectual property concerns and competitive advantage have probably also played a part in limiting public disclosure of clinical metabonomics data. From a preclinical perspective (although ironically not typically from a clinical perspective), a significant factor may be the uncertainty with how metabonomics data will be handled and used in regulatory evaluation. Like other omics data, metabonomics data may be viewed as a “can of worms” best left unopened at later stages of drug development. Spectral data, like transcript expression profiles, generates much data that is currently not understood and may be subject to many interpretations. This leads to the fear that what we currently do not understand may be used against us in some context. Clearly, this is an unacceptable situation, and steps are being taken to address these concerns within the industry and between the industry and regulatory agencies. One such approach is the voluntary genomics data submission (VGDS) which is being used as a case-study training exercise between industry and the FDA.150 Metabonomics data have recently been submitted as part of this process, and it can be anticipated that this kind of exchange will greatly reduce industry fears about using metabonomics technology in clinical trials. There is also a concerted effort by European regulators to understand the role of metabonomics (and other omic technologies) in the drug development process.151 Despite this, the clinical literature on metabonomics is quite extensive (nonpharmaceutical), covering in-born errors of metabolism,4,152 heart disease assessment153 (note caveat154), cystitis,155 hypertension,156 and schizophrenia.157,158 Beyond these medical applications, the technology is also now being used for nonmedical clinical applications such as the assessment of life-style biomarkers.159-163 From these reports, it seems quite obvious that if metabonomics has shown utility in disease states, it will be useful in the pharmaceutical clinical setting for both safety and efficacy endpoints.

6. Conclusions The cynic might ask “if metabonomics is so great why are not all pharmaceutical companies using it?” In actuality, many if not most pharmaceutical companies have made at least some effort to explore the technology with some having fairly substantial dedicated efforts. Still, it would be an overstatement to say the technology was standard practice within the industry at this point in time. Why is this? There are many legitimate criticisms about the specificity and utility of metabonomics or omic technologies in general.164-168 Some reasons include cost, technology fatigue, and the problem of data overload, to name Journal of Proteome Research • Vol. 6, No. 2, 2007 533

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Figure 3. (a) Metabonomic data from an experiment examining two structurally related MEKi inhibitors, one active (PD 254552) and one inactive (PD 320125-2). (b) Principal component scores plot of urine NMR spectra from mice treated with the active (gray circles), inactive (black circles), or vehicle (open circles). Data clearly show a metabolic differentiation between active and inactive/control samples). Note the gray circles overlapping in control space are pretest samples. (c) Principal component scores plot showing only pretest, days 1 and 5 data from mice treated with the active compound. Plot reveals early spectral changes by day 1 compared to pretest (-1) with a change in metabolic trajectory by day 5. (d) Levels of an unidentified metabolite present in high concentration in urine from rats treated with the active compound (black squares) but not the inactive (gray diamonds) or vehicle control (gray triangles).

a few.17 Clearly, as indicated by the VGDS process described previously and by statements by regulatory agency representatives,169 there is receptiveness to omics technology and an understanding of the uncertainties pharmaceutical companies 534


face within regulatory agencies. Furthermore, the NIH Roadmap Initiative described earlier also suggests that metabonomic approaches have been identified as worthy of time and investment with regard to drug development.


Like most new technologies, metabonomics has attracted its share of advocates and skeptics. However, as scientists, we should be willing to let the data speak for themselves, both good and bad. Clearly, metabonomics has brought much to the table with regard to pharmaceutical research. It is impossible to predict where the technology will be in the industry 10 years from now, as analytical and chemometric approaches are rapidly advancing. The technology will not disappear but will most likely evolve with time. For those of us in the field, it is something we look forward to.

Acknowledgment. The authors wish to acknowledge Alan Brown, Lora Robosky, and Dale Wells for their contributions to this manuscript. References (1) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Metabonomics techniques and applications to pharmaceutical research & development. Pharm. Res. 2006, 23 (6), 1075-1088. (2) Nicholson, J. K.; Lindon, J. C.; Holmes, E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29 (11), 1181-1189. (3) Beckwith-Hall, B. M.; Nicholson, J. K.; Nicholls, A. W.; Foxall, P. J.; Lindon, J. C.; Connor, S. C.; Abdi, M.; Connelly, J.; Holmes, E. Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem. Res. Toxicol. 1998, 11 (4), 260-272. (4) Holmes, E.; Foxall, P. J.; Spraul, M.; Farrant, R. D.; Nicholson, J. K.; Lindon, J. C. 750 MHz 1H NMR spectroscopy characterisation of the complex metabolic pattern of urine from patients with inborn errors of metabolism: 2-hydroxyglutaric aciduria and maple syrup urine disease. J. Pharm. Biomed. Anal. 1997, 15 (11), 1647-1659. (5) Anthony, M. L.; Rose, V. S.; Nicholson, J. K.; Lindon, J. C. Classification of toxin-induced changes in 1H NMR spectra of urine using an artificial neural network. J. Pharm. Biomed. Anal. 1995, 13 (3), 205-211. (6) Sweatman, B. C.; Farrant, R. D.; Holmes, E.; Ghauri, F. Y.; Nicholson, J. K.; Lindon, J. C. 600 MHz 1H-NMR spectroscopy of human cerebrospinal fluid: effects of sample manipulation and assignment of resonances. J. Pharm. Biomed. Anal. 1993, 11 (8), 651-664. (7) Foxall, P. J.; Spraul, M.; Farrant, R. D.; Lindon, L. C.; Neild, G. H.; Nicholson, J. K. 750 MHz 1H-NMR spectroscopy of human blood plasma. J. Pharm. Biomed. Anal. 1993, 11 (4-5), 267-276. (8) Ghauri, F. Y.; Nicholson, J. K.; Sweatman, B. C.; Wood, J.; Beddell, C. R.; Lindon, J. C.; Cairns, N. J. NMR spectroscopy of human post mortem cerebrospinal fluid: distinction of Alzheimer’s disease from control using pattern recognition and statistics. NMR Biomed. 1993, 6 (2), 163-167. (9) Foxall, P. J.; Mellotte, G. J.; Bending, M. R.; Lindon, J. C.; Nicholson, J. K. NMR spectroscopy as a novel approach to the monitoring of renal transplant function. Kidney Int. 1993, 43 (1), 234-245. (10) Anthony, M. L.; Beddell, C. R.; Lindon, J. C.; Nicholson, J. K. Studies on the effects of L(alpha S,5S)-alpha-amino-3-chloro-4,5dihydro-5-isoxazoleacetic acid (AT-125) on 4-aminophenolinduced nephrotoxicity in the Fischer 344 rat. Arch. Toxicol. 1993, 67 (10), 696-705. (11) Gartland, K. P.; Beddell, C. R.; Lindon, J. C.; Nicholson, J. K. A pattern recognition approach to the comparison of PMR and clinical chemical data for classification of nephrotoxicity. J. Pharm. Biomed. Anal. 1990, 8 (8-12), 963-968. (12) Zerhouni, E. Medicine. The NIH Roadmap. Science 2003, 302 (5642), 63-72. (13) Check, E. NIH ‘roadmap’ charts course to tackle big research issues. Nature 2003, 425 (6957), 438. (14) Anonymous The NIH RoadMap Initiative, http://nihroadmap.nih.gov/initiatives.asp. (15) Schmidt, C. W. NIH Roadmap for Medical Research. Environ. Health Perspect. 2004, 112 (3), A165-166. (16) Fiehn, O. Combining genomics, metabolome analysis, and biochemical modeling to understand metabolic networks. Comp. Func. Genomics 2001, 2, 155-168.

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multivariate statistical model to predict peroxisome proliferation in the rat, based on urinary 1H-NMR spectral patterns. Biomarkers 2004, 9 (4-5), 364-385. Atherton, H. J.; Bailey, N. J.; Zhang, W.; Taylor, J.; Major, H.; Shockcor, J.; Clarke, K.; Griffin, J. L. A combined 1H NMR spectroscopy and mass spectrometry-based metabolomic study of the PPAR-{alpha} null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome. Physiol. Genomics 2006, 27, 178-186. Brown, A. P.; Robosky, L. C.; Okerberg, C. V.; Merriman, R.; Howard, C.; Tecle, H.; Reily, M. D. The use of metabonomics to differentiate the toxicity of two MAPK kinase inhibitors in mice. Toxicol. Sci. 2004, 78 (1S), 854. Lenz, E. M.; Bright, J.; Wilson, I. D.; Hughes, A.; Morrisson, J.; Lindberg, H.; Lockton, A. Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects. J. Pharm. Biomed. Anal. 2004, 36 (4), 841-849. Lenz, E. M.; Bright, J.; Wilson, I. D.; Morgan, S. R.; Nash, A. F. A 1H NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects. J. Pharm. Biomed. Anal. 2003, 33 (5), 1103-1115. Walsh, M. C.; Brennan, L.; Malthouse, J. P.; Roche, H. M.; Gibney, M. J. Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans. Am. J. Clin. Nutr. 2006, 84 (3), 531-539. Anonymous, Genomics at FDA: Voluntary Genomics Data Submission, http://www.fda.gov/Cder/genomics/VGDS.htm. Anonymous, The Innovative Medicines Initiative: Strategic Research Agenda, http://www.efpia.org/4_pos/SRA.pdf. Constantinou, M. A. P. E.; Benaki, D.; Spraul, M.; Shulpis, K.; Koupparis, M. A.; Mikros, E. Application of nuclear magnetic resonance spectroscopy combined with principal component analysis in detecting inborn errors of metabolism using blood sposts: a metabonomic approach. Anal. Chim. Acta 2004, 511, 303-312. Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.; Bethell, H. W.; Clarke, S.; Schofield, P. M.; McKilligin, E.; Mosedale, D. E.; Grainger, D. J. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1HNMR-based metabonomics. Nat. Med. 2002, 8 (12), 1439-1444 (erratum appears in Nat. Med. 2003, 9 (4), 477). Kirschenlohr, H. L.; Griffin, J. L.; Clarke, S. C.; Rhydwen, R.; Grace, A. A.; Schofield, P. M.; Brindle, K. M.; Metcalfe, J. C. Proton NMR analysis of plasma is a weak predictor of coronary artery disease. Nat. Med. 2006, 12 (6), 705-710. Van, Q. N.; Klose, J. R.; Lucas, D. A.; Prieto, D. A.; Luke, B.; Collins, J.; Burt, S. K.; Chmurny, G. N.; Issaq, H. J.; Conrads, T. P.; Veenstra, T. D.; Keay, S. K. The use of urine proteomic and metabonomic patterns for the diagnosis of interstitial cystitis and bacterial cystitis. Dis. Markers 2003, 19 (4-5), 169-183. Brindle, J. T.; Nicholson, J. K.; Schofield, P. M.; Grainger, D. J.; Holmes, E. Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. Analyst 2003, 128 (1), 32-36. Tsang, T. M.; Huang, J. T.; Holmes, E.; Bahn, S. Metabolic profiling of plasma from discordant schizophrenia twins: correlation between lipid signals and global functioning in female schizophrenia patients. J. Proteome Res. 2006, 5 (4), 756-760. Holmes, E.; Tsang, T. M.; Huang, J. T.; Leweke, F. M.; Koethe, D.; Gerth, C. W.; Nolden, B. M.; Gross, S.; Schreiber, D.; Nicholson, J. K.; Bahn, S. Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Med. 2006, 3 (8). German, J. B.; Bauman, D. E.; Burrin, D. G.; Failla, M. L.; Freake, H. C.; King, J. C.; Klein, S.; Milner, J. A.; Pelto, G. H.; Rasmussen, K. M.; Zeisel, S. H. Metabolomics in the opening decade of the 21st century: building the roads to individualized health. J. Nutr. 2004, 134 (10), 2729-2732. German, J. B.; Roberts, M. A.; Fay, L.; Watkins, S. M. Metabolomics and individual metabolic assessment: the next great challenge for nutrition. J. Nutr. 2002, 132 (9), 2486-2487. Noguchi, Y.; Sakai, R.; Kimura, T. Metabolomics and its potential for assessment of adequacy and safety of amino acid intake. J. Nutr. 2003, 133 (6 Suppl. 1), 2097S-2100S. Teague, C.; Holmes, E.; Maibaum, E.; Nicholson, J.; Tang, H.; Chan, Q.; Elliott, P.; Stamler, J.; Ueshima, H.; Zhou, B.; Wilson, I. Ethyl glucoside in human urine following dietary exposure: detection by 1H NMR spectroscopy as a result of metabonomic screening of humans. Analyst 2004, 129 (3), 259-264.

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(167) Witkamp, R. F. Genomics and systems biology-how relevant are the developments to veterinary pharmacology, toxicology and therapeutics? J. Vet. Pharmacol. Ther. 2005, 28 (3), 235-245. (168) Luhe, A.; Suter, L.; Ruepp, S.; Singer, T.; Weiser, T.; Albertini, S. Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit? Mutat. Res. 2005, 575 (1-2), 102-115. (169) MacGregor, J. T. The future of regulatory toxicology: impact of the biotechnology revolution. Toxicol. Sci. 2003, 75 (2), 236-248.

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