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To answer this question, I need to explain a bit about what the Consortium on Metabonomics in Toxicology (COMET) project is. This is an academic collaborative project hosted by us (Biological Chemistry, Imperial College London UK). Six major pharmaceutical companies (now five, since Pfizer acquired Pharmacia, plus Hofmann La-Roche, Bristol-Myers-Squibb, NovoNordisk, Eli Lilly) have paid a subscription covering each year for three years to fund equipment and postdoctoral staff. The project set out to evaluate the utility of metabonomics as a new technology to provide information on early-stage toxicity and safety issues in the pharmaceutical industry. This was achieved by each company carrying out 25 typical acute in-life drug safety studies, collecting blood plasma and urine samples for analysis by metabonomics approaches. Selected tissues were also collected and some selected tissue studies were also performed using a technique called magic-angle-spinning NMR. The biofluid samples—in all 147 studies were eventually carried out—were shipped to Imperial College where we did high-resolution NMR spectroscopy on them to obtain metabolic profiles, which we analyzed using pattern-recognition methods. All of the data were collected into a database along with the meta-data (i.e., all the ancillary information such as details of dosing, clinical biochemistry results, and histopathology findings). We then used this database to generate a computer-based expert system to predict, on the basis of the NMR spectra of urine samples, whether a substance would be toxic to the liver or to the kidney or to other organs, or to a combination of organs; bearing in mind that liver toxicity is a major cause of attrition in drug development pipelines. The logistics of the project were aided by the formation of a steering committee with representatives from all companies and Imperial College—co-chaired by myself and my colleague Professor Jeremy Nicholson, and meeting about three times a year—and this meant that the whole project activity did not need to be mapped out before it started, and also that the project could operate very flexibly, with decisions being taken on a consensus basis (actually we never needed to vote on anything!). The whole project has been very successful and has represented the first major real-world test of the metabonomic approach. The sponsoring companies now have the databases and expert system for their own in-house use.

This highly cited paper set out the aims of the project, the methodologies and technologies to be used, the organization and logistics of the project, and reviewed the very promising preliminary results. As such, it has proven to be of major interest to scientists in pharmaceutical companies, to academic groups, and to various regulatory agencies. It is also the first in a whole series of papers that are now appearing in major journals covering both methodological developments and toxicological applications.

The methodology at the heart of the COMET project has been a new approach termed metabonomics—a word that we coined and defined [Nicholson, J.K.; Connelly, J.; Lindon, J.C.; Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nature Rev. Drug Disc., 1: 153-162 2002]. This is the systemic profiling of metabolites and metabolic pathways in whole organisms through study of biofluids and tissues [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, 29: 1181-1189 1999]. Metabonomics has been formally defined in the above citation in a biological context, as the quantitative measurement of the dynamic multi-parametric metabolic response of living systems to pathophysiological stimuli or genetic modification. This approach holds out the promise of a means by which real disease and drug-effect endpoints can be obtained.

In complex organisms, the three levels of biomolecular organization and control (transcriptomic, proteomic, and metabonomic) are highly interdependent, but they can have very different time scales of change. In addition, environmental and lifestyle effects have a large effect on gene and protein expression and metabolites levels, and these have to be considered as part of inter-sample and inter-individual variation. Interpretation of genomic data, in terms of real biological endpoints, is a major challenge because of the conditional interactions of specific gene expression levels with environmental factors that nonlinearly change disease risks. The measurement and modeling of such diverse information sets poses significant challenges at the analytical and bioinformatic-modeling levels. Highly complex animals such as man can be considered as "super-organisms" with an internal ecosystem of diverse symbiotic microbiota and parasites that have interactive metabolic processes and for which, in many cases, the genome is not known. The many levels of complexity of the mammalian system and the diverse features that need to be measured to allow "omic" data to be fully utilized have been reviewed recently [Nicholson, J.K.; Wilson, I.D. Understanding 'global' systems biology: Metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov., 2: 668-676 2003]. Novel approaches continue to be required to measure and model metabolic compartments in different interacting cell types and genomes that are connected by cometabolic processes in the global mammalian system [Nicholson, J.K.; Holmes, E.; Lindon, J.C.; Wilson, I.D. The challenges of modeling mammalian biocomplexity. Nat. Biotech., 22: 1268-1274 2004].

All metabonomics studies, which rely on analytical chemistry methods (we are using NMR spectroscopy and increasingly mass spectrometry with a prior separation step such as HPLC or UPLC) result in complex multivariate datasets that require a variety of chemometric and bioinformatic tools for effective interpretation. The aim of these procedures is to produce biochemically 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 biomarkers that reflect actual biological events. Thus, metabonomics allows real-world or biomedical endpoint observations to be related to the measurements provided by all of the "-omics" technologies. To carry out metabonomics studies, it is not necessary to have the genome sequence of all of the organisms involved.

The main application areas where we are applying metabonomics include the following:

• Validation of animal models of disease, including genetically-modified animals
• Preclinical evaluation of drug safety studies, allowing ranking of candidate compounds
• Quantitation, or ranking, of the beneficial effects of pharmaceuticals
• Improved understanding of the causes of highly sporadic idiosyncratic toxicity
• Improved differential diagnosis and prognosis of human diseases (particularly for chronic and degenerative diseases, and for diseases caused by genetic effects)
• Better understanding of large-scale human population differences through epidemiological studies
• Patient stratification for drug treatment ("pharmaco-metabonomics")
• Sports medicine and lifestyle studies—including the effects of diet, exercise, and stress
• Evaluation of the effects of interactions between drugs, and between drugs and diet and between hosts, parasites and symbiotic micro-organisms
• Microbiological characterization
• Environmental effects of pollutants monitored using marker-species

Importantly, because many metabonomics studies can use biofluids that can be collected noninvasively or minimally so, metabonomics also allows time-dependent patterns of change in response to disease, drug effects, or other stimuli to be measured. It thus provides an approach which bridges the gap between other "-omics" measurements and real world endpoints, since metabolites can easily be identified and quantified, and changes can be related to health and disease, and are changeable by therapeutic intervention.

The paper describes a successful project that will have a beneficial impact on the way in which pharmaceutical companies conduct "safety studies" as part of their drug development activities. It should reduce the number of candidate compounds discarded in the process, improve the time scales of safety studies, and hopefully result in safer new medicines reaching patients more quickly.