Metabolomics is an FDA-identified Critical Path Opportunity (1) offering a toolkit which can be potentially applied to identification of safety biomarkers, diagnostic monitoring of patient response to drug treatment, lead optimization through toxicity assessment in non-clinical drug development, biochemical pathway studies in cells, animals and humans, patient stratification, and insights on and tracking of mechanisms associated with the onset of disease or following therapeutic intervention.
However, despite its above promise, the challenges in the complexity of the biological systems studies, the experimental spectroscopy methods used and the datasets generated has restricted to this point the full commercial application of metabolomics methods in the pharmaceutical industry and in healthcare.
The complexity of the interpretation of metabolomics data points to the need for improved data analytics and visualisation methods in decision making processes and situations (2). The importance of the value-added of the integration of metabolomics data with other proteomic and genomic data to provide a more integrated, accurate and broader view of the state of the biological system studied points to the importance of explicit knowledge management techniques in critical path areas of clinical research and drug development (3) to the application of semantic web, ontology and web service approaches (4) and to the adoption of these approaches in the day-to-day scientific research activity as supported by electronic laboratory notebooks and collaboration systems (5). It also points again to the importance of co-operation on the always difficult agreement area of the definition of standards and data integration.
I also find it quite interesting that two different fields (knowledge management and metabolomics) share a common critical concept: that of context. Context is critical in the human area of knowledge management and transfer in social ecosystems and its poor treatment a reason why many early IT and informatics approaches to knowledge management worked poorly. In the biological situation of a cell or animal, metabolomics provides the critical biochemical context and data to match it, and can do so in a dynamic way over time and can track perturbations in the behaviour of such a complex system.
Recent progress in the metabolomics field includes new advances in spectroscopic and statistical and analytical techniques that strengthen and expand the accuracy and scope of the analysis possible. But the progress also increasingly includes significant application experience which already has included a significant role in the 2005 Nobel Prize in Medicine award for assigning the causative role of H. pylori bacterium in peptic ulcers and gastritis, and more recently has been applied to patient stratification in Lou Gehrig’s disease, identification of off-targeted side effects for several drugs and new chemical entities, has been applied in diagnostic roles on serum samples of diabetics and non-diabetics, the development of biomarkers for prediction of drug-induced liver injury and to insight into toxic effects from urine analysis.
Richard Beger (FDA) has pointed out (6,7) that the development of NMR- based multi-dimensional quantitative spectrometric data-activity relationships (QSDAR) provides models which could be useful for estimating chemical toxicity, risk assessment of environmental contaminants and drug-lead identifications, and that such models of biological activity should be more objective and overcome some of the unreliabilities of traditional Quantitative Structure-Activity Relationship (QSAR) approaches (8). He also indicates that Metabolomics can play an important role in the creation of better evaluation tools and models for diseases, better identification and quantification of safety biomarkers, and improving the measurement of patient response. The voluntary submission of genomics data (VGDS) to the FDA is now accepting both proteomics and metabolomics data sets (9).
In response to such demand and interest, and in addition to significant activity in academic research, a number of companies including Metabolon, Leco, Blue Gnome, Bio-Rad and Chenomx are increasingly offering commercial solutions and services in metabolomics to industry.
I provide below a description of the presentation, discussion and workshop activity for the InnovationWell Session on Application of Metabolomics to Drug Discovery & Development which will take place on Wednesday 18th October ’06 at the InnovationWell meeting at Bryn Mawr. (Follow the Continuation…)
Barry Hardy
References
1. FDA Critical Path Opportunities Report, http://www.fda.gov/oc/initiatives/criticalpath
2. Decision Support in Drug Discovery & Development, http://barryhardy.blogs.com/theferryman/2006/09/decision_suppor.html
3. Knowledge Management in Translational Research, http://barryhardy.blogs.com/theferryman/2006/09/utilising_knowl.html
4. Semantic Web & Drug Development, http://www.innovationwell.net/COMTY_semweb/
5. KM in R&D and ELNs, http://www.innovationwell.net/COMTY_conferenceopenevent/
6. Richard D. Beger, Drug Discovery Today, Vol. 11, pp 429-435, May (2006).
7. R. D. Beger, D. A. Buzata, J.G. Wilkes, Drug Discovery Handbook, Ed. Shayne C. Gad, John Wiley & Sons, pp 227-285 (2005)
8. Predictive Toxicology, http://barryhardy.blogs.com/cheminfostream/2006/09/appyling_predic.html
9. FDA’s Critical Path Initiative: Opportunities for Metabolomics http://www.innovationwell.net/COMTY_mebegerr/
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