Metabolomics Strategy

Identifying tissue-specific drug effects

Matej Oresic,  Research Professor, Systems Biology and Bioinformatics, VTT Technical Research Centre of Finland, Finland

The maturation of metabolomics technologies is expected to have profound effect on pharmaceutical R&D. Over the past few years, technologies have matured to the stage where comprehensive and quantitative investigation of global metabolome has been made possible.

Clinicians commonly rely on a tiny fraction of the information contained in the metabolome, measuring e.g. glucose and cholesterol to monitor diabetes and cardiovascular health, respectively. New analytical platforms for metabolomics and tools for informatics that afford extended and sensitive measurement of the metabolome are therefore expected to become an essential tool in pharmaceutical and clinical research.

Pharmacometabolomics - Towards personalised drug treatment

There is a clear need for personalisation of drug treatment, i.e. drug and dose selection according to individual patient characteristics in order to improve efficacy and reduce the risk of adverse reactions. In order to achieve this goal, one would need to first understand and predict how different individuals respond to specific drug-dose combination. Since metabolome is affected both by genetic and environmental factors, including variation in the diet, gut microbial composition, age, disease status and drug administration history, it provides a very sensitive quantitative measure of inter and intra-individual variation due to multitude of factors affecting the drug response.

What is metabolomics?

Metabolomics is a discipline dedicated to the global study of small molecules (i.e., metabolites) in the context of cellular, tissue, and organismal physiology. Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate and amplified response of biological systems to genetic or environmental changes.

Drug treatment may induce potentially harmful yet asymptomatic events in specific tissues. One of the promising new applications of metabolomics is therefore detection of markers of specific pathophysiological mechanisms and related biological pathways. Such markers may help identifying patients at risk of adverse effects and for individual's dose recommendations.

In a first application of this kind, effects of high dose simvastatin and atorvastatin on genome wide expression profiles of muscle tissue as well as on global plasma lipid composition were studied. The study revealed that statins at high doses may induce significant changes in the expression of multiple genes controlling metabolic and inflammatory pathways in muscle. It has led to elucidation of relevant biological pathways as well as to identification of plasma metabolic biomarker candidates related to potentially toxic statin-induced changes in muscle. Such muscle sensitive markers are not only useful for identifying patients at risk of developing adverse muscle effects, but may also help as surrogate safety biomarkers used for development of new lipid lowering drugs. The concept of tissue-specific functional biomarker as measured by biofluid metabolomics and the methodology to obtain it, as applied in author's high dose statin studies, can be extended to other areas of pharmaceutical R&D, e.g. liver toxicity or drug efficacy studies.

The metabolomic biomarkers may thus also offer a window to investigate the drug response and mechanism of action in the physiological setting. With a panel of tissue-specific markers for specific pathways of importance, one could thus track the responses of specific pathways following therapeutic intervention in different tissues using biofluid metabolic profiles in clinical setting. The key challenge to achieve such a goal will be to obtain sufficient number of representative samples, both from clinical and nonclinical studies, in order to develop reliable and validated biomarkers. However, return on such investment is potentially huge, as such approach would empower us with ability to detect subtle pathophysiological changes in responses to drug interventions much earlier than is currently possible. This would be particularly important in early clinical stages of pharmaceutical pipeline, where key decisions need to be made on clinical safety and efficacy of the drug based on studies in small populations. Tissue-specific metabolic biomarkers could thus provide important evidence on biological response to the drug, which may be extrapolated to larger populations for later clinical stages of pharmaceutical pipeline.

Role of metabolomics in translational pharmaceutical research

As metabolites are common among different species, they have more chances of representing cross-species biomarkers. Utilisation of such metabolomic phenotype links between species will have a profound effect on development of future therapies.

In a recent study, the author compared the lipidomic profiles of Zucker Diabetic Fatty rats (ZDF) and the hypertriglyceridemic patients. The lipid molecular species level changes relative to controls (lean wild type rats in preclinical study; and 2 healthy siblings of each diagnosed patient) in clinical study) were generally remarkably similar between the diabetic animal model and the human subjects. Such a panel of cross-species markers can be for example correlated with tissue-specific changes following drug interventions in nonclinical studies. The measurements of these markers in clinical drug trials offer new sensitive monitoring tools for evaluating drug safety and efficacy.

The use of metabolomics in translational pharmaceutical research may be particularly useful for diseases where animal models are difficult to validate, such as in psychiatric disorders or cancer. Application of metabolomics in such setting will require starting the drug discovery and development process top down, with the clinical studies. Clinical studies, focusing on a specific disease, should be conducted to obtain samples representative of different stages of disease progression in a representative population. Metabolomics could then be applied as a quantitative phenotyping tool, so that biomarkers can be obtained for different (sub)types and stages of the disease. Such biomarkers obtained in clinical setting are a starting point for validation of nonclinical models as well as characterisation of drug responses in such models. Access to the old and ongoing clinical studies which do not necessarily include the therapeutic intervention, as well as establishment of the biobanks, will therefore play an important role in future applications of metabolomics in pharmaceutical R&D.

Benefits of metabolic signatures

• Prognostic, diagnostic, and surrogate markers for a disease state are easily known

• Diseases can be sub-classified

• Provide biomarkers for drug response phenotypes (pharmacometabolomics)

• Information about mechanisms of disease and therapeutic intervention are easily revealed

Conclusion

Although metabolomics is one of the latest additions to the omics nomenclature, the applications of metabolite detection in clinical setting has a long tradition. The metabolites as "physiological end-points" are true systemic markers of responses to environmental, genetic or therapeutic interventions. The coming years are likely to see increasing incorporation of metabolomics platforms in non-clinical as well as clinical studies in pharmaceutical R&D.

Author Bio

Matej Oresic

Professor Matej Oresic holds a PhD in Biophysics from Cornell University. Since 2003, he is leading the research in domains of quantitative biology and bioinformatics at VTT Technical Research Centre of Finland with the main research areas being metabolomics applications in pharmacology, biomedical research and integrative bioinformatics. He is also a founder and chairman of the board of Zora Biosciences Ltd., a company dedicated to metabolomics applications in pharmaceutical R&D. Prior to joining VTT, he was the head of computational biology and statistics at Waltham / Massachusetts-based BG Medicine, Inc., and bioinformatician at LION Bioscience Research in Cambridge, USA.

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