As an important discipline within systems biology, metabolomics is being explored by a number of laboratories for its potential in pharmaceutical development. Studying metabolites can offer insights into the relationships between genotype and phenotype, as well as between genotype and environment. In addition, there is plenty to work with—there are estimated to be some 2,900 detectable metabolites in the human body, of which 309 have been identified in cerobrospinal fluid, 1,122 in serum, 458 in urine, and roughly 300 in other compartments.
Guowang Xu, Ph.D., is a researcher at the Dalian Institute of Chemical Physics. He is investigating the causes of death in China and how they have been changing over the years as the country has become a more industrialized nation. A major issue is the increase in the incidence of metabolic disorders such as diabetes, which has grown to affect 9.7% of the Chinese population.
This emergent risk is still identified by relatively primitive methods, and there is currently no means of early diagnosis of insulin-resistant subjects through blood or urine samples. These considerations are foremost in Dr. Xu’s attempt to develop a metabolomic-based strategy for identification of at-risk individuals.
Dr. Xu, working in collaboration with the laboratory of Rainer Lehman, Ph.D., of the University of Tübingen, Germany, compared urinary metabolites in samples from healthy individuals with samples taken from prediabetic, insulin-resistant subjects. Using mass spectrometry coupled with electrospray ionization in the positive mode, they observed striking dissimilarities in levels of various metabolites in the two groups.
“When we performed a comprehensive two-dimensional gas chromatography, time-of-flight mass spectrometry analysis of our samples, we observed several metabolites, including 2-hydroxybutyric acid in plasma, that could be identified as potential diabetes biomarkers,” Dr. Xu explains.
In other, unrelated studies, Dr. Xu and the German researchers used a metabolomics approach to investigate the changes in plasma metabolite profiles immediately after exercise and following a 3-hour and 24-hour period of recovery. They found that medium-chain acylcarnitines were the most distinctive exercise biomarkers, and they are released as intermediates of partial beta oxidation in human myotubes and mouse muscle tissue.
Traditionally, metabolites have been used as markers of inborn errors of metabolism, and diseases such as phenylketonuria were recognized as curable through introduction of appropriate nutritional intermediates as far back as the 1930s. Today, the availability of advanced technologies will make possible treatment of diseases with a much more complex genetic and environmental basis.
“I believe we are entering into a new era of phenotype-based diagnosis based on groups of biomarkers,” Dr. Xu says. “The traditional approach of assessment based on a singular biomarker is being superseded by the introduction of multiple marker profiles.”
Evaluation of individual metabolic profiles is becoming increasingly important in researching new pharmaceutical molecules, according to Rima Kaddurah-Daouk, Ph.D., associate professor at the Duke University Center for Pharmacometabolomics.
“The field is based on scaling up our knowledge of biochemistry to encompass not only the metabolism of the host but also that of the gut bacteria and other factors that may influence drug uptake and processing,” she explains. “We are focusing our studies of personalized medicine on psychiatric disorders and cardiovascular diseases.”
Typical of the studies under way by Dr. Kaddurah-Daouk and her colleagues is a recently published investigation highlighting the role of an SNP variant in the glycine dehydrogenase gene on individual response to antidepressants. It was determined that patients who do not respond to the selective serotonin uptake inhibitors citalopram and escitalopram carried a particular single nucleotide polymorphism in the GD gene.
“These results allow us to pinpoint a possible role for glycine in selective serotonin reuptake inhibitor response and illustrate the use of pharmacometabolomics to inform pharmacogenomics. These discoveries give us the tools for prognostics and diagnostics so that we can predict what conditions will respond to treatment.
“This approach to defining health or disease in terms of metabolic states opens a whole new paradigm. By screening hundreds of thousands of molecules, we can understand the relationship between human genetic variability and the metabolome.”
Dr. Kaddurah-Daouk talks about statins as a current model of metabolomics investigations. Whereas, these drugs were originally developed to lower cholesterol levels in individuals at risk for cardiovascular disease, it is now known that they have widespread effects, altering a range of metabolites. To sort out these changes and develop recommendations for which individuals should be receiving statins will require substantial investments of energy and resources into defining the complex web of biochemical changes that these drugs initiate.
Furthermore, Dr. Kaddurah-Daouk asserts that, “genetics only encodes part of the phenotypic response. One needs to take into account the net environment contribution in order to determine how both factors guide the changes in our metabolic state that determine the phenotype.”