BioSRQ https://www.biosrq.com Accredited Biotechnology Company Thu, 17 May 2018 15:03:19 +0000 en-US hourly 1 https://wordpress.org/?v=5.2.1 https://www.biosrq.com/wp-content/uploads/2018/10/cropped-slice3-32x32.png BioSRQ https://www.biosrq.com 32 32 Embracing diversity: Metabolomics is paving the way to personalized medicine https://www.biosrq.com/science/metabolomics-paving-personalized-medicine/ Wed, 16 May 2018 18:58:12 +0000 https://www.srqbio.com/?p=5800 AbbVie’s Humira has risen to the number one spot on the list of top ten prescription drugs in 2016 with estimated sales of $16billion (1). Despite all the success Humira has accumulated throughout the years, it still fails to treat every three out of four patients suffering from arthritis (2). The situation gets even worse […]

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AbbVie’s Humira has risen to the number one spot on the list of top ten prescription drugs in 2016 with estimated sales of $16billion (1). Despite all the success Humira has accumulated throughout the years, it still fails to treat every three out of four patients suffering from arthritis (2). The situation gets even worse with Lipitor, the uncrowned superstar of the pharmaceutical industry. Although Lipitor has amassed well over $100 billion in total sales since it was first introduced over 20 years ago as a cholesterol-lowering medication (3) it only proves to be a lifesaver remedy for just about 1 out of 34 patients (4). These astonishingly low numbers needed to treat indicate that a paradigm shift is desperately needed in the way we look at patient populations.

To address this issue, on January 30, 2015, President Obama has announced the launch of one of the most ambitious cohort studies ever, entitled as the Precision Medicine Initiative (PMI) (5). A project of this magnitude, which aims to enroll more than a million participants, requires an especially tight-knit collaboration across several agencies of the Federal government.

The new era, where personalized medicine is routinely used, will fundamentally change our health care system. Firstly, individualized clinical treatments will allow professionals to select the right drug therapy, which as a result, reduces the occurrence of adverse side effects as well as maximizes the desired therapeutic outcome (6). Moreover, the establishment of custom-designed medical treatments will permit a paradigm shift in healthcare, shifting the focus from disease management to health promotion at the earliest possible stages of the disease. This will result in an unprecedented level of customization of patient care at every stage of the disease (7,8).

To study the immensely complex and intertwined biochemical processes occurring in the human body, scientists are increasingly relying on metabolomics. The importance of metabolomics in the Precision Medicine Initiative was also greatly emphasized by President Obama himself in his mission statement (9). Ideally, the metabolic profile of an individual directly indicates system-wide alterations via signaling the loss of homeostasis. This notion is by no means unique to the modern science itself. For example, in ancient times, traditional Chinese doctors diagnosed diabetes by using ants as a means to evaluate glucose levels in patient’s urine samples. (10). The experimental objective of metabolomics is the comprehensive description of all small molecular species present in an organism (11). Therefore, the fundamental difference between these two approaches comes down to the number of metabolites detected. While in the case of ancient Chinese doctors the diagnosis was solely based on the patient’s urine glucose level, nowadays we are able to routinely screen a person’s complete metabolome, which is estimated to consist of anywhere between 5000-200.000 individual molecules. By combining the power of metabolomics with intricate statistical tools, a single biomarker, that is sufficiently specific, can unequivocally differentiate between health and disease states.

With tools such as metabolomics, we can finally acknowledge, study and utilize phenotypic and genotypic diversity among humans. Soon we will be able to do a better job at differentiating subject populations based on responders and non-responders so that patients can receive custom designed plans, hence increasing their chances of success.

 

  1. Lindsley, Craig W. “New 2016 Data and Statistics for Global Pharmaceutical Products and Projections through 2017.” ACS Chemical Neuroscience, 16 Aug. 2017, pubs.acs.org/doi/pdfplus/10.1021/acschemneuro.7b00253.
  2. Schork, Nicholas J. “Personalized Medicine: Time for One-Person Trials.” Nature News, Nature Publishing Group, 29 Apr. 2015, nature.com/news/personalized-medicine-time-for-one-person-trials-1.17411.
  3. Ledford, Heidi. “Blockbuster Drug Bows Out.” Nature News, Nature Publishing Group, 20 Dec. 2011, nature.com/news/blockbuster-drug-bows-out-1.9495.
  4. Welty, Francine K. “Atorvastatin and Cardiovascular Risk in the Elderly – Patient Considerations.” Clinical Interventions in Aging, Volume 3, 2008, pp. 299–314., doi:10.2147/cia.s2442.
  5. “FACT SHEET: President Obama’s Precision Medicine Initiative.” National Archives and Records Administration, National Archives and Records Administration, 30 Jan. 2015, obamawhitehouse.archives.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative
  6. Ramautar, R., Berger, R., Greef, J. V., & Hankemeier, T. (2013). Human metabolomics: Strategies to understand biology. Current Opinion in Chemical Biology, 17(5), 841-846.
  7. Chan, I. S., & Ginsburg, G. S. (2011). Personalized Medicine: Progress and Promise. Annual Review of Genomics and Human Genetics Annu. Rev. Genom. Human Genet., 12(1), 217-244.
  8. Kaddurah-Daouk, R., Kristal, B. S., & Weinshilboum, R. M. (2008). Metabolomics: Global
  9. Biochemical Approach to Drug Response and Disease. Annu. Rev. Pharmacol. Toxicol. Annual
  10. Review of Pharmacology and Toxicology, 48(1), 653-683.
  11. “White House Precision Medicine Initiative.” National Archives and Records Administration, National Archives and Records Administration, obamawhitehouse.archives.gov/node/333101.
  12. Greef, J. V., & Smilde, A. K. (2005). Symbiosis of chemometrics and metabolomics: Past, present, and future. J. Chemometrics Journal of Chemometrics, 19(5-7), 376-386.
  13. Oliver, S. (1998). Systematic functional analysis of the yeast genome. Trends in Biotechnology, 16(9),373-378.

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