Development of Personalized Medicine

  • Kewal K. JainEmail author


In conventional medical practice, physicians rely on their personal experience in treating patients. In spite of advances in basic medical sciences and the introduction of new technologies, physicians continue to rely on their judgment and sometimes intuition because the practice of medicine is an art as well as a science. Physicians of the last generation had limited access to information. With advances in molecular biology and its impact on medicine, a tremendous amount of new basic information has been generated, particularly in genomics and gene expression. Digitalization of information has made it accessible. The problem now is a flood of information, which requires strategies to sort out the relevant from the irrelevant. Information on a large number of studies with stratification of a large number of patients will have to be analyzed to make decisions about treatment for an individual. The massive amount of publications needs to be sorted out and analyzed for its relevance to individualized treatment. The development of personalized therapy requires the integration of various segments of clinical medicine, pharmacology and biotechnology. Genotyping is an important part of such a system. Various technologies for genotyping have been described in the following chapter and their advantages as well as limitations have been pointed out. The vast majority of relevant gene variants are rare, making it difficult to demonstrate utility − in particular for the much more frequent heterozygous carriers who have only one affected allele. Moreover, multiple factors play a role such that genetic data represent only a portion of the information needed for effective therapeutic decisions. Therapeutic areas in which personalized medicine is expected to play an important role are listed in Table 9.1.


Bayesian Approach Personalized Medicine Intestinal Microflora Personal Health Record National Human Genome Research Institute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adida B, Kohane IS (2006) GenePING: secure, scalable management of personal genomic data. BMC Genomics 7:93.CrossRefPubMedGoogle Scholar
  2. Berry DA (2006) A guide to drug discovery: Bayesian clinical trials. Nat Rev Drug Discov 5:27–36CrossRefPubMedGoogle Scholar
  3. DesRoches CM, Campbell EG, Rao SR et al (2008) Electronic health records in ambulatory care – a national survey of physicians. N Engl J Med 359:50–60CrossRefPubMedGoogle Scholar
  4. Fierz W (2004) Challenge of personalized health care: to what extent is medicine already individualized and what are the future trends? Med Sci Monit 10:RA111–RA123PubMedGoogle Scholar
  5. Fries S, Grosser T, Price TS et al (2006) Marked interindividual variability in the response to selective inhibitors of cyclooxygenase-2. Gastroenterology 130:55–64CrossRefPubMedGoogle Scholar
  6. Gill SR, Pop M, Deboy RT et al (2006) Metagenomic analysis of the human distal gut microbiome. Science 312:1355–1359CrossRefPubMedGoogle Scholar
  7. Grosser T, Fries S, FitzGerald GA (2006) Biological basis for the cardiovascular consequences of COX-2 inhibition: therapeutic challenges and opportunities. J Clin Invest 116:4–15CrossRefPubMedGoogle Scholar
  8. Robson B, Mushlin R (2004) Genomic messaging system and DNA mark-up language for information-based personalized medicine with clinical and proteome research applications. J Proteome Res 3:930–948CrossRefPubMedGoogle Scholar
  9. Ruano G, Windemuth A, Holford T (2006) Physiogenomics: Integrating systems engineering and nanotechnology for personalized health. In The Biomedical Engineering Handbook, 3rd Edition, Joseph D. Bronzino, editor, CRC Press Taylor and Francis, Chapter 28:1–9 Google Scholar
  10. Simon R (2008) Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics. Exp Opin Med Diagn 2:721–729CrossRefGoogle Scholar
  11. Ueda HR, Chen W, Minam Y et al (2004) Molecular-timetable methods for detection of body time and rhythm disorders from single-time-point genome-wide expression profiles. Proc Natl Acad Sci USA 101:11227–11232CrossRefPubMedGoogle Scholar
  12. Yang JY, Yang MQ, Zhu MM et al (2008) Promoting synergistic research and education in genomics and bioinformatics. BMC Genomics 9(Suppl 1):I1.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Jain PharmaBiotechBaselSwitzerland

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