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Moving towards individualized medicine with pharmacogenomics

Author

Listed:
  • William E. Evans

    (St Jude Children's Research Hospital
    University of Tennessee Colleges of Pharmacy and Medicine, and the Pharmacogenetics of Anticancer Agents Research Group)

  • Mary V. Relling

    (St Jude Children's Research Hospital
    University of Tennessee Colleges of Pharmacy and Medicine, and the Pharmacogenetics of Anticancer Agents Research Group)

Abstract

Individuals respond differently to drugs and sometimes the effects are unpredictable. Differences in DNA that alter the expression or function of proteins that are targeted by drugs can contribute significantly to variation in the responses of individuals. Many of the genes examined in early studies were linked to highly penetrant, single-gene traits, but future advances hinge on the more difficult challenge of elucidating multi-gene determinants of drug response. This intersection of genomics and medicine has the potential to yield a new set of molecular diagnostic tools that can be used to individualize and optimize drug therapy.

Suggested Citation

  • William E. Evans & Mary V. Relling, 2004. "Moving towards individualized medicine with pharmacogenomics," Nature, Nature, vol. 429(6990), pages 464-468, May.
    • Handle: RePEc:nat:nature:v:429:y:2004:i:6990:d:10.1038_nature02626
    DOI: 10.1038/nature02626
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    Cited by:

    1. Boon, Wouter & Moors, Ellen, 2008. "Exploring emerging technologies using metaphors - A study of orphan drugs and pharmacogenomics," Social Science & Medicine, Elsevier, vol. 66(9), pages 1915-1927, May.
    2. Lu Xia & Bin Nan & Yi Li, 2023. "Debiased lasso for generalized linear models with a diverging number of covariates," Biometrics, The International Biometric Society, vol. 79(1), pages 344-357, March.
    3. Jakob Hoffmann & Johannes Glückler, 2023. "Technological Cohesion and Convergence: A Main Path Analysis of the Bioeconomy, 1900–2020," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    4. Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.

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