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Genomics and Drug Response
Open accessAbstract
harmacogenomics is the study of the role of inherited and acquired genetic variation in drug response.1 Clinically relevant pharmacogenetic examples, mainly involving drug metabolism, have been known for decades, but recently, the field of pharmacogenetics has evolved into "pharmacogenomics," involving a shift from a focus on individual candidate genes to genomewide association studies.Such studies are based on a rapid scan of markers across the genome of persons affected by a particular disorder or drug-response phenotype and persons who are not affected, with tests for association that compare genetic variation in a case-control setting. 2 An example is provided in this issue of the Journal: McCormack and colleagues, testing for genomewide association, identified an HLA allele that is associated with hypersensitivity reactions to the anticonvulsant and mood-stabilizing drug carbamazepine in persons of European descent.3 Pharmacogenomics facilitates the identification of biomarkers that can help physicians optimize drug selection, dose, and treatment duration and avert adverse drug reactions.In addition, pharmacogenomics can provide new insights into mechanisms of drug action and as a result can contribute to the development of new therapeutic agents.In 2003, two reviews of pharmacogenetics were published the Journal.4,5 Since then, both genomic science and its application to drug response have undergone major advances.6 Here we review some of those advances, with an emphasis on discovery through genomewide association studies.We describe examples that highlight principles of pharmacogenomics that are relevant to a wide variety of drugs.The Food and Drug Administration (FDA) has altered drug labels and issued warnings about pharmacogenomic variation affecting drug response, raising the issue of the level of evidence required to show clinical utility 7 and the respective roles of regulatory agencies such as the FDA and of academic and professional societies in the evaluation of pharmacogenetic analyses for the clinic. C a r diova scul a r DrugsMany drugs have proven efficacy in the treatment and prevention of cardiovascular disease.Not uncommonly, these drugs have narrow therapeutic indexes that are influenced by genetic variation -a hallmark of drugs for which pharmacogenomic approaches are likely to provide substantial clinical benefit.The anticoagulant agents warfarin and clopidogrel are high on the list of widely prescribed cardiovascular drugs with narrow therapeutic indexes.The pharmacogenomic features of these drugs illustrate the rapid evolution of our understanding of the role of inheritance in the variation in drug efficacy and the risk of adverse drug reactions.In the case of both agents, the application of classic candidate-gene pharmacogeneticsThe New England
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Slamon, D. J., Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A., Fleming, T., Eiermann, W., Wolter, J., Pegram, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200103153441101 · Cited by 11,500
Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., Harris, P. L., Haserlat, S. M., Supko, J. G., Haluska, F. G., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040938 · Cited by 11,486
Mutations of the BRAF gene in human cancer
Davies, H., Bignell, G. R., Cox, C., Stephens, P., Edkins, S., Clegg, S., Teague, J., Woffendin, H., Garnett, M. J., Bottomley, W., et al. (2002) · Nature · doi:10.1038/nature00766 · Cited by 10,674
EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy
Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., Herman, P., Kaye, F. J., Lindeman, N., Boggon, T. J., et al. (2004) · Science · doi:10.1126/science.1099314 · Cited by 9,368
(2001) · New England Journal of Medicine · doi:10.1056/nejmoa010746 · Cited by 6,470
Interleukin-17 and Type 17 Helper T Cells
Miossec, P., Korn, T., & Kuchroo, V. K. (2009) · New England Journal of Medicine · doi:10.1056/nejmra0707449 · Cited by 1,409
Cited by
Ng, K. P., Hillmer, A. M., Chuah, C. T. H., Juan, W. C., Ko, T. K., Teo, A. S. M., Ariyaratne, P. N., Takahashi, N., Sawada, K., Fei, Y., et al. (2012) · Nature Medicine · doi:10.1038/nm.2713 · Cited by 557
Berg, J. S., Khoury, M. J., & Evans, J. P. (2011) · Genetics in Medicine · doi:10.1097/gim.0b013e318220aaba · Cited by 490
PG4KDS: A model for the clinical implementation of pre‐emptive pharmacogenetics
Hoffman, J. M., Haidar, C. E., Wilkinson, M. R., Crews, K. R., Baker, D. K., Kornegay, N. M., Yang, W., Pui, C., Reiss, U. M., Gaur, A. H., et al. (2014) · American Journal of Medical Genetics Part C: Seminars in Medical Genetics · doi:10.1002/ajmg.c.31391 · Cited by 276
In Vitro Human Cell Line Models to Predict Clinical Response to Anticancer Drugs
Niu, N., & Wang, L. (2015) · Pharmacogenomics · doi:10.2217/pgs.14.170 · Cited by 162
Self-citationMeyer, U. A., Zanger, U. M., & Schwab, M. (2013) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100502 · Cited by 157
Metadata
- Authors
- Wang, L., McLeod, H. L., & Weinshilboum, R. M.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2011-03-24
- Volume
- 364
- Issue
- 12
- Pages
- 1144-1153
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2102316672
- Cited by
- 613
- References
- 6
- FWCI
- 81.95
- Type
- journal article
- Language
- en
- Open access
- bronze
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