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Chapter 7: Pharmacogenomics

Overview of attention for article published in PLoS Computational Biology, December 2012
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Title
Chapter 7: Pharmacogenomics
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002817
Pubmed ID
Authors

Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman

Abstract

There is great variation in drug-response phenotypes, and a "one size fits all" paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 373 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 2%
Brazil 5 1%
United Kingdom 4 1%
Spain 4 1%
France 2 <1%
Ghana 1 <1%
Morocco 1 <1%
Germany 1 <1%
Canada 1 <1%
Other 3 <1%
Unknown 342 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 68 18%
Researcher 60 16%
Student > Bachelor 43 12%
Student > Master 39 10%
Professor > Associate Professor 24 6%
Other 77 21%
Unknown 62 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 90 24%
Medicine and Dentistry 58 16%
Biochemistry, Genetics and Molecular Biology 53 14%
Pharmacology, Toxicology and Pharmaceutical Science 38 10%
Computer Science 17 5%
Other 45 12%
Unknown 72 19%