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Personalized medicine: Genetic risk prediction of drug response

Overview of attention for article published in Pharmacology & Therapeutics, February 2017
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Title
Personalized medicine: Genetic risk prediction of drug response
Published in
Pharmacology & Therapeutics, February 2017
DOI 10.1016/j.pharmthera.2017.02.036
Pubmed ID
Authors

Ge Zhang, Daniel W. Nebert

Abstract

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

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Mendeley readers

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The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 158 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 14%
Student > Bachelor 20 13%
Researcher 19 12%
Student > Master 18 11%
Other 11 7%
Other 34 22%
Unknown 34 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 22%
Medicine and Dentistry 28 18%
Pharmacology, Toxicology and Pharmaceutical Science 20 13%
Agricultural and Biological Sciences 13 8%
Neuroscience 4 3%
Other 20 13%
Unknown 39 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 February 2017.
All research outputs
#20,660,571
of 25,382,440 outputs
Outputs from Pharmacology & Therapeutics
#2,128
of 2,376 outputs
Outputs of similar age
#329,110
of 433,728 outputs
Outputs of similar age from Pharmacology & Therapeutics
#32
of 40 outputs
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