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Do we need pharmacogenetics to personalize antidepressant therapy?

Overview of attention for article published in Cellular and Molecular Life Sciences, December 2012
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Title
Do we need pharmacogenetics to personalize antidepressant therapy?
Published in
Cellular and Molecular Life Sciences, December 2012
DOI 10.1007/s00018-012-1237-5
Pubmed ID
Authors

Cristina Lanni, Marco Racchi, Stefano Govoni

Abstract

This review examines the role of drug metabolism and drug target polymorphism in determining the clinical response to antidepressants. Even though antidepressants are the most effective available treatment for depressive disorders, there is still substantial need for improvement due to the slow onset of appreciable clinical improvement and the association with side effects. Moreover, a substantial group of patients receiving antidepressant therapy does not achieve remission or fails to respond entirely. Even if the large variation in antidepressant treatment outcome across individuals remains poorly understood, one possible source of this variation in treatment outcome are genetic differences. The review focuses on a few polymorphisms which have been extensively studied, while reporting a more comprehensive reference to the existing literature in table format. It is relatively easy to predict the effect of polymorphisms in drug metabolizing enzymes, such as cytochromes P450 2D6 (CYP2D6) and cytochrome P450 2C19 (CYP2C19), which may be determined in the clinical context in order to explain or prevent serious adverse effects. The role of target polymorphism, however, is much more difficult to establish and may be more relevant for disease susceptibility and presentation rather than for response to therapy.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 36 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 16%
Researcher 4 11%
Student > Ph. D. Student 4 11%
Student > Postgraduate 3 8%
Professor > Associate Professor 3 8%
Other 7 19%
Unknown 10 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 19%
Pharmacology, Toxicology and Pharmaceutical Science 6 16%
Medicine and Dentistry 5 14%
Psychology 3 8%
Agricultural and Biological Sciences 1 3%
Other 5 14%
Unknown 10 27%
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 30 December 2012.
All research outputs
#21,141,111
of 23,794,258 outputs
Outputs from Cellular and Molecular Life Sciences
#3,769
of 4,151 outputs
Outputs of similar age
#253,728
of 285,212 outputs
Outputs of similar age from Cellular and Molecular Life Sciences
#26
of 28 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,151 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.