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Mendeley readers
Attention Score in Context
| Title |
Pharmacogenotyping of CYP3A5 in predicting dose-adjusted trough levels of tacrolimus among Malaysian kidney-transplant patients
|
|---|---|
| Published in |
Canadian Journal of Physiology and Pharmacology, December 2013
|
| DOI | 10.1139/cjpp-2013-0128 |
| Pubmed ID | |
| Authors |
Sharina Hamzah, Lay Kek Teh, John Shia Kwong Siew, Ghazali Ahmad, Hin Seng Wong, Zainul Amiruddin Zakaria, Mohd Zaki Salleh |
| Abstract |
Tacrolimus (FK506) is a calcineurin inhibitor with a narrow therapeutic index that exhibits large interindividual variation. Seventy-eight kidney transplant patients treated with tacrolimus were recruited to study the correlation of dose adjusted trough level (level/dose; L/D) of tacrolimus with CYP3A5 and ABCB1 genotypes, as well as the mRNA copy number of ABCB1 in blood. Patients were genotyped for ABCB1 (C1236T, G2677T/A, and C3435T) and CYP3A5 (G6986A), while ABCB1 mRNA transcript copy number was determined by absolute quantification (real-time PCR) in 46 patients. CYP3A5*3 genotypes were found to be a good predictor of tacrolimus L/D in kidney-transplant patients. Significantly higher L/D was observed among non-expressors (2.85, 95%: 2.05-3.70 (ng·mL(-1))/(mg·kg(-1))) as compared with the expressors (1.15, 95%: 0.95-1.80 (ng·mL(-1))/(mg·kg(-1))) of CYP3A5 (Mann-Whitney U test; P < 0.001). No correlation was observed between L/D and the ABCB1 genotypes. A significant inverse correlation of blood ABCB1 mRNA level with L/D was demonstrated (Spearman's Rank Order correlation; P = 0.016, rs = -0.348). However, in multiple regression analysis, only CYP3A5*3 genotype groups were found to be significantly correlated with tacrolimus L/D (P < 0.001). These findings highlight the importance of CYP3A5*3 pharmacogenotyping among kidney-transplant patients treated with tacrolimus, and confirm the role of blood cell P-glycoprotein in influencing the L/D for tacrolimus. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 17% |
| Student > Doctoral Student | 2 | 11% |
| Student > Master | 2 | 11% |
| Researcher | 2 | 11% |
| Student > Ph. D. Student | 2 | 11% |
| Other | 4 | 22% |
| Unknown | 3 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 33% |
| Medicine and Dentistry | 4 | 22% |
| Biochemistry, Genetics and Molecular Biology | 2 | 11% |
| Unspecified | 1 | 6% |
| Computer Science | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 3 | 17% |
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 20 December 2013.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Canadian Journal of Physiology and Pharmacology
#1,331
of 1,696 outputs
Outputs of similar age
#281,853
of 320,503 outputs
Outputs of similar age from Canadian Journal of Physiology and Pharmacology
#11
of 16 outputs
Altmetric has tracked 25,373,627 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 1,696 research outputs from this source. They receive a mean Attention Score of 4.3. 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 16 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.