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Meta-analysis of the Effect of CYP3A4*18B Gene Polymorphism
on Tacrolimus Pharmacokinetics in Renal Transplant Recipients
Xueping Zhu1, MD, Hao Li2,MD, Peng Wang2,MD,Yaodong Zhao1,MD ,Zhi Cheng1,MD, Guang
Zhu1,MD, Zhongfeng Li1,MD
ABSTRACT
Background: Tacrolimus, an orally administered calcineurin inhibitor, is metabolized
mainly by the CYP3A gene subfamily. The effect of CYP3A4*18B polymorphism
on the pharmacokinetics of tacrolimus in different studies was conflicting; thus, the
aim of this study was to perform a meta-analysis to evaluate the correlation of
CYP3A4*18B gene polymorphism and the pharmacokinetics of tacrolimus in renal
transplant recipients.
Method: Two investigators independently searched the PubMed, Web of Science,
EMBASE
and
Chinese
National
Knowledge
Infrastructure
(CNKI).
The
pharmacokinetic parameters were extracted from the relevant included studies.
Weighted mean differences (WMD) and 95% confidence intervals (95% CIs) for the
1
The Fifth Affiliated Hospital of Zhengzhou University (XZ,YZ, ZC ,GZ,ZL)
2
Zhengzhou University(HL,PW)
Zhengzhou, Henan 450052, Chine
The authors have no financial or other conflicts of interest to disclose.
Correspondence: Xueping Zhu, MD,Professor of Medicine ,Department of Infectious Diseases.The
Fifth Affiliated Hospital of Zhengzhou University,Zhengzhou,Henan450052,China.
Tel: 86-13592602055; Fax: 86-0371-55615758
E-mail: [email protected]
association
between
CYP3A4*18B
gene
polymorphism
and
tacrolimus
pharmacokinetics were calculated in a fixed-effects model and a random-effects
model when appropriate and the meta-analysis was conducted using STATA11.0.
Results: A total of 5 papers concerning 519 renal transplant recipients were included
in the meta-analysis. The overall results showed CYP3A4*18B C>T polymorphism
could influence the pharmacokinetic parameters in different post transplant times, the
subjects with C allele had higher concentration to dose ratio than the subjects with T
allele.
Conclusions: Through this meta-analysis for the available studies, a definite
association between the SNP C>T in CYP3A4*18B gene and pharmacokinetics of
tacrolimus. The C>T polymorphism in CYP3A4*18B gene could be considerably
acted as a candidate biomarker for renal transplant therapy in the future. However,
additional studies with large sample size and better study designs are warranted to
verify our finding.
Introduction
So far, numerous patients with end-stage diseases were accepted organ transplantation
such as kidney transplantation, liver transplantation and heart transplantation which
was as an effective treatment. However, the immune rejection of patients after
transplantation could not be ignored. Tacrolimus, a macrolide antibiotic compound
which can effective decrease rejection rates, is widely used for preventing from organ
rejection after transplantation1-4. Many studies have confirmed tacrolimus was the
principal life-saving drugs for renal transplant patients2, 3. However, tacrolimus has a
narrow therapeutic index and a significant variability in dose requirements5. So how
to use this drug scientifically is crucial, because low blood concentrations of
tacrolimus can lead to rejection, and high blood concentration can lead to toxicosis or
infection6, 7. Thus, to avoid the rejection and adverse reaction for organ transplantation
patients, achieving the desired target blood concentration is particularly important.
CYP3A gene encodes a member of the cytochrome P450 superfamily of enzymes,
which can catalyze many reactions involved in the metabolism of almost half the
drugs in use, including tacrolimus8, 9. It is estimated that genetics can account for
20%-95% variability in drug disposition and effects. Many studies about the
association of CYP3A polymorphism and tacrolimus pharmacokinetics have been
accomplished. Up to date, numerous single nucleotide polymorphisms in human
CYP3A gene have been reported. Researches about CYP3A polymorphisms mainly
involve the CYP3A5*3, CYP3A4*18B, CYP3A4*1B, CYP3A4*22 polymorphisms.
Many studies have examined the CYP3A5*3 polymorphisms could affect the blood
concentration of tacrolimus by reducing the CYP3A5 expression10, 11. Salvatore and
his colleagues have accomplished a meta-analysis on the effect of CYP3A5*3
polymorphism on tacrolimus dose-adjusted trough levels in renal transplant patients
and revealed that the CYP3A5*3/*3 genotype could induce significantly higher
tacrolimus dose-adjusted trough levels12. Also, according to the recent studies, the
possibility of CYP3A4*18B variance is much high in Chinese, and this
polymorphism could affect the activity of CYP3A4 enzyme13. Many studies have
researched the effect of CYP3A4*18B polymorphism on tacrolimus pharmacokinetics
in renal transplant recipients. However, the results of CYP3A4*18B polymorphisms
on pharmacokinetics of tacrolimus in renal transplant recipients has not reached a
consensus. The low numbers of sample size, the post transplant time, many other
polymorphisms also influencing the CYP3A4 enzyme function, may be the reasons of
the controversial results. Thus, a confirmed result is very important for the clinical
treatment of renal transplantation.
Up to data, there is a lack of studies examining the impact of CYP3A4*18B
polymorphisms on the published data to investigate the effect of this polymorphism
on the pharmacokinetics of tacrolimus in renal transplantation. So in order to
summarize
the
influence
of
CYP3A4*18B
polymorphisms
on
tacrolimus
pharmacokinetics, several studies about this were collected to form a relatively bigger
sample size, and the meta-analysis is employed to explore the existing the evidences
of
the
association
between
CYP3A4*18B
polymorphisms
and
tacrolimus
pharmacokinetics in renal transplant recipients.
Methods
Publication Search and Inclusion Criteria
A systematic literature search was performed for all articles on the the association
between CYP3A4*18B polymorphism and pharmacokinetics of tacrolimus in renal
transplant recipients up to December 15th, 2013. The PubMed, Web of Science,
EMBASE and Chinese National Knowledge Infrastructure (CNKI) were used
simultaneously. The following search terms was used: renal transplant OR kidney
transplantation
AND
tacrolimus
AND
CYP3A4
AND
polymorphism
OR
polymorphisms OR genotype by two independent investigators. In addition, studies
were identified by manual search of the reference listed in the retrieved studies. This
electronic search was performed without language restrictions. Data from studies
were considered for inclusion in our meta-analysis only if the study met all of the
following criteria: (1) it explored the association between CYP3A4*18B
polymorphism and pharmacokinetics of tacrolimus in renal transplant recipients; (2)
the genotype frequency of CYP3A4*18B was available; (3) the pharmacokinetic
parameters were expressed as dose (mg/kg per day) or concentration (ng/ml) or
concentration/dose (ng/ml per mg/kg); (4) the post transplant times of the studies were
clear and definite. If the data from the article was insufficient, the effort to contact
with the corresponding authors was made.
Data Extraction
According to the inclusion criteria listed above, two reviewers independently
extracted the information from all the eligible articles. The characteristics information
of enrolled articles including the first author, year of publication, country of study,
ethnicity of subjects, number of total cases and male cases, age, immunosuppressive
protocol, genotype method, tacrolimus concentration measured method , was
extracted(Table 1). Furthermore, the postoperative time, cases number of
CYP3A4*18B genotypes of recipients and the tacrolimus pharmacokinetic parameters
including dose, concentration and concentration/dose (C/D) ratio were extracted and
presented in Table2. All the pharmacokinetic parameters were demonstrated by the
form of mean ± SD. If discrepancies and differences were existed after data
collection, discussion was carried out to get consensus.
Statistical Analysis
The strength of association between the CYP3A4*18B polymorphism and
pharmacokinetics of tacrolimus in renal transplant recipients was assessed by
weighted mean differences (WMD) with 95% confidence intervals (95% CIs),
according to their postoperative time. Statistical heterogeneity among studies was
tested by Chi square-based Q test and I2 14. The variation in WMD attributed to
heterogeneity was represented by I2. A random effects model (DerSimonian and Laird
method) was applied, when I2 was more than 50%, which implied statistically
significant heterogeneity. Otherwise, a fixed effects model (Inverse Variance method)
was applied. We performed Z-test to determine the statistical significance of pooled
WMD and the results would be considered significant with P < 0.05. Moreover, to
assess the publication bias of the results, the Egger’s linear regression test was
performed. All analyses were done using STATA software, version 11.0 (STATA Corp.,
College Station, TX, USA), and all tests were two-sided.
Results
Literature search
A total of 185 articles were found by literature search from the PubMed, Web of
Science, EMBASE and CNKI, using different combinations of key terms. After
screening the studies, 130 studies were removed as overlapping studies. The
remaining 55 studies were further screening the titles and abstracts, 9 studies were
removed. Among them, 6 studies were review and 3 studies were about healthy
population. The remaining 44 studies were further evaluated with the full texts.
Finally, 39 studies were excluded due to some reasons as follows: thirty-seven of
them did not focus on the association between tacrolimus pharmacokinetics and
CYP3A4*18B polymorphism; one study did not defined the postoperative time
clearly with their corresponding data15; one study; one study just investigated the
genotypes as CC and CT+TT, the effort to contact with the corresponding authors was
made, but in vain16. The flow chart described the process of our search strategy
(Fig.1)
As a result, 5 studies which included 519 renal transplant recipients were identified in
this meta-analysis17-21. The characteristics of eligible studies were summarized in
Table 1. The post transplant time is an important factor of the pharmacokinetics, so
the variation of tacrolimus concentration/dose ratio based on CYP3A4*18B C>T
polymorphisms were analyzed respectively at different post-transplant time. The
pharmacokinetics of tacrolimus in these eligible studies including concentration to
dose ratio are summarized in Table 2.
Fig. 1. Search strategy flow chart
Table 1 Characteristics of eligible studies included in the meta-analysis.
Cases/male
Country Ethnicity
(n)
Study
LI Dan-ying 2013 17
ZHU Lin 2012 18
HE Xia 2013 19
Hou Ming-ming
2010 20
LIN Ling 2012 21
Age
(years)
China
China
China
Asian
Asian
Asian
46/30
227/170
56/41
14-57
39±11
36.2±9.3
China
Asian
129/111
37±11
China
Asian
61
39.9±11.7
Body
weight
(kg)
59.5±10.1
56.7±11.8
PCR-RFLP
PCR-RFLP
TaqMan
Method of
concentration
measured
EMIT
ELISA
EMIT
61.0±7.5
PCR-RFLP
ELISA
62.4±14.1
PCR-RFLP
ELISA
Genotype
method
EMIT: Enzyme Multiplied Immunologic Technique; ELISA: Enzyme-linked
Immunosorbent Assay; PCR-RFLP: Polymerase Chain Reaction Restriction Fragment
Length Polymorphism
Table 2 Characteristics of tacrolimus pharmacokinetic parameters of the eligible
studies included in the meta-analysis for renal transplant recipients’ CYP3A4*18B
C>T polymorphisms.
Concentration/dose (ng/ml per mg/kg)
Study
Postoperative time
Cases (n)
(CC/CT/TT)
CC
CT
TT
LI Dan-ying 201317
ZHU Lin 201218
HE Xia 201319
HE Xia 201319
HE Xia 201319
HE Xia 201319
HE Xia 2013
Hou Ming-ming
201020
LIN Ling 201220
LIN Ling 201220
LIN Ling 201220
LIN Ling 201220
1 month
3 days
7 days
14 days
1 month
3 month
6 month
21/21/22
106/102/19
62/30/9
62/30/9
62/30/9
62/30/9
62/30/9
97.15±15.10
138.6±77.1
74.0±21.7
86.1±23.3
80.0±13.0
74.0±13.1
72.3±13.1
70.54±18.20
102.6±62.0
58.6±17.7
63.4±26.3
70.0±61.4
64.2±14.0
57.7±5.5
39.92±7.46
83.9±70.6
44.6±10.6
57.4±12.0
61.4±14.2
58.4±6.1
47.0±4.4
3 month
66/49/14
160.20±8.69
104.40±7.58
82.03±4.93
14 days
1 month
2 month
3 month
33/24/4
33/24/4
33/24/4
33/24/4
127.0±85.9
140.0±86.0
149.0±78.9
184.0±126.0
81.3±81.9
100.0±55.4
102.0±45.2
108.0±70.0
44.9±20.4
52.7±22.1
59.9±23.7
75.4±26.8
Effect of CYP3A4*18B C>T polymorphism on tacrolimus concentration to dose ratio
A total of 5 studies including 519 renal transplant recipients examined the association
between recipient CYP3A4*18B C>T polymorphism and tacrolimus concentration to
dose ratio. There were two studies of 7 days or less post transplantation, two studies
of 14 days post transplant, three studies of 1 month post transplant, one study of two
months post transplant, 3 studies of three months post transplant and one study of six
months post transplant. Because the number of studies about two or six months post
transplant was just only one, so this meta-analysis did not involve the two or six
months post transplant.
Table 3 showed the results of CYP3A4*18B C>T subgroup analysis for the subgroup
with tacrolimus concentration to dose ratio divided by different post transplant times.
The Q-statistic showed heterogeneity among those studies with tacrolimus
concentration dose ratio when comparing CC and CT in ≤7 days and 3 months, CC
and TT in 14 days, 1 month and 3 months, CT and TT in 14 days, 1 month and 3
months. Subjects with CC genotype had a significantly higher concentration to dose
ratio compared with subjects CT genotype in ≤7 days (P = 0.019, WMD: 23.86
[4.00-43.73]), 14 days (P < 0.001, WMD: 24.07 [13.35-34.79]), 1 month (P < 0.001,
WMD: 24.71 [15.79-33.64]) and 3 months (P = 0.032, WMD: 42.90 [3.66-82.15])
(Fig. 2). Subjects with CC genotype had a significantly higher concentration to dose
ratio compared with subjects TT genotype in ≤7 days (P < 0.001, WMD: 30.90
[22.38-39.42]), 14 days (P = 0.048, 52.56 [0.53-104.60]), 1 month (P = 0.003, WMD:
50.84 [17.82-83.87]) and 3 months (P = 0.017, WMD: 63.71 [11.53-115.89]) (Fig. 3).
Subjects with CT genotype had a significantly higher concentration to dose ratio
compared with subjects TT genotype in ≤ 7 days (P = 0.002, WMD: 14.33
[5.29-23.38]), 1 month (P = 0.001, WMD: 27.91 [11.07-44.74]) and 3 months (P =
0.031, WMD: 63.71 [1.53-31.29]) (Fig. 4).
Table 3 Stratified analysis of CYP3A4*18B C>T polymorphism on tacrolimus
concentrations to dose ratio.
Numbers of
Effects
Studies
model
CC vs CT
2
random
CC vs TT
2
CT vs TT
Subjects
WMD (95%CI)
Test of heterogeneity
I2
Z
P
0.051
73.70%
2.35
0.019
1.89
0.169
47.10%
7.11
<0.001
14.33 (5.29-23.38)
0.07
0.794
0.00%
3.11
0.002
fixed
24.07 (13.35-34.79)
0.99
0.320
0.00%
4.40
<0.001
2
random
52.56 (0.53-104.60)
8.09
0.004
87.60%
1.98
0.048
2
random
15.53 (-12.11-43.18)
2.19
0.139
54.30%
1.10
0.271
CC vs CT
3
fixed
24.71 (15.79-33.64)
2.49
0.289
19.50%
5.42
<0.001
CC vs TT
3
random
50.84 (17.82-83.87)
44.07
<0.001
95.50%
3.02
0.003
CT vs TT
3
random
27.91 (11.07-44.74)
4.28
0.117
53.30%
3.25
0.001
CC vs CT
3
random
42.90 (3.66-82.15)
183.39
<0.001
98.90%
2.14
0.032
CC vs TT
3
random
63.71 (11.53-115.89)
403.80
<0.001
99.50%
2.39
0.017
CT vs TT
3
random
16.41 (1.53-31.29)
20.72
<0.001
90.30%
2.16
0.031
Chi-squared
P
23.86 (4.00-43.73)
3.80
fixed
30.90 (22.38-39.42)
2
fixed
CC vs CT
2
CC vs TT
CT vs TT
≤ 7 days
14 days
1 month
3 months
Study
%
ID
WMD (95% CI)
Weight
ZHU Lin (2012)
36.00 (17.02, 54.98)
1.55
HE Xia (2013)
15.40 (7.08, 23.72)
8.05
Subtotal (I-squared = 73.7%, p = 0.051)
18.72 (11.10, 26.35)
9.60
HE Xia (2013)
22.70 (11.65, 33.75)
4.57
LIN Ling (2012)
45.70 (1.74, 89.66)
0.29
Subtotal (I-squared = 0.0%, p = 0.320)
24.07 (13.35, 34.79)
4.86
LI Dan-ying (2013)
26.61 (16.50, 36.72)
5.46
HE Xia (2013)
10.00 (-12.21, 32.21)
1.13
LIN Ling (2012)
40.00 (3.23, 76.77)
0.41
Subtotal (I-squared = 19.5%, p = 0.289)
24.71 (15.79, 33.64)
7.00
HE Xia (2013)
9.80 (3.82, 15.78)
15.62
Hou Ming-ming (2010)
55.80 (52.82, 58.78)
62.71
LIN Ling (2012)
76.00 (24.69, 127.31)
0.21
Subtotal (I-squared = 98.9%, p = 0.000)
46.71 (44.04, 49.37)
78.54
41.38 (39.02, 43.74)
100.00
≤ 7 days
14 days
1 month
3 month
Heterogeneity between groups: p = 0.000
Overall (I-squared = 96.6%, p = 0.000)
-127
0
127
Fig.2. Forest plot of meta-analysis of difference in tacrolimus concentrations to dose
ratio between subjects carrying CC genotype and carrying CT genotype at
CYP3A4*18B C>T polymorphism.
Study
%
ID
WMD (95% CI)
Weight
ZHU Lin (2012)
54.70 (19.73, 89.67)
0.44
HE Xia (2013)
29.40 (20.62, 38.18)
7.04
Subtotal (I-squared = 47.1%, p = 0.169)
30.90 (22.38, 39.42)
7.48
HE Xia (2013)
28.70 (18.95, 38.45)
5.71
LIN Ling (2012)
82.10 (46.62, 117.58)
0.43
Subtotal (I-squared = 87.6%, p = 0.004)
32.45 (23.05, 41.85)
6.14
LI Dan-ying (2013)
57.23 (50.06, 64.40)
10.56
HE Xia (2013)
18.60 (8.77, 28.43)
5.63
LIN Ling (2012)
87.30 (50.83, 123.77)
0.41
Subtotal (I-squared = 95.5%, p = 0.000)
44.87 (39.15, 50.59)
16.59
HE Xia (2013)
15.60 (10.45, 20.75)
20.48
Hou Ming-ming (2010)
78.17 (74.84, 81.50)
49.08
LIN Ling (2012)
108.60 (58.22, 158.98)
0.21
Subtotal (I-squared = 99.5%, p = 0.000)
59.90 (57.11, 62.69)
69.78
53.55 (51.22, 55.88)
100.00
≤ 7 days
14 days
1 month
3 month
Heterogeneity between groups: p = 0.000
Overall (I-squared = 98.3%, p = 0.000)
-159
0
159
Fig.3. Forest plot of meta-analysis of difference in tacrolimus concentrations to dose
ratio between subjects carrying CC genotype and carrying TT genotype at
CYP3A4*18B C>T polymorphism.
Study
%
ID
WMD (95% CI)
Weight
ZHU Lin (2012)
18.70 (-15.25, 52.65)
0.57
HE Xia (2013)
14.00 (4.62, 23.38)
7.51
Subtotal (I-squared = 0.0%, p = 0.794)
14.33 (5.29, 23.38)
8.08
HE Xia (2013)
6.00 (-6.25, 18.25)
4.41
LIN Ling (2012)
36.40 (-1.98, 74.78)
0.45
Subtotal (I-squared = 54.3%, p = 0.139)
8.81 (-2.86, 20.48)
4.86
LI Dan-ying (2013)
30.62 (22.23, 39.01)
9.41
HE Xia (2013)
8.60 (-15.25, 32.45)
1.16
LIN Ling (2012)
47.30 (16.31, 78.29)
0.69
Subtotal (I-squared = 53.3%, p = 0.117)
29.37 (21.70, 37.03)
11.26
HE Xia (2013)
5.80 (-0.60, 12.20)
16.14
Hou Ming-ming (2010)
22.37 (19.03, 25.71)
59.20
LIN Ling (2012)
32.60 (-5.79, 70.99)
0.45
Subtotal (I-squared = 90.3%, p = 0.000)
18.90 (15.95, 21.86)
75.80
19.22 (16.65, 21.79)
100.00
≤ 7 days
14 days
1 month
3 month
Heterogeneity between groups: p = 0.012
Overall (I-squared = 76.4%, p = 0.000)
-78.3
0
78.3
Fig.4. Forest plot of meta-analysis of difference in tacrolimus concentrations to dose
ratio between subjects carrying CT genotype and carrying TT genotype at
CYP3A4*18B C>T polymorphism.
Discussion
Our study investigated the association between CYP3A4*18B polymorphism and
tacrolimus pharmacokinetics in renal transplant recipients. Subjects with CC genotype
or CT genotype both had significantly higher concentration to dose ratio compared
with subjects with TT genotype in different post transplant times. Moreover, Subjects
with CC genotype had a significantly higher concentration to dose ratio compared
with subjects CT genotype in different post transplant times. That was mean the
subjects with C allele had higher concentration to dose ratio than the subjects with T
allele in different post transplant times.
It is very crucial for organ transplant patients to use immunosuppressive regimens in
the post transplant times to achieve successful allograft function and suppress
rejection. However, there are many adverse side effects, such as infection, cancer,
anemia and diabetes, for patients using immunosuppressive regimens22. So, it is
absolutely important and necessary to establish the individual immunosuppression
protocol for organ transplant patients. As a result of there are so many factors that
may act on immunosuppressive, so it is a challenge to identify the dose requirements
of individual immunosuppressant therapy after transplantation.
It is estimated that genetics can account for 20%-95% variability in drug disposition
and effects. Many studies have focus on the polymorphisms in genes to explain the
interindividual variability in drug absorption, distribution, metabolism and excretion
pathways. CYP3A4 is the main CYP isoform in human liver and intestine. CYP3A4
gene encodes this enzymes, which can catalyze many reactions involved in the
metabolism of almost half the drugs in use, including tacrolimus. The activity of
CYP3A4 is characterized by widespread variation in the general population, which is
thought to have a genetic basis. Many studies about the association of CYP3A4
polymorphism, such as CYP3A4*18B, CYP3A4*1B, CYP3A4*22 polymorphisms,
and tacrolimus pharmacokinetics have been accomplished. Salvatore and his
colleagues have accomplished a meta-analysis on the effect of CYP3A5*3
polymorphism on tacrolimus dose-adjusted trough levels in renal transplant patients
and revealed that the CYP3A5*3/*3 genotype could induce significantly higher
tacrolimus dose-adjusted trough levels.
CYP3A4*18B C>T is a nonsynonymous SNP, which can cause a change from leucine
to proline at codon 293. Dai D and his colleagues reported that a recombinant
CYP3A4*18B allele could enhance the metabolism of testosterone and chlorpyrifos in
vitro23. The polymorphism of CYP3A4*18B is present only in Asian individuals at an
allele frequency of 2%, this is consistent with this meta-analysis, as the studies
including in this meta-analysis are all about Chinese population. Through our
meta-analysis, the recipients harboring the CC genotype more likely to have higher
tacrolimus concentration with the recipients harboring the TT genotype. As tacrolimus
has a narrow therapeutic index and a significant variability in dose requirements, low
blood concentrations of tacrolimus can lead to rejection, and high blood concentration
can lead to toxicosis or infection. So the recipients harboring the CC genotype should
be given lower starting dose to avoid drug toxicity and the recipients harboring the TT
genotype should be given higher starting dose to avoid rejection.
Some advantages can be highlighted in the current study. Firstly, our study shed light
on
the
association
between
CYP3A4*18B
polymorphism
and
tacrolimus
pharmacokinetics in renal transplant recipients. Secondly, our study suggested that
functional polymorphism of CYP3A4*18B could be conducted and replicate these
observations, therefore it could be beneficial to achieve the interindividual therapy for
renal transplant recipients. However, some limitations of this study should be noticed
in our meta-analysis at the same time. First, the genotype distribution was unavailable
or the post transplant times were unclear in some studies, and the corresponding
authors could not be contacted. So these studies had not been included in our
meta-analysis, which may have impact on our meta-analysis. Second, many studies
did not reporter the dose administration, so our meta-analysis did not perform the
association between dose administration and the genotype in different post transplant
times. Third, lack of the original data of the including studies limited our further
evaluation of the potential interactions, because genetic factor, clinical characteristics
and their interactions with therapy may modulate the results. Fourth, only published
studies were included in this meta-analysis, ongoing studies and unpublished data
were not sought, which may have biased our results. Fifth, the included studies only
about Chinese population, there was no other race of studies, so results from this
meta-analysis may be applicable only to Chinese population.
Conclusion
Our meta-analysis suggested that the association between the SNP C>T in CYP3A4
*18B gene and pharmacokinetics of tacrolimus in Chinese population, the subjects
with C allele had higher concentration to dose ratio than the subjects with T allele in
different post transplant times. The C>T polymorphism in CYP3A4 *18B gene could
be considerably acted as a candidate biomarker for renal transplant therapy in the
future. To confirm our findings, further well-designed studies with large sample size
in diverse ethnic populations should be performed to validate the association
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