Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 REFERENCES 1. Coelho T, Tredger MDhawan A. Current status of immunosuppressive agents for solid organ transplantation in children. Pediatr Transplant 2012; 16(2): 106-22. 2. Kho M, Cransberg K, Weimar W, et al. Current immunosuppressive treatment after kidney transplantation. Expert Opin Pharmacother 2011; 12(8): 1217-31. 3. Ho ET, Wong G, Craig JC, et al. Once-daily extended-release versus twice-daily standard-release tacrolimus in kidney transplant recipients: a systematic review. Transplantation 2013; 95(9): 1120-8. 4. Penninga L, Penninga EI, Moller CH, et al. Tacrolimus versus cyclosporin as primary immunosuppression for lung transplant recipients. Cochrane Database Syst Rev 2013; 5: CD008817. 5. Mourad M, Wallemacq P, De Meyer M, et al. Biotransformation enzymes and drug transporters pharmacogenetics in relation to immunosuppressive drugs: impact on pharmacokinetics and clinical outcome. Transplantation 2008; 85(7 Suppl): S19-24. 6. Leroy S, Isapof A, Fargue S, et al. Tacrolimus nephrotoxicity: beware of the association of diarrhea, drug interaction and pharmacogenetics. Pediatr Nephrol 2010; 25(5): 965-9. 7. Picard NMarquet P. The influence of pharmacogenetics and cofactors on clinical outcomes in kidney transplantation. Expert Opin Drug Metab Toxicol 2011; 7(6): 731-43. 8. Wang J. CYP3A polymorphisms and immunosuppressive drugs in solid-organ transplantation. Expert Rev Mol Diagn 2009; 9(4): 383-90. 9. MacPhee IA. Pharmacogenetic biomarkers: cytochrome P450 3A5. Clin Chim Acta 2012; 413(17-18): 1312-7. 10. Staatz CE, Goodman L KTett SE. Effect of CYP3A and ABCB1 single nucleotide polymorphisms on the pharmacokinetics and pharmacodynamics of calcineurin inhibitors: Part II. Clin Pharmacokinet 2010; 49(4): 207-21. 11. Staatz CE, Goodman L KTett SE. Effect of CYP3A and ABCB1 single nucleotide polymorphisms on the pharmacokinetics and pharmacodynamics of calcineurin inhibitors: Part I. Clin Pharmacokinet 2010; 49(3): 141-75. 12. Terrazzino S, Quaglia M, Stratta P, et al. The effect of CYP3A5 6986A>G and ABCB1 3435C>T on tacrolimus dose-adjusted trough levels and acute rejection rates in renal transplant patients: a systematic review and meta-analysis. Pharmacogenet Genomics 2012; 22(8): 642-5. 13. Shi X J, Geng F, Jiao Z, et al. Association of ABCB1, CYP3A4*18B and CYP3A5*3 genotypes with the pharmacokinetics of tacrolimus in healthy Chinese subjects: a population pharmacokinetic analysis. J Clin Pharm Ther 2011; 36(5): 614-24. 14. Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ 2003; 327(7414): 557-60. 15. Jun KR, Lee W, Jang MS, et al. Tacrolimus concentrations in relation to CYP3A and ABCB1 polymorphisms among solid organ transplant recipients in Korea. Transplantation 2009; 87(8): 1225-31. 16. Li DY, Fang Y. Association between CYP3A4*18B Polymorphisms and Serum Tacrolimus Concentrations. Pharmaceutical and Clinical Research 2012; 20: 12-15. 17. Li DY, Teng RC, Zhu HJ, et al. CYP3A4/5 polymorphisms affect the blood level of cyclosporine and tacrolimus in Chinese renal transplant recipients. Int J Clin Pharmacol Ther 2013; 51(6): 466-74. 18. Zhu L, Song HT, Wang QH, et al. Effect of CYP3A4*18B, CYP3A5*3 gene polymorphism on dosage and concentration of tacrolimus in renal transplant patients. Acta Pharmaceutica Sinica 2012; 47: 878-883. 19. He X, Tong RS. Effect of CYP3A4 and CYP3A5 gene polymorphisms on tacrolimus plasma concentration in renal transplanted recipients in the han population. China Pharmacis 2013; 16: 497-501 20. Hou MM, Hou Y, Song HT, et al. Effects of CYP3A4 genetic polymorphism on the efficacy and adverse reaction of tacrolimus in patients with renal transplantation. China Pharmacis 2010; 21: 2427-2429. 21. Lin L, Song WL, Shen ZY, et al. Effect of CYP3A5 and CYP3A4 on pharmacokinetics of tacrolimus in renal transplantation recipients. Chin J Organ Transplant 2012; 33: 220-224. 22. Halloran P F. Immunosuppressive drugs for kidney transplantation. N Engl J Med 2004; 351(26): 2715-29 23. Dai D, Tang J, Rose R, et al. Identification of variants of CYP3A4 and characterization of their abilities to metabolize testosterone and chlorpyrifos. J Pharmacol Exp Ther 2001; 299(3): 825-31.