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A new functional CYP3A4 intron 6 polymorphism significantly affects tacrolimus pharmacokinetics in kidney transplant recipients.

CYP3A4 is the most abundant cytochrome P450 enzyme in the human liver and is responsible for the metabolism of 45%-60% of prescribed drugs (1). CYP3A4 activity varies widely, with 10- to 100-fold variation between individuals (2-5). A recent study identified a functional single-nucleotide polymorphism (SNP) [5] in intron 6 (CYP3A4*22) that was associated with decreased CYP3A4 production and activity and that was correlated with the statin dose requirement for lipid concentration control (6).

The immunosuppressive drug tacrolimus (Tac) is extensively metabolized by CYP3A4 and CYP3A5 (79). The *3 allele of the CYP3A5 [6] (cytochrome P450, family 3, subfamily A, polypeptide 5) gene, which codes for the absence of CYP3A5 (10), was previously associated with the Tac predose concentration ([C.sub.0]) and Tac dose requirements. The CYP3A5 genotype explains a major portion of the interindividual variation in Tac pharmacokinetics: carriers of 2 CYP3A5*3 nonfunctional alleles require substantially less Tac (about 50% less) to reach an identical [C.sub.0] concentration (11, 12) than kidney, liver, lung, and heart transplant recipients carrying a CYP3A5*1 active allele. The clinical benefit of CYP3A5-based Tac dosing remains debatable, however (13, 14). The drug transporter encoded by the ABCB1 [ATP-binding cassette, sub-family B (MDR/TAP), member 1] gene is also involved in Tac disposition. Genetic variants have been associated with Tac drug disposition, although contradictory results have been published (11, 12).

No studies to date have been able to identify SNPs in CYP3A4 (cytochrome P450, family 3, subfamily A, polypeptide 4) that could account for the interindividual variation in CYP3A4 activity. A newly discovered SNP in intron 6 (rs35599367C>T) may now explain this variability (6). The purpose of our study was to test whether this new CYP3A4 SNP correlates with increased Tac exposure on standard dosages and thus might predict lower dose requirements.

Materials and Methods

PATIENTS AND STUDY DESIGN

Patients were de novo kidney transplant recipients participating in a phase IV, open, prospective, randomized controlled, international multicenter trial comparing fixed-dose (FD) with concentration-controlled (CC) mycophenolate mofetil treatment (FDCC study) (15). Randomization to a fixed-dose or concentration-controlled regimen was done in blocks of 8 patients per center. Patients were randomized centrally through an automated telephone system, in a 1:1 ratio. A pharmacogenetic substudy was started in parallel. Findings on the roles of genetic polymorphisms in the UGT1A9 (UDP glucuronosyltransferase 1 family, polypeptide A9) gene for mycophenolate mofetil (16), the UGT2B7 (UDP glucuronosyltransferase 2 family, polypeptide B7) gene for acyl-glucuronide mycophenolic acid (17), and CYP3A5/ABCB1 for Tac exposure and acute rejection (18) in the FDCC study have previously been published. Patients provided separate written informed consent for the substudy. The protocol was approved by the ethics committees of all participating centers and the relevant authorities in participating countries.

Immunosuppressive therapy consisted of calcineurin inhibitor and corticosteroids. The choice of Tac or cyclosporine and the target blood concentrations for each drug were in accordance with each center's protocol. Oral Tac treatment began within 48 h before transplantation. Therapeutic drug monitoring was performed routinely, and centers were free to aim for the target concentrations they considered appropriate. A retrospective analysis showed that all centers started Tac with an aim of whole-blood concentrations of 7-15 [micro]g/L, tapering to 5-12 [micro]g/L at month 3 and to 4-10 [micro]g/L at month 12. Corticosteroid tapering was recommended but not mandatory, and tapering regimens were left to the discretion of the investigators. In general, centers used higher doses in the first 2 weeks (20-25 mg of prednisolone equivalent daily), lower doses thereafter (15 mg on week 4, 5 mg at month 3), and low-dose or no prednisolone during months 6-12. More details can be found in the original FDCC study publication (15). Genetic data were available for 185 kidney transplant recipients treated with Tac. Pharmacokinetic data were not always available for all patients at all time points. The [C.sub.0] was measured on days 3 and 10, at months 1, 3, 6, and 12 after transplantation, and whenever deemed necessary by the attending physician. Donor DNA was not collected, and no kidney biopsies were performed. Delayed graft function (DGF) was defined as a need for dialysis within the first week after transplantation. Biopsy-proven acute rejection (BPAR) was defined as any histologically confirmed episode with a Banff score [greater than or equal to] 1. All biopsy samples were assessed locally by a pathologist.

DRUG CONCENTRATION MEASUREMENT

The [C.sub.0] was measured in whole blood in laboratories in each participating center by either the Tac II microparticulate enzyme immunoassay (Abbott Laboratories) or the enzyme-multiplied immunoassay technique (EMIT 2000; Syva Company/Dade Behring). The specificities ofthe 2 assays are comparable, and high correlations exist between the immunoassay and HPLC results (19, 20 ). Although immunoassays overestimate Tac concentrations by up to 20% because of concurrent measurement of metabolites, this methodology has proved feasible for assessing differences in Tac concentrations with respect to the CYP3A5 genotype (21 ). A limited number of centers used liquid chromatography-tandem mass spectrometry to measure Tac concentrations; this method was used for 30 (16%) of the 185 patients. Proficiency testing was performed by participation of all centers in the UK Quality Assessment Scheme. Dose-adjusted predose concentrations were calculated by dividing the [C.sub.0] by the corresponding 24-h dose on a milligram-per-kilogram basis.

GENOTYPE ANALYSIS

The MagnaPure LC System (Roche Diagnostics) was used to isolate genomic DNA from 200 [micro]L EDTA-treated whole blood. The CYP3A4 intron 6 C>T genotype was determined with 50 ng genomic DNA in the allelic discrimination reaction performed with TaqMan[R] (Applied Biosystems) genotyping assays (C_59013445 10) on an ABI PRISM 7500[R] Fast RealTime PCR System (Applied Biosystems). CYP3A5*3 analysis and ABCB1 1236C>T, 2677G>T/A, and 3435C>T analyses were performed as described previously (18, 22).

STATISTICAL ANALYSIS

Statistical analyses were performed with Predictive Analytics Software (PASW) statistics (version 17.0 for Windows; SPSS/IBM). [C.sub.0] and dose-adjusted [C.sub.0] values were normalized by logarithmic transformation. Kolmogorov-Smirnov tests confirmed that log-transformed data were normally distributed. For comparisons of 2 genotype groups, Student independent t-tests were used to compare the means at single time points. With >2 groups, ANOVAs were performed under the null hypothesis that the means of the compared groups were equal. When the differences between means were significant, we carried out a post hoc analysis consisting of an a priori polynomial linear contrast test to assess any potential linear trend according to genotype classification. The corresponding linear contrast does test the probability of a positive linear trend of the dependent variable across the ordered level of genotype classifications. Differences between groups were assumed statistically significant for P values <0.05. For univariate analyses of associations between categorical data (e.g., incidence of acute rejection), we used the Fisher exact test or the Pearson [chi square] test. The Tac daily dose and the dose-adjusted [C.sub.0] of different genotypes were compared with a mixed-model analysis, which was based on the maximum likelihood ratio, with patient genotype status as the fixed factor and time after transplantation as the repeated measurement. The sex, ethnicity, and age of the patients were introduced as random effects to adjust for these covariables. No structure was imposed on the variances and covariances between and within the times of follow-up of the repeated Tac measurements. We assumed levels of covariables (sex, ethnicity, and age) to be uncorrelated and to have a constant variance across the time of follow-up. Coefficients estimated from mixed-model ANOVA were back-transformed by taking their antilogarithm so that the data could become interpretable as percentage differences in geometric mean values of untransformed outcomes. Multiple logistic regression analysis was performed according to criteria defined by McMaster et al. (23), with a fixed Tac supratherapeutic threshold set at 15 [micro]g/L. We computed genotype-specific odds ratios and 95% CIs by using backward stepwise analysis based on maximum likelihood ratios to assess the impact of genotype on the risk of Tac plasma concentrations >15 [micro]g/L. P values <0.05 were considered statistically significant for entry, and P values <0.10 were required for staying in the model. For these analyses, each genotype was coded as a "dummy variable."

Results

CYP3A4 INTRON 6 GENOTYPE AND Tac EXPOSURE

Table 1 summarizes the patient characteristics. Overall, 173 patients were homozygous for the CYP3A4 intron 6 wild type (rs35599367CC), 11 patients were heterozygous (rs35599367CT), and 1 patient was homozygous for the T variant (rs35599367TT), resulting in a minor-allele (T) frequency of 3.5%. The observed genotype distribution was in accordance with the Hardy-Weinberg equilibrium (P = 0.25, [chi square] test). Heterozygous CT and homozygous TT variants were grouped and analyzed together as carriers of the rs35599367 T allele, against the patients homozygous for the wild type (rs35599367CC). We observed no linkage disequilibrium between the CYP3A4 intron 6 SNP and either the CYP3A5*3 or CYP3A4*1B allele [[chi square] (2) = 0.24 (P = 1.0) and 1.36 (P = 0.46), respectively].

The 2 CYP3A4 intron 6 genotype groups were comparable with respect to the Tac daily dose on day 3 after transplantation: 13.3 mg/day for the wild-type CC patients vs 13.0 mg/day for the carriers of 1 or 2 T alleles ( P = 0.84, Table 2; see Fig. 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol57/issue11). With these comparable dosages, the [C.sub.0] was higher for carriers of the T variant than for CC patients: 20.5 [micro]g/L vs 14.9 [micro]g/L (P = 0.05, Table 2; see Fig. 1 in the online Data Supplement). The differences between the 2 groups in [C.sub.0] were not observed at later time points (P > 0.05, Table 2; see Fig. 1 in the online Data Supplement), but carriers of the T allele required significantly lower Tac doses than CC patients to reach this [C.sub.0], from day 10 to month 6 (Table 2; see Fig. 1 in the online Data Supplement). Identical trends were observed when dose was adjusted for weight (Table 2). Analysis of repeated measurement in a mixed model demonstrated overall mean daily Tac dose requirements (adjusted for covariates age, sex, and ethnicity) to be 33% lower for carriers of the T allele (95% CI, -46% to -20%; P = 0.018) than for CC patients. Consequently, the calculated dose-adjusted [C.sub.0] was lower for CC patients than for CT/TT patients. These differences were significantly different at day 10 and month 1 after transplantation (Table 2; see Fig. 1 in the online Data Supplement), but not at later time points, a result that might be explained by a decrease in the number of participants, thereby yielding a larger 95% CI. The mixed model for repeated measurements showed that the overall mean dose-adjusted [C.sub.0] (adjusted for the covariates of age, sex, and ethnicity) was 47% higher in carriers of the T variant (95% CI, 8%-100%; P = 0.001) than the wild-type CC patients. As stated earlier, immunoassays overestimate Tac concentrations in blood by up to 20%, which may have influenced our results; however, the proportions of T variant carriers in the immunoassay group and the HPLC group were the same (6.5% and 6.7%, respectively; P = 1.0, Fisher exact test). Moreover, when the analytical method for measuring Tac concentration was introduced as a random effect, carriers of the T allele still showed a higher overall dose-adjusted [C.sub.0] (+ 37% for T allele carriers, P < 0.001) and a lower Tac daily dose (-20% for T allele carriers, P = 0.007) than the wild-type CC patients. For the ABCB1 gene, neither 2677G>T/A nor 1236C>T was associated with differences in Tac pharmacokinetics during the entire study. Also ABCB1 3435C>T was not significantly associated with Tac dose and dose-adjusted Tac exposure in a linear mixed model when the CYP3A4 genotype was not taken into account as previously reported (18 ). When ABCB1 haplotypes were generated, no differences between the ABCB1 CGC (n = 26) and TTT haplotype groups (n = 20) were observed with respect to Tac dose and dose-adjusted Tac exposure (data not shown).

COMBINED EFFECTS OF CYP3A4 INTRON 6 GENOTYPE, CYP3A5*3, AND ABCB1 3435C>T

We investigated the effects of CYP3A4 intron 6, ABCB1 3435C>T, and CYP3A5*3 genotypes in combination. Patients carrying 1 or 2 CYP3A5*1 alleles (CYP3A5 expressers) were compared with CYP3A5*3/*3 nonexpressers. In the mixed-model analysis adjusted for the covariates of age, sex, and ethnicity and including ABCB1 3435C>T, CYP3A5*3, and CYP3A4 intron 6 genotype status as fixed effects, all investigated SNPs were significantly correlated with dose-adjusted Tac exposure. Overall, the dose-adjusted [C.sub.0] was 43% higher among patients with CYP3A4 intron 6 CT/TT (95% CI, 13%-88%; P < 0.001) than among CC patients and was 43.3% lower among CYP3A5 expressers than among nonexpressers (95% CI, -52.7% to -32.1%; P = 0.001). Regarding ABCB1 3435TT individuals, patients with ABCB1 3435CT and 3435CC genotypes had a lower overall dose-adjusted [C.sub.0]: -14.3% (95% CI, -26.2% to -0.5%; P = 0.042) and -20.9% (95% CI, -32.7% to -7.1%; P = 0.003), respectively. Only CYP3A4 intron 6 and CYP3A5 genotypes correlated significantly with the Tac dose requirement, because ABCB1 3435C>T genotype status was no longer a significant fixed effect in the mixed model. In this final model, the Tac dose requirement was 25% lower for carriers of the T allele (95% CI, -43% to -7%; P = 0.04) than for CC patients and was 63.7% higher for patients who expressed CYP3A5 than for nonexpressers (95% CI, 39.1%-88.2%; P < 0.001).

Because the effects of the CYP3A4 intron 6 and CYP3A5*3 SNPs appeared independent, we combined genotype groups. Group 1 contained CYP3A5 nonexpressers and carriers of the CYP3A4 intron 6 T variant (poor metabolizers); group 2 contained CYP3A5 nonexpressers and CYP3A4 intron 6 CC patients (intermediate-1 metabolizers); group 3 clustered CYP3A5 expressers carrying the CYP3A4 intron 6 T allele (intermediate-2 metabolizers); and group 4 merged CYP3A5 expressers with individuals with the CYP3A4 intron 6 CC wild type (extensive metabolizers) (Table 3). The [C.sub.0] values of the groups were significantly different at the first visits (Table 4). The Tac daily-dose requirements, which were based on reaching the target [C.sub.0] by therapeutic drug monitoring, were significantly different from day 10 and remained so (Table 4). Identical significant differences were observed when dose was adjusted for patient weight (Table 4). The dose-adjusted [C.sub.0] was significantly different among groups at all time points (Table 4; see Fig. 1 in the online Data Supplement). This trend was linear and was a function of genotype category classification, either for the [C.sub.0] at day 3 (group 1 > group 2 > group 3 > group 4; P < 0.004; Table 4) or for the Tac dose requirement from day 10 to month 12 (group 1 < group 2 < group 3 < group 4; P = 0.001; Table 4). This trend was also observed for the dose-adjusted [C.sub.0] and was highly significant at all investigated time points (group 1 > group 2 > group 3 > group 4; P = 0.006; Table 4). The mixed-model analysis revealed an overall increase in the Tac dose-adjusted trough blood concentration of + 179.3% for the poor metabolizer cluster (P < 0.001), +101.4% for the intermediate-1 metabolizer cluster (P < 0.001), and +64.4% for the intermediate-2 metabolizer cluster (P = 0.020), compared with the extensive metabolizers.

Patients from groups 1 and 2 had a day 3 [C.sub.0] geometric mean above the consensus supratherapeutic threshold (15 [micro]g/L): 21.5 [micro]g/L for group 1 and 15.8 [micro]g/L for group 2. Logistic regression models showed that the risk of presenting a supratherapeutic [C.sub.0] at day 3 was significantly higher for group 1 (odds ratio, 8.3; 95% CI, 1.3-57.0; P = 0.027) and group 2 (odds ratio, 4.7; 95% CI, 1.9-13.4; P = 0.002), compared with group 4 (Fig. 1). Group 3 was excluded from the analysis because [C.sub.0] data were available for only a single patient ([C.sub.0] = 14.9 [micro]g/L). No significant differences were observed across the different genotype clusters with respect to the risk of a [C.sub.0] <10 [micro]g/L (data not shown).

CYP3A4 GENOTYPE, DGF, CREATININE CLEARANCE, AND ACUTE REJECTION

Of the 185 patients, DGF was observed in 38 patients, 2 of whom carried the CYP3A4 intron 6 T allele. No significant differences were observed in DGF incidence [[chi square] (1) = 0.12; P = 0.72] or in creatinine clearance (Table 2) between T variant carriers and CC patients. Similarly, we observed no differences between groups of combined genotypes for CYP3A4 and CYP3A5 SNPs, in creatinine clearance (Table 4). BPAR occurred in 37 of the 185 patients, 4 of whom carried CYP3A4 intron 6 variant T, but no significant differences in BPAR incidence were observed between carriers of the T variant and CC patients [[chi square] (1) = 1.42; P = 0.23]. Similarly, we did not find any significant difference in the incidence of either DGF or BPAR among different clusters of combined genotypes with respect to CYP3A4 intron 6 and CYP3A5*3 allelic status [[chi square] (3) = 0.66 (P = 0.89) and 4.52 (P = 0.18), respectively].

Discussion

We show for the first time that the new CYP3A4 intron 6C> T SNP is associated with lower Tac dose requirements, in agreement with the reduced function of this CYP3A4 variant and the expected reduced clearance of Tac (6). We have demonstrated that de novo kidney transplant recipients who carry 1 or 2 T alleles require significantly lower Tac doses to reach the target [C.sub.0] than wild-type CC patients. During the first year after transplantation, carriers of the T allele required a 33% lower mean Tac dose compared with the wild-type patients.

Our findings are in agreement with those of Wang et al., who addressed the functional defect caused by this SNP (6) and showed that this SNP is significantly linked to reductions in CYP3A4 mRNA production and enzyme activity in human livers. Thus far, this CYP3A4 SNP is the only one that has a relatively high allele frequency in Caucasians (2.5%-6.9%, http://www.ncbi.nlm.nih.gov/projects/SNP) and that shows such a large effect. A recent report by Jacobson et al. (24) described 3 other CYP3A4 polymorphisms with respect to Tac pharmacokinetics, but these were observed only in Africans. In our study, only 8 of the patients were of African origin, which is why we did not include these SNPs.

Combining CYP3A4 and CYP3A5 genotypes revealed an increased significance of the observed effects on Tac pharmacokinetics compared with the CYP3A4 or CYP3A5 genotype alone. This effect was allele-dose dependent and was influenced quantitatively by genotype classification: at all time points, the Tac dose requirement was lowest for CYP3A poor metabolizers, followed by intermediate-1 metabolizers, intermediate-2 metabolizers, and, finally, extensive metabolizers. All groups were significantly different (P = 0.001), except for day 3 (P = 0.78). This latter observation reflects the fact that at this time point no dose adjustments could have been made on the basis of therapeutic drug monitoring, and dosing thus was independent of genotype or metabolizer status. Similarly, the dose-adjusted [C.sub.0] was affected significantly by the CYP3A4 and CYP3A5 combined genotype: extensive metabolizers < intermediate-2 metabolizers < intermediate-1 metabolizers < poor metabolizers--demonstrating that poor metabolizers require lower doses to achieve a target [C.sub.0] at all time points (including at day 3 after transplantation) than the other groups. Therefore, genotype classification might lead to a better prediction of the optimal Tac starting dose.

The risk of a supratherapeutic [C.sub.0] (>15 [micro]g/L) on day 3 was significantly higher for poor and intermediate-1 metabolizers than for extensive metabolizers. This risk was even more pronounced among poor metabolizers than among intermediate-1 metabolizers. We observed that both poor and intermediate-1 metabolizers had a mean [C.sub.0] at day 3 of >15 [micro]g/L (21.5 [micro]g/L and 15.8 [micro]g/L, respectively). We reported earlier that a significantly larger proportion of patients carrying the CYP3A5*1 allele had a [C.sub.0] <10 [micro]g/L (18). When the genetic status for the CYP3A4 intron 6 SNP was taken into account, no significant differences were observed with respect to the risk of presenting a Tac concentration below this threshold (data not shown). As was recently suggested, it is likely that clinicians are able to target the [C.sub.0] above this threshold rapidly after transplantation by performing simple concentration-controlled Tac dose adjustments without consideration of CYP3A5 status (13 ).In the present study, 15% of patients had a [C.sub.0] <10 [micro]g/L at day 3, and 2 patients had a [C.sub.0] <5 [micro]g/L. Approximately 50% of the patients had a [C.sub.0] >15 [micro]g/L. Neither a sub-therapeutic [C.sub.0] at day 3 nor the CYP3A5*1 allele was associated with BPAR within 1 month after surgery (data not shown), a result in accordance with previous studies (18, 21, 25-27). We found that 50% of patients did overshoot the upper limit of Tac exposure, whereas only 15% had Tac exposures of <10 [micro]g/L. This result indicates that overexposure is a problem more frequently encountered than underexposure. It may be especially relevant in patients experiencing DGF. Regarding ABCB1, we found the 3435C>T SNP to be independently associated with the dose-adjusted [C.sub.0]. The influence of this SNP (14.3% and 20.9% lower dose-adjusted [C.sub.0] for heterozygotes and homozygotes, respectively) was modest compared with the effects of the CYP3A4 and CYP3A5 polymorphisms and disappeared in the mixed-model analysis in which the CYP3A4 and CYP3A5 genotype were included. The relatively minor contribution of the ABCB1 polymorphism to Tac pharmacokinetics is in line with previous investigations (28-30).

The present study has limitations. Although most participating centers have used immunoassays to measure Tac concentrations, some centers applied a liquid chromatography-tandem mass spectrometry approach. In an additional mixed-model analysis in which we adjusted for Tac assay by introducing a dummy variable as a random effect, the effect of CYP3A4 intron 6 genotype was still significant, both for the Tac daily dose (-20% for carriers of the T allele, P = 0.007) and for the dose-adjusted [C.sub.0] (+37% for T allele carriers, P < 0.001). Second, corticosteroids are known to influence Tac exposure (31, 32). Given that corticosteroid tapering was recommended but not mandatory, the different centers may have used different corticosteroid regimens, which we cannot exclude from having influenced the analysis. If all patients had been treated with the same dose, the influence of genotype might have been stronger by reducing the uncontrolled variation generated by different tapering regimens. Unfortunately, the corticosteroid dose could not be included in the mixed-model analysis because different formulations with different immunosuppressive potencies were used. Third, diabetic gastrointestinal-motility disorders can affect Tac pharmacokinetics. Although diabetic gastropathy may alter the curve of the Tac area under the ROC curve during a dosing interval, [C.sub.0] values are generally less affected (33), and we therefore believe that the influence of diabetes on the outcomes of the present study was limited. Fourth, our set contained some missing data points. To overcome this limitation, we performed mixed-model analysis, which compensates for missing records. The correct use of mixed-model analysis requires that data be missing at random. We investigated this criterion, and, indeed, the proportion of missing data for carriers of the CYP3A4 intron 6 T allele was not significantly different from that of noncarriers with respect to [C.sub.0] (P = 0.71), Tac dose (P = 0.14), [C.sub.0]/dose (P = 0.28), and creatinine clearance (P = 0.24). Finally, we realize that our findings are significant at a 95% CI. Therefore, our results need to be confirmed with independent cohorts.

In conclusion, we have shown that the new genetic CYP3A4 intron 6 polymorphism was associated with reduced Tac clearance in our patient cohort. Therefore, pretransplantation genotyping of the CYP3A4 intron 6 C>T SNP, along with CYP3A5*3, could potentially benefit patients by reducing initial Tac doses among CYP3A poor metabolizers and thereby reduce the risk of reaching supratherapeutic Tac concentrations.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the Disclosures of Potential Conflict of Interest form. Potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: D.A. Hesselink, Astellas Pharma; T. van Gelder, Roche Pharmaceuticals.

Research Funding: D.A. Hesselink, Astellas Pharma; T. van Gelder, FDCC study sponsored by Roche Pharmaceuticals; R.H.N. van Schaik, Hoffmann-La Roche.

Expert Testimony: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.

Acknowledgments: Laure Elens is a research fellow with the Wallonie-Bruxelles International (WBI.WORLD) program and Fonds Special de Recherche [FSR (UCL)]. The authors acknowledge the important contribution of all FDCC investigators in this study, especially J.W. de Fijter, A. Hartmann, J. Schmidt, K. Budde, D. Kuypers, Y. Le Meur, S. Powis, I. MacPhee, M. Zeier, P. Pisarski, and R. Mamelok, who contributed to the pharmacogenetic substudy.

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(25.) Kuypers DR, de Jonge H, Naesens M, Lerut E, Verbeke K, Vanrenterghem Y. CYP3A5 and CYP3A4 but not MDR1 single-nucleotide polymorphisms determine long-term tacrolimus disposition and drug-related nephrotoxicity in renal recipients. Clin Pharmacol Ther 2007;82:711-25.

(26.) MacPhee IA, Fredericks S, Tai T, Syrris P, Carter ND, Johnston A, et al. The influence of pharmacogenetics on the time to achieve target tacrolimus concentrations after kidney transplantation. Am J Transplant 2004;4:914-9.

(27.) Roy JN, Barama A, Poirier C, Vinet B, Roger M. Cyp3A4, Cyp3A5, and MDR-1 genetic influences on tacrolimus pharmacokinetics in renal transplant recipients. Pharmacogenet Genomics 2006; 16:659-65.

(28.) Fredericks S, Moreton M, Reboux S, Carter ND, Goldberg L, Holt DW, MacPhee IA. Multidrug resistance gene-1 (MDR-1) haplotypes have a minor influence on tacrolimus dose requirements. Transplantation 2006;82:705-8.

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Laure Elens, [1,2] Rachida Bouamar, [3] Dennis A. Hesselink, [4] Vincent Haufroid, [2] Ilse P. van der Heiden, [1] Teun van Gelder, [3,4] and Ron H.N. van Schaik [1] *

[1] Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, the Netherlands; [2] Cliniques Universitaires Saint-Luc--UCL, Laboratory of Analytical Biochemistry and Louvain Centre for Toxicology and Applied Pharmacology (LTAP), Brussels, Belgium; Departments of [3] Hospital Pharmacy and [4] Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.

* Address correspondence to this author at: Department of Clinical Chemistry, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands. Fax +31-10-4367894; e-mail r.vanschaik@erasmusmc.nl.

Received March 18, 2011; accepted August 23, 2011.

Previously published online at DOI: 10.1373/clinchem.2011.165613

[5] Nonstandard abbreviations: SNP; single-nucleotide polymorphism; Tac, tacrolimus; [C.sub.0], Tac predose concentration; FDCC, fixed-dose, concentration-controlled; DGF, delayed graft function; BPAR, biopsy-proven acute rejection.

[6] Human genes: CYP3A5, cytochrome P450, family 3, subfamily A, polypeptide 5; ABCB1, ATP-binding cassette, sub-family B (MDR/TAP), member 1; CYP3A4, cytochrome P450, family 3, subfamily A, polypeptide 4; UGT1A9, UDP glucuronosyltransferase 1 family, polypeptide A9; UGT2B7, UDP glucuronosyltransferase 2 family, polypeptide B7.
Table 1. Patient demographics.

 All patients

Patients, n 185
Male/female sex, n 112 (61%)/73 (40%)
Age, years (a) 47.9 (13.8)
Weight, kg (a) 72.6 (14.0)
Transplantation no., n
 First 156(84%)
 Second 19(10%)
 Third or more 5 (2.7%)
 Missing information 5 (2.7%)
Living/deceased donor, n 71 (38%)/114 (62%)
FDCC MMF therapy, n 98 (53%)/87 (47%)
Induction therapy, n (b) 67 (36%)
Primary kidney disease, n
 Diabetic nephropathy 16(8.6%)
 Glomerulonephritis 51 (28%)
 Hypertensive nephropathy 18(9.7%)
 Obstructive/reflux nephropathy 9 (4.9%)
 Other 42 (23%)
 Polycystic kidney disease 29(16%)
 Pyelonephritis/interstitial nephritis 9 (4.9%)
 Unknown 9 (4.9%)
HLA mismatches, n (c) 2.9 (3.0%)
Panel reactive antibodies, n
 <10%/[greater than or equal to] 10% 167 (90%)/18 (9.7%)
Ethnicity, n
 Asian 9 (4.9%)
 Black 8 (4.3%)
 Caucasian 164 (89%)
 Other 4 (2.2%)

 CYP3A4 intron 6 CC
 homozygote

Patients, n 173
Male/female sex, n 108 (62%)/65 (38%)
Age, years (a) 47.7 (14.0)
Weight, kg (a) 72.8 (14.3)
Transplantation no., n
 First 145 (84%)
 Second 18(10%)
 Third or more 5 (2.9%)
 Missing information 5 (2.9%)
Living/deceased donor, n 66 (38%)/107 (62%)
FDCC MMF therapy, n 92 (53%)/81 (47%)
Induction therapy, n (b) 62 (36%)
Primary kidney disease, n
 Diabetic nephropathy 15(8.7%)
 Glomerulonephritis 48 (28%)
 Hypertensive nephropathy 18(10.4%)
 Obstructive/reflux nephropathy 8 (4.6%)
 Other 42 (24%)
 Polycystic kidney disease 24 (14%)
 Pyelonephritis/interstitial nephritis 8 (4.6%)
 Unknown 8 (4.6%)
HLA mismatches, n (c) 2.9 (3.0%)
Panel reactive antibodies, n
 <10%/[greater than or equal to] 10% 155 (90%)/18 (10.4%)
Ethnicity, n
 Asian 9 (5.2%)
 Black 8 (4.6%)
 Caucasian 152 (88%)
 Other 4 (2.3%)

 CYP3A4 intron 6
 allele T carriers
 (CT plus TT) P

Patients, n 12 --
Male/female sex, n 4 (33%)/8 (67%) 0.07
Age, years (a) 51.8 (11.3) 0.32
Weight, kg (a) 71.1 (10.4) 0.69
Transplantation no., n 0 80
 First 11 (92%) --
 Second 1 (8.3%) --
 Third or more 0 (0.0%) --
 Missing information 0 (0.0%) --
Living/deceased donor, n 5 (42%)/7 (58%) 0.81
FDCC MMF therapy, n 6 (50%)/6 (50%) 0.83
Induction therapy, n (b) 5 (42%) 0.69
Primary kidney disease, n 0.29
 Diabetic nephropathy 1 (8.3%) --
 Glomerulonephritis 3 (25%) --
 Hypertensive nephropathy 0 (0.0%) --
 Obstructive/reflux nephropathy 1 (8.3%) --
 Other 0 (0.0%) --
 Polycystic kidney disease 5 (42%) --
 Pyelonephritis/interstitial nephritis 1 (8.3%) --
 Unknown 1 (8.3%) --
HLA mismatches, n (c) 3.2 (3.0%) 0.55
Panel reactive antibodies, n
 <10%/[greater than or equal to] 10% 12 (100.0%)/0(0.0%) 0.24
Ethnicity, n 0.65
 Asian 0 (0.0%) --
 Black 0 (0.0%) --
 Caucasian 12 (100%) --
 Other 0 (0.0%) --

(a) Data are presented as the mean (SD).

(b) All patients who received induction therapy were treated with
antibody against the interleukin-2 receptor; none were treated with
antithymocyte globulin.

(c) Data are presented as the mean (% of total).

Table 2. Tac dose, [C.sub.0], and dose-adjusted [C.sub.0]
([C.sub.0]/dose) according to CYP3A4 intron 6 C>T SNP genotype.

 CYP3A4 intron 6 CC
 homozygote n

Tac dose, mg/day
 Day 3 13.3 (12.6-13.9) 136
 Day 10 12.9 (11.9-13.9) 134
 Month 1 11.2 (10.4-11.9) 137
 Month 3 7.5 (6.8-8.2) 131
 Month 6 6.2 (5.5-6.8) 120
 Month 12 5.2 (4.7-5.8) 112
Weight-adjusted Tac dose,
mg x [day.sup.-1] x
[(kg body weight).sup.-1]
 Day 3 0.181 (0.174-0.189) 135
 Day 10 0.176 (0.162-0.190) 133
 Month 1 0.156 (0.15-0.166) 136
 Month 3 0.105 (0.095-0.115) 130
 Month 6 0.087 (0.078-0.097) 119
 Month 12 0.072 (0.063-0.081) 111
[C.sub.0], [micro]g/L (b)
 Day 3 14.9 (13.8-16.0) 144
 Day 10 11.5 (10.9-12.1) 133
 Month 1 12.5 (11.8-13.2) 145
 Month 3 10.2 (9.8-10.8) 145
 Month 6 8.6 (8.1-9.2) 125
 Month 12 7.2 (6.5-7.9) 110
[C.sub.0]/dose,
[micro]g/L per mg/kg (b)
 Day 3 84.7 (78.2-91.8) 125
 Day 10 74.3 (67.3-82.0) 120
 Month 1 87.5 (80.3-95.4) 128
 Month 3 112.2 (101.0-124.6) 124
 Month 6 121.3 (106.4-138.3) 105
 Month 12 114.7 (100.7-130.5) 98
Creatinine clearance, mL/min
 Day 3 35.8 (31.7-39.9) 151
 Day 10 45.1 (41.2-49.0) 148
 Month 1 55.6 (52.0-59.3) 148
 Month 3 60.1 (56.3-63.9) 144
 Month 6 63.7 (59.8-67.7) 130
 Month 12 65.6 (61.5-69.6) 114

 CYP3A4 intron 6 allele
 T carriers (CT plus TT) n p (a)
Tac dose, mg/day
 Day 3 13.0 (11.7-14.3) 9 0.84
 Day 10 9.2 (7.2-11.7) 10 0.05
 Month 1 6.6 (5.5-7.7) 10 <0.001
 Month 3 5.2 (4.3-6.1) 11 <0.001
 Month 6 4.5 (3.7-5.3) 10 0.004
 Month 12 4.6 (3.1-6.1) 9 0.55
Weight-adjusted Tac dose,
mg x [day.sup.-1] x
[(kg body weight).sup.-1]
 Day 3 0.193 (0.178-0.207) 9 0.45
 Day 10 0.134 (0.101-0.168) 10 0.10
 Month 1 0.097 (0.078-0.116) 10 0.004
 Month 3 0.076 (0.061-0.091) 11 0.10
 Month 6 0.066 (0.054-0.079) 10 0.25
 Month 12 0.069 (0.041-0.097) 9 0.84
[C.sub.0], [micro]g/L (b)
 Day 3 20.5 (15.2-27.7) 8 0.05
 Day 10 13.2 (11.3-15.5) 9 0.21
 Month 1 12.8 (10.9-15.0) 11 0.81
 Month 3 11.0 (9.7-12.4) 11 0.47
 Month 6 9.1 (7.6-11.0) 10 0.63
 Month 12 9.5 (6.9-13.1) 8 0.12
[C.sub.0]/dose,
[micro]g/L per mg/kg (b)
 Day 3 108.7 (82.9-142.6) 8 0.14
 Day 10 101.3 (86.5-118.6) 8 0.006
 Month 1 136.6 (107.9-173.1) 10 0.006
 Month 3 154.2 (117.6-202.3) 10 0.10
 Month 6 144.3 (111.7-186.3) 9 0.26
 Month 12 171.8 (120.2-245.5) 8 0.09
Creatinine clearance, mL/min
 Day 3 42.0 (20.1-63.8) 10 0.47
 Day 10 47.4 (29.0-65.7) 11 0.77
 Month 1 57.3 (42.1-72.5) 11 0.82
 Month 3 66.2 (50.9-81.5) 11 0.40
 Month 6 66.1 (50.0-82.3) 10 0.74
 Month 12 59.1 (41.3-76.9) 9 0.40

(a) Statistically significant results (P < 0.05) are highlighted in
boldface.

(b) Values are expressed as the geometric mean (95% CI).

Table 3. CYP3A4/CYP3A5 genotype cluster classification.

 CYP3A4 intron 6 CT or TT CYP3A4 intron 6 CC

CYP3A5*1 Group 1, poor Group 2, intermediate-1
 noncarriers metabolizers metabolizers
 n = 10 (5.4%) n = 142 (76.8%)

CYP3A5*1 Group 3, intermediate-2 Group 4, extensive
 carriers metabolizers metabolizers
 n = 2 (1.1%) n = 31 (16.8%)

Table 4. Tac dose, [C.sub.0], and dose-adjusted [C.sub.0] according
to the combined CYP3A4 intron 6 C>T SNP and CYP3A5 genotype.

 Group 1 n
Tac dose, mg/day
 Day 3 13.0 (10.9-15.1) 7
 Day 10 8.3 (5.9-10.6) 8
 Month 1 6.5 (5.0-8.0) 8
 Month 3 5.0 (3.8-6.2) 9
 Month 6 4.3 (3.2-5.3) 8
 Month 12 3.9 (2.9-4.9) 8
Weight-adjusted Tac dose,
 mg x [day.sup.-1] x
 [(kg body weight).sup.-1]
 Day 3 0.190 (0.173-0.207) 7
 Day 10 0.118 (0.089-0.146) 8
 Month 1 0.094 (0.070-0.118) 8
 Month 3 0.072 (0.054-0.089) 9
 Month 6 0.062 (0.048-0.075) 8
 Month 12 0.058 (0.042-0.074) 8
[C.sub.0], [micro]g/L (c)
 Day 3 21.5 (14.2-32.5) 7
 Day 10 13.0 (10.5-16.0) 8
 Month 1 12.8 (10.2-16.2) 9
 Month 3 11.1 (8.9-13.0) 10
 Month 6 9.1 (7.1-11.7) 9
 Month 12 9.5 (6.4-14.0) 8
[C.sub.0]/dose,
 [micro]g/L per mg/kg (c)
 Day 3 113.6 (78.4-164.7) 7
 Day 10 104.3 (84.5-128.9) 7
 Month 1 142.3 (100.9-200.6) 8
 Month 3 123.3 (115.9-226.2) 9
 Month 6 148.3 (105.3-208.7) 8
 Month 12 171.8 (111.7-276.8) 8
Creatinine clearance, mL/min
 Day 3 43.9 (16.4-71.3) 8
 Day 10 49.0 (25.9-72.0) 9
 Month 1 59.1 (40.0-78.2) 9
 Month 3 67.6 (50.8-84.4) 10
 Month 6 68.1 (50.5-85.8) 9
 Month 12 60.9 (40.8-81.0) 8

 Group 2 n
Tac dose, mg/day
 Day 3 13.1 (12.4-13.8) 111
 Day 10 11.8 (10.8-12.8) 109
 Month 1 10.3 (9.5-11.0) 112
 Month 3 6.7 (6.2-7.3) 105
 Month 6 5.3 (4.8-5.9) 97
 Month 12 4.6 (4.1-5.1) 91
Weight-adjusted Tac dose,
 mg x [day.sup.-1] x
 [(kg body weight).sup.-1]
 Day 3 0.178 (0.171-0.185) 110
 Day 10 0.160 (0.147-0.174) 108
 Month 1 0.143 (0.132-0.154) 111
 Month 3 0.093 (0.084-0.102) 104
 Month 6 0.074 (0.065-0.082) 96
 Month 12 0.062 (0.054-0.070) 90
[C.sub.0], [micro]g/L (c)
 Day 3 15.8 (14.7-17.1) 118
 Day 10 11.7 (11.0-12.4) 110
 Month 1 12.7 (12.0-13.4) 120
 Month 3 10.4 (9.9-11.0) 120
 Month 6 8.7 (8.1-9.3) 105
 Month 12 7.2 (6.6-8.0) 91
[C.sub.0]/dose,
 [micro]g/L per mg/kg (c)
 Day 3 91.7 (83.9-100.3) 100
 Day 10 83.0 (74.6-92.3) 97
 Month 1 97.5 (89.3-106.3) 103
 Month 3 127.9 (114.9-142.3) 100
 Month 6 140.2 (123.0-159.9) 86
 Month 12 131.2 (116.4-147.8) 80
Creatinine clearance, mL/min
 Day 3 36.1 (31.6-40.6) 125
 Day 10 44.1 (39.8-48.3) 123
 Month 1 55.2 (51.1-59.2) 123
 Month 3 60.0 (56.0-63.9) 118
 Month 6 63.5 (59.2-67.7) 107
 Month 12 65.4 (61.1-69.8) 94

 Group 3 n
Tac dose, mg/day
 Day 3 13.0 (0.3-25.7) 3
 Day 10 13.0(0.3-25.7) 2
 Month 1 7.0 (-5.7 to 19.7) 2
 Month 3 6.0 (6.0-6.0) 2
 Month 6 5.5 (-0.9 to 11.9) 2
 Month 12 10.0 1
Weight-adjusted Tac dose,
 mg x [day.sup.-1] x
 [(kg body weight).sup.-1]
 Day 3 0.201 (0.012-0.222) 2
 Day 10 0.201 (0.012-0.390) 2
 Month 1 0.108 (-0.084 to 0.300) 2
 Month 3 0.093 (0.089-0.096) 2
 Month 6 0.085 (-0.010 to 0.180) 2
 Month 12 0.155 1
[C.sub.0], [micro]g/L (c)
 Day 3 14.9 1
 Day 10 15.3 1
 Month 1 12.4 (7.9-19.7)
 Month 3 9.3 1
 Month 6 9.0 1
 Month 12 --
[C.sub.0]/dose,
 [micro]g/L per mg/kg (c)
 Day 3 80.1 1
 Day 10 82.2 1
 Month 1 116.2 (12.3-1100.0)
 Month 3 100.0 1
 Month 6 116.1 1
 Month 12 -- 0
Creatinine clearance, mL/min
 Day 3 34.3 (-202.9 to 271.4) 2
 Day 10 40.2 (-64.8 to 145.2) 2
 Month 1 49.2 (19.5-78.9) 2
 Month 3 52.5 1
 Month 6 48.5 1
 Month 12 45.0 1

 Group 4 n
Tac dose, mg/day
 Day 3 14.0 (12.0-15.9) 25
 Day 10 17.6 (15.0-20.2) 25
 Month 1 15.2 (13.1-17.2) 25
 Month 3 10.5 (8.5-12.6) 26
 Month 6 9.7 (7.9-11.6) 23
 Month 12 7.9 (6.2-9.6) 21
Weight-adjusted Tac dose,
 mg x [day.sup.-1] x
 [(kg body weight).sup.-1]
 Day 3 0.197 (0.172-0.222) 25
 Day 10 0.245 (0.212-0.277) 25
 Month 1 0.212 (0.189-0.235) 25
 Month 3 0.152 (0.122-0.183) 26
 Month 6 0.142 (0.112-0.173) 23
 Month 12 0.114 (0.088-0.140) 21
[C.sub.0], [micro]g/L (c)
 Day 3 11.2 (9.1-13.7) 26
 Day 10 10.8 (9.3-12.5) 23
 Month 1 11.5 (9.7-13.6) 25
 Month 3 9.4 (8.4-10.5) 25
 Month 6 8.2 (6.9-9.9) 20
 Month 12 6.8 (5.1-9.0) 19
[C.sub.0]/dose,
 [micro]g/L per mg/kg (c)
 Day 3 61.6 (53.3-71.1) 25
 Day 10 46.6 (39.8-54.4) 23
 Month 1 56.2 (46.8-67.4) 25
 Month 3 65.0 (52.3-80.7) 24
 Month 6 62.8 (46.4-85.0) 19
 Month 12 63.0 (42.3-93.9) 18
Creatinine clearance, mL/min
 Day 3 34.6 (23.6-45.2) 26
 Day 10 50.0 (39.4-60.7) 25
 Month 1 58.0 (48.8-67.2) 25
 Month 3 60.9 (49.1-72.7) 26
 Month 6 64.9 (53.6-76.1) 23
 Month 12 66.1 (55.1-77.1) 20

 P, ANOVA (a) P, polynomial (a,b)

Tac dose, mg/day
 Day 3 0.78 --
 Day 10 <0.001 <0.001
 Month 1 <0.001 <0.001
 Month 3 <0.001 0.001
 Month 6 <0.001 <0.001
 Month 12 <0.001 <0.001
Weight-adjusted Tac dose,
 mg x [day.sup.-1] x
 [(kg body weight).sup.-1]
 Day 3 0.18 --
 Day 10 <0.001 <0.001
 Month 1 <0.001 <0.001
 Month 3 <0.001 0.001
 Month 6 <0.001 <0.001
 Month 12 <0.001 <0.001
[C.sub.0], [micro]g/L (c)
 Day 3 <0.001 0.004
 Day 10 0.40 --
 Month 1 0.62 --
 Month 3 0.36 --
 Month 6 0.90 --
 Month 12 0.27 --
[C.sub.0]/dose,
 [micro]g/L per mg/kg (c)
 Day 3 <0.001 0.006
 Day 10 <0.001 0.003
 Month 1 <0.001 <0.001
 Month 3 <0.001 <0.001
 Month 6 <0.001 0.001
 Month 12 <0.001 <0.001
Creatinine clearance, mL/min
 Day 3 0.85 --
 Day 10 0.67 --
 Month 1 0.88 --
 Month 3 0.77 --
 Month 6 0.84 --
 Month 12 0.75 --

(a) Statistically significant results (P < 0.05) are highlighted in
boldface.

(b) The corresponding linear contrast tested the probability of a
positive linear trend of the dependent variable across the ordered
level of the genotype classification (a priori polynomial linear
contrast test).

(c) Values are expressed as the geometric mean, (95% CI).
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Title Annotation:Molecular Diagnostics and Genetics
Author:Elens, Laure; Bouamar, Rachida; Hesselink, Dennis A.; Haufroid, Vincent; van der Heiden, Ilse P.; va
Publication:Clinical Chemistry
Date:Nov 1, 2011
Words:8277
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