Sirolimus (SRL) (Rapamune, Wyeth Pharmaceuticals Inc., Philadelphia, PA) is a potent immunosuppressant that is used for the prevention of renal transplant rejection. SRL pharmacokinetics exhibits wide inter- and intrasubject variability and dosages used in kidney transplantation range from 2 to 15 mg/d. Steady state SRL trough concentrations less than 5 ng/mL and more than 15 ng/mL were associated with the occurrence of acute rejection episodes and adverse reactions such as leukopenia and thrombocytopenia, respectively (1). Therapeutic drug monitoring (TDM) of SRL is therefore suggested to be a useful tool to optimize the outcomes of patients with renal transplantation (2). The systemic availability of oral SRL is low, likely because SRL is a substrate for the drug efflux pump p-glycoprotein (P-gp), and the hepatic and intestinal cytochrome P450, family 3, subfamily A, polypeptide 4 (CYP3A4) and cytochrome P450, family 3, subfamily A, polypeptide 5 (CYP3A5) enzymes (3, 4). The genes encoding P-gp (i.e., ABCB1), CYP3A4, and CYP3A5 could therefore be important determinants of SRL bioavailability and metabolism.
Some of the most commonly studied ABCB1, CYP3A4, and CYP3A5 variants include ABCB1 1236C>T, 2677G>T/A, and 3435C>T, CYP3A4 −392A>G, and CYP3A5 6986A>G and 14690G>A. The ABCB1 1236C>T and 3435C>T are synonymous single-nucleotide polymorphisms (SNPs); the latter was correlated with ABCB1 function (5) and resulted in substrate specificity changes (6). The ABCB1 2677G>T/A causes an amino acid change in exon 21, which led to increased vincristine transport rates in the variants (7). CYP3A5 6986A>G and 14690G>A represent two splice variants that cause alternative splicing and protein truncation result in the absence of CYP3A5 protein (8). Allelic frequency for 6986A>G is highest in whites (93%), whereas 14690G>A occurs almost exclusively in blacks (12%) (9). Although CYP3A4 −392A>G represents a mutation in the nifedipine-specific element in the 5′-flanking region of the gene, its associations with higher prostate cancer tumor stage (10) and reduced hepatic CYP3A activity (11) were likely attributed to its strong linkage equilibrium with CYP3A5 6986A>G (8).
Cytokines such as interleukin (IL)-10 and tumor necrosis factor (TNF) have been shown to decrease CYP3A activity (12, 13) and affect P-gp expression (14). Therefore, SRL pharmacokinetics may be influenced by IL-10 and TNF SNPs as well. It has been shown that IL-10 basal protein expression levels were higher in healthy individuals with IL-10 −1082GG genotype (15), and TNF −308A variant allele was associated with much powerful transcriptional activation of TNF in human B cells (16).
The effects of genetic polymorphisms on SRL dose requirements and pharmacokinetics in renal transplantation have been studied previously (17–21). However, all these studies were cross-sectional in nature. Because physiological factors affecting drug absorption and disposition could be different during early versus late posttransplantation, it is important to examine the influence of genetics on long-term SRL usage. This retrospective study was designed to explore the associations of ABCB1, CYP3A4, CYP3A5, IL-10, and TNF genetic polymorphisms with longitudinal dose requirements of SRL in kidney transplant recipients. Information obtained from this study can potentially help develop an optimal dosing strategy for long-term therapeutic monitoring of SRL.
Ninety-three renal transplant patients were enrolled in this study; a complete genotype set was obtained in 87 subjects. The variant allele frequencies for the ABCB1 1236C>T, 2677 G>T/A, and 3435C>T, CYP3A4 −392A>G, CYP3A5 6986A>G and 14690G>A, IL-10 −1082G>A, and TNF −308G>A polymorphisms were 0.359, 0.352/0.033, 0.413, 0.167, 0.806, 0.269, 0.544, and 0.161, respectively (see Table 1, SDC 2,http://links.lww.com/TP/A555). With the exception of CYP3A4 −392A>G and CYP3A5 6986A>G, all SNPs were in Hardy-Weinberg equilibrium (i.e., no significant differences between observed and expected genotype frequencies).
Among the 93 subjects enrolled, 86 recipients were included in data analysis. Six subjects were excluded because of incomplete genetic information and one is known to be noncompliant with medications. Patient demographics and immunosuppressive therapies are summarized in Table 1. Using a linear mixed-effects model, body mass index and weight-normalized doses of tacrolimus, prednisone, clotrimazole, and statins were found to have significant effects on log-transformed, dose adjusted, weight-normalized trough concentrations of SRL (ng/mL per mg/kg body weight) (expressed as C/D ratio) and were included as covariates in subsequent modeling.
Table 2 summarizes the univariate effects of each SNP under the dominant, codominant, and recessive genetic models on SRL log (C/D), with adjustments for the significant covariates as described earlier. The ABCB1 1236C>T and 3435C>T were significantly associated with log (C/D) under both the codominant and dominant models, whereas ABCB1 2677G>T/A was significant under all three genetic models. For all these ABCB1 SNPs, the dominant model had the lowest Akaike's information criterion (AIC) value and was used in the multivariate analysis. The IL-10 −1082G>A was significant under both the codominant and recessive models, with the recessive model having the lower AIC and was used in the multivariate analysis.
The ABCB1 3435C>T under the dominant model and IL-10 −1082G>A under the recessive model remained significant in the multivariate analysis, with P values of 0.0016 and 0.0394, respectively. The coefficients (standard errors) for ABCB1 3435C>T and IL-10-1082G>A were 0.171 (0.0521) and 0.0930 (0.0444), respectively. These represent a 48% higher mean SRL C/D in patients with at least one ABCB1 3435T allele than those with 3435CC genotype, and a 24% higher mean SRL C/D in IL-10-1082GG homozygotes compared with −1082A heterozygotes and homozygotes. There were no significant interactions between ABCB1 3435C>T and IL-10 −1082G>A and between these SNPs with the covariates in the final model.
In this study, SRL trough concentrations were obtained longitudinally from the start to 1 year after the initiation of SRL therapy, with postoperative days ranging from immediate to 2 years posttransplantation. These data, obtained from TDM, reflected the actual use of SRL in clinical practice but were subjected to the influences of various confounding factors such as demographics, time posttransplantation, and concomitant medications. We minimized the effects of these confounding factors by selecting and adjusting for those factors that have significant impact on the mixed-effects model. Among all significant covariates, correlated positively with SRL log (C/D), similar to a previous finding showing higher initial tacrolimus blood levels in overweight renal transplant recipients (22). Additionally, SRL concentration/dose ratios were increased during clotrimazole or statins coadministration and were decreased during tacrolimus or prednisone coadministration, suggesting CYP3A enzyme competition between substrates (statins and tacrolimus) and CYP3A induction and inhibition by corticosteroids (23, 24) and clotrimazole, respectively (25, 26). The effect of steroids may influence the observation that prednisone sparing SRL protocols have high rates of SRL related adverse effects such as mouth ulcers (27, 28).
Various studies examining drug-metabolizing enzyme and transporter polymorphisms reported conflicting results on the associations of CYP3A5 genotypes with SRL dose requirements and pharmacokinetics, whereas no associations were observed with ABCB1 SNPs to date. SRL concentration/dose ratios were significantly lower in patients who are homozygous and heterozygous for the wild type CYP3A5 6986A allele (expressors) than in CYP3A5 6986GG homozygotes (nonexpressors) in a subgroup of 69 renal transplant recipients receiving SRL as rescue therapy without calcineurin inhibitors (17). However, no association was observed in the same study in the entire studied population (n=149), in which patients receiving de novo SRL and SRL-calcineurin inhibitor combination were included (17). Two other studies showed significant influences of CYP3A5 status on SRL pharmacokinetics in renal transplant recipients. SRL concentration/dose ratios and dose-adjusted C0, Cmax, and AUC0–9 at steady state were lower in 47 CYP3A5 Chinese (21) and Caucasian expressors, respectively (18). On the contrary, no differences were observed between CYP3A5 expressors and nonexpressors in dose-adjusted SRL trough in 85 renal transplant patients (19), and in SRL dose/trough ratio and dose-adjusted AUC0–24 in a small patient population (n=20) (20). The lack of associations between SRL log (C/D) with CYP3A5 genotypes in our study suggest that CYP3A5 6986A>G and 14690G>A do not contribute significantly to the variability in SRL dose requirements. This is consistent with findings from an in vitro study showing that CYP3A4 was a more efficient catalyst of SRL metabolism than CYP3A5 (29). It is noted that CYP3A4 −392A>G and CYP3A5 6986A>G were not in Hardy-Weinberg equilibrium, and typically, they should be excluded from data analysis in genetic association study such as ours. However, inclusion of these SNPs did not affect our final results. CYP3A4 −392A>G and CYP3A5 6986A>G were not found to be significant factors.
Although our results on CYP3A5 polymorphisms are not totally unexpected, it is surprising to find a significant association between ABCB1 3435C>T and SRL log (C/D). As a matter of fact, this study is the first to report such an association despite all negative findings previously. The reason for the discrepancy in results may be related to the differences in patient population, type of concentration data, study duration, and the methodology used in statistical analysis. The patient population in the current study was relatively heterogeneous when compared with those of the others; 16% of our patients were blacks versus almost all whites or Chinese in the other studies (17–21). Our data were longitudinal with an average of 4.4 data points/patient lasting up to 1 year post-SRL initiation, and were analyzed by a mixed-effects model that took into account the intrasubject variability and various confounding factors. It is calculated that the posthoc powers to detect the observed mean differences in SRL log (C/D) at day 7, assuming the observed effects are true effects, were 67% and 6.6% for ABCB1 3435C>T and IL-10 −1082G>A, respectively. These powers were increased to 97% and 18%, respectively, when the observed average means across all time points and the “ball park” sample sizes (n=190) for longitudinal studies were used. The “ball park” sample sizes represent the numbers of study subjects as if each subject only contributes one observation, taking into consideration the correlation between repeated observations (ρ=0.291 in this study) (30). Finally, we did not make assumptions on the genetic model for the loci being examined; the codominant, dominant, and recessive models were all tested for each SNP. This testing allowed us to select the model that best fitted our data for the ABCB1 3435C>T, which was found to be the dominant model and was significantly associated with SRL log (C/D). The codominant model, in which the mean of the heterozygotes falls between the two homozygote means, may not be the best model for our data because SRL concentration/dose ratios were higher in the CT heterozygotes than in the TT homozygotes (Table 2). Notably, concentration data from all previous studies were also inconsistent with a codominant model (17–21). Presumably, the lack of associations in some of these studies may be attributed to analyzing the data with respect to three instead of two genotype groups, and the cross-sectional nature of the studies. The “true” genetic model of ABCB1 3435C>T, and its association with long-term SRL dose requirements remain to be confirmed in future studies.
The higher SRL log (C/D) in ABCB1 3435CT heterozygotes compared with the TT homozygotes is perplexing. It may be related to the complex interplay between ABCB1 genotype/P-gp phenotype, CYP3A4 expression, and physiological and environmental influences. As discussed earlier, CYP3A4 was a more efficient catalyst of SRL metabolism than CYP3A5 in vitro (29). It was also found to be the major factor, rather than P-gp, in limiting SRL absorption in CYP3A4-transfected Caco-2 cells (31). Therefore, CYP3A4 phenotype may play a more significant role in SRL trough concentrations compared with other drug metabolizing enzymes and transporters. Previous studies have shown that baseline hepatic CYP3A4 expression was increased in Abcb1 knockout mice (32) and ABCB1 2677T allele carriers (33), and higher jejunal CYP3A4 mRNA levels were observed in women with ABCB1 2677TT-3435TT haplotype than those with 2677GT-3435CT haplotype (34). Presumably, CYP3A4 is increased in ABCB1 variants due to the upregulation of the major CYP enzyme regulator pregnane X receptor, an effect that could be caused by higher concentrations of pregnane X receptor ligands such as endogenous steroids, which are also P-gp substrates (35). This increase could in turn compensate for the effect of reduced P-gp activity as a result of ABCB1 3435C>T. It can be hypothesized that in 3435TT homozygotes, the enhanced CYP3A4 activity overrode the effect of reduced P-gp transport causing a smaller overall increase in SRL log (C/D). It remains to be determined the relative contribution of ABCB1 3435CT heterozygotes and TT homozygotes to SRL disposition and pharmacokinetics.
In addition to ABCB1 34354C>T, our results demonstrated that IL-10 −1082GG homozygotes had higher SRL concentration/dose ratios, consistent with enhanced IL-10 expression (15), leading to lowered CYP3A activity (13) and reduced SRL metabolism in patients with this genotype. This result seems to agree with previous finding showing IL-10 −1082A carriers as a risk factor for steroid dependency at 1 year after transplantation (36). However, because of multiple testing in this study and the borderline significance of this SNP in the final model, the association of IL-10 −1082G>A with SRL dose requirements cannot be certain. Regardless of this IL-10 genotype, simulation of our final model showed that SRL trough concentrations would be in the range of 8 to 10 ng/mL for a 70 kg patient with ABCB1 3435CC genotype receiving 5 mg SRL without concomitant medications (see Table 2, SDC 3,http://links.lww.com/TP/A556). These levels are lower than the 12 to 20 ng/mL concentration range that is recommended in the package insert for cyclosporine withdrawal in patients receiving SRL-cyclosporine combination and suggest that patients with ABCB1 3435CC genotype may be at a higher risk for rejection and may require more intensive immunosuppression and slow steroid weaning. Although it is important to perform future studies to validate our results and evaluate the effects of ABCB1 SNPs on outcomes and adverse effects, the current study indicates that genetics can play a significant role in SRL dosing and may be useful in TDM of SRL in renal transplantation.
MATERIALS AND METHODS
Patients and Data Collection
The study population consisted of renal transplant recipients who participated in one of the treatment protocols at the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Transplantation Branch between 1999 and 2006. Patients were enrolled if they received or switched to SRL as the maintenance immunosuppressive therapy. Informed consent was obtained from all patients in the screening protocol for genetic analysis. This study, and the screening and treatment protocols, was approved by the NIDDK Institutional Review Board. SRL, and all other prescribed immunosuppressive medications, was provided for all patients free of charge to lessen the variability associated with limited access to drug.
Clinical data including patient demographics, type and date of transplantation, induction and maintenance immunosuppression therapies, hematocrit, and concomitant medications were collected for all patients from the NIDDK transplant database. Most of the patients were started on oral SRL loading dose followed by daily maintenance therapy. Whole blood SRL trough concentrations were measured as clinically indicated. SRL dosage was adjusted to maintain levels between 15 and 20 ng/mL when given alone or between 10 and 12 ng/mL when administered concurrently with tacrolimus (Prograf, Astellas, Deerfield, IL) as specified in the parent protocols. Dose adjustments were also performed if patients experience thrombocytopenia, severe arthralgias or other side effects known to be associated with this agent. SRL daily dosages and predose concentrations (C) at 7 days and at 1, 3, 6, and 12 months after initiation of SRL were collected for each patient, and SRL log (C/D) was calculated. SRL whole blood concentrations were measured by a validated high-performance liquid chromatography tandem mass spectrometric assay with a quantification limit of 2 ng/mL.
Genomic DNA was isolated from whole blood using the Gentra DNA purification kit (Gentra Systems, Minneapolis, MN) according to the manufacturer's instructions. Genotyping of ABCB1 1236C>T (rs1128503), 2677G>T/A (rs2032582), and 3435C>T (rs1045642), CYP3A4 −392A>G (rs2740574), CYP3A5 6986 A>G (rs776746) and 14690G>A (rs10264272), IL-10 −1082G>A (rs1800896), and TNF −308G>A (rs1800629) were determined using the Sequenom MassArray iPLEX platform (37) by Bioserve (Beltsville, MD). This platform had an average error rate of less than 0.2% and provided call rates of more than or equal to 95% on our samples. Quality control was achieved by typing internal positive control samples of known genotypes with no template controls. In initial assay development, DNAs from 20 individuals from Coriell's Polymorphism Discovery Resource were used.
Deviation from the Hardy-Weinberg equilibrium was assessed using the Pearson's chi square test, using the observed genotype frequencies obtained from the data and the expected frequencies obtained from the Hardy-Weinberg principle. The longitudinal changes in SRL log C/D were analyzed using a linear mixed-effects model, which incorporates correlation inherent within observations from the same individual. The outcome variable, C/D, and the primary independent time variable, number of postoperative day was log-transformed before analysis to achieve a normal distribution.
Initially, various covariates were examined using a mixed-effects model and backward elimination to select the confounding factors that had significant impact on SRL log (C/D). Univariate analysis was then performed to investigate the effect of each SNP on log (C/D) under the codominant, dominant, or the recessive genetic model, with adjustments for the selected covariates. Under the codominant model, genotypes were coded into three separate groups, that is, major allele homozygote, heterozygote, and minor allele homozygote. Under the dominant model, genotypes were coded into two groups, which consisted of the major allele homozygote as one group and heterozygote and the minor allele homozygote as the other group. Under the recessive model, the minor allele homozygote was coded as one group and the heterozygote and the major allele homozygote as the other group. The major allele homozygote was used as the reference group for the codominant and dominant models. The group containing at least one major allele was designated as the reference group for the recessive model. The major allele is the more common allele and the minor allele is the less common allele. The genetic model that best fitted the concentration data for each SNP was chosen based on the AIC. The model with the lowest AIC value was the best model.
Finally, a multivariate analysis was performed using the genetic models that showed significant associations in the univariate analysis, with adjustments for the same covariates as previously determined. Statistical analysis was performed using the open source statistical software* R (version 2.5.0). P values less than 0.05 were considered statistically significant. Adjustments for multiple testing were not performed. As such, our results are considered exploratory and hypothesis generating.
The authors thank Dr. Xin Tian for her advices on the statistical analysis including the posthoc power calculation. They also thank Dr. Michael Ring for assistance in database management and Dr. James Taylor for helpful discussion on the genetic analysis.
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