Increase in tacrolimus exposure after steroid tapering is influenced by CYP3A5 and pregnane X receptor genetic polymorphisms in renal transplant recipients

Increase in tacrolimus exposure after steroid tapering is influenced by CYP3A5 and pregnane X... Abstract Background Tacrolimus, a drug for prevention of rejection after kidney transplantation, has a narrow therapeutic window and is metabolized by the cytochrome P540 3A (CYP3A) system. Tacrolimus exposure increases after steroid tapering in many patients. The pregnane X receptor (PXR)—a mediator for CYP3A—has a steroid receptor and might regulate CYP3A5 activity depending on single nucleotide polymorphisms (SNPs) of CYP3A5 or PXR. This may contribute to differences in tacrolimus exposure after steroid tapering. Methods In a cohort of renal transplant recipients, the influence of CYP3A5 and PXR SNPs (A7635G, C8055T and C25385T) on the dose-normalized Tacrolimus trough concentration (DnC0) and their potential interaction with each other after steroid taper were analysed by linear regression. Eligible were all 83 outpatient renal transplant patients on tacrolimus and steroids in a pharmacokinetic steady state at least 6 weeks after transplantation and whose blood was available for genetic analysis. Results Compared with the CYP3A5*1/*3 genotype, the CYP3A5*3/*3 SNP showed a significantly stronger increase in DnC0 after steroid taper (+0.29 µg/L/mg; P = 0.002). Of the tested PXR SNPs, PXR G7635G individuals had a significantly stronger increase in DnC0 (compared with A7635A, +0.31 µg/L/mg; P = 0.02), with a weaker increase in A7635G heterozygotes (+0.17 µg/L/mg; P = 0.124). There was neither interaction nor association between CYP3A5 and PXR SNPs. Conclusions The magnitude of the DnC0 increase due to steroid taper after renal transplantation is related to CYP3A5 SNPs. Independently, the PXR G7635G SNP is related to this increase, proving the role of PXR in tacrolimus metabolism. pharmacokinetics, pharmacogenetics, pregnane X receptor (PXR), tacrolimus, therapeutic drug monitoring INTRODUCTION Tacrolimus is a frequently used immunosuppressive drug after renal transplantation. Because of its narrow therapeutic window between underexposure (with risk of rejection) and overexposure (with risk of side effects), therapeutic drug monitoring is advised. In clinical practice, dosing is adapted on the trough concentration (as surrogate for the exposure). With increasing time post-transplant, a decrease in the dosage of tacrolimus required to maintain similar trough concentrations has been reported [1], the reason being a decrease in tacrolimus clearance with time [2]. However, tacrolimus dose requirements also diminish after steroid withdrawal [1, 3–5]. Clinically relevant increases (>20% rise of trough concentration) have been reported in 43% of patients after withdrawal of 5 mg and in 61% of patients after withdrawal of 10 mg prednisolone [4]. This increase in trough concentration after steroid tapering has been demonstrated to be present also when tapering takes place >6 months after transplantation, when mechanisms like improving graft function, liver function and an increase in haemoglobin or albumin concentration are less likely to occur [1, 4]. The reason for this phenomenon has not yet been clearly elucidated. Relevant pharmacokinetic interaction between corticosteroids and tacrolimus has been linked to cytochrome P450 3A (CYP3A) and P-glycoprotein (P-gp), a drug efflux pump produced by the multidrug resistance 1 (MDR-1) gene [3]. Since steroids are reported to induce the activity of CYP3A enzymes [6], it has been postulated that steroid tapering may result in diminished CYP3A5 activity [4]. CYP3A enzymes in the liver and small intestine are majorly responsible for tacrolimus metabolism leading to a substantial first-pass effect [7, 8]. Tacrolimus exposure is also related to single nucleotide polymorphisms (SNPs) of CYP3A5. To our knowledge, it is not known whether the effect of tapering steroids on tacrolimus exposure is different between these SNPs. The induction of CYP3A, on the other hand, is mediated by the pregnane X receptor [PXR, also called NR1I2 (nuclear receptor 1I2)] [9]. It is a regulatory factor of the nuclear receptor family with a steroid receptor and is involved in the upregulation of many drug-metabolizing enzymes and drug transporters [10, 11]. Induction of the CYP3A gene in response to treatment with a variety of compounds, such as the glucocorticoid dexamethasone, the antibiotic rifampicin and the antimycotic clotrimazole [9, 12], could then be linked to PXR. Several SNPs in the PXR gene have been found to increase CYP3A induction [6]. However, it is unknown whether systemic exposure to tacrolimus is then (in-)directly related to the inducing capacity mediated by SNPs in the PXR gene. If so, carriers of these inducing SNPs should have higher tacrolimus requirements while on steroids and thus have a more pronounced increase in dose-corrected trough concentrations after steroid withdrawal compared with the wild-type allele carriers. In this study, we hypothesize that the steroid-mediated induction of PXR upregulates metabolic clearance through CYP3A5 and therefore contributes to the increase in tacrolimus exposure after steroid tapering or withdrawal. The aim of the current study is to determine whether the observed increase in tacrolimus exposure after steroid tapering is associated with certain SNPs of the CYP3A5 or PXR gene and whether there is an interaction between SNPs of the CYP3A5 and PXR genes. To test this hypothesis we retrospectively analysed data in a population of renal transplant patients for whom we obatined relevant biochemical, pharmaceutical and genetic data. MATERIALS AND METHODS Patients, samples and clinical data Between December 2002 and February 2005, leftover ethylenediaminetetraacetic acid (EDTA) blood samples of renal transplant patients routinely visiting our outpatient clinic were collected. Patient data and tacrolimus trough concentrations were retrieved from the patient files and hospital information system. Collection, storage and use of blood and patient data were performed in agreement with the Federation of Dutch University Medical Centers (FEDERA) code of conduct (www.nfu.nl). Secondary usage of this leftover material was approved by the Medical Ethical Committee of the Maastricht University Medical Centre (MEC 04-188). All clinically stable renal transplant patients at least 6 weeks post-transplant on tacrolimus-based immunosuppression and whose leftover samples for DNA analysis were available were included. In addition, pharmacokinetic steady-state tacrolimus trough concentrations had to be present at a prednisolone dose of 10 mg/day and/or 5 mg/day and after complete steroid withdrawal. According to our centre protocol at that time, the prednisolone dose was 10 mg during the first 6 weeks and 7.5 mg for 4 weeks and 5 mg for 4 weeks during the ensuing 2 months and complete withdrawal thereafter, provided there were no signs of rejection or a higher degree of immunization. The tacrolimus trough concentration in the pharmacokinetic steady state was defined as at least 7 days after the last prednisolone dose reduction and/or the last tacrolimus dose change. Excluded from analysis were patients who were treated for acute rejection within 1 month before measurements and who were suffering from any condition influencing absorption or elimination of tacrolimus, like gastrointestinal disorders, hepatic dysfunction or medication interfering with tacrolimus pharmacokinetics. Parameters We collected the following biochemical, pharmaceutical and genetic parameters from hospital clinical and laboratory files: age, time since transplantation, body weight, dose-normalized Tacrolimus trough concentration (DnC0) [calculated from the tacrolimus trough level (C0)] and tacrolimus total daily dose), haemoglobin and serum albumin. The CYP3A5*1 and *3 alleles as well as three different SNPs of the PXR gene (A7635G, C8055T and C25385T) were determined. The choice of the PXR SNPs was based on previous reports: A7635G and C8055T are associated with a higher magnitude of (intestinal) CYP3A inducibility [6], while C25385T has been reported to be related to differences in tacrolimus apparent clearance [13]. DNA analysis Genomic DNA from a venous blood sample was extracted according to the manufacturers’ instructions (Qiagen, Leusden, The Netherlands). Real-time polymerase chain reaction (PCR) fluorescence resonance transfer (FRET) assays were used for genotyping with the LightCycler (Roche Diagnostics, Almere, The Netherlands), as described previously (R. Op Den Buijsch, unpublished work). Statistical analysis The baseline characteristics of the patients are presented as mean and standard deviation (SD) or absolute value and percentage of all three prednisolone dosages. DnC0 values were compared using an independent sample t-test. The unadjusted association of both CYP3A5 and PXR SNPs and the DnC0 value for 10 mg steroid use were first computed using linear regression. In addition, we computed the adjusted associations after correction for all other SNPs and potential confounding factors [i.e. gender, age, weight at 10 mg steroid use (baseline), elapsed time since transplantation, haemoglobin concentration, serum albumin concentration, DnC0 at 10 mg steroid use, time interval between 10 and 0 mg steroid use]. These potential confounders were tested for significance using backward stepwise elimination. To assess interactions between CYP3A5 and PXR SNPs for the individual slopes of the DnC0 value depending on steroid use, we computed interaction terms and tested using linear regression. Next, we determined DnC0 changes over the three measures by performing a linear regression analysis per patient to obtain each patient’s individual DnC0 slope. These slopes were used for subsequent analyses using the same methods as for the associations with DnC0 at 10 mg steroid use. Finally, we used Fisher’s exact test to assess whether there were any associations between CYP3A5 and PXR SNPs. All analyses were performed using SPSS, version 23 (IBM, Armonk, NY, USA). P-values ≤0.05 were considered statistically significant. RESULTS Patients screened for this study received a renal transplant between March 1993 and January 2003. After this time our centre protocol changed to early steroid withdrawal, therefore patients transplanted after 2003 could not be included. By collecting leftover EDTA blood samples for DNA analyses between December 2002 and February 2005, we were able to collect material for all patients who were under regular control in our centre. This way we had a database consisting of 325 patients. When retrospectively applying the criteria for eligibility (at least 6 weeks post-transplant on tacrolimus-based immunosuppression, pharmacokinetic steady-state tacrolimus trough concentration at a prednisolone dose of 10 mg/day and/or 5 mg/day and after complete steroid withdrawal), 105 patients were eligible for further analyses. After application of the exclusion criteria we had tacrolimus trough concentration measurements for 83 patients after complete steroid withdrawal. Of these, 66 patients also had a measurement at 5 mg prednisolone dose and 81 at 10 mg. Tacrolimus trough concentrations were available at all three prednisolone doses in 64 patients. Of these, 17 patients did not have a stable tacrolimus trough concentration at prednisolone 5 mg and 2 patients at 10 mg. The baseline characteristics of the entire cohort for the respective prednisolone dosages are shown in Table 1. Table 1 Baseline characteristics of the study cohort at three different prednisolone dosages Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Data are presented as mean (SD) unless stated otherwise. ALAT, Alanine Aminotransferase; Dn, dose-normalized. Table 1 Baseline characteristics of the study cohort at three different prednisolone dosages Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Data are presented as mean (SD) unless stated otherwise. ALAT, Alanine Aminotransferase; Dn, dose-normalized. Correlation of CYP3A5 and PXR SNPs for DnC0 at 10 mg steroid dose The unadjusted (univariable) and adjusted (multivariable) associations between the tested CYP3A5 and PXR SNPs and DnC0 for 10 mg steroid dose (baseline measurement) are shown in Table 2. In the multivariable model, all SNPs were entered simultaneously together with all potential relevant confounders (i.e. except DnC0 at 10 mg steroid use and time interval between 10 and 0 mg steroid use). None of the potential confounders were statistically significant and they were therefore left out of the final multivariable model. Both in the univariable and multivariable analysis, the CYP3A5 carrier state was the only significant factor correlated with DnC0: *3/*3 SNP individuals had a 42% higher DnC0 compared with *1/*3 SNP individuals (1.4  versus 0.8 µg/L/mg). At baseline, i.e. <10 mg prednisolone dose, none of the PXR SNPs showed a statistically significant difference in DnC0. Table 2 Association ofPXRand cytochrome gene SNPs with DnC0for 10 mg of steroid use Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Table 2 Association ofPXRand cytochrome gene SNPs with DnC0for 10 mg of steroid use Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Influence of steroid withdrawal on DnC0 DnC0 increased by 58% (from a mean of 1.2 to 1.9 μg/L/mg) after withdrawal of prednisolone from 10 to 0 mg/day (P = <0.001; Table 1). As illustrated in Figure 1, DnC0 increased with every step of prednisolone tapering, with a large interindividual variability. The individual changes in DnC0 after steroid withdrawal are demonstrated in Figure 2. By considering (arbitrarily) a 20% change to be clinically relevant, ∼75% of the individuals (n = 62) had a clinically relevant increase up to 260% (with a single outlier >400%), while 18 remained stable and 3 individuals had a clinically relevant decline in DnC0. Since we observed one patient with an extreme increase in DnC0 after steroid withdrawal of >400% (Figure 2), we performed a sensitivity analysis in which the extreme outlier was omitted from the analyses. The sensitivity analysis did not result in different conclusions, nor did it result in different effect sizes or the signs of effects (results not shown), as described in the following sections. FIGURE 1 View largeDownload slide Box plots of the tacrolimus DnC0 for three different prednisolone dosages. The band inside the box shows the median value, whereas the bottom and top of the box represent the first and third quartiles, respectively. The small vertical lines at the end of the whiskers denote the lowest and highest values that are still within 1.5 times the interquartile range. The individual points are the outliers that fall outside of this boundary. FIGURE 1 View largeDownload slide Box plots of the tacrolimus DnC0 for three different prednisolone dosages. The band inside the box shows the median value, whereas the bottom and top of the box represent the first and third quartiles, respectively. The small vertical lines at the end of the whiskers denote the lowest and highest values that are still within 1.5 times the interquartile range. The individual points are the outliers that fall outside of this boundary. FIGURE 2 View largeDownload slide Distribution of frequencies of percentage change in tacrolimus DnC0 after prednisone dose reduction from 10 to 0 mg/day. FIGURE 2 View largeDownload slide Distribution of frequencies of percentage change in tacrolimus DnC0 after prednisone dose reduction from 10 to 0 mg/day. Relationship of CYP3A5 and PXR SNPs with the change in DnC0 for different steroid dosages In accordance with the literature, CYP3A5*3/*3 homozygotes have a higher DnC0 compared with CYP3A5*1/*3 carriers. This finding is valid at all prednisolone dosages (Figure 3 and Table 3). As shown in Table 3, not only the DnC0 but also its increase by steroid tapering is significantly higher in CYP3A5*3/*3 individuals (+64%) compared with CYP3A5*1/*3 individuals (+37%) (P< 0.001). Table 3 Main outcomes stratified by CYP3A5 genotype CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) Data are presented as mean (SD). Dn, dose-normalized. Table 3 Main outcomes stratified by CYP3A5 genotype CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) Data are presented as mean (SD). Dn, dose-normalized. FIGURE 3 View largeDownload slide Tacrolimus DnC0 stratified by CYP3A5 genotype. FIGURE 3 View largeDownload slide Tacrolimus DnC0 stratified by CYP3A5 genotype. The univariable and multivariable associations between CYP3A5 and PXR SNPs and the change in DnC0 with every 5-mg decline in prednisolone dose are depicted in Table 4. In the multivariable model, all SNPs were entered together with all potential confounders. As none of the latter were statistically significant related to the outcome parameter, the final multivariable model contains only the CYP3A5 and PXR SNPs. For CYP3A5, both the univariable and the multivariable models show that, compared with CYP3A5*1/*3 heterozygotes, CYP3A5*3/*3 homozygotes had a clinically significant additional ±0.30 µg/L/mg increase in DnC0 for every 5 mg steroid taper (P = 0.002). Table 4 Association ofPXRand cytochrome gene SNPs with steroid decrease–induced DnC0increase Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Table 4 Association ofPXRand cytochrome gene SNPs with steroid decrease–induced DnC0increase Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Compared with the homozygote PXR A7635A genotype, the A7635G heterozygotes had a trend towards a greater increase in DnC0 (+0.17 µg/L/mg for every 5 mg prednisolone tapering), while for the G7635G homozygotes this increase was statistically and clinically significant and nearly twice as high (+0.31 µg/L/mg; P = 0.02; Figure 4). In contrast, the homozygote PXR T8055T genotype had a statistically and clinically relevant lower DnC0 compared with the homozygote PXR C8055C genotype in both univariable and multivariable analyses (−0.50 and −0.72, respectively, for every 5 mg prednisolone tapering). However, interpretation of this has to be cautious because of the small number of individuals with PXR T8055T (n = 3; Table 1) and the fact that there was no indication for a dose–effect relationship (no decline in the C8055T genotype). FIGURE 4 View largeDownload slide Tacrolimus DnC0 stratified by PXR 7635 genotype at different prednisolone doses per day. FIGURE 4 View largeDownload slide Tacrolimus DnC0 stratified by PXR 7635 genotype at different prednisolone doses per day. There were no statistically significant interactions between CYP3A5 and the two statistically significant PXR SNPs (interaction with PXR A7635G GG: B = 0.14, SE = 0.23, P = 0.559; interaction with PXR C8055T TT: B = −0.70, SE = 0.48, P = 0.149). Fisher’s exact test revealed no associations between the presence of PXR SNPs and CYP3A5 SNPs. P-values derived from these tests were 0.840, 0.131 and 0.433, respectively, for PXR C25385T, A7635G and C8055T. DISCUSSION This study was designed to determine whether the observed increase in tacrolimus exposure after steroid tapering correlates with certain SNPs of the CYP3A5 or PXR gene and whether these SNPs showed any interaction. First, we confirmed the former finding of increasing DnC0 with steroid tapering (Figure 1) with a magnitude consistent with an earlier report from our group [4]. Second, in line with CYP3A5 activity, carriers of the CYP3A5*1 genotype had a lower DnC0 compared with the CYP3A5*3 SNP homozygotes (Figure 3 and Tables 2 and 3). Third, not only do CYP3A5*1 carriers have a lower DnC0, but a new finding was that they also exhibit a significant ∼30% lower increase after steroid taper compared with the CYP3A5*3/*3 SNP. Finally, we identified a correlation between the PXR 7635 carrier state and the change in DnC0 and that this correlation is independent from the CYP3A5 carrier state. The mechanism for the smaller increase in DnC0 in CYP3A5*1 carriers is not known. Although the increased drug clearance in stable long-term post-transplant patients in CYP3A5*1 carriers has been extensively described and reviewed [14–17], the majority of this increase was already within the diminished increase in DnC0 over time and has only been described by Kuypers et al. [18], who found a 39% increase in dose-corrected tacrolimus exposure only in the CYP3A5*3/*3 group during the first 5 years after renal transplant. The majority of this increase was within the first year post-transplant. As most of the steroid reduction takes place during this first year and we found a 25% increase in drug exposure per 5 mg steroid reduction, we conclude that this increase in tacrolimus exposure can be primarily attributed to the steroid taper. This is strengthened by the fact that the time since transplantation of the steroid tapering was not related to the change in tacrolimus exposure in our analysis. More recently, it has been suggested that the phenomenon of maturation of tacrolimus exposure in the first year after renal transplantation observed in CYP3A5*3/*3 homozygous patients can partly be explained by a (steroid tapering–related) decline in CYP3A4 activity (measured by diminished apparent oral clearance of midazolam) and a progressive increase in haematocrit [19]. We measured a progressive increase of the haemoglobin concentration and assume that this is equivalent to an increase in haematocrit. However, we did not establish a significant relationship of the increase in haemoglobin concentration and the increasing dose-corrected tacrolimus concentration during the total time of observation, which was longer than the reported 1-year period in the study of de Jonge et al. [20]. Since PXR is a transcriptional regulator of CYP3A5 [21–23] and this regulation is mediated by steroids [24, 25], carriers of inducing SNPs could then have higher tacrolimus requirements while on steroids and thus have a more pronounced increase in DnC0 after steroid withdrawal compared with the wild-type allele carriers. Our analysis revealed two PXR SNPs to be related to the change in DnC0 with steroid tapering (Table 4). First, compared with PXR A7635A patients, patients with the homozygote G7635G allele had a 0.31 µg/L/mg higher increase in DnC0 after complete steroid withdrawal, and this was nearly double the increase of heterozygote PXR A7635G carriers (P = 0.02). These results are in line with the results of Zhang et al. [25], who found a 2-fold higher CYP3A4 mRNA content after 2 days of rifampicine exposure in homozygous G7635G carriers compared with homozygous A7635A carriers. This should translate into higher CYP3A activity. Reducing the inducing influence of steroids through dose reduction would then result in diminished CYP3A activity, leading to a greater increase in DnC0. Notably, at a stable steroid dose we did not find differences in DnC0 between heterozygote PXR A7635G carriers and homozygote PXR G7635G carriers. However, PXR G7635G homozygotes display a significantly greater increase after steroid withdrawal, which is compatible with our hypothesis that carriers of inducing SNPs have higher tacrolimus requirements while on steroids and thus have a more pronounced increase in DnC0 after steroid withdrawal. Our analysis reveals that this greater increase in DnC0 is independent of the CYP3A5 genotype. One has to be aware that the PXR 7635 genotype was the only tested genotype not in Hardy–Weinberg (HW) equilibrium: our study cohort had a relative abundance of homozygous 7635G patients. We were unable to identify any plausible explanation for this finding in this population that was not pre-selected by any other parameter than the availability of steady-state tacrolimus trough concentration while on prednisone (10 mg and/or 5 mg) and after complete tapering of prednisone. Also, the entire pharmacogenetic renal transplant database (n = 325; without differences in patient characteristics compared with the studied cohort) was in HW equilibrium. We therefore think that the HW disequilibrium of the A7635G genotype is due to coincidence. The second SNP statistically related to the change in DnC0 with steroid tapering was the PXR T8055T genotype. It showed a significantly lower DnC0 increase compared with the heterozygote PXR C8055T or the PXR C8055C reference (multivariable coefficient −0.72; P = 0.06; Table 4), which means there is less effect of steroid withdrawal. This is not consistent with what would be expected given the intestinal CYP3A inducibility as shown by Zhang et al. [6]. One should consider that in our study only three patients carried this PXR T8055T genotype. So no firm conclusions can be drawn from these findings concerning the C8055T SNP unless they are confirmed in an independent larger cohort. An advantage of our study was that we had longitudinal data of pharmacokinetically stable patients at different phases of steroid taper during the first 2 years of transplantation instead of studying cross-sectional data. Since we adapted our immunosuppressive protocol towards early steroid withdrawal in our centre later on, we are unable to extend the number of patients in this study. As could be expected in a Caucasian population of this size, we lack in this study CYP3A5*1/*1 homozygotes and therefore we have to refrain from making any conclusions about them. Given the low genotype frequency of the PXR T8055T SNP, we could not elucidate in detail the potential influence of this apparently rare SNP. In conclusion, we confirmed the clinically relevant increase in tacrolimus exposure due to steroid tapering in renal transplantation. This phenomenon is not time dependent and probably largely explains the reported increase in drug exposure in the first year post-transplant. Given the large interpatient variability, the fact that tacrolimus exposure increases in all CYP3A5 groups after steroid tapering and the dose–response relation with every 5 mg prednisone taper, our advice is to monitor the tacrolimus trough concentration after every steroid dose change. Above all, we found that the increase in DnC0 after steroid tapering will be larger in CYP3A5*3 homozygotes compared with CYP3A5*1 carriers (and probably CYP3A5*1/*1 homozygotes). In addition, we also demonstrated that some SNPs of the PXR gene (especially G7635G, but also possibly T8055T) were related to a clinically relevant change in tacrolimus exposure due to steroid tapering and that this was independent of the CYP3A5 SNP. Therefore this study is the first clinical study showing that the steroid receptor PXR might be of clinical relevance for tacrolimus metabolism. ACKNOWLEDGEMENTS We would like to thank our trial nurse Monique Mullens for her help in collecting the clinical and laboratory data. We would also like to thank our laboratory co-worker Petal Wijnen for her help in performing the SNP determination. AUTHORS’ CONTRIBUTIONS F.S. was responsible for performance of the research, data analysis and writing and revision of the manuscript. S.M.J.v.K. performed the statistical analysis. O.B. was responsible for the contribution of analytical tools and revision of the manuscript. M.H.L.C. was the principal investigator, designed the research, provided data analysis and wrote and revised the manuscript. CONFLICTS OF INTEREST STATEMENT F.S., O.B. and S.M.J.v.K. have no conflicts of interest that are relevant to the content of this research. M.H.L.C. has been an investigator in company-driven studies by Novartis and Astellas and his institute has received consulting and lecture fees from Astellas. The results presented in this article have not been published previously in whole or part. REFERENCES 1 Kuypers DR , Claes K , Evenepoel P et al. Time-related clinical determinants of long-term tacrolimus pharmacokinetics in combination therapy with mycophenolic acid and corticosteroids: a prospective study in one hundred de novo renal transplant recipients . 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The induction of cytochrome P450 3A5 (CYP3A5) in the human liver and intestine is mediated by the xenobiotic sensors pregnane X receptor (PXR) and constitutively activated receptor (CAR) . J Biol Chem 2004 ; 279 : 38379 – 38385 Google Scholar CrossRef Search ADS PubMed 10 Kliewer SA , Moore JT , Wade L et al. An orphan nuclear receptor activated by pregnanes defines a novel steroid signaling pathway . Cell 1998 ; 92 : 73 – 82 Google Scholar CrossRef Search ADS PubMed 11 Ma X , Idle JR , Gonzalez FJ. The pregnane X receptor: from bench to bedside . Expert Opin Drug Metab Toxicol 2008 ; 4 : 895 – 908 Google Scholar CrossRef Search ADS PubMed 12 Lehmann JM , McKee DD , Watson MA et al. The human orphan nuclear receptor PXR is activated by compounds that regulate CYP3A4 gene expression and cause drug interactions . J Clin Invest 1998 ; 102 : 1016 – 1023 Google Scholar CrossRef Search ADS PubMed 13 Benkali K , Premaud A , Picard N et al. Tacrolimus population pharmacokinetic-pharmacogenetic analysis and Bayesian estimation in renal transplant recipients . Clin Pharmacokinet 2009 ; 48 : 805 – 816 Google Scholar CrossRef Search ADS PubMed 14 Hesselink DA , van Schaik RH , van der Heiden IP et al. Genetic polymorphisms of the CYP3A4, CYP3A5, and MDR-1 genes and pharmacokinetics of the calcineurin inhibitors cyclosporine and tacrolimus . Clin Pharmacol Ther 2003 ; 74 : 245 – 254 Google Scholar CrossRef Search ADS PubMed 15 Tada H , Tsuchiya N , Satoh S et al. Impact of CYP3A5 and MDR1(ABCB1) C3435T polymorphisms on the pharmacokinetics of tacrolimus in renal transplant recipients . Transplant Proc 2005 ; 37 : 1730 – 1732 Google Scholar CrossRef Search ADS PubMed 16 de Jonge H , Kuypers DR. Pharmacogenetics in solid organ transplantation: current status and future directions . Transplant Rev (Orlando) 2008 ; 22 : 6 – 20 Google Scholar CrossRef Search ADS PubMed 17 Thervet E , Anglicheau D , Legendre C et al. Role of pharmacogenetics of immunosuppressive drugs in organ transplantation . Ther Drug Monit 2008 ; 30 : 143 – 150 Google Scholar CrossRef Search ADS PubMed 18 Kuypers DR , de Jonge H , Naesens M et al. 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 – 725 Google Scholar CrossRef Search ADS PubMed 19 de Jonge H , Vanhove T , de Loor H et al. Progressive decline in tacrolimus clearance after renal transplantation is partially explained by decreasing CYP3A4 activity and increasing haematocrit . Br J Clin Pharmacol 2015 ; 80 : 548 – 559 Google Scholar CrossRef Search ADS PubMed 20 Lamba JK , Lin YS , Schuetz EG et al. Genetic contribution to variable human CYP3A-mediated metabolism . Adv Drug Deliv Rev 2002 ; 54 : 1271 – 1294 Google Scholar CrossRef Search ADS PubMed 21 Wang H , LeCluyse EL. Role of orphan nuclear receptors in the regulation of drug-metabolising enzymes . Clin Pharmacokinet 2003 ; 42 : 1331 – 1357 Google Scholar CrossRef Search ADS PubMed 22 Zhou SF. Structure, function and regulation of P-glycoprotein and its clinical relevance in drug disposition . Xenobiotica 2008 ; 38 : 802 – 832 Google Scholar CrossRef Search ADS PubMed 23 Pascussi JM , Drocourt L , Fabre JM et al. Dexamethasone induces pregnane X receptor and retinoid X receptor-alpha expression in human hepatocytes: synergistic increase of CYP3A4 induction by pregnane X receptor activators . Mol Pharmacol 2000 ; 58 : 361 – 372 Google Scholar CrossRef Search ADS PubMed 24 Pascussi JM , Drocourt L , Gerbal-Chaloin S et al. Dual effect of dexamethasone on CYP3A4 gene expression in human hepatocytes. Sequential role of glucocorticoid receptor and pregnane X receptor . Eur J Biochem 2001 ; 268 : 6346 – 6358 Google Scholar CrossRef Search ADS PubMed 25 Zhang Y , Benet LZ. The gut as a barrier to drug absorption: combined role of cytochrome P450 3A and P-glycoprotein . Clin Pharmacokinet 2001 ; 40 : 159 – 168 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nephrology Dialysis Transplantation Oxford University Press

Increase in tacrolimus exposure after steroid tapering is influenced by CYP3A5 and pregnane X receptor genetic polymorphisms in renal transplant recipients

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
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0931-0509
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Abstract

Abstract Background Tacrolimus, a drug for prevention of rejection after kidney transplantation, has a narrow therapeutic window and is metabolized by the cytochrome P540 3A (CYP3A) system. Tacrolimus exposure increases after steroid tapering in many patients. The pregnane X receptor (PXR)—a mediator for CYP3A—has a steroid receptor and might regulate CYP3A5 activity depending on single nucleotide polymorphisms (SNPs) of CYP3A5 or PXR. This may contribute to differences in tacrolimus exposure after steroid tapering. Methods In a cohort of renal transplant recipients, the influence of CYP3A5 and PXR SNPs (A7635G, C8055T and C25385T) on the dose-normalized Tacrolimus trough concentration (DnC0) and their potential interaction with each other after steroid taper were analysed by linear regression. Eligible were all 83 outpatient renal transplant patients on tacrolimus and steroids in a pharmacokinetic steady state at least 6 weeks after transplantation and whose blood was available for genetic analysis. Results Compared with the CYP3A5*1/*3 genotype, the CYP3A5*3/*3 SNP showed a significantly stronger increase in DnC0 after steroid taper (+0.29 µg/L/mg; P = 0.002). Of the tested PXR SNPs, PXR G7635G individuals had a significantly stronger increase in DnC0 (compared with A7635A, +0.31 µg/L/mg; P = 0.02), with a weaker increase in A7635G heterozygotes (+0.17 µg/L/mg; P = 0.124). There was neither interaction nor association between CYP3A5 and PXR SNPs. Conclusions The magnitude of the DnC0 increase due to steroid taper after renal transplantation is related to CYP3A5 SNPs. Independently, the PXR G7635G SNP is related to this increase, proving the role of PXR in tacrolimus metabolism. pharmacokinetics, pharmacogenetics, pregnane X receptor (PXR), tacrolimus, therapeutic drug monitoring INTRODUCTION Tacrolimus is a frequently used immunosuppressive drug after renal transplantation. Because of its narrow therapeutic window between underexposure (with risk of rejection) and overexposure (with risk of side effects), therapeutic drug monitoring is advised. In clinical practice, dosing is adapted on the trough concentration (as surrogate for the exposure). With increasing time post-transplant, a decrease in the dosage of tacrolimus required to maintain similar trough concentrations has been reported [1], the reason being a decrease in tacrolimus clearance with time [2]. However, tacrolimus dose requirements also diminish after steroid withdrawal [1, 3–5]. Clinically relevant increases (>20% rise of trough concentration) have been reported in 43% of patients after withdrawal of 5 mg and in 61% of patients after withdrawal of 10 mg prednisolone [4]. This increase in trough concentration after steroid tapering has been demonstrated to be present also when tapering takes place >6 months after transplantation, when mechanisms like improving graft function, liver function and an increase in haemoglobin or albumin concentration are less likely to occur [1, 4]. The reason for this phenomenon has not yet been clearly elucidated. Relevant pharmacokinetic interaction between corticosteroids and tacrolimus has been linked to cytochrome P450 3A (CYP3A) and P-glycoprotein (P-gp), a drug efflux pump produced by the multidrug resistance 1 (MDR-1) gene [3]. Since steroids are reported to induce the activity of CYP3A enzymes [6], it has been postulated that steroid tapering may result in diminished CYP3A5 activity [4]. CYP3A enzymes in the liver and small intestine are majorly responsible for tacrolimus metabolism leading to a substantial first-pass effect [7, 8]. Tacrolimus exposure is also related to single nucleotide polymorphisms (SNPs) of CYP3A5. To our knowledge, it is not known whether the effect of tapering steroids on tacrolimus exposure is different between these SNPs. The induction of CYP3A, on the other hand, is mediated by the pregnane X receptor [PXR, also called NR1I2 (nuclear receptor 1I2)] [9]. It is a regulatory factor of the nuclear receptor family with a steroid receptor and is involved in the upregulation of many drug-metabolizing enzymes and drug transporters [10, 11]. Induction of the CYP3A gene in response to treatment with a variety of compounds, such as the glucocorticoid dexamethasone, the antibiotic rifampicin and the antimycotic clotrimazole [9, 12], could then be linked to PXR. Several SNPs in the PXR gene have been found to increase CYP3A induction [6]. However, it is unknown whether systemic exposure to tacrolimus is then (in-)directly related to the inducing capacity mediated by SNPs in the PXR gene. If so, carriers of these inducing SNPs should have higher tacrolimus requirements while on steroids and thus have a more pronounced increase in dose-corrected trough concentrations after steroid withdrawal compared with the wild-type allele carriers. In this study, we hypothesize that the steroid-mediated induction of PXR upregulates metabolic clearance through CYP3A5 and therefore contributes to the increase in tacrolimus exposure after steroid tapering or withdrawal. The aim of the current study is to determine whether the observed increase in tacrolimus exposure after steroid tapering is associated with certain SNPs of the CYP3A5 or PXR gene and whether there is an interaction between SNPs of the CYP3A5 and PXR genes. To test this hypothesis we retrospectively analysed data in a population of renal transplant patients for whom we obatined relevant biochemical, pharmaceutical and genetic data. MATERIALS AND METHODS Patients, samples and clinical data Between December 2002 and February 2005, leftover ethylenediaminetetraacetic acid (EDTA) blood samples of renal transplant patients routinely visiting our outpatient clinic were collected. Patient data and tacrolimus trough concentrations were retrieved from the patient files and hospital information system. Collection, storage and use of blood and patient data were performed in agreement with the Federation of Dutch University Medical Centers (FEDERA) code of conduct (www.nfu.nl). Secondary usage of this leftover material was approved by the Medical Ethical Committee of the Maastricht University Medical Centre (MEC 04-188). All clinically stable renal transplant patients at least 6 weeks post-transplant on tacrolimus-based immunosuppression and whose leftover samples for DNA analysis were available were included. In addition, pharmacokinetic steady-state tacrolimus trough concentrations had to be present at a prednisolone dose of 10 mg/day and/or 5 mg/day and after complete steroid withdrawal. According to our centre protocol at that time, the prednisolone dose was 10 mg during the first 6 weeks and 7.5 mg for 4 weeks and 5 mg for 4 weeks during the ensuing 2 months and complete withdrawal thereafter, provided there were no signs of rejection or a higher degree of immunization. The tacrolimus trough concentration in the pharmacokinetic steady state was defined as at least 7 days after the last prednisolone dose reduction and/or the last tacrolimus dose change. Excluded from analysis were patients who were treated for acute rejection within 1 month before measurements and who were suffering from any condition influencing absorption or elimination of tacrolimus, like gastrointestinal disorders, hepatic dysfunction or medication interfering with tacrolimus pharmacokinetics. Parameters We collected the following biochemical, pharmaceutical and genetic parameters from hospital clinical and laboratory files: age, time since transplantation, body weight, dose-normalized Tacrolimus trough concentration (DnC0) [calculated from the tacrolimus trough level (C0)] and tacrolimus total daily dose), haemoglobin and serum albumin. The CYP3A5*1 and *3 alleles as well as three different SNPs of the PXR gene (A7635G, C8055T and C25385T) were determined. The choice of the PXR SNPs was based on previous reports: A7635G and C8055T are associated with a higher magnitude of (intestinal) CYP3A inducibility [6], while C25385T has been reported to be related to differences in tacrolimus apparent clearance [13]. DNA analysis Genomic DNA from a venous blood sample was extracted according to the manufacturers’ instructions (Qiagen, Leusden, The Netherlands). Real-time polymerase chain reaction (PCR) fluorescence resonance transfer (FRET) assays were used for genotyping with the LightCycler (Roche Diagnostics, Almere, The Netherlands), as described previously (R. Op Den Buijsch, unpublished work). Statistical analysis The baseline characteristics of the patients are presented as mean and standard deviation (SD) or absolute value and percentage of all three prednisolone dosages. DnC0 values were compared using an independent sample t-test. The unadjusted association of both CYP3A5 and PXR SNPs and the DnC0 value for 10 mg steroid use were first computed using linear regression. In addition, we computed the adjusted associations after correction for all other SNPs and potential confounding factors [i.e. gender, age, weight at 10 mg steroid use (baseline), elapsed time since transplantation, haemoglobin concentration, serum albumin concentration, DnC0 at 10 mg steroid use, time interval between 10 and 0 mg steroid use]. These potential confounders were tested for significance using backward stepwise elimination. To assess interactions between CYP3A5 and PXR SNPs for the individual slopes of the DnC0 value depending on steroid use, we computed interaction terms and tested using linear regression. Next, we determined DnC0 changes over the three measures by performing a linear regression analysis per patient to obtain each patient’s individual DnC0 slope. These slopes were used for subsequent analyses using the same methods as for the associations with DnC0 at 10 mg steroid use. Finally, we used Fisher’s exact test to assess whether there were any associations between CYP3A5 and PXR SNPs. All analyses were performed using SPSS, version 23 (IBM, Armonk, NY, USA). P-values ≤0.05 were considered statistically significant. RESULTS Patients screened for this study received a renal transplant between March 1993 and January 2003. After this time our centre protocol changed to early steroid withdrawal, therefore patients transplanted after 2003 could not be included. By collecting leftover EDTA blood samples for DNA analyses between December 2002 and February 2005, we were able to collect material for all patients who were under regular control in our centre. This way we had a database consisting of 325 patients. When retrospectively applying the criteria for eligibility (at least 6 weeks post-transplant on tacrolimus-based immunosuppression, pharmacokinetic steady-state tacrolimus trough concentration at a prednisolone dose of 10 mg/day and/or 5 mg/day and after complete steroid withdrawal), 105 patients were eligible for further analyses. After application of the exclusion criteria we had tacrolimus trough concentration measurements for 83 patients after complete steroid withdrawal. Of these, 66 patients also had a measurement at 5 mg prednisolone dose and 81 at 10 mg. Tacrolimus trough concentrations were available at all three prednisolone doses in 64 patients. Of these, 17 patients did not have a stable tacrolimus trough concentration at prednisolone 5 mg and 2 patients at 10 mg. The baseline characteristics of the entire cohort for the respective prednisolone dosages are shown in Table 1. Table 1 Baseline characteristics of the study cohort at three different prednisolone dosages Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Data are presented as mean (SD) unless stated otherwise. ALAT, Alanine Aminotransferase; Dn, dose-normalized. Table 1 Baseline characteristics of the study cohort at three different prednisolone dosages Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Prednisolone 10 mg (n = 81) Prednisolone 5 mg (n = 66) Prednisolone 0 mg (n = 83) Age (years) 49.1 (12.5) 48.4 (11.9) 50.2 (12.7) Gender (male), n (%) 50 (61.0) 39 (59) 51 (61.4) Time since transplant (days) 88.5 (276.8) 151 (218.9) 580 (759.5) Days on stable prednidolone dose 41.5 (132.8) 48.8 (115.1) 95.1 (293.9) Kreatinin (μmol/L) 222.5 (168.6) 168.9 (67.7) 146.1 (50.8) Haemoglobin (mmol/L) 6.8 (5.6) 7.4 (1.2) 8.1 (1.1) Albumin (g/L) 33.8 (5.1) 39.5 (4.3) 38.8 (4.1) ALAT (mmol/L) 28.1 (20.4) 21.3 (9.8) 24.7 (14.1) Daily tacrolimus dose (mg) 15.5 (9) 10.2 (6.1) 6.7 (4.4) Tacrolimus trough concentration (μg/L) 13.9 (4.1) 11.9 (3.7) 9.6 (3.2) Dn trough concentration (μg/L/mg) 1.2 (0.7) 1.6 (1.1) 1.9 (1.1) CYP3A5, n (%)  *3/*3 52 (64.2) 43 (65.1) 54 (65)  *1/*3 29 (35.8) 23 (34.9) 29 (34.9) PXR C25385T, n (%)  CC 38 (46.9) 36 (54.5) 39 (47.0)  CT 35 (43.2) 25 (37.9) 35 (43.4)  TT 8 (9.8) 5 (7.6) 8 (9.6) PXR A7635G, n (%)  AA 33 (40.7) 24 (36.4) 33 (39.7)  AG 29 (35.8) 25 (37.9) 30 (36.1)  GG 19 (23.5) 16 (25.7) 19 (24.0) PXR C8055T, n (%)  CC 56 (69.1) 48 (72.3) 59 (71.1)  CT 22 (27.2) 15 (22.7) 22 (26.5)  TT 3 (3.7) 3 (4.5) 3 (3.6) Data are presented as mean (SD) unless stated otherwise. ALAT, Alanine Aminotransferase; Dn, dose-normalized. Correlation of CYP3A5 and PXR SNPs for DnC0 at 10 mg steroid dose The unadjusted (univariable) and adjusted (multivariable) associations between the tested CYP3A5 and PXR SNPs and DnC0 for 10 mg steroid dose (baseline measurement) are shown in Table 2. In the multivariable model, all SNPs were entered simultaneously together with all potential relevant confounders (i.e. except DnC0 at 10 mg steroid use and time interval between 10 and 0 mg steroid use). None of the potential confounders were statistically significant and they were therefore left out of the final multivariable model. Both in the univariable and multivariable analysis, the CYP3A5 carrier state was the only significant factor correlated with DnC0: *3/*3 SNP individuals had a 42% higher DnC0 compared with *1/*3 SNP individuals (1.4  versus 0.8 µg/L/mg). At baseline, i.e. <10 mg prednisolone dose, none of the PXR SNPs showed a statistically significant difference in DnC0. Table 2 Association ofPXRand cytochrome gene SNPs with DnC0for 10 mg of steroid use Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Table 2 Association ofPXRand cytochrome gene SNPs with DnC0for 10 mg of steroid use Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT −0.02 0.15 0.916 −0.10 0.14 0.497  TT −0.11 0.25 0.650 −0.27 0.24 0.257 PXR A7635G (AA is reference)  AG −0.05 0.15 0.740 0.13 0.17 0.446  GG 0.24 0.17 0.170 0.15 0.21 0.477 PXR C8055T (CC is reference)  CT 0.01 0.17 0.977 0.04 0.18 0.832  TT 0.56 0.38 0.151 0.58 0.38 0.143 CYP3A5 (*3/*3 is reference)  *1/*3 −0.66 0.13 <0.001 −0.71 0.14 <0.001 Influence of steroid withdrawal on DnC0 DnC0 increased by 58% (from a mean of 1.2 to 1.9 μg/L/mg) after withdrawal of prednisolone from 10 to 0 mg/day (P = <0.001; Table 1). As illustrated in Figure 1, DnC0 increased with every step of prednisolone tapering, with a large interindividual variability. The individual changes in DnC0 after steroid withdrawal are demonstrated in Figure 2. By considering (arbitrarily) a 20% change to be clinically relevant, ∼75% of the individuals (n = 62) had a clinically relevant increase up to 260% (with a single outlier >400%), while 18 remained stable and 3 individuals had a clinically relevant decline in DnC0. Since we observed one patient with an extreme increase in DnC0 after steroid withdrawal of >400% (Figure 2), we performed a sensitivity analysis in which the extreme outlier was omitted from the analyses. The sensitivity analysis did not result in different conclusions, nor did it result in different effect sizes or the signs of effects (results not shown), as described in the following sections. FIGURE 1 View largeDownload slide Box plots of the tacrolimus DnC0 for three different prednisolone dosages. The band inside the box shows the median value, whereas the bottom and top of the box represent the first and third quartiles, respectively. The small vertical lines at the end of the whiskers denote the lowest and highest values that are still within 1.5 times the interquartile range. The individual points are the outliers that fall outside of this boundary. FIGURE 1 View largeDownload slide Box plots of the tacrolimus DnC0 for three different prednisolone dosages. The band inside the box shows the median value, whereas the bottom and top of the box represent the first and third quartiles, respectively. The small vertical lines at the end of the whiskers denote the lowest and highest values that are still within 1.5 times the interquartile range. The individual points are the outliers that fall outside of this boundary. FIGURE 2 View largeDownload slide Distribution of frequencies of percentage change in tacrolimus DnC0 after prednisone dose reduction from 10 to 0 mg/day. FIGURE 2 View largeDownload slide Distribution of frequencies of percentage change in tacrolimus DnC0 after prednisone dose reduction from 10 to 0 mg/day. Relationship of CYP3A5 and PXR SNPs with the change in DnC0 for different steroid dosages In accordance with the literature, CYP3A5*3/*3 homozygotes have a higher DnC0 compared with CYP3A5*1/*3 carriers. This finding is valid at all prednisolone dosages (Figure 3 and Table 3). As shown in Table 3, not only the DnC0 but also its increase by steroid tapering is significantly higher in CYP3A5*3/*3 individuals (+64%) compared with CYP3A5*1/*3 individuals (+37%) (P< 0.001). Table 3 Main outcomes stratified by CYP3A5 genotype CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) Data are presented as mean (SD). Dn, dose-normalized. Table 3 Main outcomes stratified by CYP3A5 genotype CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) CYP3A5*3/*3 CYP3A5*1/*3 Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg Prednisolone 10 mg Prednisolone 5 mg Prednisolone 0 mg (n = 52) (n = 43) (n = 54) (n = 29) (n = 23) (n = 29) Daily tacrolimus dose (mg) 11.5 (6.1) 7.3 (3.8) 4.6 (2.2) 23.0 (9.3) 15.5 (6.0) 10.8 (5.2) Tacrolimus trough concentration (μg/L) 13.1 (3.5) 11.8 (3.9) 9.2 (3.0) 15.3 (4.7) 12.0 (3.2) 10.3 (3.3) Dn trough concentration (μg/L/mg) 1.4 (0.7) 2.0 (1.2) 2.3 (1.1) 0.8 (0.3) 0.9 (0.4) 1.1 (0.5) Data are presented as mean (SD). Dn, dose-normalized. FIGURE 3 View largeDownload slide Tacrolimus DnC0 stratified by CYP3A5 genotype. FIGURE 3 View largeDownload slide Tacrolimus DnC0 stratified by CYP3A5 genotype. The univariable and multivariable associations between CYP3A5 and PXR SNPs and the change in DnC0 with every 5-mg decline in prednisolone dose are depicted in Table 4. In the multivariable model, all SNPs were entered together with all potential confounders. As none of the latter were statistically significant related to the outcome parameter, the final multivariable model contains only the CYP3A5 and PXR SNPs. For CYP3A5, both the univariable and the multivariable models show that, compared with CYP3A5*1/*3 heterozygotes, CYP3A5*3/*3 homozygotes had a clinically significant additional ±0.30 µg/L/mg increase in DnC0 for every 5 mg steroid taper (P = 0.002). Table 4 Association ofPXRand cytochrome gene SNPs with steroid decrease–induced DnC0increase Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Table 4 Association ofPXRand cytochrome gene SNPs with steroid decrease–induced DnC0increase Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Univariable Multivariable Coefficient SE P-value Coefficient SE P-value PXR C25385T (CC is reference)  CT 0.07 0.09 0.489 0.04 0.09 0.644  TT −0.16 0.16 0.308 −0.11 0.15 0.486 PXR A7635G (AA is reference)  AG −0.02 0.10 0.837 0.17 0.11 0.138  GG 0.11 0.11 0.316 0.31 0.13 0.020 PXR C8055T (CC is reference)  CT −0.08 0.11 0.478 −0.18 0.11 0.110  TT −0.50 0.24 0.044 −0.72 0.25 0.006 CYP3A5 (*3/*3 is reference)  *1/*3 −0.30 0.09 0.001 −0.29 0.09 0.002 Compared with the homozygote PXR A7635A genotype, the A7635G heterozygotes had a trend towards a greater increase in DnC0 (+0.17 µg/L/mg for every 5 mg prednisolone tapering), while for the G7635G homozygotes this increase was statistically and clinically significant and nearly twice as high (+0.31 µg/L/mg; P = 0.02; Figure 4). In contrast, the homozygote PXR T8055T genotype had a statistically and clinically relevant lower DnC0 compared with the homozygote PXR C8055C genotype in both univariable and multivariable analyses (−0.50 and −0.72, respectively, for every 5 mg prednisolone tapering). However, interpretation of this has to be cautious because of the small number of individuals with PXR T8055T (n = 3; Table 1) and the fact that there was no indication for a dose–effect relationship (no decline in the C8055T genotype). FIGURE 4 View largeDownload slide Tacrolimus DnC0 stratified by PXR 7635 genotype at different prednisolone doses per day. FIGURE 4 View largeDownload slide Tacrolimus DnC0 stratified by PXR 7635 genotype at different prednisolone doses per day. There were no statistically significant interactions between CYP3A5 and the two statistically significant PXR SNPs (interaction with PXR A7635G GG: B = 0.14, SE = 0.23, P = 0.559; interaction with PXR C8055T TT: B = −0.70, SE = 0.48, P = 0.149). Fisher’s exact test revealed no associations between the presence of PXR SNPs and CYP3A5 SNPs. P-values derived from these tests were 0.840, 0.131 and 0.433, respectively, for PXR C25385T, A7635G and C8055T. DISCUSSION This study was designed to determine whether the observed increase in tacrolimus exposure after steroid tapering correlates with certain SNPs of the CYP3A5 or PXR gene and whether these SNPs showed any interaction. First, we confirmed the former finding of increasing DnC0 with steroid tapering (Figure 1) with a magnitude consistent with an earlier report from our group [4]. Second, in line with CYP3A5 activity, carriers of the CYP3A5*1 genotype had a lower DnC0 compared with the CYP3A5*3 SNP homozygotes (Figure 3 and Tables 2 and 3). Third, not only do CYP3A5*1 carriers have a lower DnC0, but a new finding was that they also exhibit a significant ∼30% lower increase after steroid taper compared with the CYP3A5*3/*3 SNP. Finally, we identified a correlation between the PXR 7635 carrier state and the change in DnC0 and that this correlation is independent from the CYP3A5 carrier state. The mechanism for the smaller increase in DnC0 in CYP3A5*1 carriers is not known. Although the increased drug clearance in stable long-term post-transplant patients in CYP3A5*1 carriers has been extensively described and reviewed [14–17], the majority of this increase was already within the diminished increase in DnC0 over time and has only been described by Kuypers et al. [18], who found a 39% increase in dose-corrected tacrolimus exposure only in the CYP3A5*3/*3 group during the first 5 years after renal transplant. The majority of this increase was within the first year post-transplant. As most of the steroid reduction takes place during this first year and we found a 25% increase in drug exposure per 5 mg steroid reduction, we conclude that this increase in tacrolimus exposure can be primarily attributed to the steroid taper. This is strengthened by the fact that the time since transplantation of the steroid tapering was not related to the change in tacrolimus exposure in our analysis. More recently, it has been suggested that the phenomenon of maturation of tacrolimus exposure in the first year after renal transplantation observed in CYP3A5*3/*3 homozygous patients can partly be explained by a (steroid tapering–related) decline in CYP3A4 activity (measured by diminished apparent oral clearance of midazolam) and a progressive increase in haematocrit [19]. We measured a progressive increase of the haemoglobin concentration and assume that this is equivalent to an increase in haematocrit. However, we did not establish a significant relationship of the increase in haemoglobin concentration and the increasing dose-corrected tacrolimus concentration during the total time of observation, which was longer than the reported 1-year period in the study of de Jonge et al. [20]. Since PXR is a transcriptional regulator of CYP3A5 [21–23] and this regulation is mediated by steroids [24, 25], carriers of inducing SNPs could then have higher tacrolimus requirements while on steroids and thus have a more pronounced increase in DnC0 after steroid withdrawal compared with the wild-type allele carriers. Our analysis revealed two PXR SNPs to be related to the change in DnC0 with steroid tapering (Table 4). First, compared with PXR A7635A patients, patients with the homozygote G7635G allele had a 0.31 µg/L/mg higher increase in DnC0 after complete steroid withdrawal, and this was nearly double the increase of heterozygote PXR A7635G carriers (P = 0.02). These results are in line with the results of Zhang et al. [25], who found a 2-fold higher CYP3A4 mRNA content after 2 days of rifampicine exposure in homozygous G7635G carriers compared with homozygous A7635A carriers. This should translate into higher CYP3A activity. Reducing the inducing influence of steroids through dose reduction would then result in diminished CYP3A activity, leading to a greater increase in DnC0. Notably, at a stable steroid dose we did not find differences in DnC0 between heterozygote PXR A7635G carriers and homozygote PXR G7635G carriers. However, PXR G7635G homozygotes display a significantly greater increase after steroid withdrawal, which is compatible with our hypothesis that carriers of inducing SNPs have higher tacrolimus requirements while on steroids and thus have a more pronounced increase in DnC0 after steroid withdrawal. Our analysis reveals that this greater increase in DnC0 is independent of the CYP3A5 genotype. One has to be aware that the PXR 7635 genotype was the only tested genotype not in Hardy–Weinberg (HW) equilibrium: our study cohort had a relative abundance of homozygous 7635G patients. We were unable to identify any plausible explanation for this finding in this population that was not pre-selected by any other parameter than the availability of steady-state tacrolimus trough concentration while on prednisone (10 mg and/or 5 mg) and after complete tapering of prednisone. Also, the entire pharmacogenetic renal transplant database (n = 325; without differences in patient characteristics compared with the studied cohort) was in HW equilibrium. We therefore think that the HW disequilibrium of the A7635G genotype is due to coincidence. The second SNP statistically related to the change in DnC0 with steroid tapering was the PXR T8055T genotype. It showed a significantly lower DnC0 increase compared with the heterozygote PXR C8055T or the PXR C8055C reference (multivariable coefficient −0.72; P = 0.06; Table 4), which means there is less effect of steroid withdrawal. This is not consistent with what would be expected given the intestinal CYP3A inducibility as shown by Zhang et al. [6]. One should consider that in our study only three patients carried this PXR T8055T genotype. So no firm conclusions can be drawn from these findings concerning the C8055T SNP unless they are confirmed in an independent larger cohort. An advantage of our study was that we had longitudinal data of pharmacokinetically stable patients at different phases of steroid taper during the first 2 years of transplantation instead of studying cross-sectional data. Since we adapted our immunosuppressive protocol towards early steroid withdrawal in our centre later on, we are unable to extend the number of patients in this study. As could be expected in a Caucasian population of this size, we lack in this study CYP3A5*1/*1 homozygotes and therefore we have to refrain from making any conclusions about them. Given the low genotype frequency of the PXR T8055T SNP, we could not elucidate in detail the potential influence of this apparently rare SNP. In conclusion, we confirmed the clinically relevant increase in tacrolimus exposure due to steroid tapering in renal transplantation. This phenomenon is not time dependent and probably largely explains the reported increase in drug exposure in the first year post-transplant. Given the large interpatient variability, the fact that tacrolimus exposure increases in all CYP3A5 groups after steroid tapering and the dose–response relation with every 5 mg prednisone taper, our advice is to monitor the tacrolimus trough concentration after every steroid dose change. Above all, we found that the increase in DnC0 after steroid tapering will be larger in CYP3A5*3 homozygotes compared with CYP3A5*1 carriers (and probably CYP3A5*1/*1 homozygotes). In addition, we also demonstrated that some SNPs of the PXR gene (especially G7635G, but also possibly T8055T) were related to a clinically relevant change in tacrolimus exposure due to steroid tapering and that this was independent of the CYP3A5 SNP. Therefore this study is the first clinical study showing that the steroid receptor PXR might be of clinical relevance for tacrolimus metabolism. ACKNOWLEDGEMENTS We would like to thank our trial nurse Monique Mullens for her help in collecting the clinical and laboratory data. We would also like to thank our laboratory co-worker Petal Wijnen for her help in performing the SNP determination. AUTHORS’ CONTRIBUTIONS F.S. was responsible for performance of the research, data analysis and writing and revision of the manuscript. S.M.J.v.K. performed the statistical analysis. O.B. was responsible for the contribution of analytical tools and revision of the manuscript. M.H.L.C. was the principal investigator, designed the research, provided data analysis and wrote and revised the manuscript. CONFLICTS OF INTEREST STATEMENT F.S., O.B. and S.M.J.v.K. have no conflicts of interest that are relevant to the content of this research. M.H.L.C. has been an investigator in company-driven studies by Novartis and Astellas and his institute has received consulting and lecture fees from Astellas. The results presented in this article have not been published previously in whole or part. REFERENCES 1 Kuypers DR , Claes K , Evenepoel P et al. Time-related clinical determinants of long-term tacrolimus pharmacokinetics in combination therapy with mycophenolic acid and corticosteroids: a prospective study in one hundred de novo renal transplant recipients . 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The gut as a barrier to drug absorption: combined role of cytochrome P450 3A and P-glycoprotein . Clin Pharmacokinet 2001 ; 40 : 159 – 168 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Nephrology Dialysis TransplantationOxford University Press

Published: Sep 1, 2018

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