Exploring genetic and non‐genetic risk factors for delayed graft function, acute and subclinical rejection in renal transplant recipients

Abstract

Aims

This study aimed at identifying pharmacological factors such as pharmacogenetics and drug exposure as new predictive biomarkers for delayed graft function (DGF), acute rejection (AR) and/or subclinical rejection (SCR).

Methods

Adult renal transplant recipients (n = 361) on cyclosporine‐based immunosuppression were followed for the first 6 months after transplantation. The incidence of DGF and AR were documented as well as the prevalence of SCR at 6 months in surveillance biopsies. Demographic, transplant‐related factors, pharmacological and pharmacogenetic factors (ABCB1, CYP3A5, CYP3A4, CYP2C8, NR1I2, PPP3CA and PPP3CB) were analysed in a combined approach in relation to the occurrence of DGF, AR and prevalence of SCR at month 6 using a proportional odds model and time to event model.

Results

Fourteen per cent of the patients experienced at least one clinical rejection episode and only DGF showed a significant effect on the time to AR. The incidence of DGF correlated with a deceased donor kidney transplant (27% vs. 0.6% of living donors). Pharmacogenetic factors were not associated with risk for DGF, AR or SCR. A deceased donor kidney and acute rejection history were the most important determinants for SCR, resulting in a 52% risk of SCR at 6 months (vs. 11% average). In a sub‐analysis of the patients with AR, those treated with rejection treatment including ATG, significantly less frequent SCR was found in the 6‐month biopsy (13% vs. 50%).

Conclusions

Transplant‐related factors remain the most important determinants of DGF, AR and SCR. Furthermore, rejection treatment with depleting antibodies effectively prevented SCR in 6‐month surveillance biopsies.

What is Already Known about this Subject

• Acute rejection rates have decreased dramatically in the past decades. However, long‐term outcome has not improved accordingly.

• Transplant‐related factors are important determinants of delayed graft function, acute rejection and subclinical rejection, which in its turn leads to progressive fibrosis and loss of graft function.

• Pharmacogenetics can have an influence on cyclosporine A pharmacokinetics, but whether this also influences delayed graft function, acute rejection and subclinical rejection is subject to debate.

What this Study Adds

• The combined approach of analysing a wide range of time‐varying and time‐constant pharmacological factors in addition to known transplant‐related factors did not identify new factors suitable for prediction of delayed graft function, acute rejection and subclinical rejection.

• Transplant‐related factors currently remain the most important determinants of delayed graft function, acute rejection and subclinical rejection in this therapeutic drug monitoring (TDM)‐guided setting. Furthermore, rejection treatment with ATG prevented SCR.

• Pharmacogenetic factors, although theoretically plausible, are not suitable as predictive factors for delayed graft function, acute rejection and subclinical rejection and subsequently long‐term graft survival.

Introduction

In renal transplantation, acute rejection rates have decreased dramatically, mainly due to calcineurin inhibitor (CNI)‐based immunosuppressive regimens. However, long‐term outcome has not improved concomitantly 1. One of the dominant risk factors for acute rejection is delayed graft function which in its turn is highly related to transplant‐related factors such as vulnerability of the allograft and/or prolonged preservation times 2. Clinical episodes of acute rejection have been identified as a risk factor for subclinical rejection 3. Subclinical rejection has been associated with interstitial fibrosis and tubular atrophy (IF/TA) and with time progressive deterioration of renal function and inferior graft survival 4. Protocol biopsies two years after transplantation have shown high prevalence of chronic allograft nephropathy, defined by renal IF/TA, in CNI‐treated patients 5. The causes are multifactorial and determined by transplantation‐related factors including donor organ quality, ischemic/reperfusion injury, acute rejection and/or CNI toxicity. Subclinical rejection has been associated with IF/TA in subsequent biopsies and inadequate immune suppression may turn out to be a key factor in persistent or recurrent (chronic) cellular rejection and/or humoral rejection, finally leading to IF/TA and progressive loss of renal function 6, 7, 8.

Subclinical rejection is defined by (cortical) tubulo‐interstitial mononuclear cell infiltration without detectable functional renal deterioration. The prevalence of subclinical rejection decreases over time after transplantation 7, largely depending on the intensity of clinical immunosuppression 9, 10, 11, 12 and type of induction therapy 13, 14. This is illustrated by a decrease in subclinical rejection at 3 months post‐transplantation from 63% in the era of ciclosporin A (CsA)/azathioprine, towards only 5% with tacrolimus/mycophenolate 8.

Besides immunosuppressive therapy, prior acute rejection, histo‐incompatibility, degree of sensitization and donor age have been reported as risk factors for subclinical rejection 9, 10, 15, 16. The additional role of pharmacological factors, such as drug exposure and pharmacogenetic parameters for the occurrence of subclinical rejection is still unclear. It has been suggested that optimal CNI exposure may prevent subclinical rejection and progressive renal dysfunction 12. In this context, variability in the genes coding for the metabolic cytochrome enzymes (CYP3A4 and CYP3A5), transporter proteins (ABCB1), and the nuclear factor pregnane‐X‐receptor (NR1I2) are of interest. While there are no clear relationships between single nucleotide polymorphisms (SNPs) in ABCB1 and CsA exposure 17, associations between genetic variants in ABCB1 and graft function and graft survival have been described 18, 19, 20, 21. Furthermore, graft survival was not altered in renal transplant recipients on CsA therapy, when either these recipients or their donors were carriers of the CYP3A5*1 allele 22, but these recipients were found to have a survival benefit 23. CYP3A4*22 has previously been associated with DGF but this finding has never been replicated 24. Another metabolic enzyme of potential relevance to CsA therapy could be CYP2C8. CYP2C8*3 was related to an increased risk of developing renal toxicity in liver transplant recipients on CNIs, predominantly tacrolimus 25. However, no pharmacogenetic risk factors for subclinical rejection have been reported for renal transplant recipients on CsA therapy. Genetic variability in genes coding for calcineurin could alter the susceptibility for CsA as has been shown previously for tacrolimus 26. Polymorphisms in these genes could potentially be related to acute rejection and/or subclinical rejection 17. Acute rejection is caused by a complex interplay of time‐varying and time‐constant pharmacological, transplant‐related, genetic and nongenetic factors. In the present study a systematic analysis is performed using a parametric survival model to describe the time to acute rejection in renal transplant recipients to explore potential risk factors. Such an approach has advantages compared to nonparametric and semiparametric analyses, because it enables inclusion of time‐varying covariates and allows simulation based on the final model.

The principal aim of this study was to identify the contribution of CsA exposure and/or genetic variability in the genes coding for PPP3CA, PPP3CB, ABCB1, CYP3A4, CYP3A5, CYP2C8 and NR1I2 in addition to known transplant‐related risk factors to the risk for delayed graft function, acute‐ and ultimately subclinical rejection.

Patients and methods

Study design and patient population

Renal transplant recipients (n = 361) participating in the run‐in phase for a multicentre, randomized prospective trial 27 were used for this analysis. Induction therapy consisted of two doses of 20 mg basiliximab (Simulect®) intravenously before transplantation and on day 4, rapidly tapered prednisolone dose (50 mg b.i.d. intravenous tapered to 10 mg once daily oral prednisolone at day 4), twice daily mycophenolate sodium (Myfortic®) and twice daily CsA (Neoral®). CsA was initially dosed 4 mg/kg b.i.d. and supported by routine TDM based on a predefined whole blood target area under the blood‐concentration vs. time curve (AUC_0‐12h) of 5400 μg h l⁻¹ the first 6 weeks and 3250 μg h l⁻¹ thereafter. Myfortic was initially dosed twice daily 720 mg and supported by routine TDM on a predefined AUC_0‐12h of 35 mg h l⁻¹. TDM was performed on weeks 1 and 6 and months 3 and 6, after transplantation.

To guide safe reduction of immunosuppressive drugs, a protocol biopsy was performed at 6 months after transplantation and examined for histological signs of acute rejection according to the Banff 2005 grading system. The biopsy scores used in this study were not divided into borderline changes or at least grade IA rejection. We considered this justified by the fact that these criteria are based on for‐cause biopsies and not protocol biopsies. In addition, especially for borderline changes, there may be issues related to sampling error and interobserver variability 28, 29, 30. Furthermore, not only is serum creatinine a poor marker for changes in renal function 31, but also the definition for stable renal function in different studies was not strict and ranged from 10% to 25% difference in creatinine relative to baseline. Medical ethics approval was provided by the review boards of all participating centres and written informed consent was obtained from each patient.

Therapeutic drug monitoring and pharmacokinetic modelling

TDM was performed on the basis of Bayesian estimation 32 using MW/Pharm(v3.5) (Mediware, Groningen, The Netherlands) (blood concentration at t = 0, 1, 2, 3, 4, 5 and 6 up to 12 h after dose). Whole blood concentrations were determined with fluorescence polarization immunoassay (FPIA) (Axsym®, Abbott Laboratories) in the laboratories of the three participating centres. Pharmacokinetic parameters of interest were CsA AUC_0‐12h, clearance and dose. These pharmacokinetic parameters were derived using a previously published population pharmacokinetic model 33.

Pharmacogenetics

Renal transplant recipients (n = 302) were genotyped for genetic variants in the relevant genes PPP3CA and PPP3CB and in the genes ABCB1, CYP3A5, CYP3A4, CYP2C8 and NR1I2. Genetic information could not be obtained from all participants primarily owing to early dropout of patients or low quality of the collected material. Genotype distributions are presented in Supporting Information Table S1.

Statistical analysis

Delayed graft function

The binary endpoint for delayed graft function (yes/no) was analysed with a proportional odds model. Dropout was not included in the analysis as delayed graft function only occurred directly after transplantation.

Subclinical rejection (SCR)

The binary endpoint for subclinical rejection (yes/no) was analysed with a proportional odds model. Patients that dropped out during the first 6 months were included in the analysis to avoid overprediction of subclinical rejection. The base model for patients with a biopsy at 6 months was defined by:

$$Y=\left(1-P_{\mathit{do}}\right)*\left(P_{\mathit{SCR}}*\mathit{SCR}+\left(1-P_{\mathit{SCR}}\right)*\left(1-\mathit{SCR}\right)\right)$$

where Y is the likelihood of the model, P _do is the probability of dropping out and P _SCR is the probability of subclinical rejection. The variable SCR is a binary outcome with SCR = 1 if SCR is present and SCR = 0 if SCR is absent. For individuals with a premature study‐end (dropout), the likelihood of the model is Y = P _do. For the analysis of rejection treatment on the development of subclinical rejection, a sub‐analysis was performed on the 50 patients who had experienced an acute rejection in the first 6 months.

The model parameters for the analysis of delayed graft function and subclinical rejection were estimated by maximizing its likelihood using the Laplacian method. Throughout the model‐building process, an altered model was chosen over a precursor model if the difference in the objective function (OF), defined as −2 times the log‐likelihood, was >3.84 (P < 0.05, with 1 degree of freedom, assuming χ² distribution). All preselected covariates were evaluated one by one. Subsequently, selected covariate relationships were evaluated by forward inclusion and backward deletion.

Acute rejection – time‐to‐event

The time at which first acute rejection occurred was recorded, and for the patients who did not experience an acute rejection the time to dropout or end of study was recorded and treated as a censored observation. The time to acute rejection was analysed using a parametric survival model. The model was developed in two steps. First a base model was built to describe the time to first acute rejection with taking the dropout into account (right‐censoring). Second, whether covariates could influence the time to first acute rejection was investigated. To describe the time to the first acute allograft rejection, a parametric survival function according to the following equation was used:

$$S\left(t\right)=e^{‐{\int }_0^{t}h\left(t\right)\mathit{dt}}$$ (1)

where h(t) is the hazard, and S(t) is the survival, which is a function of the cumulative hazard within the time interval between time zero and time t describing the probability of not experiencing an acute rejection (‘surviving’) within this interval. The base model was developed by exploring different functions for the hazard h(t), varying from time independent constant hazard functions (e.g. exponential) to more complex functions such as Weibull, Gompertz and log‐logistic distributions. Of the preselected covariates, potential covariates were selected after a stepwise approach: in a first step, a graphical analysis was performed to select potential covariates that could be investigated in a full covariate analysis. To this end, Kaplan–Meier plots, stratified per group, were inspected visually for each covariate. In the case of continuous covariates, data were divided into quartiles, resulting in an equal number of subjects in each quartile. In a second step, based on the graphical analysis, covariates were selected to be carried forward to the single addition step. The selected covariates were added to the model one by one, and were retained in the model if the drop in objective function value (ΔOFV) > 6.63 (P < 0.01, assuming χ²‐distribution). In a third step, forward inclusion, the covariates that were selected were added one by one in ranking order of significance. The covariates were retained in the model if the drop in OFV was larger than 6.63 (P < 0.01). In a fourth step, backward deletion, each covariate that was included in the full model, based on the forward inclusion step, was removed. This step was repeated until each remaining covariate caused an increase of at least 10.8 points in OFV. The covariates were retained in the model if the increase in OFV > 10.8 (P < 0.001) to correct for multiple testing.

Model files and estimation algorithms and options

The NONMEM code including estimation algorithms and options of the final models can be found in the supporting information.

Software

The nonlinear mixed effect modelling package NONMEM (v7.2.1, Icon Development Solutions, Ellicott City, MD) was used for modelling, using PsN toolkit 3.4.2 and Piranã version 2.8.0 as modelling environment 34. Results were analysed and plotted using statistical software package R (v2.15.2) and RStudio (v0.97.248; Boston, MA, USA).

More detailed information regarding the methods are presented in the Supplementary Methods in the supporting information.

Results

Patient characteristics are provided in Table 1. At the time of transplantation, 361 renal transplant recipients were included and a 6‐month protocol biopsy was obtained from 275 (76%) patients. Reasons for exclusion were mainly nonmedical (withdrawal of consent), return to dialysis, insufficient graft function or patient death. There were no relevant differences in the demographic or transplant characteristics. Overall delayed graft function was observed in 14% of the patients (28% in the case of a kidney from a deceased donor, including those after cardiac death) and subclinical acute rejection was observed in 18% (n = 50) of protocol biopsies. The prevalence of subclinical rejection was higher in male recipients and patients with a history of acute rejection (Supporting Information Table S1).

Table 1.

Clinical characteristics

Characteristic	Inclusion at the time of transplantation
Inclusion at transplantation (n)	361
Recipient age (yr)	51 ± 13
Recipient gender (% male)	63
Race (% Caucasian)	86
Diabetes at baseline (%)	12
Primary kidney disease (%):
Polycystic kidney disease	78
Glomerulonephritis,‐sclerosis	77
Hypertension	60
Urological origin	26
Diabetic nephropathy	18
Interstitial disease	11
Etiology uncertain (e.c.i.)	14
Other	77
Cold ischemia (h) of cad donor	17
Donor age (yr)	49 ± 13
Donor type:
Living donor, related	76
Living donor, unrelated	93
Deceased donor, heart beating	121
Deceased donor, non‐heart beating	70
HLA‐mismatches:
Class 1 mismatches	1.94 ± 1.15
Class 2 mismatches	0.84 ± 0.63
Delayed graft function (%; living donor excl.)	28
Patients with at least 1 BPAR (%)	13.85
Patients treated with ATG (%)	34
Serum creatinine at baseline (μmol l−1)	770 ± 277
Serum creatinine at week 2 (μmol l−1)	246 ± 244
Serum creatinine at week 4 (μmol l−1)	145 ± 62
Serum creatinine at month 2 (μmol l−1)	138 ± 70
Serum creatinine at month 6 (μmol l−1)	129 ± 39
Patients with a 6‐months biopsy	276
Drop‐out (no biopsy) reasons:
Withdrawal of consent	55
Graft loss, dialysis or eGFR < 15 ml/min	18
Patient death	7
Infection	2
Intolerability to immunosuppressive drugs	1
Other	2

Patients were genotyped for the polymorphisms in genes encoding the CYP3A5, CYP3A4 and CYP2C8 enzymes, P‐glycoprotein and the calcineurin protein. Haplotypes and genotypes are summarized in Supporting Information Table S2. Besides these pharmacogenetic factors, inadequate systemic drug exposure is also a potential important pharmacological risk factor for subclinical rejection. CsA exposure was monitored throughout the study period and the change in AUCs over time after transplantation is presented in Figure 1.

Figure 1.

AUC_0‐12h in time after transplantation. Target AUC (horizontal striped lines) was 5400 μg h l⁻¹ up to 6 weeks after transplantation and 3250 μg h l⁻¹ thereafter

In the univariate analysis the covariates related to the incidence of delayed graft function (Table 2) and subclinical rejection were identified (Table 3).

Table 2.

Factors with significant effect on the incidence of delayed graft function

Model	Incidence of DGF	∆OF	P‐value
Univariate
BASE‐model*	14%
Deceased donor		−63.408	<0.00001
if yes	27%
if no	0.6%
Cold ischemic time > 12 h†		−36.515	<0.00001
if yes	26%
if no	7%
PPP3CB ‐genotype†		−5.142	0.0234
no TAC block	15%
carriers of TAC	35%
Multivariate
forward inclusion
BASE‐model
AND effect deceased donor on DGF		−63.408	<0.00001
AND effect cold ischemic time on DGF*		−2.501	0.1338
backward deletion
FINAL‐model
MINUS effect deceased donor on DGF		63.408	<0.00001

∆OF/LL > 6.64 (P < 0.01, chi‐square test)

^* Dropped from the final model

^† Based on a smaller dataset due to missing data.

Table 3.

Factors with significant effects on either the incidence of subclinical acute rejection or the incidence of drop‐outs

Covariate	Incidence of SCR	∆OF/LL	P‐value	Drop‐out frequency	∆OF/LL *	P‐value
BASE‐model *	18%			24%
Previous acute rejection episode		−6.645	0.0099		−16.829	0.0000
if yes	38%			48%
if no	16%			20%
Type of donation		−5.489	0.0191		−7.473	0.0063
if deceased	24%			29%
if living	13%			17%
Gender		−3.814	0.0508		−6.926	0.0085
male	21%			19%
female	12%			31%
					−4.388	0.0362
ABCB1 TTT‐genotype † no TTT block		N.S.		10%
carriers of TTT		N.S.		19%
Previous rejection treatment		−7.811	0.0052
if yes	34%				N.S.
if no	15%				N.S.

^* ∆OF > 3.84 (P < 0.05, chi‐square test),

^† Based on a smaller dataset due to missing data. N.S. not significant

Of the pharmacological factors, only PPP3B was related to the occurrence of delayed graft function. Carriers of a at least one TAC block had a higher incidence of delayed graft function (35% vs. 15%). The only other covariate related to delayed graft function was a deceased kidney donor (27% vs. 0.6% of living donors) and a cold ischemic time over 12 hours (26% vs. 7% if not).

The most significant covariates related to the prevalence of subclinical rejection were: a previous acute rejection episode and recipient of a kidney from a deceased donor. A history of acute rejection increased the incidence of SCR to 38% vs. 16% without acute rejection. Receiving a deceased donor kidney was associated with an SCR prevalence of 24% vs. 13% in recipients with a living donor kidney. Covariates related to an increased risk of dropping out (not biopsied at 6 months) were a previous acute rejection episode, a deceased donor kidney, female sex and the ABCB1 TTT‐haplotype (Table 3). In cases where patients did not carry a TTT‐haplotype, dropout was 10%, otherwise 19%.

For delayed graft function only a deceased kidney donor remained significantly related in the multivariate analysis with an incidence of 28% (Table 2). The highest risk category for subclinical rejection was identified with the final model (Table 4), identifying its prevalence at 6 months of 47% in the case of a deceased donor kidney and a history of (treated) acute rejection. In contrast, living donation without acute rejection resulted in a subclinical rejection prevalence of 11%.

Table 4.

Multivariate analysis of SCR: forward inclusion/backward deletion

Model	Absolute OF	∆OF *	P‐value
Forward inclusion
BASE‐model	654.297
AND effect previous acute rejection on dropout	637.468	−16.829	0.00004
AND effect recipient gender on dropout	628.991	−8.477	0.0036
AND effect donation type on dropout	620.677	−8.314	0.0039
AND effect previous acute rejection on SCR	614.032	−6.645	0.0099
AND effect donation type on SCR	608.458	−5.574	0.0182
Backward deletion
FINAL‐model	608.458
MINUS effect donation type on SCR	614.032	5.574	0.0182
MINUS effect previous acute rejection on SCR	620.032	6	0.0143
MINUS effect donation type on dropout	628.991	8.959	0.0028
MINUS effect recipient gender on dropout	637.468	8.477	0.0036
MINUS effect previous acute rejection on dropout	654.297	16.829	0.00004

^* ∆OF > 3.84 (P < 0.05, chi‐square test)

During the study period patients dropped out, and recipients with a deceased donor kidney had the highest drop‐out rate, 60% vs. 27% with or without acute rejection, respectively. After splitting the results according to gender as an additional risk factor for dropping out, female recipients displayed a dropout rate of 70% and males 51% in the case of a deceased donor kidney and previous acute rejection. For comparison, after living donations, without a previous acute rejection, the dropout rates were 19% (females) vs. 10% (males), respectively. In the multivariate analysis the ABCB1 TTT‐haplotype was deliberately left out owing to the small effect (P = 0.04, Table 3) on dropout, as well as the fact that the genotypes were not available for all individuals.

In a subanalysis with patients that experienced at least one acute rejection episode (n = 50), the effect of the type of rejection treatment on prevention of subsequent SCR was investigated (Supporting Information Table S3). Rejection treatment that included ATG resulted in a significantly (P < 0.05) lower prevalence of subclinical rejection (13% vs. 50%).

In the time to first acute rejection analysis, three patients had to be excluded because they dropped out of the study before the start of the 6‐month observation period. In some of the patients, more than one acute rejection was observed. Due to the limited number of these observations, the analysis took into account only occurrence of the first acute rejection episode. A Gompertz model described the time to first acute rejection most adequately (Figure 2). The equations used for the hazard function can be found in Supporting Information Table S4 The Kaplan–Meier plots showed adequate agreement between the observed and the simulated time to rejection. Furthermore, the dropout was adequately described by a log‐logistic model.

Figure 2.

Kaplan–Meier plots of the percentage of patients without acute rejection. The shaded area represents the 95% prediction intervals for the simulated data. The continuous line represents the real data, the dashed lines represent the 90% confidence interval of the real data

Based on graphical non‐parametric Kaplan–Meier plot analysis of the different covariates, 12 covariates were selected for parametric covariate analysis with the model: body mass index and CSA exposure (AUC_0‐12h) as time‐varying and ‐continuous covariates and DGF, PPP3CB variant TAT, pre‐existing diabetes mellitus (DM), CYP3A5*1 genotype, five different HLA‐mismatch categories, age category and underlying disease as categorical covariates (immunological vs. non‐immunological). The results of the univariate and multivariate analysis are presented in Table 5.

Table 5.

Analysis of time to first AR

Model	∆OF	P‐value
Univariate
Time varying covariates
AUC (linear)	−0.5908	0.4421
AUC (below target AUC)	−4.9	0.0269
Continuous covariates
BMI on λ (linear)*	−1.06	0.3032
Cyclosporine AUC0‐12h
AUC on λ	−0.642	0.4230
AUC on γ	−0.823	0.3643
Categorical covariates
DGF on λ†	−12.33	0.0004
DGF on γ	−1.34	0.2470
DM on λ	−0.71	0.3994
DM on γ	−0.12	0.7290
PPP3CB ‐ TAT on λ	−4.65	0.0311
PPP3CB ‐ TAT ‐ on γ	−3.80	0.0513
CYP3A5*1 on λ†	−8.36	0.0038
CYP3A5*1 on γ†	−7.04	0.0080
HLA mismatch CLASS II on λ	−4.57	0.0325
HLAMISS on λ	−3.67	0.0554
HLAMISS on γ	−2.72	0.0991
HLA mismatch‐A on λ	−1.74	0.1871
HLA mismatch‐A on γ	−3.53	0.0603
HLA mismatch‐B on λ	−3.93	0.0474
HLA mismatch‐B on γ	−3.84	0.0500
HLA mismatch‐DR on λ	−4.57	0.0325
HLA mismatch‐DR MISDR on γ	−3.46	0.0629
Age category on λ†	−8.16	0.0028
Age category on γ†	−8.95	0.0028
Underlying disease on λ	−0.72	0.3961
Underlying disease on γ	−5.24	0.0221
(HLAMISS defined as 2 or more HLA DR mismatches or at least 1 HLA‐B and 1 HLA DR mismatch)
Multivariate
Forward inclusion
Basic survival
AND effect DGF on λ	−12.327	0.0004
AND effect AGEcat on γ	−8.647	0.0033
AND effect CYP3A5*1 on λ	−6.373	0.0116
AND effect AGEcat on λ	−1.887	0.1695
AND effect CYP3A5.1 on γ	−1.726	0.1889
Backward deletion
Final survival model
MINUS effect AGECAT as covariate on γ	8.647	0.0033
MINUS effect DGF as covariate on λ	12.054	0.0005

^* Continuous covariates were tested for linear, log‐linear, allometric and Emax relationship, the relationship with the largest ∆OFV is shown.

^† Selected for multivariate analysis ∆OF > 6.64 (P < 0.01). ∆OFV > 6.64 (P < 0.01) forward inclusion, ∆OFV > 10.8 (P < 0.001) backward deletion

After forward inclusion and backward deletion, only delayed graft function remained a significant risk factor for the time to acute rejection. In Figure 3, the Kaplan–Meier visual predictive check (VPC) plot shows the difference in survival (freedom of acute rejection) in the two groups: with or without delayed graft function. The survival model prediction shows adequate match with the observed data. Patients experiencing delayed graft function had an increased risk of developing early acute rejection.

Figure 3.

Kaplan–Meier VPC of final time‐to‐event model stratified by presence or absence of delayed graft function (DGF). The shaded area represents the 95% prediction intervals for the simulated data. The continuous line represents the real data, the dashed lines represent the 90% confidence interval of the real data

Discussion

This analysis on a homogeneous group of standard to low risk transplant recipients treated with quadruple therapy with basiliximab, prednisolone, mycophenolate sodium and CsA with controlled systemic drug exposure, aimed to identify pharmacological risk factors for delayed graft function (DGF), acute rejection and subclinical rejection 6 months after renal transplantation in addition to known transplant‐related factors. Especially, the variability in CsA exposure and/or genetic variability in genes encoding calcineurin, P‐glycoprotein and CYP3A5 were of interest. The incidence of acute and subclinical rejection with controlled and early reduced systemic CsA exposure within 6 months was found to be 14% and 18%, respectively. Time‐constant and time‐varying pharmacological factors, including exposure and genetic variability in the selected genes, were not found to be related to the risk for delayed graft function, acute or subclinical rejection. Receiving a kidney from a deceased donor was the dominant risk factor for delayed graft function, with delayed graft function being the primary risk factor for time to first acute rejection. For subclinical rejection, the most important risk factors were a previous acute rejection episode, and being recipient of a deceased donor kidney. Finally a significant relationship (P < 0.05) was found between rejection treatment including ATG and a lower subsequent prevalence of subclinical rejection.

The results of this study confirm previous findings and those of Nankivell et al. 8, 35. The prevalence of subclinical rejection depends on time after transplantation and the centre policy on the use/type of induction/maintenance immunosuppressive therapy and the immunologic risk profile of the recipients 36. Subclinical rejection in early protocol biopsies was found to be associated with HLA‐matching 10, 35, 37, prior acute rejection episode 35, donor age 10 and donor source 16, 37. Although CsA exposure was not related to the incidence of subclinical rejection at 6 months even not as a time‐varying covariate, it is relevant to note that exposure was relatively high the first 6 weeks after transplantation (generally over 5400 μg h l⁻¹) and, after early reduction maintained between 2000 and 4500 μg h l⁻¹ thereafter. In current more aggressive CNI reduction strategies, CNI exposure might be of more importance. The findings of the subanalysis of rejection treatment on the prevalence of subsequent SCR confirms the previously reported low prevalence observed with induction therapy with depleting antibodies in patient cohorts dominated by living donor kidney transplant recipients. No relationship between the genetic variability in gene coding for calcineurin isoforms, PPP3CA and PPP3CB and acute or subclinical rejection was found. PPP3CB could be primarily of relevance as this gene principally encodes the calcineurin present in cells of the immune system, whereas PPP3CA is thought to be more relevant in other tissues including renal tubular epithelial cells. The selected haplotype may not be specific for the actual calmodulin and calcineurin binding parts 17 and expression of this protein may be regulated by other (nuclear) factors.

Furthermore, no relationship could be identified between any of the selected genes in drug transport (ABCB1), metabolism (CYP3A5, CYP3A4, CYP2C8) and the regulation of these genes (PXR – NR1I2). Carrying at least one copy of the ABCB1 TTT‐haplotype, however, was related to an almost two‐fold higher dropout rate for a 6‐month protocol biopsy. At least theoretically, these patients may be prone to a higher frequency of adverse events, since the TTT‐haplotype is associated with lower P‐glycoprotein activity. This effect is independent from kidney survival, where the ABCB1 genotype of the donor may be of higher relevance 18, 21. A combined donor‐recipient homozygosity for the C3435T variant in ABCB1 was associated with chronic allograft damage 38. In accordance with earlier results, no relation has been found between tacrolimus, carrying the CYP3A5*1 allele and AR or SCR 39, 40.

There are some limitations to the current analysis. Exposure of basiliximab and prednisolone could not be quantified, which in theory could also have an influence on the incidence of acute rejection and subclinical rejection. Exposure to mycophenolate was of less interest as all patients had an exposure above 35 mg h l⁻¹ and differentiation was difficult because of the absence of a validated PK model for myfortic. However, as has previously been determined, mycophenolate exposure seems to have less influence on rejection when used in the current regimen 41.

To the best of our knowledge this is the first comprehensive analysis using a combined pharmacometric approach to analyse the relationship between time‐constant and time‐varying pharmacological factors, including exposure and genetic variability risk for possible relationship with delayed graft function, acute or subclinical rejection in adult renal transplant recipients. We identified only delayed graft function as a risk factor for early acute rejection. Moreover, a history of acute rejection and being recipients of kidneys from a deceased donor were identified as the dominant risk factors for inflammation in 6‐month protocol biopsies despite controlled systemic drug exposure. Although effects of exposure and genetics could not be identified in this analysis, it is likely that this approach can be successful in identifying risk factors for chronic nephrotoxicity, or other forms of drug‐related toxicity, in transplant recipients. Indeed, kidneys from donors carrying the ABCB1 variant haplotype 1236T/2677T/3435T have been associated with inferior graft survival and renal function 21, while donors carrying the 3435TT genotype were associated with nephrotoxicity 18. Such a conclusive analysis should at least include genetic variability in the genes ABCB1, CYP3A5, PPP3CA of the donor.

Competing Interests

All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare no support from any organization for the submitted work, no financial relationship with any organization that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work.

The data used for this specific study presented in this manuscript was derived from a clinical study which was financially supported by Novartis Pharma.

Contributors

DJARM and RRP contributed equally to the manuscript. All authors contributed to the writing of the manuscript, and research design. DJARM, RRP, OA, BAP, CD, JAMW, TvdS, MD, HJG and JWdF performed the research and analysed the data. In addition, TvdS and JWdF contributed analytical tools, and FJB, JSS, JJHvdH and JWdF included patients. JWdF was the principal investigator.

Supporting information

Table S1 Demographic and transplant related factors within the groups with and without biopsies displaying subclinical acute rejection (SCR)

Table S2 Haplotype and genotype frequencies in renal transplant recipients for the genes coding for calcineurin alpha (PPP3CA) and beta (PPP3CB), CYP2C8, P‐glycoprotein (ABCB1), CYP3A5 and Pregnane‐X‐Receptor (NR1I2)

Table S3 Sub analysis of rejection treatment and incidence of SCR in patients experiencing acute rejection

Table S4 Equations for the Time to Event model

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Moes, D. J. A. R. , Press, R. R. , Ackaert, O. , Ploeger, B. A. , Bemelman, F. J. , Diack, C. , Wessels, J. A. M. , van der Straaten, T. , Danhof, M. , Sanders, J.‐S. F. , Homan van der Heide, J. J. , Guchelaar, H. J. , and de Fijter, J. W. (2016) Exploring genetic and non‐genetic risk factors for delayed graft function, acute and subclinical rejection in renal transplant recipients. Br J Clin Pharmacol, 82: 227–237. doi: 10.1111/bcp.12946.