Edited by: Ulrich M. Zanger, Dr. Margarete Fischer-Bosch-Institut für Klinische Pharmakologie (IKP), Germany
Reviewed by: Julio Benitez, Universidad de Extremadura, Spain; Wenndy Hernandez, University of Chicago, United States
This article was submitted to Pharmacogenetics and Pharmacogenomics, a section of the journal Frontiers in Pharmacology
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Monitoring of immunosuppressive drugs, such as calcineurin and mTOR inhibitors, is essential to avoid undesirable kidney transplant outcomes. Polymorphisms in pharmacokinetics-related genes have been associated with variability in blood levels of immunosuppressive drugs and adverse effects, but influence of pharmacodynamics-related genes remains to be elucidated. The influence of polymorphisms in genes of the mTOR and calcineurin signaling pathways on long-term clinical outcomes was investigated in Brazilian kidney transplant recipients within the 1-year post-transplant. Two-hundred and sixty-nine kidney transplant recipients were enrolled at a kidney transplant center in São Paulo city, Brazil, and treated with tacrolimus plus everolimus or mycophenolate sodium (clinical trial NCT01354301). Clinical and laboratory data, including renal function parameters and drug blood levels were recorded. Genomic DNA was extracted from blood samples. Polymorphisms in
The calcineurin inhibitor (CNI), such as tacrolimus (TAC) and mTOR inhibitor (mTORi), such as everolimus (EVR) are immunosuppressive drugs helpful to prevent allograft rejection and simultaneously improve graft and patient survival in kidney transplantation (
TAC and EVR binding to their cytoplasmic protein receptor, the FK506-binding protein 12 (FKBP12). The TAC-FKBP12 complex interacts with calcineurin while the EVR-FKBP12 targets mTOR. These complexes lead to inhibition of T cell activation and proliferation, besides other important implications in alloimmune responses (
The therapeutic monitoring of these immunosuppressants is widely accepted because there is high between-patient variability in their pharmacokinetics and concentration–effect relationship (
In kidney transplant recipients on CNI-based immunosuppression, some polymorphisms in three calcineurin subunit genes (
Five variants in
This study investigated the influence of polymorphisms in
This pharmacogenetic study was carried out in a sample of kidney transplant recipients previously enrolled in the clinical trial registered as NCT01354301 at the US Clinical Trials database (
The aforementioned trial included low/moderate-immunological risk adult recipients indicated for first ABO-compatible kidney transplantation, from either alive or deceased donor, of which, the 269 patients who completed the study were selected. Exclusion criteria considered kidneys from HLA identical or expanded criteria deceased donors, positive cytotoxic cross match or panel reactive antibody equal to or above 50%, either class I or class II. Use of contraceptives methods during the trial were requested for women of childbearing potential (
The patients were randomized in three study groups of immunossupressive treatment, as follows: (i) Group TAC5/EVR: single dose of anti-thymocyte globulin at first day post-transplant, TAC 0.05 mg/kg b.i.d. and EVR 1.5 mg daily b.i.d.; (ii) TAC10/EVR: basiliximab induction on days 0 and 4. On day 1, TAC 0.1 mg/kg b.i.d. and EVR 1.5 mg b.i.d.; (iii) TAC10/MPS: basiliximab induction on days 0 and 4. On day 1, TAC 0.1 mg/kg b.i.d. and MPS 720-mg b.i.d. Before transplantation, all patients were treated with 1 g of methylprednisolone, and began receiving on day 1 post-transplant 0.5 mg/kg/day oral prednisone (not exceeding a 30 mg dose) tapered to 5 mg/day dose by day 45.
Clinical characteristics and laboratory data of the kidney recipients were recorded pre- and post-transplant). Clinical characteristics included cause of CKD, dialysis procedure, maintenance hemodialysis, cold ischemia time, graft loss and DGF. Laboratory data consisted of serum creatinine, eGFR, glucose, insulin, total cholesterol, HDL and LDL cholesterol, triglycerides, hemoglobin, erythrocytes and leukocyte counts, urinary proteins, immunodiagnostic test for CMV, kidney graft biopsies. eGFR was estimated by MDRD formula (
TAC and EVR blood concentration, the prescribed doses and adjusted concentration for dose administered (Co/D) were recorded. Blood concentrations were analyzed by chemoluminiscent microparticle immunoassay (Abbott Diagnostics, Lake Forest, IL, United States) and liquid chromatography-tandem mass spectrometry, respectively.
Primary clinical outcomes were considered serum creatinine, eGFR, cellular and humoral acute kidney rejection, and CMV events, including asymptomatic (infection) or symptomatic (disease) viremia (
Secondary clinical outcomes were considered the adverse events that were classified according to the Medical Dictionary for Regulatory activities (
Genomic DNA was isolated from peripheral blood sample using the QIAamp® DNA Blood Mini Kit (Qiagen Inc., Valencia, CA, United States) on the QIAcube system (Qiagen Sciences Inc., Germantown, MD, United States). DNA quantification and purity were evaluated by 260/280 nm spectrophotometry using NanoDrop ND-1000 (NanoDrop Technologies Inc., Wilmington, NC, United States) and DNA integrity was evaluated by 1.0% agarose gel electrophoresis. Samples were stored at −20°C.
Gene polymorphisms [
Genotyping was performed using the following pre-designed TaqMan SNP genotyping assays (Thermo Fisher Scientific Inc., Foster City, CA, United States): C_8862305_1 (rs1057079), C_8862346_10 (rs1135172), C_28027381_10 (rs1064261), C_25618069_10 (rs3730251), C__30149598_20 (rs6033557), C___2981262_20 (rs2159370), C__15942641_10 (rs2232365), C__27476877_10 (rs3761548). The PCR mixture contained 1X TaqMan genotyping master mix, 1X TaqMan SNP genotyping assay and 40 ng of template DNA. The real-time PCR assays were carried out according to the manufacturer’s instructions using an ABI 7500 FAST system (Applied Biosystems, Foster City, CA, United States). Genotype calling was performed using 7500 SDS software (Thermo Fisher Scientific Inc., Foster City, CA, United States). To assess the accuracy of the genotype calling, 20% of the DNA samples were randomly genotyped twice.
Continuous variables are expressed as median and interquartile range and compared by Mann–Whitney or Kruskal–Wallis test and Bonferroni’s correction test for multiple comparisons. Categorical variables are expressed as percentage and number of individuals in parenthesis, and compared by chi-square test or likelihood ratio test (for number of individuals less than 5).
Allele and genotype frequencies were estimated by counting. Hardy–Weinberg equilibrium (HWE) was estimated using Haploview program (
The influence of gene polymorphisms on clinical outcomes was studied using dominant, co-dominant and recessive inheritance models. The dominant model, in which the presence of at least one minor allele has an effect on the phenotype or clinical outcome, provides the highest power values of the statistical tests (
Cumulative incidence of acute rejection episodes (time-dependent variable) during the follow-up was analyzed by Kaplan–Meier survival analysis, and comparisons between major and minor allele carriers were performed using the log-rank test.
Univariable logistic regression analysis was used to assess the relationship between gene variants and primary and secondary clinical outcomes. Continuous variables such as eGFR and serum creatinine were divided by tertiles to be used as categorical variables in the logistic regression analysis. The combined effect of covariates and SNP was investigated using multivariable logistic regression analysis. The covariates considered in the models were age, weight, gender, donor type, time on hemodialysis, DGF time, cold ischemia time, acute rejection, proteinuria, immunosuppressive therapy group and CMV. Only covariates with
Association analysis of
The statistical analyses were performed using the SPSS for windows (SPSS Inc., Chicago, IL, United States), GraphPad Prism (GraphPad Software Inc., La Jolla, CA, United States) and SigmaStat (Systat Software Inc., San Jose, CA, United States). The
Biodemographic and clinical characteristics of the kidney transplant recipients are shown in Table
Biodemographic and pre-transplant clinical data of kidney recipients.
Variables | Total (269) | TAC5/EVR (80) | TAC10/EVR (96) | TAC10/MPS (93) | |
---|---|---|---|---|---|
Recipient age, years | 44.0 [34.0–56.0] | 43.5 [32.3–55.8] | 46.0 [33.5–57.0] | 43.0 [35.0–55.5] | 0.667 |
Recipient weight, kg | 68.3 [59.0–77.9] | 68.8 [62.4–77.5] | 67.5 [58.1–76.9] | 68.5 [57.3–79.8] | 0.652 |
Recipient gender, male | 66.2% (178) | 63.8% (51) | 65.6% (63) | 68.8% (64) | |
Recipient ethnics, Caucasian | 51.7% (139) | 47.5% (38) | 51.0% (49) | 55.9% (52) | 0.386 |
Cause of CKD | 0.260 | ||||
Glomerulonephritis | 12.3% (33) | 13.8% (11) | 14.6% (14) | 8.6% (8) | |
Hypertension | 10.0% (27) | 10.0% (8) | 7.3% (7) | 12.9% (12) | |
Diabetes | 10.0% (27) | 8.8% (7) | 5.2% (5) | 16.1% (15) | |
Undetermined | 41.6% (112) | 41.3% (33) | 46.9% (45) | 36.6% (34) | |
Other | 26.0% (70) | 26.3% (21) | 26.0% (25) | 25.8% (24) | |
Hemodialysis | 93.3% (251) | 91.3% (73) | 95.8% (92) | 92.5% (86) | 0.586∗ |
Time on hemodialysis, months | 33.0 [16.0–54.0] | 36.0 [16.0–48.0] | 27.5 [16.3–49.8] | 29.5 [16.0–61.5] | 0.894 |
Cold ischemia timea, h | 20.3 [17.6–23.0] | 20.3 [17.6–23.7] | 20.5 [17.8–23.0] | 20.1 [17.2–23.0] | 0.839 |
Delayed graft function | 33.8% (91) | 37.5% (30) | 34.4% (33) | 30.1% (28) | 0.586 |
DGF time, days | 10.0 [6.0–15.0] | 12.0 [6.8–16.0] | 11.0 [6.0–14.0] | 9.0 [5.3–13.5] | 0.396 |
Graft loss | 3.3% (9) | 1.3% (1) | 1.0% (1) | 7.5% (7) | 0.026∗ |
Donor type, deceased | 69.1% (186) | 77.5% (62) | 64.6% (62) | 66.7% (62) | 0.148 |
Drug monitoring data of the kidney recipients at 1-year post-transplant is shown in Supplementary Table
Primary and secondary kidney outcomes at 1-year post-transplant are shown in Table
Primary and secondary clinical outcomes of kidney recipients at 1-year post-transplant.
Variables | Total (269) | TAC5/EVR (80) | TAC10/EVR (96) | TAC10/MPS (93) | |
---|---|---|---|---|---|
Serum creatinine, mg/dL | 1.3 [1.1–1.6] | 1.3 [1.1–1.7]a,b | 1.4 [1.1–1.8]a | 1.2 [1.0–1.5]b | 0.034 |
eGFR, ml/min/1.73 m2 | 63.5 [49.7–78.2] | 64.6 [48.9–79.1]a,b | 57.8 [44.6–70.7]a | 67.3 [55.7–81.5]b | 0.014 |
Acute rejection, | 14.9% (40) | 10.0% (8) | 17.7% (17) | 16.2% (15) | 0.198 |
Cellular | 13.4% (36) | 7.5% (6) | 17.7% (17) | 14.0% (13) | |
Humoral | 1.5% (4) | 2.5% (2) | 0.0% (0) | 2.2% (2) | |
Anemia | 8.2% (22) | 10.0% (8) | 9.4% (9) | 5.4% (5) | 0.470 |
Leukopenia | 5.2% (14) | 5.0% (4) | 1.0% (1) | 9.7% (9) | 0.018∗ |
Constipation | 12.6% (34) | 11.3% (9) | 10.4% (10) | 16.1% (15) | 0.450 |
Diarrhea | 25.7% (69) | 25.0% (20) | 22.9% (22) | 29.0% (27) | 0.621 |
Dyspeptic disorder | 8.6% (23) | 3.8% (3) | 9.4% (9) | 11.8% (11) | 0.156 |
Epigastric pain | 12.3% (33) | 11.3% (9) | 9.4% (9) | 16.1% (15) | 0.348 |
Nausea and/or vomiting | 9.7% (26) | 8.8% (7) | 10.4% (10) | 9.7% (9) | 0.933 |
Edema | 41.3% (111) | 45.0% (36) | 47.9% (46) | 31.2% (29) | 0.047 |
Cytomegalovirus | 18.2% (49) | 5.0% (4) | 10.4% (10) | 37.6% (35) | <0.001∗ |
Hyperglycemia | 17.8% (48) | 25.0% (20) | 15.6% (15) | 14.0% (13) | 0.131 |
Post-transplant diabetes | 10.4% (28) | 13.8% (11) | 11.5% (11) | 6.5% (6) | 0.268 |
Proteinuria | 8.9% (24) | 10.0% (8) | 9.4% (9) | 7.5% (7) | 0.835 |
Analysis of the secondary outcomes showed that the most common blood and lymphatic system disorders were anemia (8.2%) and leucopenia (5.2%). Leukopenia was more frequent in the TAC10/MPS group (9.7%) than the TAC5/EVR (5.0%) and TAC10/EVR (1.0%) groups (
Frequencies of gene polymorphisms in the sample population are summarized in Table
Frequencies of gene polymorphisms in kidney transplant recipients.
Polymorphism | Genotypes | Total (269) | TAC5/EVR (80) | TAC10/EVR (96) | TAC10/MPS (93) | |
---|---|---|---|---|---|---|
CC | 33.5% (90) | 32.5% (26) | 39.6% (38) | 28.0% (26) | 0.534 | |
c.1437T>C | CT | 47.2% (127) | 46.3% (37) | 43.8% (42) | 51.6% (48) | |
TT | 19.3% (52) | 21.3% (17) | 16.7% (16) | 20.4% (19) | ||
T allele | 42.9% | 44.4% | 38.5% | 46.2% | 0.290 | |
TT | 42.4% (114) | 35.0% (28) | 46.9% (45) | 44.1% (41) | 0.530 | |
TC | 45.7% (123) | 50.0% (40) | 43.8% (42) | 44.1% (41) | ||
CC | 11.9% (32) | 15.0% (12) | 9.4% (9) | 11.8% (11) | ||
C allele | 34.8% | 40.0% | 31.3% | 33.9% | 0.218 | |
AA | 33.1% (89) | 30.0% (24) | 40.6% (39) | 28.0% (26) | 0.138 | |
c.4731G>A | AG | 43.9% (118) | 40.0% (32) | 39.6% (38) | 51.6% (48) | |
GG | 23.0% (62) | 30.0% (24) | 19.8% (19) | 20.4% (19) | ||
G allele | 45.0% | 50.0% | 39.6% | 46.2% | 0.135 | |
GG | 85.5% (230) | 86.3% (69) | 87.5% (84) | 82.8% (77) | 0.481∗ | |
c.249G>A | GA | 14.1% (38) | 12.5% (10) | 12.5% (12) | 17.2% (16) | |
AA | 0.4% (1) | 1.3% (1) | 0.0% (0) | 0.0% (0) | ||
A allele | 7.4% | 7.5% | 6.3% | 8.6% | 0.684∗ | |
TT | 43.9% (118) | 37.5% (30) | 46.9% (45) | 46.2% (43) | 0.209 | |
n.259+24936T>C | TC | 44.6% (120) | 48.8% (39) | 46.9% (45) | 38.7% (36) | |
CC | 11.5% (31) | 13.8% (11) | 6.3% (6) | 15.1% (14) | ||
C allele | 33.8% | 38.1% | 29.7% | 34.4% | 0.244 | |
GG | 33.5% (90) | 32.5% (26) | 34.4% (33) | 33.3% (31) | 0.997 | |
c.-2110G>T | GT | 52.0% (140) | 53.8% (43) | 51.0% (49) | 51.6% (48) | |
TT | 14.5% (39) | 13.8% (11) | 14.6% (14) | 15.1% (14) | ||
T allele | 40.5% | 40.6% | 40.1% | 40.9% | 0.988 | |
GG | 52.8% (142) | 51.3% (41) | 55.2% (53) | 51.6% (48) | 0.721 | |
c.-22-902A>G | GA | 18.2% (49) | 16.3% (13) | 20.8% (20) | 17.2% (16) | |
AA | 29.0% (78) | 32.5% (26) | 24.0% (23) | 31.2% (29) | ||
A allele | 38.1% | 40.6% | 34.4% | 39.8% | 0.426 | |
CC | 65.1% (175) | 67.5% (54) | 63.5% (61) | 64.5% (60) | 0.666 | |
c.-23+2882A>C | CA | 13.0% (35) | 15.0% (12) | 10.4% (10) | 14.0% (13) | |
AA | 21.9% (59) | 17.5% (14) | 26.0% (25) | 21.5% (20) | ||
A allele | 28.4% | 25.0% | 26.0% | 28.5% | 0.433 |
Median values of serum creatinine at 1-year post-transplant were higher in subjects carrying
Influence of
Univariable logistic regression showed that
Multivariable regression analysis revealed that male gender remained a risk factor for high serum creatinine at 1-year post-transplant using both model 1 (TAC5/EVR and TAC10/EVR) and model 2 (all therapy groups) (adjusted
Variables associated with primary outcomes in kidney recipients at 1-year post-transplant: Multivariable logistic regression analysis.
Dependent variables | Independent variables | Risk factor | OR (95% CI) | Adjusted |
|
---|---|---|---|---|---|
Model 1 | CC genotype | 1.66 (0.86–3.32) | 0.104 | 0.312 | |
Weight | Per 1 kg | 1.01 (0.99–1.04) | 0.325 | 0.442 | |
Gender | Male | 2.97 (1.46–7.49) | 0.001 | 0.005 | |
Acute rejection | Presence | 3.51 (1.35–13.04) | 0.015 | 0.060 | |
Proteinuria | Presence | 1.72 (0.69–4.85) | 0.221 | 0.442 | |
Model 2 | CC genotype | 1.22 (0.69–2.22) | 0.465 | 0.516 | |
Age | Per 1 year | 0.99 (0.97–1.00) | 0.112 | 0.448 | |
Weight | Per 1 kg | 1.02 (0.99–1.04) | 0.172 | 0.516 | |
Gender | Male | 3.67 (1.96–7.80) | 0.0001 | 0.001 | |
Acute rejection | Presence | 3.74 (1.71–10.40) | 0.004 | 0.024 | |
Proteinuria | Presence | 1.71 (0.73–4.55) | 0.226 | 0.516 | |
Therapy group | TAC+EVR | 1.82 (1.05–3.31) | 0.019 | 0.095 | |
Model 1 | AG+GG genotype | 3.53 (1.09–11.48) | 0.037 | 0.111 | |
Age | Per 1 year | 0.97 (0.94–1.01) | 0.130 | 0.260 | |
Weight | Per 1 kg | 1.03 (0.99–1.06) | 0.144 | 0.260 | |
Delayed graft function time | Per 1 day | 1.09 (1.03–1.15) | 0.003 | 0.012 | |
Model 2 | Age | Per 1 year | 0.98 (0.95–1.00) | 0.072 | 0.072 |
Delayed graft function time | Per 1 day | 1.11 (1.06–1.16) | 0.0001 | 0.0001 |
Association of genetic and non-genetic variables associated with acute rejection within 1-year post-transplant was also tested by univariate regression analysis (Supplementary Table
Multivariable regression analysis showed that
The influence of
One-year cumulative incidence of acute rejection in kidney recipients stratified by
The results of the univariable regression analysis for gene polymorphism and other variables associated with secondary clinical outcomes within 1-year post-transplant of kidney recipients are shown in Supplementary Table
Multivariable regression analysis showed that
Variables associated with secondary outcomes in kidney recipients at 1-year post-transplant: Multivariable logistic regression analysis.
Dependent variable | Independent variable | Risk factor | OR (95% CI) | Adjusted |
|
---|---|---|---|---|---|
Leukopenia | GG genotype | 4.45 (1.81–20.11) | 0.017 | 0.085 | |
CMV | Use of ganciclovir | 6.85 (1.81–26.02) | 0.005 | 0.030 | |
Constipation | TC+CC genotype | 2.41 (1.05–7.92) | 0.030 | 0.150 | |
Epigastric pain | GA+AA genotype | 2.41 (1.05–6.33) | 0.022 | 0.115 | |
Nausea and/or vomiting | CA+AA genotype | 2.24 (0.89–6.23) | 0.063 | 0.189 |
This study investigated the influence of polymorphisms in genes of the mTOR and calcineurin signaling pathway on long-term clinical outcomes in kidney recipients treated with TAC and EVR-based immunosuppressive therapy. Factors related to long-term acute rejection and adverse events were also investigated. Pre-transplant clinical and laboratory characteristics of the kidney recipients were similar among the immunosuppressive therapy groups, as it was previously reported (
A meta-analysis examined long-term renal outcomes comparing different immunosuppressant regimens. The authors observed that cohorts using combination of non-reduced doses of CNI and mycophenolic acid, as compared with mTORi group, showed higher creatinine clearance and lower serum creatinine values (
In this study,
It is likely that the C allele of
Carriers of the G allele of
These evidences are suggestive that the subjects carrying the
Alloantigens are immunomodulatory molecules that stimulate T cell receptor (
mTOR signaling pathway also exerts a role regulating the differentiation of regulatory and effectors T cells (
In this study, the CC genotype of the
Additional analysis used an additive model, which grouped male hemizygous genotypes with each one of the corresponding female homozygous genotypes. Our results indicated that females carrying the CA genotype, but not CC/C, of the
One study explored the impact of
Interestingly both
Likewise, an association between the C allele of
Both MPS-based immunosuppressive therapy and ganciclovir were also individually associated with leukopenia, but only the latter remained significant after adjustment. It is likely that carriers of the GG genotype of
It has been described that up to 35% of patients under treatment with MPS develops episodes of leukopenia (
In our work, among the non-genetic factors influencing kidney outcomes, the time of DGF was strongly associated with acute rejection, even in the presence of other confounders such as recipient and donor factors or cold ischemia time. Likewise, in a cohort of kidney recipients treated with TAC-based therapy, the presence of DGF was associated with increased risk of acute rejection (
To best of our knowledge, this is the first study to investigate the association of
The small sample size and the population substructure are limitations of this study, and in particular, the low number of female patients for the analysis of polymorphisms located in the X chromosome. The low number of cases with acute rejection and graft failure also limited the study of associations between these outcomes and polymorphisms. Also a heterogeneity in the therapeutic regimens of calcineurin and mTOR inhibitors for every study group is observed. The analyses of this work are exploratory considering that the main study was not designed to evaluate pharmacogenetic aspects.
In conclusion, this work shows that
AC-S, FG, AC, and RH wrote the article. AC-S, CF, HT-S, JM-P, MH, and RH designed the research. AC-S, GM, and RB performed the research. AC-S, FG, AC, and RH analyzed the data.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors thank Cristina M. Fajardo, Nagilla Oliveira, Vivian Bonezi, Alvaro M. Nishikawa, Patricia Salgado, and Andreia Dietzius for the technical support and for helping with the patient selection.
The Supplementary Material for this article can be found online at:
drug concentration for dose administered
chronic kidney disease
cytomegalovirus
calcineurin inhibitor
delayed graft function
estimated glomerular filtration rate
everolimus
mycophenolate sodium
mechanistic target of rapamycin
mTOR inhibitor
tacrolimus
regulatory T cells.