Impact of IL-6 and IL-10 genotypes on tacrolimus dose requirements in kidney transplant recipients: Monte Carlo analysis

Abstract

Introduction: IL-6 and IL-10 may affect the activity of cytochrome P450 (CYP) 3A enzymes involved in tacrolimus (Tac) metabolism. Moreover, the effect of IL-6 and IL-10 on Tac pharmacokinetics may differ with respect to the genetic variations in their genes.

Aim: To examine the influence of IL-6 and IL-10 gene polymorphisms on Tac dose requirements and exposure over a 5-year period following kidney transplantation. Univariate and standard multivariate linear regression and Monte Carlo analysis were performed to investigate potential covariates influencing Tac dose-adjusted trough concentration (C₀/D) in various post-transplantation periods.

Materials & methods: IL-6 (-174G > C), IL-10 (-1082G > A, -819C > T and -592C > A) genotype, Tac daily dose, C₀, C₀/D and intrapatient variability data were collected from 113 patients.

Results: Multivariate regression analysis and accompanied Monte Carlo simulation underscore the importance of considering IL-6 -174G > C and IL-10 -1082G > A gene polymorphisms, alongside Tac metabolic phenotype and post-transplantation period, when tailoring Tac dosage regimen.

Conclusion: This study provides valuable insights regarding the individualized adjustment of Tac treatment in various post-transplantation periods.

Plain language summary

Article highlights.

• Inter-individual variability in Tac exposure can be explained to a considerable extent by Tac metabolism rate or phenotype. In addition, it was assumed that interleukin gene polymorphisms may account for a portion of the residual variability.

• Patients in the present study were classified into three groups based on the Tac metabolic phenotype: <1.05 ng/ml/mg (FM), 1.05–1.54 ng/ml/mg (IM) and ≥1.55 ng/ml/mg (SM), and it was demonstrated that Tac metabolic phenotype significantly and independently affected Tac exposure.

• Concerning the IL-6 -174G > C polymorphism, the carriers of the -174GG genotype exhibit higher Tac dose requirements (expressed as Tac C₀/D) compared to the carriers of the IL-6 -174C allele during the entire observation period of five years post-transplantation.

• Regarding the IL-10 polymorphisms, it was found that IL-10 -1082G > A polymorphism was associated with Tac pharmacokinetics (predominantly in later post-transplantation periods), while such an association was not demonstrated for the IL-10 -819C > T or IL-10 -592C > A polymorphism (both polymorphisms were in complete linkage disequilibrium).

• The interleukin gene polymorphisms did not correlate with the Tac IPV.

• The significant predictors in multivariate regression analysis, in other words, Tac metabolic phenotype, IL-6 -174G > C polymorphisms, IL-10 -1082G > A polymorphism and period after transplantation, accounted for 35.8% of the Tac C₀/D variance.

• Using the MC simulation approach, the present study showed that fast metabolizers carrying IL-6 GG + IL-10 A genotype combination tended to have the lowest predicted Tac C₀/D values, while slow metabolizers with the IL-6 C + IL-10 GG genotype combination exhibited the highest predicted Tac C₀/D values.

• The assessment of the combined effect of the metabolic phenotype and interleukin gene polymorphisms on Tac exposure could potentially facilitate optimizing Tac dosage regimen.

1. Background

Tacrolimus (Tac) serves as the backbone in most contemporary immunosuppressive protocols following kidney transplantation and is expected to maintain this role in the coming years. However, Tac is characterized by significant inter-individual (inter-patient) and intra-individual (intra-patient, IPV) variability in pharmacokinetics and chronic nephrotoxicity [1–3]. Previous studies have demonstrated that the 6986A > G polymorphism in the cytochrome P450 (CYP) 3A5 gene is one of the most important determinants of inter-individual pharmacokinetic variability, leading to varying Tac daily dose (TDD) requirements among individuals [4–7]. However, this gene polymorphism only partially accounts for the inter-individual pharmacokinetic variability of Tac [2,5]. An emerging parameter, Tac metabolic phenotype, which is based on the determination of Tac trough concentration (C₀) to TDD ratio (C₀/D) mainly in the early post-transplantation period, enables the categorization of patients as fast, intermediate and slow Tac metabolizers [8]. It has been postulated that all patients with low Tac C₀/D values exclusively possess the fast metabolizer phenotype, while CYP3A5 expressers do not ultimately have low Tac C₀/D values, in other words, fast metabolizer phenotype, indicating the greater prognostic potential of Tac C₀/D than the CYP3A5 genotype [9]. Although, categorization based on the metabolic phenotype explains a more substantial portion of variability than CYP3A5 polymorphism, there is a constant need for new genetic and non-genetic biomarkers, which would provide a more comprehensive insight into the overall pharmacokinetic variability of Tac. Numerous studies confirmed that CYP3A5 genotype-based dosing does not bring an evident clinical advantage compared with classic concentration-controlled dosing, thus a comprehensive dosing algorithm that should impregnate Tac pharmacogenetic considerations along with non-genetic covariates could be a step forward in personalized medicine [10]. Determining polymorphisms genes for interleukins (IL), specifically IL-6 and IL-10, may play a significant role in kidney transplantation. The rationale for studying IL-6 and IL-10 in the context of kidney transplantation lies in their indicative impact on Tac dose requirements and exposure, as well as their correlation with adverse post-transplantation outcomes [11]. Interleukin-6 is believed to play a crucial role in modulating CYP activity during inflammation, displaying a unique characteristic of downregulating all CYP isoenzymes, unlike other cytokines [12,13]. Meanwhile, IL-10 selectively downregulates CYP3A, with no discernible impact on other members within the CYP family [14,15]. Taking into account that Tac undergoes the CYP3A-dependent metabolism, variation in CYP3A enzymes' expression and activity can influence the variability in Tac C₀ and C₀/D [4]. The balance between IL-6 and IL-10 can contribute to inter-individual variability in Tac pharmacokinetics. Existing studies have predominantly focused on investigating the impact of polymorphisms in IL genes on Tac dose requirements and exposure, particularly during the early post-transplantation period [15]. However, to the best of our knowledge, there is a lack of data in the available literature regarding the influence of interleukin polymorphisms on Tac pharmacokinetic parameters in the long-term post-transplantation period. In addition to well-acknowledged inter-individual variability, Tac exhibits notable IPV in drug exposure, referring to the fluctuation in C₀ within an individual over a specific period, even when the TDD remains constant. In contrast to the inter-individual pharmacokinetic variability of Tac, well-known pharmacogenetic factors did not exhibit a substantial role in influencing IPV [1]. This study aimed to examine the influence of IL-6 and IL-10 gene polymorphisms on Tac dose requirements and exposure over a 5-year period following kidney transplantation. In addition, univariate and standard multivariate regression and Monte Carlo (MC) numerical analysis were performed to investigate potential covariates influencing Tac C₀/D values in various post-transplantation periods.

2. Materials & methods

2.1. Study design

The single-center pharmacokinetic-pharmacogenetic retrospective study was conducted at the Research Centre for Biomedicine, Faculty of Medicine, University of Nis and the Clinic of Nephrology, University Clinical Center Nis, Nis, Serbia. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The protocols were approved by the Ethics Committee of the Faculty of Medicine, University of Nis (No 12-6972-2/5 from 07 February 2018. and No 12-3588-2/2 from 07 April 2022). Patients provided their informed written consent to participate in the study.

The cohort comprised 113 Caucasian patients who underwent transplantation surgery between 2008 and 2018. All patients underwent routine controls, including therapeutic drug monitoring (TDM) of Tac and biochemical monitoring, at the Clinic of Nephrology, University Clinical Center Nis, Serbia. The present study spanned a period of 6 to 60 months after kidney transplantation. This study included patients on a Tac-based immunosuppressive protocol who had a kidney transplant for at least five years and whose graft is stable. Furthermore, patients displaying signs of graft failure necessitating regular dialysis within the first post-transplantation year were not included in the study. In addition, patients with uncontrolled hypertension (more precisely, the therapy prescribed for it) that could affect the study results were not included. Of the patients enrolled in the study, 74 were men and 39 were women, with a mean age of 39.68 ± 10.91 years at the time of transplantation surgery. Eighty-two patients had living donor transplantation (LDT), while 31 received their kidney grafts from deceased donors (DDT). Patients were prescribed Tac in either conventional preparation, immediate-release hard capsules (Prograf®, Astellas Ireland Co. Ltd.), administered twice daily (Tac-TD), or prolonged-release hard capsules (Advagraf®, Astellas Ireland Co. Ltd.), administered once daily (Tac-OD). The data used in the study were taken from the medical records of kidney transplant recipients. In order to protect patient data, each patient was assigned a code at the beginning of the study, which was subsequently employed in statistical analyses instead of personal identifiers.

2.2. Immunosuppressive protocol

All patients initiated a comprehensive immunosuppressive protocol, featuring Tac as a cornerstone. Intravenous methylprednisolone was administered initially at a dose of 0.5 g/day, later switched to oral prednisone at an initial dose of 1 mg/kg/day. The regimen also encompassed mycophenolate mofetil at 1.5–2 g/day or mycophenolic acid at 1080–1440 mg/day orally, and 20 mg of the monoclonal antibody basiliximab which was administered on the first and the fourth day after transplantation. The first oral Tac dose was usually given on the fifth day post-transplantation at 8:00 a.m. before breakfast (0.1 mg/kg or 0.2 mg/kg). Furthermore, Tac was administered either twice daily (at 08:00 a.m. and 08:00 p.m.) or once daily (at 08:00 a.m.) depending on the prescribed drug. Dose adjustments were made through TDM to achieve target therapeutic ranges: 8–12 ng/ml for the initial 90 days, 6–10 ng/ml in the first post-transplantation year and 5–9 ng/ml thereafter. In addition to the standard immunosuppressive therapy, patients were prescribed antihypertensive medications, including beta-blockers (bisoprolol, carvedilol, or metoprolol) and/or calcium channel blockers (amlodipine), either as monotherapy or in combination. Some patients also received angiotensin-converting enzyme (ACE) inhibitors such as fosinopril and zofenopril. All patients were prescribed proton pump inhibitors (omeprazole or pantoprazole) as part of the overall management plan.

2.3. Biochemical monitoring

Routine biochemical parameters, such as serum level of albumin (ALB), total proteins (TP), glucose (GLU), blood urea nitrogen (BUN) and creatinine (CRE) were measured using standard methods in the Biochemical laboratory of the Clinic of Nephrology. Analyses were performed on an automated random access clinical chemistry analyzer (Dimension RxL Max, Siemens, Germany). The Modification of Diet in Renal Disease (MDRD) equation was employed to calculate the eGFR [16]. The body mass index (BMI) was also calculated.

2.4. Pharmacokinetic data

For the purpose of analysis, the following data were collected: TDD, C₀ determined in the whole blood, C₀/D and IPV. Tacrolimus daily dose was retrieved from medical records or obtained through oral communication with patients, while Tac C₀ was measured using the chemiluminescent microparticle immunoassay (CMIA) method according to the manufacturer's instructions (Architect, IL, USA). The dose-adjusted trough concentration was calculated as Tac C₀ divided by the corresponding TDD. Tacrolimus daily dose and C₀ were recorded throughout the entire five-year observation period. Since TDD was not fixed, C₀/D instead of C₀ was used for IPV calculation, although in most patients, we preferred periods with constant TDD during 6–12 months post-transplantation. Tacrolimus IPV was calculated as: $IPV=\frac{SD}{\mathrm{Mean}}\times 100\%$ where Mean and SD are the mean and corresponding standard deviation of Tac C₀/D of available samples calculated for each patient during 6–12 months after transplantation. The average number of C₀ used for IPV calculation was 7 per patient, with a range of 4–11. Data obtained during the patient's hospitalization were not included.

2.5. Genotyping of interleukin gene polymorphisms

Genetic testing was not a routine procedure and was performed specifically for the purpose of this study. A fasting blood sample was taken from each patient during the routine control at the Clinic. Of the whole blood sample, 200 μl was used for DNA extraction. DNA was extracted from the whole blood with EDTA as an anticoagulant using a Genomic DNA Purification Kit (Fermentas, Thermo Scientific, Lithuania) according to the manufacturer's instructions. Genotyping of the IL-6 -174G > C (rs1800795) polymorphism was performed using the Polymerase Chain Reaction–Restriction Fragment Length Polymorphism (PCR-RFLP) method, adopted from Losito et al. [17]. All three IL-10 polymorphisms are located in the gene promoter, and they were genotyped using the PCR-RFLP “touchdown” method [18]. The primers used for PCR amplification of the sequence IL-10 -1082G > A (rs1800896) were adopted from Moudi et al. [19], while for the IL-10 -819 C > T (rs1800871) and -592C > A (rs1800872) polymorphisms, primers were adopted from Rad et al. [20]. Detailed genotyping methods are given in Supplementary Material.

2.6. Statistical analysis

The characteristics of the study group were expressed as the median and interquartile range or mean and standard deviation or frequency (%). The distribution of genotypes and alleles for the tested polymorphisms were assessed for deviation from Hardy-Weinberg equilibrium (HWE). Student's t test (normally distributed data) or the Mann-Whitney U test (non-normally distributed data) was used to compare TDD, Tac C₀ and Tac C₀/D concerning the tested genotypes. Linear regression analysis was conducted to evaluate the potential influence of independent predictors on C₀/D values. Furthermore, the derived regression equation was validated by MC simulation method. All statistical analyses were performed with SPSS software, version 22.0 (SPSS, BM Corp, Armonk, NY, USA) at the significance level set at p < 0.05. MATLAB R2017b (MathWorks) software was used to perform the MC simulation.

3. Results

The characteristics of the study population are outlined in Table 1.

Table 1.

Characteristics of the study population.

Characteristics of the patients (n = 113)	Value
Demographic data
Gender of the recipient (male/female)	74/39 (65.5%/34.5%)
Age of recipient at time of Tx (years)	39.68 ± 10.91 39.00 (31.00–48.00)
Donor type (living/deceased)	82/31 (72.6%/27.4%)
Number of kidney transplantations: 1st 2nd 3rd	109 (96.46%) 3 (2.65%) 1 (0.88 %)
Body mass (kg)a	70.00 (61.75–78.00)
BMI (kg/m2)a	23.25 (22.19–26.05)

Hematological and biochemical parametersa
WBC (109/L)	7.42 ± 2.29 7.30 (5.75–8.90)
RBC (1012/L)	4.53 ± 0.78 4.46 (3.92–5.04)
Hemoglobin (g/l)	130.57 ± 19.50 131.00 (116.00–143.00)
Hematocrit (l/l)	39.45 (34.58–43.62)
AST (U/l)	20.00 (15.15–24.92)
ALT (U/l)	26.60 (18.00–38.75)
ALP (U/l)	74.50 (55.00–103.00)
GGT (U/l)	24.00 (16.50–38.50)
Glucose (mmol/l)	5.05 (4.60–5.65)
Cholesterol (mmol/l)	5.77 ± 1.29 5.70 (4.90–6.50)
Triglycerides (mmol/l)	2.00 (1.48–2.58)
Creatinine (μmol/L)	130.00 (110.00–158.00)
eGFR (ml/min/1.73 m2)	46.85 (37.95–57.52)
Urea (mmol/l)	7.20 (5.80–9.12)
Albumin (g/l)	42.00 (39.00–44.00)
Total proteins (g/l)	70.66 ± 5.58 71.00 (67.00–74.00)

Tac pharmacokinetic parametersa
TDD (mg)	5.00 (3.50–7.00)
TDD per kilogram of body weight (mg/kg)	0.072 (0.046–0.107)
C0 (ng/ml)	7.64 ± 2.53 7.50 (5.90–9.00)
Tac C0/D (ng ml-1/mg)	1.45 (1.00–2.13)

Data are expressed as absolute number and percentage or mean ± standard deviation or median (interquartile range).

^aAt the 6th post-transplantation month when follow-up began.

ALP: Alkaline phosphatase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; BMI: Body mass index; C0-Tac: Trough concentration in the whole blood; C0/D-Tac: Dose-adjusted trough concentration; eGFR: Estimated glomerular filtration rate; GGT: Gamma-glutamyl transferase; RBC: Red blood cell; Tac: Tacrolimus; TDD: Tac daily dose; WBC: White blood cell.

In the majority of cases, the primary kidney disease was glomerulonephritis, followed by hypertensive nephropathy, diabetic nephropathy, vesicoureteral reflux, or cases where the cause was unknown. Additionally, a few patients presented with pyelonephritis, IgA nephropathy, Alport syndrome, or polycystic kidney disease.

The obtained results demonstrated that genotype frequencies did not deviate from HWE for the tested gene polymorphisms (Table 2). Additionally, -819C > T and -592C > A polymorphisms of the IL-10 gene were found to be in complete linkage disequilibrium, and in the subsequent analysis, -819C > T will be considered. Furthermore, Table 2 presents the IL-10 gene haplotypes. However, their expression potential is primarily dependent on the IL-10 -1082G > A gene polymorphism: IL-10*GCC/GCC is a high-producer (H), *GCC/ACC and *GCC/ATA are intermediate-producers (I) and *ACC/ACC, *ACC/ATA and *ATA/ATA are low-producers (L) [21]. In the further analysis, gene polymorphism data were presented independently using a genetic dominant model.

Table 2.

Distribution of genotypes and alleles of examined gene polymorphisms.

Gene polymorphism	Genotype	Frequency	Allele	Frequency	HWE
IL-6 (-174G > C)	GG	45 (39.8%)	G	143 (63.3%)	X2 = 0.010; p = 0.922
	GC	53 (46.9%)	C	83 (36.7%)
	CC	15 (13.3%)
IL-10 (-1082G > A)	GG	18 (15.9%)	G	90 (39.8%)	X2 = 0.001; p = 0.975
	GA	54 (47.8%)	A	136 (60.2%)
	AA	41 (36.3%)
IL-10 (-819C > T)	CC	53 (46.9%)	C	157 (69.5%)	X2 = 0.462; p = 0.496
	CT	51 (45.1%)	T	69 (30.5%)
	TT	9 (8%)
IL-10 (-592C > A)	CC	53 (46.9%)	C	157 (69.5%)	X2 = 0.462; p = 0.496
	CA	51 (45.1%)	A	69 (30.5%)
	AA	9 (8%)
IL-10 producer haplotype	Haplotype	Frequency
High High/intermediate	GCC/GCC GCC/GTA	16 (14.2%) 2 (1.7%)
Intermediate	GCC/ACC GCC/ATA	27 (23.9%) 27 (23.9%)
Low	ACC/ACC ACC/ATA ATA/ATA	10 (8.8%) 22 (19.5%) 9 (8.0%)

HWE: Hardy Weinberg equilibrium.

Table 3 shows that the carriers of the IL-6 -174GG genotype had higher TDD at all observed time points after transplantation compared to the carriers of -174GC and CC genotypes. Regarding IL-10 polymorphisms, -1082GG carriers had lower TDD compared to -1082GA and -1082AA carriers at the 6th, 36th, 48th and 60th month post-transplantation, while -819CC carriers had lower dose requirements only at the 6th post-transplantation month.

Table 3.

Comparison of TDD (mg) with respect to tested gene polymorphisms.

Gene polymorphism	Genotype	Period after kidney transplantation (months)
		6th	12th	24th	36th	48th	60th
IL-6 (-174G > C)	GG	6.57 ± 3.83 5.90 (4.10)	5.45 ± 3.11 4.80 (2.65)	4.50 ± 2.58 4.00 (3.21)	4.12 ± 2.22 4.00 (2.50)	4.05 ± 2.18 4.00 (2.63)	4.23 ± 2.43 3.00 (2.00)
	GC + CC	4.86 ± 2.63 4.75 (3.00)	4.13 ± 2.54 3.56 (2.31)	3.47 ± 1.99 3.00 (1.96)	2.95 ± 1.69 2.59 (1.50)	2.82 ± 1.42 2.75 (1.13)	2.73 ± 1.47 2.33 (1.00)
	Statistics.	Z = -2.475a p = 0.013	Z = -2.748a p = 0.006	Z = -2.196a p = 0.028	Z = -3.387a p = 0.001	Z = -3.279a p = 0.001	Z = -3.883a p < 0.001
IL-10 (-1082G > A)	GG	4.64 ± 3.45 3.50 (2.81)	4.02 ± 3.29 3.50 (2.25)	3.64 ± 2.90 2.50 (2.67)	2.96 ± 2.63 2.33 (1.13)	2.66 ± 2.14 2.00 (1.50)	2.40 ± 2.00 2.00 (0.96)
	GA + AA	5.69 ± 3.16 5.00 (3.50)	4.77 ± 2.67 4.00 (2.67)	3.92 ± 2.16 3.50 (2.50)	3.50 ± 1.84 3.00 (1.75)	3.41 ± 1.75 3.00 (1.75)	3.50 ± 1.98 3.00 (1.88)
	Statistics	Z = -1.987a p = 0.047	Z = -1.938a p = 0.053	Z = -1.408a p = 0.159	Z = -2.374a p = 0.018	Z = -2.634a p = 0.008	Z = -3.372a p = 0.001
IL-10 (-819C > T)	CC	5.13 ± 3.47 4.25 (2.69)	4.58 ± 3.16 3.56 (2.68)	3.80 ± 2.39 3.08 (2.37)	3.38 ± 2.10 3.00 (2.00)	3.20 ± 1.85 2.75 (2.00)	3.04 ± 1.86 3.00 (1.75)
	CT + TT	5.82 ± 2.99 6.00 (3.00)	4.70 ± 2.45 4.00 (2.59)	3.93 ± 2.21 3.50 (2.56)	3.43 ± 1.91 3.00 (2.13)	3.35 ± 1.84 3.00 (1.94)	3.58 ± 2.16 3.00 (2.44)
	Statistics	Z = -2.121a p = 0.034	Z = -1.308a p = 0.191	Z = -0.690a p = 0.490	Z = -0.458a p = 0.647	Z = -0.731a p = 0.465	Z = -1.449a p = 0.147

^aMann-Whitney Test.

Conversely, there was no difference in Tac C₀ among patients with different genotypes throughout the observed period, except for IL-10 -819C > T at the 60^th month after kidney transplantation (Table 4).

Table 4.

Comparison of Tac C₀ (ng/ml) with respect to tested gene polymorphisms.

Gene polymorphism	Genotype	Period after kidney transplantation (months)
		6th	12th	24th	36th	48th	60th
IL-6 (-174G > C)	GG	7.87 ± 1.77 8.18 (2.77)	7.19 ± 1.44 7.30 (1.86)	6.51 ± 1.22 6.55 (1.37)	6.18 ± 1.30 6.08 (1.84)	6.10 ± 1.64 5.88 (2.02)	6.28 ± 1.34 6.20 (1.44)
	GC + CC	7.48 ± 2.24 7.47 (2.62)	6.88 ± 1.82 7.44 (2.11)	6.35 ± 1.78 6.65 (2.14)	6.11 ± 1.97 6.24 (2.68)	5.63 ± 1.69 5.99 (2.10)	5.74 ± 2.03 5.95 (2.05)
	Statistics.	t = 0.911b p = 0.364	Z = -0.503a p = 0.615	Z = -0.187a p = 0.851	t = 0.230b p = 0.819	Z = -0.825a p = 0.409	Z = -1.056a P = 0.291
IL-10 (-1082G > A)	GG	6.99 ± 1.54 6.89 (1.93)	6.46 ± 1.77 6.48 (2.56)	6.69 ± 1.96 6.90 (1.92)	6.16 ± 1.87 6.53 (2.56)	5.43 ± 1.97 5.65 (2.13)	6.18 ± 2.47 6.55 (1.55)
	GA + AA	7.77 ± 2.16 7.78 (2.79)	7.12 ± 1.65 7.50 (1.80)	6.35 ± 1.50 6.55 (2.08)	6.13 ± 1.72 6.13 (2.34)	5.89 ± 1.61 5.97 (2.00)	5.10 ± 1.64 6.05 (1.51)
	Statistics	t = -1.444b p = 0.152	Z = -1.408a p = 0.159	Z = -0.539a p = 0.590	t = 0.060b p = 0.952	t = -1.023b p = 0.309	Z = -1.298a P = 0.194
IL-10 (-819C > T)	CC	7.42 ± 1.95 7.04 (2.51)	6.91 ± 1.78 7.08 (2.37)	6.36 ± 1.60 6.33 (1.85)	6.34 ± 1.82 6.57 (2.37)	5.89 ± 1.82 5.95 (2.05)	6.24 ± 2.11 6.30 (1.43)
	CT + TT	7.81 ± 2.18 7.78 (2.42)	7.08 ± 1.60 7.50 (1.34)	6.45 ± 1.58 6.70 (2.18)	5.95 ± 1.64 5.82 (2.29)	5.73 ± 1.55 5.97 (2.00)	5.65 ± 1.40 5.55 (1.75)
	Statistics	t = -0.942b p = 0.348	Z = -0.499a p = 0.618	Z = -0.695a p = 0.487	t = 1.136b p = 0.259	t = 0.461b p = 0.646	Z = -2.023a p = 0.043

^aMann-Whitney Test.

^bStudent's t test.

Table 5. demonstrates that the carriers of the IL-6 -174GG had significantly lower Tac C₀/D compared to the carriers of the C allele during the entire observed period (except at the 24^th month). As regards IL-10 polymorphisms, -1082GG genotype carriers had higher Tac C₀/D at the 36^th and 60^th post-transplantation months, while there was no difference in Tac C₀/D between the carriers of different IL-10 -819C > T genotypes.

Table 5.

Comparison of Tac C₀/D (ng/ml/mg) with respect to tested gene polymorphisms.

Gene polymorphism	Genotype	Period after kidney transplantation (months)
		6th	12th	24th	36th	48th	60th
IL-6 (-174G > C)	GG	1.49 ± 0.73 1.30 (0.76)	1.64 ± 0.82 1.42 (0.80)	1.80 ± 0.83 1.68 (1.06)	1.84 ± 0.89 1.74 (0.99)	1.78 ± 0.74 1.73 (1.16)	1.89 ± 0.99 1.75 (1.40)
	GC + CC	1.92 ± 1.04 1.62 (1.05)	2.08 ± 1.03 1.89 (1.30)	2.26 ± 1.27 1.95 (1.25)	2.48 ± 1.40 2.24 (1.17)	2.34 ± 1.23 2.10 (1.39)	2.53 ± 1.44 2.17 (1.61)
	Statistics.	Z = -2.075a p = 0.038	Z = -2.306a p = 0.021	Z = -1.688a p = 0.091	Z = -2.899a p = 0.004	Z = -2.111a p = 0.035	Z = -2.208a p = 0.027
IL-10 (-1082G > A)	GG	2.20 ± 1.36 1.80 (2.08)	2.21 ± 1.22 1.89 (1.78)	2.43 ± 1.33 2.28 (1.31)	2.59 ± 1.03 2.48 (1.05)	2.53 ± 1.44 2.43 (1.17)	3.19 ± 1.63 3.05 (2.25)
	GA + AA	1.66 ± 0.82 1.40 (0.87)	1.85 ± 0.91 1.66 (0.96)	2.02 ± 1.09 1.84(0.92)	2.16 ± 1.30 1.88 (0.99)	2.05 ± 1.01 1.40 (0.94)	2.09 ± 1.15 1.95 (1.15)
	Statistics	Z = -1.486a p = 0.137	Z = -1.178a p = 0.239	Z = -1.440a p = 0.150	Z = -2.339a p = 0.019	Z = -1.862a p = 0.063	Z = -2.858a p = 0.004
IL-10 (-819C > T)	CC	1.94 ± 1.07 1.79 (1.14)	1.98 ± 1.02 1.79 (1.45)	2.11 ± 1.10 1.93 (1.27)	2.21 ± 0.89 2.07 (0.99)	2.20 ± 1.15 1.97 (1.10)	2.51 ± 1.36 2.12 (1.56)
	CT + TT	1.61 ± 0.83 1.36 (0.63)	1.85 ± 0.93 1.65 (0.96)	2.06 ± 1.18 1.85 (1.01)	2.24 ± 1.52 1.78 (1.23)	2.07 ± 1.07 1.96 (1.17)	2.05 ± 1.24 1.98 (1.31)
	Statistics	Z = -1.708a p = 0.088	Z = -0.753a p = 0.452	Z = -0.537a p = 0.591	Z = -1.069a p = 0.285	Z = -0.619a p = 0.536	Z = -1.789a p = 0.074

^aMann-Whitney Test.

As previously stated, the C₀/D determined at the 3^rd month post-transplantation could be assumed as an index of Tac metabolism rate. Therefore, patients were classified into three groups: <1.05 ng/ml/mg (FM), 1.05–1.54 ng/ml/mg (IM) and ≥1.55 ng/ml/mg (SM). Standard multiple regression analysis was conducted to examine the independent influence of the different post-transplantation periods, Tac metabolic phenotypes according to C₀/D at the 3^rd month, IL-10 -1082G > A (IL-10 GG, IL-10 GA + AA), IL-6 -174G > C (IL-6 GG, IL-6 GC + CC), age of the kidney recipients and sex on C₀/D values (Table 6). Furthermore, IL-10 -819C > T was not significant in univariate regression analysis; therefore, it was not included in the multiple regression. The tested model accounted for 35.8% of the C₀/D variance, and based on the significant parameters identified through regression analysis, a regression equation was formulated (equation 1).

Table 6.

Multivariate regression analysis of factors influencing Tac C₀/D after kidney transplantation.

Parameters	B (SE)	95% CI for B		Beta	Sig.	Model R2, Sig.
Constant	0.913 (0.230)	0.461	1.365		<0.001	35.8%, p < 0.001
Age of kidney recipient (years)	0.006 (0.003)	0.000	0.013	0.065	0.065
Sex (female)	-0.002 (0.079)	-0.157	0.154	-0.001	0.983
Period after kidney transplantation (ref: 6th month)
12th month	0.155 (0.123)	-0.086	0.397	0.055	0.207
24th month	0.295 (0.124)	0.051	0.538	0.103	0.018
36th, 48th, 60th month	0.411 (0.103)	0.209	0.612	0.189	<0.001
Metabolic phenotype according to Tac C0/D at the 3rd month (ref: FM)
IM	0.606 (0.093)	0.425	0.788	0.256	<0.001
SM	1.303 (0.092)	1.123	1.484	0.578	<0.001
IL-6 (-174G > C) genotype (ref: GG)
IL-6 GC + CC	0.276 (0.079)	0.121	0.430	0.123	<0.001
IL-10 (-1082G > A) genotype (ref: GG)
IL-10 GA + AA	-0.286 (0.102)	-0.486	-0.086	-0.099	0.005

Constant: 6^th month post-transplantation; FM metabolic phenotype; male sex; IL-6 GG genotype; IL-10 GG genotype.

B: Unstandardized coefficient; Beta: Standardized coefficient; CI: Confidence interval; R2-% of explained variance of Tac C0/D by the given model; SE: Standard error; Sig.: Significance.

3.1. Regression equation 1

C₀/D of Tac (between 6 and 60 months post-transplantation) = 0.913 + [0.295 if period after transplantation is 24^th month or 0.411 if period after transplantation is 36^th, 48^th or 60^th month] + [0.606 if patient is IM or 1.303 if patient is SM] + [0.276 if patient is a carrier of IL-6 -174C allele] + [-0.286 if patient is a carrier of IL-10 -1082A allele]. The proposed model was validated using the MC simulation method.

3.2. Monte Carlo numerical analysis

Given that multiple parameters were significant in regression analysis, the next step involved validating the obtained model using MC simulation. The MC simulation was performed based on the regression equation derived from the analysis of experimental data. Moreover, the equation coefficients were simulated within the range of unstandardized coefficients ± standard error. The input values for each category of significant covariates were represented using probability distributions based on the coefficients derived from the regression analysis. Monte Carlo simulation involves repeatedly simulating the model, drawing different random sets of values (inputs) from the sampling distribution of model parameters. This iterative process produces distributions of possible outcome values (outputs), providing valuable insights into long-term predictions after transplantation. Using this approach, we obtained 1000 values for each of 36 independent simulations, considering Tac C₀/D between 6 and 60 months after transplantation. These values collectively represent the most anticipated values for Tac C₀/D in the study (Figure 1a–c).

Figure 1.

Monte Carlo simulation of IL-6 and IL-10 genotype effect on Tac C₀/D values < 24 months (A), at the 24th month (B) and between 36 and 60 months (C) after kidney transplantation.

In accordance with the proposed model, the minimum obtained value is 0.629 ± 0.143 ng/ml/mg (FM, IL-6 GG + IL-10 A), while the maximum value is 2.488 ± 0.151 ng/ml/mg (SM, IL-6 C + IL-10 GG) in period less than 24 months. Considering C₀/D values at the 24^th post-transplantation month, the minimum value was 0.923 ± 0.165 ng/ml/mg (FM, IL-6 GG + IL-10 A) and maximum value was 2.777 ± 0.165 ng/ml/mg (SM, IL-6 C + IL-10 GG). Finally, the predicted minimum and maximum C₀/D values between 36 and 60 months were 1.034 ± 0.155 ng/ml/mg (FM, IL-6 GG + IL-10 A) and 2.911 ± 0.163 ng/ml/mg (SM, IL-6 C + IL-10 GG), respectively.

Besides marked inter-individual pharmacokinetic variability, Tac exerts significant IPV. The mean IPV of the study population being 22.55 ± 9.59%, while median being 21.11% (12.7%). Figure 2. illustrates that there is no statistically significant association between Tac IPV and interleukin gene polymorphisms: IL-6 (-174GG vs. GC + CC): 20.81 ± 6.88% vs. 23.64 ± 10.85%, z = -0.875, p = 0.381; IL-10 (-1082GG vs. GA + AA): 24.35 ± 11.00% vs. 22.17 ± 9.29%, z = -0.756, p = 0.450; IL-10 (-819CC vs. CT + TT): 21.11 ± 9.20% vs. 23.79 ± 9.82%, z = -1.611, p = 0.107.

Figure 2.

The comparison of Tac IPV in relation to tested gene polymorphisms.

4. Discussion

The goal of personalized medicine is to provide optimal treatment for individual patients. Achieving this involves a comprehensive understanding of the patient-related factors, such as lifestyle, coexisting medical conditions, current medications and their pharmacokinetic (PK)/pharmacodynamic (PD) characteristics, along with pharmacogenetic information [22]. This approach is particularly crucial for drugs with a narrow therapeutic index and variable pharmacokinetics, such as Tac. Therefore, the continuous investigation of factors influencing PK/PD variability remains essential, even if their clinical relevance may not be immediately apparent. Novel insights, integrated into a comprehensive patient assessment, significantly enhance the achievement of optimal therapeutic outcomes for a broader spectrum of patients [23].

The IL-6 -174G > C (rs1800795) gene polymorphism is located in proximity to a glucocorticoid response element and within a region crucial for IL-6 transcription (-180 to -123 bp) [24]. It influences the transcription of the IL-6 gene, with the C allele assumed to be associated with reduced expression and lower plasma levels of IL-6 (IL-6*C/C, low producer—L) [21]. Although traditionally considered a pro-inflammatory cytokine, the IL-6 signaling represents a fine balance of both pro-inflammatory and anti-inflammatory effects, so that in recent perspectives, IL-6 is increasingly recognized as an anti-inflammatory cytokine when the homeostatic mechanisms are maintained [25,26]. Under a homeostatic state, characterized by a plasma concentration of IL-6 typically below 5 pg/ml, a substantial impediment is present, preventing the systemic activities of IL-6 [27]. However, in the cases of infection, inflammation, inflammatory diseases and cancer, IL-6 plasma concentrations can surge over 1000-fold, and in sepsis, it can increase by six orders of magnitude [28,29].

Numerous studies have suggested the influence of IL-6 on Tac pharmacokinetics, with this effect potentially varying based on the IL-6 -174G > C genotype. It is hypothesized that a moderate to high level of inflammation is necessary for IL-6 to impact basal CYP3A-dependent Tac metabolism, contributing to variability in C₀ and Tac C₀/D [30]. In our study, individuals with the -174GG genotype required higher TDD throughout the 5-year observation period compared to those with the C allele. Subsequently, higher TDD in this group was accompanied by lower Tac C₀/D values. While one might assume that patients carrying the -174GG genotype needed higher doses due to lower C₀ [31], our study did not observe this pattern. Kotowski et al. [32] demonstrated that -174GG genotype carriers received the highest mean dose of the immunosuppressant cyclosporine A compared to the -174GC and -174CC genotypes. Despite the expectation that carriers of the -174GG genotype might need lower TDD due to decreased drug clearance from assumed elevated IL-6 production, it is crucial to emphasize that inter-individual variability in Tac exposure might also be influenced by the CYP3A5 gene polymorphism, as well as other cytokine gene polymorphisms [31,33]. The CYP3A5 polymorphism undoubtedly plays an important role in Tac exposure variability, which was extensively investigated in our previous published studies [5,23]. The identification of genotypes assists in anticipating a patient's phenotype and, consequently, facilitates the recommendation of a suitable drug and/or initial dosage regimen when initiating treatment. Nevertheless, evidence suggests that the ability to predict metabolic capacity and manage treatments based on pharmacogenetic information might face challenges or limitations due to additional factors. These factors encompass concurrent medications that affect drug-metabolizing enzymes and transporters (DMETs), resulting in phenoconversion, as well as any other coexisting comorbidities that may have emerged over an individual's lifetime [34,35]. Therefore, the present study shifts its focus to an emerging parameter—the metabolic phenotype (C₀/D determined early after transplantation), which proves to account for a greater extent of variability in Tac exposure compared to the CYP3A5 polymorphism. To our knowledge, this is one of the first studies to investigate the independent, as well as the combined impact of the metabolic phenotype and interleukin gene polymorphisms on Tac C₀/D in the long-term period after kidney transplantation.

The classical view of IL-10 is that of an immunosuppressive and anti-inflammatory cytokine [36], due to its potent ability to downregulate the production of cytokines and chemokines by antigen-presenting cells, as well as the expression of MHCII molecules, costimulatory and adhesion molecules. However, IL-10 also exhibits certain immunostimulatory effects, stimulating thymocytes, mast cells, and notably, the survival, proliferation and antibody production by B cells [36,37]. While protective during acute inflammation, prolonged IL-10 overstimulation may induce and accelerate tissue fibrosis [38]. Despite the prevailing view of IL-10 as a beneficial agent, it can clearly exert opposing and context-specific effects [39]. The promoter region of the IL-10 gene is highly variable, with -1082G > A (rs1800896), -819C > T (rs1800871), and -592C > A (rs1800872) influencing IL-10 levels [21]. While all three IL-10 gene polymorphisms have been extensively studied for their potential influence on Tac pharmacokinetics in kidney, liver and lung transplant recipients, the majority of research has focused on the early post-transplantation period [15,40–43]. However, the results are inconsistent, and uncertainties persist regarding which of the three polymorphisms exerts a significant influence when an association is observed. In our current study, we found that the IL-10 -819C > T polymorphism did not significantly affect Tac pharmacokinetics, contradicting the findings of Chen et al., and Cheng et al. [15,44]. Concerning the IL-10 -1082G > A polymorphism, we observed an association with TTD and Tac C₀/D, predominantly in later post-transplantation periods. Patients carrying at least one A allele had significantly lower Tac C₀/D than patients with the GG genotype in the 36^th and 60^th month post-transplantation. Khaleel and associates reproduced the same finding in male recipients, but during the first month post-transplantation [43]. While some authors did not demonstrate the effect of IL-10 -1082G > A polymorphism on Tac exposure in earlier studies [15,41], others postulated that the G allele at position -1082 is the primary genetic determinant influencing the regulation of constitutive IL-10 mRNA [43], as reflected in haplotype expression potential. In the early post-transplantation period, several factors, including inflammation, healing processes and changes in the patient's physiology, could influence drug metabolism. However, it is crucial to shift the focus toward the late post-transplantation period when the patient is likely to have achieved a more stable condition. This shift enables a consideration of long-term effects and the potential influence of genetic variations on Tac levels.

The conducted MC simulation unequivocally confirmed that fast metabolizers carrying IL-6 GG + IL-10 A genotype combination tended to have the lowest predicted Tac C₀/D values, while slow metabolizers with the IL-6 C + IL-10 GG genotype combination exhibited the highest predicted Tac C₀/D values. Furthermore, the magnitude of the combined effect of metabolic phenotype and interleukin gene polymorphisms on Tac C₀/D values did not display dependence on post-transplantation period.

Moreover, it is important to consider chronic inflammation in the context of interleukin gene polymorphisms, given its common association with chronic allograft injury [45]. Kidney transplant recipients are considered to have chronic kidney disease, even immediately after surgery [46]. The pro-inflammatory mediators, such as cytokines, chemokines, leukotrienes, or prostaglandins play a crucial role in regulating the production of acute-phase proteins. The systemic changes induced by this process are commonly referred to as ‘acute-phase responses,’ and they manifest in both acute and chronic inflammatory conditions. During an acute episode within a chronic inflammatory state, phenoconversion may result in a transition toward a less efficient metabolizing phenotype. Conversely, in cases of chronic inflammation, the resolution of inflammation might revert the phenotype back to its physiological status. Consequently, inflammation has the potential to influence not only inter-individual, but also intra-individual variability in drug responses [45,47]. However, the present study did not demonstrate a statistical difference in Tac IPV concerning IL-6 and IL-10 genotypes. Addressing the role of inflammation in the variability of drug exposure in patients with chronic diseases is challenging due to the presence of numerous confounding factors. Moreover, one could hypothesize that these effects may be more pronounced in patients with high-expression genotypes of these interleukins.

Some limitations of the study need to be mentioned. The first drawback of the present research was a small number of patients and therefore a small number of patients per genotype group, but still this is a single-center study. However, the potential constraints arising from the limited patient cohort were partially mitigated through the implementation of MC simulation. In addition, it would be of great interest to examine inflammatory status among study participants. However, this study is retrospective and therefore it was not possible to explore this for the entire population. The study would be strengthened by including the IL-6 and IL-10 genotypes of the graft donor. Nonetheless, approximately 30% of patients received a kidney from deceased donors.

5. Conclusion

In conclusion, individuals with the IL-6 -174GG genotype exhibit higher Tac dose requirements (expressed as Tac C₀/D) compared to the carriers of the IL-6 -174C allele over the course of five years post-kidney transplantation. Regarding IL-10 gene polymorphism, patients with the IL-10 -1082GG genotype demonstrated lower Tac dose requirements than carriers of the IL-10 -1082A allele in the later post-transplantation period (>36 months). Furthermore, regression analysis and MC numerical analysis underscore the importance of considering IL-6 -174G > C and IL-10 -1082G > A gene polymorphisms, alongside Tac metabolic phenotype and post-transplantation period, when tailoring Tac dosage regimen. These factors play a significant role in influencing Tac pharmacokinetics. In addition, tested gene polymorphism did not affect Tac IPV in the first post-transplantation year. The findings of this study provide valuable insights for the monitoring of patients undergoing long-term Tac treatment, facilitating the individualized adjustments of daily doses.

Funding Statement

This work was supported by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (grant No: 451-03-65/2024-03/200113).

Supplemental material

Supplemental data for this article can be accessed at https://doi.org/10.1080/14622416.2024.2379227

Author contributions

Conceptualization: N Stefanović and K Danković; methodology: N Stefanović, S Vujić, T Jevtović-Stoimenov; software: N Stefanović and I Pavlović; investigation: N Stefanović, K Danković and B Mitić; writing-review and editing: N Stefanović, K Danković and S Vujić; supervision: N Stefanović, R Veliĉković-Radovanović, B Mitić and T Cvetković; funding acquisition: T Cvetković and T Jevtović-Stoimenov. All authors have read and agreed to the published version of the manuscript.

Financial disclosure

This work was supported by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (grant No: 451-03-65/2024-03/200113). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained institutional review board approval from the Ethics Committee of the Faculty of Medicine, University of Nis (No 12-6972-2/5 from 07 February 2018. and No 12-3588-2/2 from 07 April 2022.) for the research described. In addition, the written informed consent was obtained from all patients for the inclusion of their medical and treatment history within this work.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.