Long-Term Influence of CYP3A5,CYP3A4,ABCB1,and NR1I2 Polymorphisms on Tacrolimus Concentration in Chinese Renal Transplant Recipients

Abstract

Background:

The highly pharmacokinetic variability of tacrolimus makes it difficult to adjust the dose. In the current study, we investigated the influence of gene polymorphisms and other clinical factors on long-term tacrolimus dosing in Chinese renal transplant recipients.

Methods:

A total of 276 renal transplant recipients were enrolled. The tacrolimus trough concentration and other clinical variables were recorded for 5 years following transplantation. Eight single nucleotide polymorphisms in four genes (CYP3A5, CYP3A4, ABCB1, and NR1I2) were genotyped using polymerase chain reaction-restriction fragment length polymorphism analysis and sequencing. The dose-adjusted tacrolimus trough concentrations were calculated and compared among patients according to allelic status.

Results:

The alleles CYP3A5*3 and CYP3A4*18B were significantly associated with dose-adjusted tacrolimus blood trough concentrations and had a strong time-genotype interaction with tacrolimus pharmacokinetics. NR1I2 g.7635A>G had a significant interaction with time, but the dose-adjusted tacrolimus concentration did not significantly differ over 5 years posttransplantation, except for the GG genotype of NR1I2 g.7635A>G. Sex differences had an important influence on tacrolimus concentration during the later post-transplantation period.

Conclusions:

The interindividual variability of tacrolimus concentration appears to be due in part to the effects of these identified genetic variants and clinical characteristics. Thus, genotyping of the CYP3A4 and CYP3A5 genes should be considered with respect to determining tacrolimus dose regimens during the post-transplantation period.

Introduction

Tacrolimus is a first-line immunosuppressive agent for prophylaxis of organ transplant rejection. The dose regimen of tacrolimus is adjusted by routine therapeutic drug monitoring. However, tacrolimus has a narrow therapeutic index (the whole blood trough concentration must be 5-10 ng/mL) and shows high pharmacokinetic variability (t_1/2 = 3.5-40.5 h; oral bioavailability, 5-67%), which results in highly variable primary tacrolimus blood levels (Staatz and Tett, 2004; Barry and Levine, 2010). Acute rejection, drug-related adverse effects, and even transplant failure may occur during the dose adjustment of tacrolimus, which results in a decreased survival rate and deterioration of the patient's quality of life. It is essential to adjust the dose of tacrolimus frequently and significantly to achieve a good maintenance dose.

Recent studies have clearly demonstrated that 20-90% of interindividual variability of drug effects can be explained by genetic factors (Kuehl et al., 2001; Xin et al., 2014; Mooij et al., 2016; Relling and Evans, 2015; Shu et al., 2015). Tacrolimus is mainly metabolized by P450s (CYP3A enzymes), such as CYP3A4 and CYP3A5. CYP3A5 and CYP3A4 are located on chromosome 7q22.1 along with other CYP3A family members. The most common nonfunctional variant of CYP3A5 is designated CYP3A5*3 (6986A>G, rs776746). A change from A to G at this position creates a cryptic splice site in intron 3, resulting in altered mRNA splicing and a nonfunctional protein. Individuals with the CYP3A5*3/*3 genotype are considered to be CYP3A5 nonexpressors. The CYP3A5*3 polymorphism is strongly correlated with the pharmacokinetics and pharmacodynamics of tacrolimus (Barry and Levine, 2010; Nair et al., 2015; Wang et al., 2015). The CYP3A4*18B is located in the tenth intron of CYP3A4, which effects cyclosporine and tacrolimus pharmacokinetics (Hu et al., 2007; Qiu et al., 2008; Li et al., 2014).

P-Glycoprotein, encoded by ATP-binding cassette subfamily B member 1 (ABCB1), regulates the absorption of tacrolimus. A synonymous single nucleotide polymorphism (SNP) in exon 26 of ABCB1 (ABCB1 3435C>T) has been recognized to play a role in drug transport (Wang et al., 2005; Hawwa et al., 2009). Other polymorphisms of ABCB1, such as in exon 21 (ABCB1 2677G>T) and exon 26 (ABCB1 3435C>T), have been reported to be associated with cyclosporine pharmacokinetics (Ferraresso et al., 2013).

Pregnane X receptor (PXR, also known as steroid and xenobiotic receptor), belongs to a large family of nuclear receptors. PXR is encoded by NR1I2, which is responsible for the upstream regulation of drug-metabolizing enzymes and transporters, including the CYP3A family and multidrug resistance protein 1 [MDR1] (Zhang et al., 2001). As a transcription factor, PXR can affect the metabolism of tacrolimus through regulating the expression of these genes. In the current retrospective study, we assessed the effect of genetic factors (CYP3A4*18B, CYP3A5*3, ABCB1 1236C>T, ABCB1 2677G>T/A, ABCB1 3435C>T, NR1I2 6-bp indel, NR1I2 g.7635A>G, and NR1I2 − 24381C>A) and time-genotype interaction factors on tacrolimus dose-adjusted concentrations in renal transplant recipients over 5 years posttransplantation.

Materials and Methods

Study design

Renal transplant patients (496) at Wuhan General Hospital of Guangzhou Command (China) were recorded as potential participants between January 1997 and December 2010. Of them, 220 were excluded because of lost to follow-up, preoperative abnormal liver function, or combined usage of nephrotoxic drugs (e.g., amphotericin B, aminoglycosides, etc.) or inducers/inhibitors of CYP3A (e.g., rifampin, macrolides, etc.). The remaining 276 patients were all maintained on a triple immunosuppressive regimen consisting of tacrolimus, mycophenolate mofetil, and steroids.

The ethical aspects of this study were approved by the Wuhan General Hospital of Guangzhou Command Ethics Committee. All patients recruited were asked to read and sign an informed consent form. The following variables were recorded: sex; age; dialysis duration before transplantation; number of months posttransplantation; history of smoking, primary renal diseases, hepatitis B or C, hypertension, and diabetes; source of graft; type of immunosuppressive therapy; liver and renal function; fasting blood glucose, blood lipids, and blood pressure and routine; and drug combination.

Tacrolimus blood concentration

Tacrolimus blood concentration levels were determined using an automated chemiluminescent immunoassay with the ARCHITECT Tacrolimus Assay (Abbott Laboratories, IL). The blood trough concentrations (C₀) were recorded by taking blood samples 7 days, 15 days, 1, 3, 6, and 12 months, 2, 3, and 5 years after transplantation. Dose-adjusted C₀ (C₀/D) were calculated by dividing C₀ (ng/mL) by the corresponding 24-h dose.

Genotyping

Genomic DNA was extracted from whole blood using a commercially available DNA isolation kit (WIZARD Genomic DNA Purification Kit; Promega Corp, Madison, WI). Polymerase chain reaction (PCR) followed by restriction fragment length polymorphism analysis were performed to genotype CYP3A4*18B, CYP3A5*3, ABCB1 1236C>T, ABCB1 2677G>T/A, and ABCB1 3435C>T. The PXR 6-bp deletion (NR1I2 6-bp indel, rs3842689) was performed by allelic special touchdown PCR using normal primers (WP) and mutation detection primers (MP). The genotypes of NR1I2 6-bp indel were wild type (WW, products amplified only by WP), homozygous (MM, products amplified only by MP), and heterozygous (WM, products amplified by WP and MP). NR1I2 g.7635A>G and −24381C>A were detected by direct sequencing (Wang et al., 2007).

The specific primers were synthesized by Sangon (Sangon Biotech, Shanghai, China) and details are shown in Supplementary Table S1 (Supplementary Data are available online at www.liebertpub.com/gtmb). The final volume of the reaction was 25 μL, consisting of 2 μL of genomic DNA, 0.4 μM concentrations of each primer pair, and 12.5 μL of 2 × PCR Master Mix (Promega). The thermocycling profiles are shown in Supplementary Table S2. The PCR products of CYP3A4*18B, CYP3A5*3, ABCB1 1236C>T, ABCB1 2677G>T/A, ABCB1 3435C>T, and NR1I2 6-bp indel were digested using restriction enzymes at 37°C before separation on a 3% agarose gel. The digested fragments were separated and visualized on gel imaging system.

Statistical analysis

A Pearson's chi-square test was performed to assess the deviation of gene frequencies from Hardy-Weinberg equilibrium. Linkage disequilibrium (LD) maps were constructed by SHEsis online software (Shi and He, 2005). Comparisons between different genotype groups at different time points were carried out by nonparametric tests. The Mann-Whitney U-test was used for comparisons between two groups, and the Kruskal-Wallis H-test was used for comparisons among several groups. To reduce distribution skewness, tacrolimus C₀/D ratios were log transformed. Multivariable linear regression was used to assess the relative influence of genotypes and other clinical characteristics on dose-adjusted tacrolimus exposure. We used a generalized estimation equation (GEE) model to analyze longitudinal relationships between genotypes and tacrolimus C₀/D (Zeger and Liang, 1986; Shi and He, 2005). All continuous data are shown as mean values ± standard deviations. Data were analyzed using SPSS 19.0 for Windows (SPSS, Inc., Chicago, IL). A p < 0.05 was considered statistically significant.

Results

Patient characteristics and genotypes

A total of 276 Han Chinese patients (male 184, female 92) were enrolled in the current study, and their demographic and clinical data are described in Table 1. The average age of these patients was 40.46 ± 11.31 years. The mean C₀/D (ng/mL/mg) for these patients at 7 days, 15 days, 1, 3, 6, and 12 months, 2, 3, and 5 years were 1.63 ± 1.33, 1.73 ± 1.32, 1.87 ± 1.07, 2.05 ± 1.2, 2.17 ± 1.43, 2.13 ± 1.38, 2.4 ± 1.7, 2.22 ± 1.45, and 2.13 ± 1.3, respectively.

Table 1.

Demographics and Clinical Characteristics of Renal Transplant Recipients

		Day 7		Day 15		Month 1		Month 3		Month 6		Year 1		Year 2		Year 3		Year 5
Characteristics		Mean ± SD	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	n	Mean ± SD	n
Gender, n (%):
Male	184 (66.7)
Female	92 (33.3)
Age (years), mean ± SD	40.46 ± 11.31
Tacrolimus blood concentration (C0, ng/mL)		6.13 ± 3.49	213	6.55 ± 3.13	176	6.58 ± 2.78	213	6.54 ± 2.71	218	6.09 ± 2.66	233	5.65 ± 2.20	211	5.8 ± 3.04	174	5.24 ± 2.16	126	4.92 ± 1.77	72
Daily dose (D, mg)		4.34 ± 1.73	210	4.49 ± 1.8	172	4.15 ± 1.76	205	5.13 ± 20.13	218	3.39 ± 1.46	225	3.21 ± 1.43	206	3.03 ± 1.44	171	3.05 ± 1.47	123	2.84 ± 1.43	70
Dose-adjusted trough blood concentrations (C0, ng/mL/mg)		1.63 ± 1.33	206	1.73 ± 1.32	169	1.87 ± 1.07	201	2.05 ± 1.2	213	2.17 ± 1.43	223	2.13 ± 1.38	206	2.4 ± 1.7	170	2.22 ± 1.45	122	2.13 ± 1.3	69
Total bilirubin (TB, g/L)		10.82 ± 5.27	168	10.89 ± 8.81	187	11.67 ± 11.95	190	12.49 ± 12.86	201	12.87 ± 9.97	211	13.5 ± 9.28	194	13.77 ± 10.67	160	13.21 ± 11.04	114	14.67 ± 12.07	64
Direct bilirubin (DB, μM)		2.54 ± 1.60	168	2.79 ± 5.51	187	3.01 ± 6.97	188	2.8 ± 5.11	200	3.23 ± 5.65	209	3.15 ± 4.61	195	3.36 ± 5.45	160	3.31 ± 6.41	114	3.99 ± 6.26	64
Alanine aminotransferase (ALT, U/L)		49.88 ± 330.49	170	29.09 ± 28.91	187	23.21 ± 17.55	190	20.6 ± 13.31	201	23.1 ± 20.09	212	20.65 ± 11.85	196	21.98 ± 12.56	160	23.81 ± 22.88	115	21.68 ± 10.55	63
Aspartate aminotransferase (AST, U/L)		40.5 ± 275.93	166	18.75 ± 9.51	184	19.09 ± 10.18	188	18.13 ± 7.44	198	19.95 ± 13.01	210	18.82 ± 9.93	190	20.04 ± 10.02	155	20.38 ± 14.84	115	19.05 ± 10.35	62
Total protein (TP, g/L)		66.84 ± 10.56	164	69.24 ± 8.54	184	71.36 ± 7.64	187	71.62 ± 8.75	192	72.63 ± 6.94	207	73.11 ± 5.87	189	73.56 ± 5.18	154	71.81 ± 8.13	113	73.47 ± 7.21	59
Blood urea nitrogen (BUN, mM)		11.01 ± 9.16	186	10.52 ± 6.65	211	8.61 ± 4.68	197	7.63 ± 4.71	208	7.4 ± 3.45	220	12.01 ± 55.66	201	7.72 ± 4.12	165	7.65 ± 3.68	120	7.9 ± 4.18	64
Creatinine (Cr, μM)		168.12 ± 134.78	186	155.64 ± 108.24	209	132.16 ± 83.98	197	122.68 ± 54.76	209	118.53 ± 61.55	221	133.22 ± 114.12	202	128.7 ± 77.24	167	140.67 ± 89	120	145.01 ± 142.87	66
Uric acid (UA, μM)		384.86 ± 125.64	182	386.62 ± 110.05	209	381.64 ± 109.51	196	386.41 ± 93.65	204	386.05 ± 100.39	217	383.41 ± 107.93	198	389.02 ± 91.75	164	389.74 ± 97.04	118	398.26 ± 95.17	64
Blood glucose (GLU, μM)		5.56 ± 0.97	154	5.58 ± 0.98	166	6.33 ± 6.2	166	5.84 ± 0.94	183	6.43 ± 8.01	198	5.74 ± 0.83	176	8.3 ± 30.51	145	5.74 ± 0.76	102	5.84 ± 0.91	54
Red blood cell count (RBC,10E12/L)		3.62 ± 0.96	171	3.65 ± 0.84	204	4.05 ± 1.96	189	4.12 ± 0.75	198	4.34 ± 0.78	215	4.33 ± 0.79	191	4.43 ± 0.78	158	4.4 ± 0.73	108	4.4 ± 0.76	66
White blood cell count (WBC, 10E9/L)		7.59 ± 3.00	172	7.18 ± 3.24	207	6.87 ± 2.38	189	7.37 ± 10.79	199	6.43 ± 1.89	214	6.36 ± 2.02	194	6.30 ± 2.02	158	6.35 ± 2.32	109	6.05 ± 1.69	66
Hemoglobin (Hb, g/L)		107.46 ± 24.83	169	108.56 ± 23.73	201	117.45 ± 21.69	186	123.52 ± 21.29	194	127.48 ± 22.6	212	128.53 ± 23.71	192	130.69 ± 22.95	152	128.84 ± 21.4	104	127.59 ± 24.13	62
Platelet count (PLT, 10E9/L)		189.91 ± 76.63	169	184.05 ± 71.93	201	177.32 ± 61.95	189	179.2 ± 56.94	198	178.43 ± 57.06	209	178.81 ± 59.59	191	175.11 ± 56.36	155	176.53 ± 53	105	177.57 ± 56.44	63

SD, standard deviation; n = Number of patients evaluated at each time point.

The genotyping results of patients are shown in Table 2. The variant allele frequencies of CYP3A4*18B, CYP3A5*3, ABCB1 1236C>T, ABCB1 2677G>T/A, ABCB1 3435C>T, NR1I2 6-bp indel, NR1I2 g.7635A>G, and NR1I2 − 24381C>A were 28.99%, 69.02%, 43.48%, 48.52%, 36.23%, 74.46%, 60.49%, and 77.43%, respectively. Genotypic frequencies of 6 SNPs (CYP3A4*18B, CYP3A5*3, ABCB1 2677G>T/A, ABCB1 3435C>T, NR1I2 g.7635A>G, and NR1I2 − 24381C>A) did not show statistically significant differences (p > 0.05) by the Hardy-Weinberg principle. The observed frequencies of ABCB1 1236C>T and NR1I2 6-bp indel were significantly different from Hardy-Weinberg equilibrium (p < 0.05, Table 2).

Table 2.

Genotype Results for CYP3A4 ^* 18B, CYP3A5 ^* 3, ABCB1 1236C>T, ABCB1 2677G>T/A, ABCB1 3435C>T, NR1I2 6-bp indel, NR1I2 g.7635A>G, and NR1I2 − 24381C>A

Gene	SNP	Alternative nomenclature	Allelic status	Number of patients	Genotype frequencies, n (%)	Allelic status	Allelic frequencies	p^a
CYP3A4	rs2242480	CYP3A4^18B*	^1/^1	140	140 (8.7)
			^1/^18B	112	112 (40.6)	^*1	71.01%
			^18B/^18B	24	24 (50.7)	^*18	28.99%	0.8124
			Missing values	0
CYP3A5	rs776746	CYP3A5^3*	^1/^1	30	30 (10.9)
			^1/^3	111	111 (40.2)	^*1	30.98%
			^3/^3	135	135 (48.9)	^*3	69.02%	0.3226
			Missing values	0
ABCB1	rs1128503	ABCB1 1236C>T	CC	102	102 (37.0)
			CT	108	108 (39.1)	C	56.52%
			TT	66	66 (23.9)	T	43.48%	0.0007
			Missing values	0
ABCB1	rs2032582	ABCB1 2677G>T/A	GG	72	72 (26.7)
			GT	109	109 (40.4)	G	51.48%
			GA	25	25 (9.3)	T	38.52%
			TT	39	39 (14.4)	A	10.00%	0.8073
			TA	21	21 (7.8)
			AA	4	4 (1.5)
			Missing values	6
ABCB1	rs1045642	ABCB1 3435C>T	CC	117	117 (42.4)
			CT	118	118 (42.8)	C	63.77%
			TT	41	41 (14.9)	T	36.23%	0.2141
			Missing values	0
NR1I2	rs3842689	NR1I2 6-bp indel	WW	41	41 (14.9)
			WM	59	59 (21.4)	W	25.54%
			MM	176	176 (63.8)	M	74.46%	0.0000
			Missing values	0
NR1I2	rs6785049	NR1I2 g.7635A>G	AA	43	43 (16.1)
			AG	125	125 (45.8)	A	39.51%
			GG	99	99 (37.1)	G	60.49%	0.7366
			Missing values	9
NR1I2	rs1523127	NR1I2 − 24381C>A	CC	14	14 (5.2)
			CA	93	93 (34.7)	C	22.57%
			AA	161	161 (60.1)	A	77.43%	0.9047
			Missing values	8

p for Hardy-Weinberg test

SNP, single nucleotide polymorphism; WW, wild-type; WM, heterozygous; MM, homozygous.

Genetic factors associated with dose-adjusted tacrolimus concentration

Eight SNPs were examined to identify their association with tacrolimus C₀/D. The mean C₀/D of different SNPs at different time points are shown in Table 3 and Supplementary Figure S1. The genotypes of CYP3A4*18B and CYP3A5*3 were significantly associated with the tacrolimus C₀/D using the Kruskal-Wallis H-test (p < 0.001) over a period of 5 years posttransplantation, except for year 5. However, a significantly higher C₀/D was observed for CYP3A4*1/*1 and CYP3A5*3/*3 genotype carriers (Table 3 and Fig. 1a, b). ABCB1 2677G>T/A and NR1I2 g.7635A>G were found to be significantly associated with tacrolimus C₀/D (p < 0.05) at year 5, whereas NR1I2 6-bp indel was associated with tacrolimus C₀/D at year 1 (p = 0.023); NR1I2 g.7635A>G was found to be marginally associated with tacrolimus C₀/D.

FIG. 1.

Time interaction between dose-adjusted tacrolimus concentration and genotypes and sex. Data represent means ± standard deviations. C₀/D: dose-adjusted tacrolimus concentration. (a) Genotypes of CYP3A4*18B; (b) genotypes of CYP3A5*3; (c) genotypes of NR1I2 g.7635A>G; (d) AA, the AA genotype of ABCB1 2677G>T/A; WM, the heterozygote of NR1I2 6-bp indel; CC, the CC genotype of ABCB1 1236C>T; (e) male and female.

Table 3.

Mean Dose-Adjusted Tacrolimus Concentration of Different Single Nucleotide Polymorphisms at Different Time Points

		Day 7			Day 15			Month 1			Month 3			Month 6
Gene	Genotypes	Mean ± SD	n	p^a	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p
CYP3A4	CYP3A4^18B*
	^1/^1	2.13 ± 1.59	102		2.23 ± 1.62	83		2.29 ± 1.13	98		2.43 ± 1.23	107		2.6 ± 1.64	113
	^1/^18B	1.14 ± 0.73	86		1.27 ± 0.71	71		1.46 ± 0.83	86		1.72 ± 1.08	88		1.79 ± 1.05	92
	^18B/^18B	1.11 ± 0.84	18	0.000	1.11 ± 0.44	15	0.000	1.49 ± 0.9	17	0.000	1.42 ± 0.79	18	0.000	1.36 ± 0.71	18	0.000
CYP3A5	CYP3A5^3*
	^1/^1	0.88 ± 0.63	23		1.05 ± 0.48	19		1.21 ± 0.64	23		1.33 ± 0.68	23		1.41 ± 0.79	23
	^1/^3	1.11 ± 0.65	85		1.28 ± 0.79	73		1.38 ± 0.66	79		1.56 ± 0.82	85		1.64 ± 0.98	88
	^3/^3	2.26 ± 1.59	98	0.000	2.32 ± 1.6	77	0.000	2.41 ± 1.15	99	0.000	2.61 ± 1.3	105	0.000	2.74 ± 1.6	112	0.000
ABCB1	ABCB1 1236C>T
	CC	1.89 ± 1.8	78		1.84 ± 1.62	66		1.89 ± 1.2	73		2.22 ± 1.4	81		2.36 ± 1.82	80
	CT	1.57 ± 0.97	82		1.71 ± 1.22	66		1.9 ± 1.04	83		2.05 ± 1.12	85		2.12 ± 1.26	90
	TT	1.27 ± 0.74	46	0.166	1.56 ± 0.82	37	0.943	1.76 ± 0.91	45	0.799	1.76 ± 0.9	47	0.26	1.97 ± 0.96	53	0.871
ABCB1	ABCB1 2677G>T/A
	GG	1.82 ± 1.64	57		1.86 ± 1.26	46		1.96 ± 1.11	55		2.02 ± 1.07	56		2.19 ± 1.54	60
	GT	1.45 ± 0.84	84		1.64 ± 1.1	69		1.81 ± 1.02	83		2.1 ± 1.29	91		2.02 ± 1.08	90
	GA	1.72 ± 1.96	19		1.73 ± 1.55	12		1.38 ± 0.63	15		2.23 ± 1.21	16		2.26 ± 1.33	21
	TT	1.74 ± 1.06	22		1.51 ± 0.88	22		1.93 ± 1.18	27		2.02 ± 1.26	29		2.56 ± 2	30
	TA	1.52 ± 1.56	18		2.3 ± 2.84	12		1.98 ± 1.16	14		1.59 ± 1.13	13		1.69 ± 0.86	16
	AA	1.17 ± 0.39	2	0.744	1.34 ± 0.37	4	0.827	1.61 ± 0.44	2	0.622	1.51 ± 0.75	3	0.401	2.18 ± 0.94	2	0.671
ABCB1	ABCB1 3435C>T
	CC	1.65 ± 1.4	92		1.78 ± 1.27	74		1.82 ± 1.02	87		2.03 ± 1.1	90		2.21 ± 1.49	93
	CT	1.6 ± 1.35	89		1.7 ± 1.5	69		1.87 ± 1.08	86		1.95 ± 1.08	91		1.99 ± 1.15	100
	TT	1.63 ± 0.99	25	0.825	1.64 ± 0.94	26	0.876	2.01 ± 1.22	28	0.884	2.41 ± 1.69	32	0.533	2.64 ± 1.95	30	0.249
NR1I2	NR1I2 6-bp indel
	WW	1.71 ± 1.04	134		1.56 ± 0.91	100		1.91 ± 1.07	128		1.79 ± 1.07	136		2.15 ± 1.52	143
	WM	1.49 ± 1.28	43		1.77 ± 1.69	46		1.57 ± 0.82	42		2.06 ± 1.39	45		1.9 ± 0.95	48
	MM	1.65 ± 1.4	29	0.244	1.75 ± 1.22	23	0.923	1.95 ± 1.13	31	0.229	2.11 ± 1.17	32	0.282	2.26 ± 1.54	32	0.488
NR1I2	NR1I2 g.7635A>G
	AA	1.42 ± 0.91	30		1.45 ± 0.65	24		1.83 ± 0.91	27		1.76 ± 1.01	27		1.85 ± 1.35	33
	AG	1.65 ± 1.51	92		1.63 ± 1.01	84		1.72 ± 0.96	97		1.94 ± 1.11	102		2.03 ± 1.08	106
	GG	1.71 ± 1.28	76	0.52	2.05 ± 1.86	55	0.71	2.13 ± 1.26	69	0.227	2.3 ± 1.36	75	0.071	2.54 ± 1.84	76	0.097
NR1I2	NR1I2 − 24381C>A
	CC	1.46 ± 1.37	10		1.59 ± 1.46	7		2.07 ± 1.31	10		2.07 ± 0.8	11		2.73 ± 2.24	10
	CA	1.72 ± 1.41	70		1.77 ± 1.07	65		1.79 ± 0.95	72		2.19 ± 1.42	80		2.04 ± 0.98	77
	AA	1.62 ± 1.3	121	0.546	1.74 ± 1.5	92	0.434	1.92 ± 1.15	111	0.917	1.99 ± 1.08	115	0.762	2.2 ± 1.6	129	0.87

		Year 1			Year 2			Year 3			Year 5
Gene	Genotypes	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p
CYP3A4	CYP3A4^18B*
	^1/^1	2.61 ± 1.61	106		2.99 ± 2.02	89		2.75 ± 1.63	63		2.38 ± 1.42	36
	^1/^18B	1.63 ± 0.83	83		1.82 ± 0.89	68		1.72 ± 0.97	48		1.98 ± 1.18	27
	^18B/^18B	1.6 ± 0.77	17	0.000	1.42 ± 0.85	13	0.000	1.35 ± 0.87	11	0.000	1.35 ± 0.49	6	0.130
CYP3A5	CYP3A5^3*
	^1/^1	1.47 ± 0.71	23		1.47 ± 0.79	20		1.5 ± 1.11	16		1.58 ± 0.58	8
	^1/^3	1.52 ± 0.84	78		1.73 ± 0.94	63		1.61 ± 0.9	46		1.89 ± 0.93	27
	^3/^3	2.73 ± 1.54	105	0.000	3.1 ± 1.96	87	0.000	2.88 ± 1.59	60	0.000	2.46 ± 1.58	34	0.237
ABCB1	ABCB1 1236C>T
	CC	2.39 ± 1.81	72		2.56 ± 1.91	66		2.2 ± 1.56	56		2.16 ± 1.52	30
	CT	2.05 ± 1.13	84		2.43 ± 1.76	64		2.25 ± 1.55	40		2.16 ± 1.28	28
	TT	1.9 ± 0.92	50	0.705	2.08 ± 1.1	40	0.756	2.21 ± 1.05	26	0.68	1.98 ± 0.55	11	0.644
ABCB1	ABCB1 2677G>T/A
	GG	2.3 ± 1.52	54		2.77 ± 2.15	44		2.69 ± 1.03	33		2.18 ± 1.15	20
	GT	2.09 ± 1.12	77		2.37 ± 1.35	63		2.27 ± 1.79	48		2.23 ± 1.15	31
	GA	1.86 ± 1.05	20		2.4 ± 1.09	18		2.11 ± 0.88	14		2.97 ± 2.86	3
	TT	1.93 ± 1.79	30		2.32 ± 2.19	23		1.82 ± 1.48	13		1.54 ± 0.27	6
	TA	2.2 ± 1.06	18		1.97 ± 1.55	15		1.39 ± 1.55	10		1.21 ± 0.5	8
	AA	1.42 ± 0.45	3	0.228	1.4 ± 0.19	3	0.219	0.9 ± 0	1	0.158		0	0.037
ABCB1	ABCB1 3435C>T
	CC	2.14 ± 1.44	86		2.51 ± 1.84	72		2.46 ± 1.55	59		2.26 ± 1.23	31
	CT	2.17 ± 1.15	89		2.3 ± 1.39	73		2.07 ± 1.43	49		1.97 ± 1.24	31
	TT	2.01 ± 1.78	31	0.177	2.38 ± 2.1	25	0.591	1.74 ± 0.85	14	0.196	2.26 ± 1.93	7	0.228
NR1I2	NR1I2 6-bp indel
	MM	2.6 ± 1.76	133		2.58 ± 1.62	114		2.34 ± 1.69	84		2.65 ± 1.68	50
	WM	1.65 ± 0.85	45		2.01 ± 1.16	31		1.87 ± 0.89	21		1.6 ± 0.67	9
	WW	2.2 ± 1.39	28	0.023	2.47 ± 1.83	25	0.433	2.28 ± 1.51	17	0.804	2.12 ± 1.28	10	0.38
NR1I2	NR1I2 g.7635A>G
	AA	2.02 ± 1.56	33		2.28 ± 1.52	27		1.93 ± 1.03	19		1.74 ± 0.6	11
	AG	1.91 ± 0.95	94		2.07 ± 1.06	82		1.99 ± 1.14	59		1.88 ± 1.09	32
	GG	2.47 ± 1.72	72	0.218	2.97 ± 2.37	55	0.373	2.69 ± 1.9	41	0.244	2.73 ± 1.59	24	0.033
NR1I2	NR1I2 − 24381C>A
	CC	3.52 ± 2.46	9		2.16 ± 1.31	10		1.91 ± 0.96	7		1.63 ± 0.41	3
	CA	2.01 ± 1	70		2.42 ± 1.27	52		2.35 ± 1.45	36		2 ± 1.35	22
	AA	2.11 ± 1.44	122	0.119	2.42 ± 1.93	104	0.356	2.16 ± 1.47	76	0.64	2.22 ± 1.33	43	0.532

p for the Kruskal-Wallis H test

n = Number of patients evaluated at each time point.

We also examined the association of the haplotypes of CYP3A4 and CYP3A5 with the tacrolimus C₀ using the Mann-Whitney U-test (Table 4). The tacrolimus C₀/D of CYP3A4*18B noncarriers (CYP3A4*1/*1) were higher than that of CYP3A4*18B carriers (CYP3A4*1/*18B and CYP3A4*18B/*18B), but the result of nonparametric tests showed no significant difference at year 5. The same result was observed for CYP3A5*3 expressors (CYP3A5*3/*3) and nonexpressors (CYP3A5*1/*1 and CYP3A5*1/*3).

Table 4.

The Nonparametric Tests of Haplotypes of CYP3A and Sex

		Day 7			Day 15			Month 1			Month 3			Month 6
Gene	Genotypes	Mean ± SD	n	p^a	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p
CYP3A4	CYP3A4^18B*
	CYP3A4^18B* Noncarrier	2.13 ± 1.59	102		2.23 ± 1.62	83		2.29 ± 1.13	98		2.43 ± 1.23	107		2.6 ± 1.64	113
	CYP3A4^18B* Carrier	1.14 ± 0.75	104	0.0000	1.25 ± 0.68	86	0.0000	1.47 ± 0.84	103	0.0000	1.67 ± 1.04	106	0.0000	1.72 ± 1.01	110	0.0000
CYP3A5	CYP3A5^3*
	CYP3A5 Nonexpressor	2.26 ± 1.59	98		2.32 ± 1.6	77		2.41 ± 1.15	99		2.61 ± 1.3	105		2.74 ± 1.6	112
	CYP3A5 Expressor	1.06 ± 0.65	108	0.0000	1.23 ± 0.74	92	0.0000	1.34 ± 0.66	102	0.0000	1.51 ± 0.79	108	0.0000	1.59 ± 0.94	111	0.0000
Gender
Male		1.65 ± 1.41	137		1.78 ± 1.44	115		1.96 ± 1.15	134		2.17 ± 1.19	145		2.33 ± 1.53	147
Female		1.58 ± 1.16	69	0.4904	1.61 ± 1.03	54	0.7271	1.67 ± 0.86	67	0.1748	1.8 ± 1.21	68	0.0133	1.85 ± 1.17	76	0.0070

		Year 1			Year 2			Year 3			Year 5
Gene	Genotypes	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p	Mean ± SD	n	p
CYP3A4	CYP3A4^18B*
	CYP3A4^18B* Noncarrier	2.61 ± 1.61	106		2.99 ± 2.02	89		2.75 ± 1.63	63		2.38 ± 1.42	36
	CYP3A4^18B* Carrier	1.63 ± 0.82	100	0.0000	1.76 ± 0.89	81	0.0000	1.65 ± 0.96	59	0.0000	1.86 ± 1.11	33	0.1746
CYP3A5	CYP3A5^3*
	CYP3A5 Nonexpressor	2.73 ± 1.54	105		3.1 ± 1.96	87		2.88 ± 1.59	60		2.46 ± 1.58	34
	CYP3A5 Expressor	1.51 ± 0.81	101	0.0000	1.66 ± 0.9	83	0.0000	1.58 ± 0.95	62	0.0000	1.82 ± 0.86	35	0.1131
Gender
Male		2.26 ± 1.43	136		2.54 ± 1.7	116		2.43 ± 1.53	89		2.26 ± 1.28	49
Female		1.88 ± 1.24	70	0.0378	2.11 ± 1.68	54	0.0228	1.65 ± 1.02	33	0.0061	1.81 ± 1.32	20	0.0519

n = Number of patients evaluated at each time point.

p for the Mann-Whitney U test.

Relative influence of haplotypes and other clinical characteristics on dose-adjusted tacrolimus concentration

Multivariable linear regression was used to assess the relative influence of haplotypes (CYP3A4*18B, CYP3A5*3, ABCB1 1236C>T, ABCB1 2677G>T/A, ABCB1 3435C>T, NR1I2 6-bp indel, NR1I2 g.7635A>G, and NR1I2 − 24381C>A) and clinical characteristics on tacrolimus C₀/D. Final models were developed separately for all time points (Table 5). The models explained 26.3-45.8% of the total variation of tacrolimus C₀/D. The haplotypes of CYP3A4*18B (CYP3A4*18B carriers and noncarriers) were important, explaining 8.1-17.6% of the variation, whereas CYP3A5*3 haplotypes can only explain 3-5.4%. Four additional variants were significant at different time points: NR1I2 6-bp indel was significant at 3 months, accounting for 6.5% of the variation; ABCB1 2677G>T/A accounted for 2.6% of the total variation at 7 days; ABCB1 3435C>T explained 4.8% of the variation at 1 month; NR1I2 g.7635A>G was significant at year 5 (15.9% of the variation). ABCB1 1236C>T and NR1I2 − 24381C>A, however, showed no significant influence on tacrolimus C₀/D.

Table 5.

The Contribution of Variables to the Total Variation in Dose-Adjusted Tacrolimus Concentration

Variable	% Cumulative contribution to variation
Day 7
Hb	16.6
ALT	29.0
CYP3A4^18B* genotypes	37.1
CYP3A5^3* genotypes	40.1
WBC	43.2
ABCB1 2677G>T/A	45.8
Day 15
RBC	11.5
CYP3A4^18B* genotypes	23.0
GLU	26.3
Month 1
CYP3A4^18B* genotypes	17.6
AST	25.2
ABCB1 3435C>T	30.0
Hb	33.3
Month 3
CYP3A4^18B* genotypes	12.6
Gender	19.9
NR1I2 6-bp indel	26.4
CYP3A5^3* genotypes	30.7
AST	34.0
Month 6
RBC	13.7
CYP3A4^18B* genotypes	23.8
AST	30.2
Year 1
CYP3A4^18B* genotypes	14.5
AST	20.1
UA	24.4
Cr	30.0
Year 2
CYP3A4^18B* genotypes	10.5
CYP3A5^3* genotypes	15.4
AST	20.9
Hb	25.5
DB	28.6
Year 3
DB	14.6
Gender	25.8
CYP3A4^18B* genotypes	36.9
Year 5
NR1I2 g.7635A>G	15.9
Gender	25.5
AST	34.6

TB, total bilirubin; DB, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TP, total protein; BUN, blood urea nitrogen; Cr, creatinine; GLU, blood glucose; RBC, red blood cell count; WBC, white blood cell count; Hb, hemoglobin; PLT, platelet count.

Sex was a significant factor in the later posttransplantation periods (11.2% at year 2, and 9.6% at year 5). Hemoglobin level was a significant factor explaining 16.6% and 3.3% of the variation at 7 days and 1 month, whereas direct bilirubin accounted for 3.1% and 14.6% of the total variation in tacrolimus C₀/D at years 2 and 3. As indices of liver function, the alanine aminotransferase level was a significant factor at 7 days (12.4% of the variation), whereas aspartate aminotransferase levels accounted for 3.3-9.1% of the total variation from 1 month to 5 years. Renal function parameters creatinine and uric acid explained 5.6% and 4.3% of the tacrolimus concentration variation 1 year posttransplantation.

The longitudinal relationships between genotypes and dose-adjusted tacrolimus concentration

Table 6 shows longitudinal analysis results of the group-time interactions using GEE. A significant interaction was observed with CYP3A5*3 and CYP3A4*18B haplotypes (p < 0.001). During the posttransplantation period, between-group analysis of CYP3A5*3 and CYP3A4*18B haplotypes showed values that were almost comparable (Table 4), except those at year 5. NR1I2 g.7635A>G genotypes also showed group-time interactions (AA, p = 0.006; AG, p = 0.003). For ABCB1 1236C>T and NR1I2 6-bp indel, only one genotype of each had a significant interaction with time (CC of ABCB1 1236C>T, p = 0.014; WM of NR1I2 6-bp indel, p = 0.029); the p-value of the AA genotype of ABCB1 2677G>T/A was 0.016. No statistically significant association over time was found among ABCB1 3435C>T and NR1I2 − 24381C>A genotypes according to GEE analysis.

Table 6.

Influence of Genotypes and Sex on Longitudinal Exposure to Dose-Adjusted Tacrolimus Concentration According to the General Estimation Equation Model

Genotype	p^a
CYP3A5^3*
CYP3A5^*3 Nonexpressor	0.000
CYP3A5^*3 Expressor	0^b
CYP3A4^18B*
CYP3A4^*18B Noncarrier	0.000
CYP3A4^*18B Carrier	0
ABCB1 1236C>T
CC	0.014
CT	0.134
TT	0
ABCB1 2677G>T/A
AA	0.016
TA	0.478
GA	0.981
GG	0.490
GT	0.851
TT	0
ABCB1 3435C>T
CC	0.850
CT	0.453
TT	0
NR1I2 6-bp indel
MM	0.970
WM	0.029
WW	0
NR1I2 g.7635A>G
AA	0.006
AG	0.003
GG	0
NR1I2 − 24381C>A
AA	0.647
CA	0.613
CC	0
Gender
Male	0.004
Female	0

Group-time interaction (p-value) of GEE.

Set to zero.

Discussion

Recently, many studies have reported on the clinically significant association of CYP3A5 SNPs with dose, pharmacokinetics, pharmacodynamics, and pharmacogenomics of tacrolimus (Anglicheau et al., 2005; Relling and Evans, 2015). The results of our present study showed the frequency of CYP3A5*3 to be 69.02%. The tacrolimus C₀/D were significantly different among CYP3A5*3 genotypes. The mean C₀/D of CYP3A5*3 expressors were 35-130% higher than that of nonexpressors at different time points posttransplantation (Table 4). Even though CYP3A5*3 is the only genetic factor to have reached extensive consensus to date in tacrolimus pharmacogenetic/pharmacogenomic studies, interindividual differences in pharmacokinetics cannot be fully explained by the CYP3A5*3 genotype (Qiu et al., 2008; Lamba et al., 2012; Lunde et al., 2014; Aouam et al., 2015; Relling and Evans, 2015). In our study, the haplotypes of CYP3A5*3 only accounted for 3-5.4% of the total variation involved in tacrolimus concentration (Table 5).

The frequency of the CYP3A4*18B allele was 28.99% (Table 2). Hu et al. (2007) showed that the CYP3A4*18B genotype affects cyclosporine pharmacokinetics, probably resulting from a higher enzymatic activity of this mutation in healthy subjects. Although previous research found patients with CYP3A4*18B alleles may require higher doses of cyclosporine to reach the target levels, Qiu et al. (2008) suggested a combined analysis of CYP3A4 and CYP3A5 genotypes was necessary due to the strong LD between CYP3A4*18B and CYP3A5*3. In our previous investigation, CYP3A4*18B noncarriers were found to be at risk for development of cyclosporine-related liver injuries in Chinese renal transplant recipients (Xin et al., 2014). Similar to CYP3A5*3, CYP3A4*18B carriers were found to have significant lower tacrolimus C₀/D than that of CYP3A4*18B noncarriers posttransplantation in the present study (Table 4). Analysis of the association of CYP3A4*18B with tacrolimus C₀ after being stratified by CYP3A5*3 genotypes showed that the tacrolimus C₀/D of CYP3A4*18B carriers was significantly higher compared with that of CYP3A4*18B noncarriers posttransplantation in CYP3A5*3 expressors (Supplementary Table S3). In CYP3A5*3 nonexpressors, however, no significant difference was found between the CYP3A4*18B genotypes (Supplementary Table S4). It should be noted that no more than 1 patient with CYP3A4*1/*1 and CYP3A5*1/*1 (CYP3A4*18B noncarriers and CYP3A5*3 nonexpressors) was enrolled at different time points (Supplementary Table S4).

The variety of CYP3A4*18B haplotypes have a significant influence on the variation of tacrolimus C₀/D (Table 5 and Supplementary Table S5). After stratified by CYP3A4*18B genotypes, the associations of CYP3A5*3 genotypes with tacrolimus C₀/D were analyzed as well (Supplementary Table S5). The mean tacrolimus C₀/D of different CYP3A5*3 genotypes at different time points showed no significant difference, except on day 7 in CYP3A4*18B carriers (p < 0.05). In our study, the CYP3A4*18B genotypes showed more important contribution to the tacrolimus C₀/D than CYP3A5*3 genotypes. As the CYP3A isozymes have overlapping substrate activity and moderate extent of LD between CYP3A4*18B and CYP3A5*3 (D′ = 0.747), it is necessary to have a combined analysis of CYP3A4 and CYP3A5 genotypes in the clinical use of tacrolimus. The exact mechanisms of CYP3A5*3 and CYP3A4*18B influence on tacrolimus pharmacokinetics are not clear and need further investigation.

Three SNPs of MDR1 were investigated in the present study, including ABCB1 1236C>T, ABCB1 2677G>T/A, and ABCB1 3435C>T. Only the genotypes of ABCB1 2677G>T/A were significantly associated with tacrolimus C₀/D at year 5 (p = 0.037, Table 3) and these genotypes accounted for 2.6% of the total variation (Table 5). As the most studied SNP in MDR1, ABCB1 3435C>T has been reported to be associated with reduced P-glycoprotein mRNA or protein expression (Wang et al., 2005), but the effect of this SNP on tacrolimus disposition remains contradictory (Mai et al., 2004; Hawwa et al., 2009; Li et al., 2015; Tavira et al., 2015). According to our current findings, ABCB1 3435C>T of recipients had no association with tacrolimus C₀/D posttransplantation, whereas the regression models showed that it can influence tacrolimus C₀/D at 1 month (4.8% of total variation, Table 5).

As one of the functional SNPs of NR1I2, the 6-bp deletion can diminish NR1I2 promoter activity in HepG2 cells (Uno et al., 2003). In our present study, NR1I2 6-bp indel was found to be significantly associated with tacrolimus C₀/D at 1 year (Table 3) and accounted for 6.5% of the variation at 3 months (Table 5). NR1I2 g.7635A>G was significantly associated with tacrolimus C₀/D (p = 0.033, Table 3) and accounted for 15.9% of its variation at year 5 (Table 5). Previous studies have shown that variant alleles of NR1I2 g.7635A>G were associated with increased NR1I2 transcriptional activity (Zhang et al., 2001; Chung et al., 2011). As the CYP3A5*3 is the unanimous genetic factor in tacrolimus pharmacogenetic studies, we investigated the independent influence of MDR1 and NR1I2 polymorphisms on C₀/D of tacrolimus after stratification by CYP3A5*3 genotypes and the results are shown in Supplementary Tables S3 and S4. However, no statistically significant association was found for all time points, except at years 5.

For studying the long-term relationships between genotypes and tacrolimus C₀/D, a GEE was used to assess the gene-time interaction, with the time points (repeated measurement variable) being within-subject variables. GEE analysis of the interactions showed a significant group-time interaction for all genotypes of CYP3A5*3, CYP3A4*18B, and NR1I2 g.7635A>G (Table 6 and Fig. 1a-c). Specific genotypes of ABCB1 (CC genotype of ABCB1 1236C>T and AA genotype of ABCB1 2677G>T/A), as well as the heterozygote of NR1I2 6-bp indel, were also found to have statistically significant associations over time with tacrolimus C₀/D according to our data (Table 6 and Fig. 1d).

Previously, the long-term effects of ABCB1 and NR1I2 SNPs on cyclosporine pharmacokinetics were confirmed during the pediatric renal transplant patient's aging process (Ferraresso et al., 2013). The same trend in tacrolimus C₀/D was discovered for CYP3A5*3, CYP3A4*18B, NR1I2 g.7635A>G, and sex in the present study (Fig. 1). We found that tacrolimus C₀/D increased significantly over time in CYP3A5*3/*3, CYP3A4*1/*1, and the GG genotype of NR1I2 g.7635A>G. On the other hand, the line of AA genotype of ABCB1 2677G>T/A was different from others (Fig. 1d) largely because a limited number of patients with the AA genotype were enrolled, and no tacrolimus C₀ data for this genotype were recorded at 5 years.

Herein, sex was observed to have significant influence on the variation of tacrolimus C₀/D during the later posttransplantation period (Table 5). The mean tacrolimus C₀/D was significantly different between males and females from 3 months to 3 years (Table 4). Furthermore, the difference between the sexes had a long-term interaction with tacrolimus concentration (Table 6 and Fig. 1e). Velicković-Radovanović et al. (2011) also observed significant differences in tacrolimus pharmacokinetics between men and women. Sex is also an important variable for constructing a regression equation of prediction population pharmacokinetics of tacrolimus (Staatz et al., 2002; Luo et al., 2016). According to our current study, it was necessary to consider the sex of patients using tacrolimus immunosuppressive regimens.

In conclusion, the results of the present study demonstrate that (1) CYP3A5*3 genotypes were significantly associated with tacrolimus C₀/D and had strong time-genotype interactions with tacrolimus pharmacokinetics; (2) the variety of CYP3A4*18B haplotypes had a significant influence on the variation of tacrolimus C₀/D, which was more important than CYP3A5*3 genotypes.(3) NR1I2 g.7635A>G genotypes were shown to have a significant interaction with time, but the tacrolimus C₀/D of these genotypes was not significantly different over 5 years posttransplantation (except for that of the GG genotype of NR1I2 g.7635A>G at 5 years); (4) sex had a significant influence on tacrolimus concentration during the later posttransplantation period. Further studies exploring the mechanisms behind genotype- and time-related tacrolimus disposition are necessary.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

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