The impact of CYP3A4 and CYP3A5 genetic variations on tacrolimus treatment of living‐donor Egyptian kidney transplanted patients

Abstract

Background

Tacrolimus (TAC) is the mainstay of immunosuppressive regimen for kidney transplantations. Its clinical use is complex due to high inter‐individual variations which can be partially attributed to genetic variations at the metabolizing enzymes CYP3A4 and CYP3A5. Two single nucleotide polymorphisms (SNPs), CYP3A4*22 and CYP3A5*3, have been reported as important causes of differences in pharmacokinetics that can affect efficacy and/or toxicity of TAC.

Objective

Investigating the effect of CYP3A4*22 and CYP3A5*3 SNPs individually and in combination on the TAC concentration in Egyptian renal recipients.

Methods

Overall, 72 Egyptian kidney transplant recipients were genotyped for CYP3A4*22 G>A and CYP3A5*3 T>C. According to the functional defect associated with CYP3A variants, patients were clustered into: poor (PM) and non‐poor metabolizers (Non‐PM). The impact on dose adjusted through TAC concentrations (C0) and daily doses at different time points after transplantation was evaluated.

Results

Cyp3A4*1/*22 and PM groups require significantly lower dose of TAC (mg/kg) at different time points with significantly higher concentration/dose (C0/D) ratio at day 10 in comparison to Cyp3A4*1/*1 and Non‐PM groups respectively. However, CyP3A5*3 heterozygous individuals did not show any significant difference in comparison to CyP3A5*1/*3 individuals. By comparing between PM and Non‐PM, the PM group had a significantly lower rate of recipients not reaching target C0 at day 14.

Conclusion

This is the first study on Egyptian population to investigate the impact of CYP3A4*22 and CYP3A5*3 SNPs individually and in combination on the TAC concentration. This study and future multicenter studies can contribute to the individualization of TAC dosing in Egyptian patients.

Overall, 72 Egyptian living donor kidney transplant recipients were genotyped for CYP3A4 and CYP3A5. Cyp3A4*1/*22 group require significantly lower dose of TAC (mg/kg) with significantly higher concentration/dose (C0/D) ratio in comparison to Cyp3A4*1/*1. CyP3A5*3 heterozygous individuals did not show any significant difference in comparison to CyP3A5*1/*3 individuals.

1. INTRODUCTION

Tacrolimus (TAC) is the main component in current immunosuppressive therapies for preventing rejection after kidney transplantation. The clinical use of TAC is complex due to its narrow therapeutic index and the high degree of inter‐ and intra‐individual pharmacokinetics (PK) variability. ¹ , ² , ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ So, individual dose titration is needed to reach an early and stable therapeutic trough concentration (C0) within the required therapeutic range to achieve the maximal efficacy and to minimize the toxicity. Sub‐therapeutic concentration in the early post‐transplant period increases the risk of rejection, while supra‐therapeutic concentration is linked to nephrotoxicity, neurotoxicity, diabetes, opportunistic infections, increased cardiovascular risk, and malignancies. ⁹ , ¹⁰ , ¹¹ , ¹²

Different factors have been identified as reasons of TAC PK variability including demographic features, liver functions, hematocrit (ht), food intake, drug–drug interactions, and the pharmacogenetic markers. ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ , ²¹

The large variability observed in TAC PK makes therapeutic drug monitoring (TDM) essential to optimize tacrolimus dosing. However, TDM support is less accurate during the first critical days and is limited by the time delay needed to ensure attainment of pharmacokinetic steady state before performing any dosage adjustment. Pharmacogenetic biomarkers have been shown to have the potential to enable individualization of the starting dose. ²² , ²³

Enzymes in the cytochrome P450 (CYP) 3A family are responsible for the oxidative metabolism of TAC. Four genes in this family have been described, but only CYP3A4 and CYP3A5 are thought to be relevant in adults and control both first pass metabolism and systemic clearance of TAC. The activity of these metabolizing isoenzymes varies markedly between individuals and appears to be explained, in part, by the presence of certain single‐nucleotide polymorphisms (SNPs) in the CYP3A4 and CYP3A5 genes. ²⁴ Two SNPs in the CYP3A4 and CYP3A5 genes, CYP3A4*22 and CYP3A5*3, have been reported as important causes of inter‐individual variability due to their effect on the activity of the metabolizing enzymes and their expression. ²⁵

The role of these two variants to the inter‐individual PK variability has been studied through the years. In some studies, it has been shown that CYP3A4*22 carriers had higher Tac C0 during the first week after transplantation and had more supra‐therapeutic C0. Also, various studies showed that CYP3A5 expressers require approximately twofold higher Tac doses to reach the same blood exposure compared with CYP3A5 non‐expressers. ¹⁸ , ¹⁹ , ²¹ , ²⁶ , ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶

As reported previously, ethnicity plays an important role in the impact of genetic variants on TAC inter‐individual variability and its therapeutic effect. ³⁷ , ³⁸ Up to our knowledge, such research was not conducted before on the Egyptian population. Thus, the aim of this study is to investigate the effect of CYP3A4*22 and CYP3A5*3 SNPS individually and in combination with the TAC concentration in Egyptian recipients of renal transplantation.

2. METHODS

2.1. Patients and immunosuppressive therapy

The protocol of our retrospective study ethics approval for genetic testing was obtained from the Research Ethics Committee of the Faculty of Medicine, Cairo University (ref. no. 133/2020), and all patients included provided written informed consent.

Adult Egyptian living donor kidney transplant recipients were recruited between 2016 and 2018 at the renal transplant units of both the Cairo Kidney Center and Salam International Hospital, Cairo, Egypt. Overall, 72 patients were included with a minimum of 30 patients from each center after applying inclusion and exclusion criteria.

All patients were treated with an immunosuppressive drug regimen containing oral TAC (Prograf; Astellas Pharma Europe Ltd.) administered in two divided doses. In the majority of patients, TAC was given in combination with 0.5–1.0 g mycophenolate mofetil (Cellcept; Roche) twice daily or 360–720 mg mycophenolic acid (Myofortic; Novartis Pharmaceuticals UK Ltd) twice daily. A TAC oral loading dose of 0.1–0.2 mg/kg/day in two divided doses was started 2 days before the day of transplantation and subsequently adjusted to achieve the predefined target whole‐blood C0 range of 10–12 ng/mL. The target Tac C0 was equal for all patients, irrespective of the need for induction therapy. Clinicians adjusted the TAC daily dose to achieve C0 within the target range, according to the previous day C0 result using the following formula: New dose = (target C0/measured C0) × daily dose.

Induction therapy was used depending on the perceived immunological risk. Induction therapy (anti‐thymocyte globulin (Thymoglobulin; Genzyme Europe) or basiliximab (Simulect; Novartis farmaceutica) was used in the case of increased immunological risk. In addition, all patients received intraoperative glucocorticoids according to the local protocol (250 mg intravenous methylprednisolone perfusion on day −1, 0 with daily reduction of 50 mg/day until reaching 100 mg/day then shifted to oral prednisolone 50 mg/day with further daily reduction of 5 mg until reaching 20 mg/day). Then, prednisone was tapered progressively more than several months to either a daily maintenance dose of 5 mg or complete discontinuation.

Five milliliters of venous whole blood was taken in ethylene diamine tetra‐acetic acid containing blood tubes and stored at –20°C. Demographic characteristics (age, sex, ethnicity, and weight) of the patients, repeated TAC C0 and dosages were obtained retrospectively from case notes. Data collected included cause of end stage renal disease, co‐prescribed medications, total ischemia time, human leukocyte antigen (HLA) mismatch, and panel reactive antibodies (PRA) results. Serum creatinine concentrations (mg/dL) and estimated glomerular filtration rates (eGFR; calculated using the Chronic Kidney Disease Epidemiology Collaboration formula) were recorded at day 14 and month 3. TAC C0 (ng/mL) and TAC daily doses (mg/kg/day) were retrieved from the medical files at the following time points: day 3 ± 2, day 10 ± 2, day 14 ± 3 and month 1 ± 7 days.

Nonsmokers and stable recipients, with no major intercurrent illness during the follow‐up period, were included in the analysis. Recipients taking any of the following drugs concomitantly with TAC: rifampicin, verapamil, clarithromycin, phenytoin, fluconazole, or amiodarone were excluded from the analysis.

2.2. TAC C0 measurement and Genotyping

Tacrolimus C0 (ng/mL) was obtained by means of a microparticle enzyme immunoassay method using the ARCHITECT analyzer (Abbott Laboratories).

Genomic DNA extraction was performed from peripheral whole blood using Thermo Scientific GeneJet Whole blood genomic DNA purification mini kit (Thermo Scientific, Fermentas UAB, V.Graiciuno 8, LT‐02241 Vilnius, Lithuania).

For Genotyping, an allelic discrimination was performed using specific Taqman™ Genotyping Master Mix, specific TaqMan™ SNP Genotyping Assay human Attached (rs35599367) and TaqMan Drug metabolism Assays (DME) (rs776746) (Applied Biosystems) for each SNP on StepOne™ Real‐Time PCR Systems (Applied Biosystems). All recipients were genotyped for CYP3A4*22 G>A (rs35599367), CYP3A5*3 T>C (rs776746). According to the functional defect associated with CYP3A variants, patients were clustered into: poor (PM) (CYP3A4*22 carriers with CYP3A5*3/*3), whereas the others were considered as non‐poor metabolizers (Non‐PM).

2.3. Statistical analysis

Statistical analysis was done by SPSS version 28 (IBM Co.). Quantitative non‐parametric data were presented as the median and interquartile range (IQR) and were analyzed by Mann–Whitney U‐test to compare each two groups and Kruskal–Wallis test for more than two groups. Qualitative variables were presented as frequency and percentage (%) and were analyzed using the Chi‐square test or Fisher's exact test when appropriate.

Linear regression was performed to assess the different factors associated with clinical outcome. A two tailed p value <0.05 was considered statistically significant.

3. RESULTS

3.1. Baseline characteristics of the studied recipients

A total of 72 adult recipients of a living kidney donor were included in the present study (45 males and 27 females), their ages ranged from 18 to 73 years with a mean age of 43.14 ± 15.15 years. The studied participants had a mean body weight of 76.92 ± 14.12 kg (ranged between 44 and 112 kg).

Diabetic nephropathy (25%) was the leading indication for transplantation, followed by congenital abnormality of kidneys and urinary tract (CAKUT) (18.1%), hypertensive nephropathy (15.28%), glomerulonephritis (15.28%), autosomal dominant polycystic kidney disease (ADPKD) (12.5%), and obstructive uropathy (5.6%). Other causes included chronic pyelonephritis, analgesic nephropathy, and unknown etiologies. Baseline demographics and clinical characteristics are shown in Table 1.

TABLE 1.

Baseline demographics and clinical characteristics of the studied recipients.

	Study participants (n = 72)
Age (years)
Mean ± SD	43.14 ± 15.15
Range	18–73
BW (kg)
Mean ± SD	76.92 ± 14.12
Range	44–112
Sex
Male	45 (62.5%)
Female	27 (37.5%)
Primary kidney disease	N (%)
Diabetic nephropathy	18 (25%)
Hypertensive nephropathy	11 (15.28%)
ADPKD	9 (12.5%)
Glomerulonephritis	11 (15.28%)
CAKUT	13 (18.1%)
Obstructive uropathy	4 (5.6%)
Previous graft failure	1 (1.4%)
Others	5 (6.9%)
Total HLA mismatches	N (%)
0	1 (1.4%)
1	1 (1.4%)
2	24 (33.3%)
3	30 (41.7%)
4	16 (22.2%)
Induction therapy	N (%)
None	65 (90.2%)
ATG	4 (5.6%)
Basiliximab	3 (4.2%)
Baseline immunosuppressive therapy	N (%)
Prednisone	72 (100%)
Tacrolimus	72 (100%)
MMF	71 (98.6)
Everolimus	1 (1.4%)

Note: Data are presented as frequency (n) and percentage (%) unless otherwise mentioned.

Abbreviations: ATG, anti‐thymocyte globulin; BW, Body weight; CAKUT, congenital abnormalities of the kidney and urinary tract; IQR, Interquartile range.

3.2. Genotyping results/genotype frequencies

Table 2 demonstrates the genotype frequencies of the two studied genes among the studied recipients. The frequencies of the CYP3A4*1/*1, *1/*22, and *22/*22 genotypes were 31 (43.3%), 41 (56.9%), and 0 (0%), respectively. As regards CYP3A5, the frequencies of the CYP3A5*1/*1, *1/*3, and *3/*3 genotypes were, 0 (0%), 12 (16.7%), and 60 (83.3%), respectively.

TABLE 2.

Recipient allelic frequencies and comparison of basic characteristics.

Gene (SNIPs)	Genotype	N (%)	Mean age (SD)	p Value	Male N (%)	Female N (%)	p Value
CYP3A4 G > A (Rs35599367)	GG (*1/*1)	31 (43.1%)	39.0 (14.2)	0.045	16 (51.6%)	16 (51.6%)	0.097
	GA (*1/*22)	41 (56.9%)	39.0 (14.2)		29 (70.7%)	29 (70.7%)
	AA (*22/*22)	0 (0%)
CYP3A5 T > C (Rs776746)	CC (*3/*3)	60 (83.3%)	42.8 (15.3)	0.097	36 (60.0%)	36 (60.0%)	0.515
	TC (*1/*3)	12 (16.7%)	42.8 (15.3)		9 (75.0%)	9 (75.0%)
	TT (*1/*1)	0 (0%)
CYP3A4 and CYP3A5 combined genotypes	PM (GA‐CC)	32 (44.44%)
	Non‐PM (GA‐TC) + (GG‐CC) + (GG‐TC)	40 (55.56%)

Note: Data are presented as frequency (N) and percentage (%). Statistical significance at p value <0.05.

Bold indicates statistical significance at p < 0.05.

Patients were clustered according to both CYP3A4*22 and CYP3A5*3 allelic status into two groups: poor metabolizers (CYP3A4*22 carriers with CYP3A5*3/*3) and non‐poor metabolizers (CYP3A4*1/*1 with CYP3A5*3/*3), (CYP3A4*22 carriers with CYP3A5*1 carriers) or (CYP3A4*1/*1 and CYP3A5*1 carriers). The frequencies of PM and Non‐PM were 32 (44.44%) and 40 (55.56%), respectively.

The mean age for the *22 carriers was higher than the mean age of the *1/*1 group, p = 0.045. There was no statistically significant difference between the two groups regarding sex. No statistically significant difference between the CYP3A5 groups regarding age or sex.

3.3. Influence of genetic variations of each individual gene on TAC dosing

Table 3 shows the association between different allelic distribution and different parameters. In terms of CYP3A4 SNPs, recipients with GG genotype had significantly higher doses per kg at days 3, 10, 14, and month 1 (p value = 0.009, 0.004, 0.006, 0.01, respectively) while C0/D at day 10 was significantly lower in recipients with GG genotype as compared to those with GA (p value = 0.004). On the other hand, no significant difference was found with regards the CYP3A5 genotyping.

TABLE 3.

Association between different allelic distribution and different parameters among the studied participants.

	CYP3A4 (Rs35599367)		p Value	CYP3A5 (Rs776746)		p Value
	GG	GA		CC	TC
Days needed to reach target C0	3 (3–3)	3 (3–8)	0.055	3 (3–7)	3 (3–4)	0.493

	Mean (range)	Mean (range)		Mean (range)	Mean (range)
TAC dose (mg/kg/day)
Day 3	0.16 (0.13–0.18)	0.1 (0.1–0.17)	0.009	0.14 (0.1–0.17)	0.15 (0.1–0.17)	0.982
Day 10	0.16 (0.14–0.18)	0.1 (0.08–0.16)	0.004	0.15 (0.09–0.17)	0.15 (0.08–0.16)	0.784
Day 14	0.17 (0.13–0.18)	0.1 (0.07–0.16)	0.006	0.15 (0.1–0.17)	0.14 (0.08–0.17)	0.722
1 month	0.16 (0.12–0.17)	0.1 (0.07–0.16)	0.01	0.16 (0.1–0.17)	0.14 (0.1–0.16)	0.565
C0 (ng/mL)
Day 3	16 (10.25–16.85)	13.9 (9.3–18)	0.977	14.15 (10–17.65)	14.3 (11.33–16.1)	0.928
Day 10	11 (9.05–13.6)	12.2 (9.55–15.2)	0.366	11.75 (9.3–14.83)	12.15 (9.6–13.65)	0.975
Day 14	11.2 (9.45–14.3)	10.2 (8.4–13.3)	0.352	10.75 (9.35–14.25)	11.45 (8.8–12.35)	0.67
1 month	11.2 (10.3–13.4)	11 (9.3–12.85)	0.238	11 (10–12.7)	12.5 (10.7–14.7)	0.179
C0/D (ng.mL−1/mg.kg−1)
Day 3	95.88 (66.46–108.75)	105.88 (83.33–163.75)	0.117	100 (71.68–124.15)	90.94 (78–136.54)	0.711
Day 10	69.13 (54.85–97.55)	102.67 (76.04–136.87)	0.004	91.78 (61.5–123)	86.86 (74.93–100.7)	0.95
Day 14	75.88 (57.33–107.44)	100.22 (75.47–147.72)	0.057	86.56 (58.77–126.75)	96.15 (74.06–127.59)	0.8
1 month	75.84 (59.47–102.71)	94.29 (72.9–127)	0.069	83.75 (63.29–106.21)	99.67 (88.54–119.06)	0.209
Cr (mg/dL)
Day 14	1.06 (0.75–1.43)	0.83 (0.74–1.04)	0.594	0.96 (0.75–1.24)	0.8 (0.74–0.8)	0.206
3 months	1.2 (1.13–1.37)	0.9 (0.82–1.01)	0.089	0.93 (0.85–1.2)	1 (1–1)	0.828
eGFR (mL/min/1.73 m2)
Day 14	110 (100–120)	102 (91–110)	0.158	110 (98–112.5)	109.4 (100–120)	0.556
3 months	60.1 (54.55–70.25)	92.5 (74.35–100.5)	0.106	83.4 (61.75–95)	–	–

Note: Bold indicates statistical significance at p < 0.05.

3.4. Influence of CYP3A4 and CYP3A5 combined genotype on TAC dosing and C0

Table 4 shows the association between metabolism status (PM vs. Non‐PM) and different parameters. In terms of TAC doses and C0, PM had significantly lower doses per kg at days 3, 10, 14, and month 1 as compared to the Non‐PM (p = 0.039, 0.034, 0.029, 0.038, 0.024, respectively) with significantly higher C0/D at day 10 (p value = 0.004).

TABLE 4.

Association between metabolism and different parameters among the studied participants.

	PM (n = 32) Mean (range)	Non‐PM (n = 40) Mean (range)	p Value
Days needed to reach target C0	3 (3–9)	3 (3–3)	0.052

	Mean (range)	Mean (range)
TAC dose (mg/kg/day)
Day 3	0.1 (0.1–0.17)	0.16 (0.1–0.18)	0.043
Day 10	0.1 (0.08–0.16)	0.16 (0.12–0.17)	0.029
Day 14	0.1 (0.07–0.17)	0.16 (0.12–0.17)	0.038
1 month	0.1 (0.07–0.16)	0.16 (0.12–0.17)	0.042
C0 (ng/mL)
Day 3	13.2 (9.8–18)	15.5 (10.08–16.68)	0.995
Day 10	12.25 (9.63–15.55)	11 (8.83–13.55)	0.172
Day 14	10.7 (9.15–14.05)	11.05 (9.15–13.23)	0.965
1 month	10.9 (9.1–12.85)	11.2 (10.2–13.4)	0.251
C0/D (ng.mL−1/mg.kg−1)
Day 3	106.44 (82.25–164.31)	95 (68.95–112.67)	0.155
Day 10	106.34 (77.95–139.15)	78.61 (55.42–99.99)	0.004
Day 14	107.14 (73.45–139.55)	80 (59.69–109.5)	0.087
1 month	92.3 (72.96–125.25)	78 (61.47–102.92)	0.135
Cr (mg/dL)
Day 14	0.96 (0.75–1.11)	0.8 (0.73–1.15)	0.668
3 months	0.87 (0.79–1.02)	1.2 (1–1.2)	0.089
eGFR (mL/min/1.73 m2)
Day 14	100 (82.35–110)	110 (100–120)	0.074
3 months	92.5 (74.35–100.5)	60.1 (54.55–70.25)	0.106

Note: Bold indicates statistical significance at p < 0.05.

By comparing between Poor and non‐poor metabolizers, PM group had significantly lower rate of recipients not reaching target C0 at day 14 (p value = 0.035). However, there was no significant statistical difference between the two groups with regards the rate of patients with supra‐therapeutic or sub‐therapeutic TAC C0. See Table 5.

TABLE 5.

The effect of metabolic status on TAC C0.

	PM (N = 32) Mean (range)	Non‐PM (N = 40) Mean (range)	p Value
	N (%)	N (%)
Patients with C0 > 15 ng/mL
Day 3	12 (37.5%)	20 (50%)	0.289
Day 10	9 (28.13%)	5 (12.5%)	0.07
Day 14	6 (18.75%)	6 (15%)	0.532
1 month	1 (3.13%)	6 (15%)	0.223
Patients with C0 < 5 ng/mL
Day 3	0 (0%)	3 (7.5%)	0.249
Day 10	2 (6.25%)	1 (2.5%)	0.581
Day 14	5 (15.63%)	2 (5%)	0.23
1 month	5 (15.63%)	4 (10%)	0.498
Patients not reaching target C0
Day 10	0 (0%)	2 (6.3%)	0.194
Day 14	0 (0%)	4 (12.5%)	0.035

Note: Data are presented as frequency (N) and percentage (%).

Bold indicates statistical significance at p < 0.05.

3.5. Influence on the graft function

Renal function was not statistically significantly different between studied groups Tables 1 and 2. No statistically significant differences were observed in creatinine and eGFR values at day 14 and at 3 months.

The multiple linear regression analysis given in Table 6 showed that total ischemia time was significantly associated with eGFR at day 14 with (Coefficient: −1.046, 95% CI: −1.707 to −0.385, p value = 0.003).

TABLE 6.

Multiple linear regression of characteristics associated with eGFR at day 14.

	Day 14
	Coefficient	p‐Value	95% CI
Age (years)	−0.022	0.907	−0.399 to 0.355
Sex
Male	Ref
Female	−1.461	0.789	−12.380 to 9.457
BW (kg)	−0.445	0.051	−0.892 to 0.002
Starting dose (mg/kg)	103.943	0.321	−104.313 to 312.198
Time needed to reach target C0	0.563	0.063	−0.032 to 1.157
HB	4.130	0.261	−3.168 to 11.429
Ht at day 3	−0.221	0.793	−1.907 to 1.464
HLA mismatch	−0.765	0.819	−7.429 to 5.899
Total ischemia time	−1.046	0.003	−1.707 to −0.385
CYP3A4 (Rs35599367)
GG	Ref
GA	4.010	0.772	−23.659 to 31.679
CYP3A5 (Rs776746)
CC	Ref
TC	6.192	0.614	−18.306 to 30.690
Metabolism
Non‐poor	Ref
Poor	4.039	0.788	−25.914 to 33.993

Note: Bold indicates statistical significance at p < 0.05.

Abbreviations: BW, body weight; CI, Confidence interval; HB, hemoglobin; Ht, Hematocrit; TAC, Tacrolimus.

4. DISCUSSION

Nowadays, TAC is considered the main immunosuppressive agent that is widely used for preventing rejection after kidney transplantation. ³⁹ SNPs in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of TAC in renal transplant patients. ²⁵ , ⁴⁰ CYP3A4*22 SNP is associated with functional defect that leads to decrease in CYP3A4 hepatic mRNA production and a lower microsomal CYP3A4‐driven activity. ⁴¹ The CYP3A5*3 variant is the predominant allele in many populations. It may result in truncated mRNA with loss of expression of the functional protein in homozygotes or compound heterozygotes, or encode nonfunctional protein. ¹⁸ , ²⁷ , ⁴² , ⁴³ The aim of the present study was to evaluate the influence of the individual CYP3A4*22 and CYP3A5*3 SNPs and their combinations in poor metabolizers on TAC dosing in the post‐transplant period.

We found that TAC metabolism, as assessed by the dose needed to reach the target drug concentration, was influenced by CYP3A4*22 SNP as recipients who carry the CYP3A4*22 variant alleles required significantly lower tacrolimus doses than non‐carriers. Our results are in agreement with ⁴⁴ Kitzmiller et al. ⁴⁴ who showed that CYP3A4*22 carriers required lower statin doses to control lipid levels compared with CYP3A4*1/*1 individuals. Similarly, Elens et al. ³⁴ reported that carriers of CYP3A4*22 SNP needed lower Tac doses to reach the target C0 compared with CYP3A4*1/*1 patients in de‐novo kidney transplant patients.

In our results, there was no significant difference between CYP3A4*1/*1 and CYP3A4*1/*22 genotypes with the trough C0 level at different time points. However, CYP3A4*1/*22 individuals showed significantly higher C0/D at day 10. This can reflect that the functional defect in CYP3A4 activity by carrying CYP3A4*22 allele makes a lower dose sufficient to reach the same blood concentrations as CYP3A4*1/*1 individuals. In contrast to our results, some investigators reported higher mean Tac C0 in CYPA3A4*22 carriers. ⁴⁵ However, some other studies did not find an association between the CYP3A4*22 allele and Tac C0 in renal transplant recipients. ⁴⁶ , ⁴⁷

On the other hand, our results did not find a significant influence of carrying CYP3A5*3 on TAC dose requirements. On contrast to our results, the study on Egyptian population of Mendrinou et al. ⁴⁸ showed that mean TAC daily requirements for heterozygous patients CYP3A5*1/*3 were significantly higher compared to homozygous patients CYP3A5*3/*3 during the first year after kidney transplantation. Despite of the strong association between the CYP3A5 genotype and tacrolimus exposure in previous reports, ¹⁸ , ¹⁹ , ²¹ , ²⁶ , ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ⁴⁵ , ⁴⁹ on average, the CYP3A5 *1 genotype requires a higher tacrolimus dose, a previous study reported that some individuals who were CYP3A5 expressers did not have a real fast metabolizer phenotype, and may reach target concentrations when receiving the conventional TAC doses. ¹⁸ Based on numerous published guidelines for the genotype‐based selection of the initial TAC dose, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) recommended increasing the starting dose with CYP3A5*1 carriers with subsequent dose adjustment based on TDM. They conclude that whereas a CYP3A5‐based starting TAC dose may help better dose adjustments, there is right now no persuading clinical proof that the utilization of pharmacogenetic‐based TAC dosing improves clinical outcomes after solid organ transplantation. ²⁵ , ⁴⁰ , ⁵⁰

Furthermore, our results showed that on combining both CYP3A4 and CYP3A5 alleles into a metabolism status and defining two clusters (PM versus Non‐PM), we found that poor metabolizers, by being CYP3A4*22 carrier and homozygous for CYP3A5*3, had significantly lower doses per kg at days 3, 10, 14, and month 1 as compared to the non‐poor ones with significantly higher C0/D. These results are in agreement with those of a previously published report ⁵¹ , ⁵² where PM had a higher TAC dose‐adjusted C0 compared with Non‐PM. Despite that our results did not show significant statistical difference between the two groups with regards the rate of patients with supra‐therapeutic or sub‐therapeutic TAC C0, PM group had significantly lower rate of recipients not reaching the predefined target C0 at day 14.

The genotyping initial TAC dosing is being considered in some transplantation centers. However, data reported to date on the potential value of such strategy are still not clear. In our study, we did not observe any differences between all studied groups and the graft function represented by eGFR at day 14 and month 3. Multiple linear regression analysis showed that the only factor that influenced the eGFR at day 14 was only the total ischemia time. Similarly, a previous study conducted by Tactique and his colleagues ⁵³ showed that genotype‐based TAC dosing did not improve the clinical outcome despite the shorter time to reach an adequate C0.

The study has limitations. First, it includes only Egyptian patients and there might be differences in CYP3A genotype cluster activity with other studies. The second limitation is the relatively low number of extensive metabolizers (n:3) in comparison to intermediate and poor metabolizers.

This is the first study on Egyptian population to investigate the impact of CYP3A4*22 and CYP3A5*3 SNPs individually and in combination on the TAC concentration. Our results showed that Cyp3A4*1/*22 and PM groups require significantly lower dose of TAC (mg/kg) at days 3, 10, 14, and month 1 with significantly higher concentration/dose (C0/D) ratio at day 10 in comparison to Cyp3A4*1/*1 and Non‐PM groups, respectively. However, CyP3A5*3 heterozygous individuals did not show any significant difference in comparison to CyP3A5*1/*3 individuals. By comparing between PM and Non‐PM, the PM group had a significantly lower rate of recipients not reaching target C0 at day 14. Interest in pharmacogenetics has grown significantly in the last decade. The impact of pharmacogenetics in both TAC pharmacokinetics and pharmacodynamics still needs further investigation aiming to assist personalized dose adjustment that can potentially improve the clinical outcome. This study and future multicenter studies can contribute to the individualization of TAC dosing in Egyptian patients.

AUTHOR CONTRIBUTIONS

Hanaa Wanas analyzed data and wrote the main manuscript text; Mai Hamed Kamel and Emily Samir Mikhael analyzed the genotyping; Emad Adel William, Tarek Fayad, and Mohamed Essmat Abdelfattah collected the data, and Hossein Mostafa Elbadawy reviewed the final version. All authors reviewed the manuscript.

FUNDING INFORMATION

This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

Wanas H, Kamel MH, William EA, et al. The impact of CYP3A4 and CYP3A5 genetic variations on tacrolimus treatment of living‐donor Egyptian kidney transplanted patients. J Clin Lab Anal. 2023;37:e24969. doi: 10.1002/jcla.24969

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author, [HW], on special request.