Impact of CYP3A4 and CYP3A5 polymorphisms on tacrolimus dose requirements in Saudi kidney transplant patients

Abstract

Background

Limited research has explored the genetic variability of CYP3A4 and CYP3A5 in the Saudi population, particularly concerning tacrolimus (Tac) therapy among Saudi kidney transplant patients (SKTP).

Objectives

To investigate specific CYP3A4 and CYP3A5 polymorphisms in SKTP and evaluate their influence on Tac dose requirements and trough levels.

Methods

A total of 251 Saudi participants were recruited, comprising 129 kidney transplant patients and 122 healthy volunteers. Genomic DNA was extracted, and polymorphisms in CYP3A4 (*1B, *6, *18, *22) and CYP3A5 (*2, *3, *4) were analyzed using real-time PCR and allele-specific sequencing. Genotype frequencies and minor allele frequencies (MAF) were calculated, and the impact of CYP3A variants on Tac dosing and trough levels (C0) was assessed in SKTP.

Results

The CYP3A41B polymorphism was absent, with all participants being homozygous wild type (G/G). For CYP3A5*3, 98.4% of participants carried the mutant genotype (*3/*3), while 1.6% carried the wild-type genotype (*1/*1). Patients with the wild-type allele (*1/*1) required significantly higher Tac doses and exhibited lower trough concentrations (C0) compared to those with the mutant genotype (*3/3). Other polymorphisms, such as CYP3A4*22, were rare, with approximately 90% of participants carrying the wild-type allele.

Conclusion

This study highlights the high prevalence of the CYP3A5*3/*3 genotype and wild-type CYP3A4 alleles in the Saudi population. The genetic variability significantly affects Tac trough levels and dosing requirements necessary to achieve therapeutic targets. These findings underscore the importance of pharmacogeneticguided Tac dosing to optimize therapeutic outcomes in SKTP

Supplementary Information

The online version contains supplementary material available at 10.1007/s44446-025-00035-1.

Introduction

Cytochrome P450 (CYP) enzymes are a superfamily of membrane-bound hemoproteins responsible for the oxidative metabolism of numerous endogenous and exogenous compounds. Among these, CYP3A4 and CYP3A5 are the most clinically significant isoforms, accounting for the metabolism of over 50% of all prescribed drugs, including the cornerstone immunosuppressant tacrolimus (Tac) (Zhao et al. 2021; Manikandan and Nagini 2018; Song et al. 2021). These enzymes are predominantly expressed in the liver and intestine and exhibit considerable inter-individual and interethnic variability, largely due to genetic polymorphisms (Song et al. 2021; Margreiter 2002; Hesselink et al. 2014).

Tacrolimus is a hydrophobic macrolide lactone widely used in kidney transplantation to prevent acute rejection. It is notably more potent than cyclosporine, with a 50–100-fold higher immunosuppressive activity and a significantly lower incidence of acute rejection (Margreiter 2002; Ong and Gaston 2021). However, tacrolimus has a narrow therapeutic window (3–12 ng/mL) and substantial inter-patient pharmacokinetic variability, which complicates dosing strategies and increases the risk of rejection or toxicity (Nguyen et al. 2023; Brunet and Pastor-Anglada 2022). Several factors contribute to this variability, with genetic polymorphisms in CYP3A4 and CYP3A5 being among the most influential (Yu et al. 2018; Bruckmueller et al. 2015; Lee et al. 2022; Ebid et al. 2022; Chauhan et al. 2023).

Tacrolimus is primarily metabolized by CYP3A4 and CYP3A5 through hepatic and intestinal oxidative pathways. The CYP3A5*3 polymorphism (6986A > G, rs776746) results in a splicing defect that renders the enzyme non-functional. Individuals with the CYP3A5*1 allele (expressers) metabolize tacrolimus more rapidly and typically require ~ 50% higher doses than non-expressers (3/3 genotype) to maintain therapeutic levels (Brunet and Pastor-Anglada 2022; Bruckmueller et al. 2015; Ebid et al. 2022). In contrast, the CYP3A4*22 variant (rs35599367) leads to reduced hepatic enzyme expression and slower metabolism of tacrolimus. Carriers of this T-variant allele exhibit lower tacrolimus dose requirements, independent of CYP3A5 status (Brunet and Pastor-Anglada 2022; Bruckmueller et al. 2015).

Importantly, patients carrying both the CYP3A5 3/3 and CYP3A4*22 genotypes are considered poor metabolizers and require significantly lower doses of tacrolimus to avoid overexposure (Bruckmueller et al. 2015; Ebid et al. 2022; Almeman 2021). These findings highlight the clinical value of pharmacogenetic testing to guide individualized tacrolimus dosing and improve therapeutic outcomes (Bruckmueller et al. 2015; Chauhan et al. 2023).

While global research on tacrolimus pharmacogenetics is well established, data from Saudi Arabia remain limited. Previous local studies have largely focused on other CYP enzymes such as CYP2C19 and CYP2C9, primarily in the context of cardiovascular pharmacotherapy (Almeman 2021; Goljan et al. 2022a). However, investigations addressing CYP3A4 and CYP3A5 polymorphisms in relation to immunosuppressive therapy are scarce, despite their clinical significance.

With the growing prevalence of chronic kidney disease (CKD) in Saudi Arabia—estimated at nearly 5%—and an increasing number of kidney transplants performed annually (Alshehri et al. 2025; Saudi Center for Organ Transplantation 2023), understanding population-specific pharmacogenetic profiles is essential for optimizing immunosuppressive regimens.

Objective of the study

This study had the following objectives:

1. To genotype key CYP3A4 and CYP3A5 polymorphisms in Saudi kidney transplant patients and healthy volunteers.

2. To evaluate the association between these polymorphisms and tacrolimus dose requirements.

3. To examine the relationship between the polymorphisms and tacrolimus trough concentrations over time.

4. To inform the development of genotype-guided dosing strategies for tacrolimus.

Methodology

Ethical approval

The study received approval from the Institutional Research Ethics Committee. All participants provided written informed consent after receiving comprehensive verbal and written explanations of the study’s objectives and procedures.

Inclusion criteria for Saudi kidney transplant patients

This retrospective cohort study included all adult Saudi kidney transplant patients (SKTPs), aged 18 to 60 years, who underwent kidney transplantation and received tacrolimus (Tac) (Prograf®, Astellas Toyama Co., Ltd., Japan), prednisolone, and mycophenolate mofetil (MMF) at the Armed Forces Hospital, SouthernRegion, Khamis Mushayt, Saudi Arabia, between 2012 and 2017. All patients had a minimum follow-up of 24 months post-transplantation.

Eligible patients were identified via the hospital’s electronic health information system, and relevant clinical data were retrospectively extracted from their medical records.

Exclusion criteria

Patients were excluded if they had liver cirrhosis, cancer, hepatitis B or C, repeat transplants, concurrent cyclosporine use, nephrotoxic medications, CYP3A-modifying drugs, early graft loss (within 24 months), or non-compliance with prescribed medications.

Maintenance immunosuppressive protocol

The initial tacrolimus dose was 0.1 mg/kg/day, administered in two divided doses and adjusted based on trough concentration (C₀) levels. Target C₀ levels were maintained at 10–12 ng/mL during the early postoperative phase (first month), then gradually reduced to 5–7 ng/mL by six months post-transplant, in accordance with international guidelines for therapeutic drug monitoring in kidney transplantation.

Patients were followed according to the institutional post-transplantation protocol. Tacrolimus trough levels were measured weekly during the first month, biweekly during the second and third months, monthly from the fourth to sixth months, and every two months thereafter.

Healthy volunteers for genetic study

A total of 122 healthy volunteers, aged 18–60 years and of both genders, were recruited for the genetic polymorphism analysis. All participants underwent physical examinations and routine laboratory tests, including a complete blood count (CBC), and assessments of liver and renal function, to confirm their health status before participation. Healthy individuals were included as a reference group to characterize the baseline distribution of CYP3A4 and CYP3A5 polymorphisms in the general Saudi population. This provided a genetic background for comparison with the patient cohort and supported population-specific interpretation of genotype frequencies.

Sample collection for genotyping

Genotypic sampling was conducted through the hospital laboratory phlebotomy unit, one ml of venous blood was collected from each participant and placed into an EDTA tube. The samples were securely transported on dry ice to the Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, KSA. All participants underwent genotyping for key polymorphisms in the CYP3A4 and CYP3A5 genes. Specific single nucleotide polymorphisms (SNPs) analyzed included:

• CYP3A5 Gene: CYP3A5*2 (rs28365083), CYP3A5*3 (rs776746), and CYP3A5*4 (rs56411402)

• CYP3A4 Gene: CYP3A4*1B (rs2740574), CYP3A46 (rs4646438), CYP3A418 (rs28371759), and CYP3A4*22 (rs35599367)

DNA Isolation

DNA was extracted from 0.25 mL of blood, isolated from leukocytes, using the QIAamp DNA Mini Kit (Cat. No. 56304, Qiagen, Alameda, CA, USA), following the manufacturer's protocol. The concentration of the isolated genomic DNA was determined using a Nanodrop-2000 spectrophotometer (Thermo Scientific, USA). The extracted DNA samples were stored at −20°C until further analysis.

TaqMan assay (Real-time PCR)

TaqMan genotyping was performed for the selected SNPs using the 7500 FAST Real-Time PCR system (Applied Biosystems) with TaqMan® SNP Genotyping Assay (Cat. No. 4351376), which included specific oligonucleotide primers and Minor Groove Binder (MGB) probes labelled with VIC™ (green fluorophore) and FAM™ (blue fluorophore). Genotyping focused on the selected SNPs in the CYP3A5 genes specifically CYP3A5*2, CYP3A5*3, and CYP3A5*4. Additionally, SNPs in the CYP3A4 gene, including CYP3A4*1B, CYP3A4*6, CYP3A4*18, and CYP3A4*22, were also analyzed. Detailed assay information is provided in Table 1. Table 2. Shows a list of primer sequences and product sizes.

Table 1.

CYP3A4 and CYP3A5 Polymorphism Characteristics and TaqMan Assay Details

Gene Symbol	SNP ID	Chromosome Position	Variation	TaqMan Assay ID	VIC/FAM
CYP3A4	CYP3A4*1B	rs2740574	Chr.7: 99,784,473 T > C	C__1837671_50	[C/T]
CYP3A4	CYP3A4*6	rs4646438	Chr.7: 99,766,411 T/-, Ins/Del	C__32787140_40	[T/-]
CYP3A4	CYP3A4*18	rs28371759	Chr.7: 99,764,003 A > G	C__27859823_20	[A/G]
CYP3A4	CYP3A4*22	rs35599367	Chr.7: 99,768,693 G > A	C__59013445_10	[G/A]
CYP3A5	CYP3A5*2	rs28365083	Chr.7: 99,652,613 G > T	C__30633862_10	[G/T]
CYP3A5	CYP3A5*3	rs776746	Chr.7: 99,672,916 T > C	C__26201809_30	[T/C]
CYP3A5	CYP3A5*4	rs56411402	Chr.7: 99,665,237 T > C	C__30633864_10	[C/T]

Table 2.

List of primer sequences and product sizes

SNP ID	Forward 5` to 3`	Reverse 5` to 3`	Product size
rs2740574	GGAGCTCACCTCTGTTCAGG	CTGGGATGAGAGCCATCACT	711
rs4646438	ACACACGCTACACTTCAGCA	TGGCTTCCAGTTGAGAACCTT	717
rs28371759	ACCTTGGGGAAAACTGGAT	TTCTCCTGGGAAGTGGTGAG	449
rs35599367	CTGAAGAGGAATCGGCTCTG	TGTTCACTCCAAATGATGTGC	491
rs28365083	GGGTGAGGATGGTCTTGAAT	AAAATGCCCACAGGGACATA	698
rs776746	TGGTGAGAGCAGTGGATGAG	CAGCACAGGGAGTTGACCTT	698
rs56411402	TGGGGTGTTGACAGCTAAAG	AGCTTGGTGTCTCCATCACC	572

SNP Validation by sanger sequencing

Sanger sequencing was performed to validate seven SNPs. A total of 175 samples (25 samples per SNP) were randomly selected for cross-validation of the TaqMan assay results. PCR amplification was performed using a Veriti™ 96-well thermal cycler (Applied Biosystems) with GoTaq® Green Master Mix. Primers were designed using Primer 3 software (https://www.primer3plus.com/index.html). The PCR amplicons were resolved on a 2% agarose gel stained with SYBR Safe DNA gel stain and visualized using a UV trans-illuminator (Bio-Rad, USA). Subsequently, PCR products were purified using the QIAquick PCR Purification Kit (Qiagen). Cycle sequencing PCR was conducted using the BigDye Terminator v3.1 Cycle Sequencing Kit (Life Technologies), followed by purification with the BigDye XTerminator (Applied Biosystems). Sequencing was performed on an ABI 3500 Genetic Analyzer (Life Technologies) using rapid electrophoresis run modules. Sequence alignment and variant identification were carried out using BioEdit software.

Blood samples and drug analysis of Tac

Three millilitres of blood were collected in EDTA tubes 12 h post-dose at scheduled intervals following transplantation. The samples were stored at −4°C and analyzed in batches within 7 days. Quality control was rigorously maintained by the CAP-accredited AFHSR laboratory, with continuous monitoring and quarterly blind sample analyses. Tacrolimus concentrations were measured using the Dimension RxL system (Siemens Healthcare Diagnostics), employing an enzyme-multiplied immunoassay. The lower limit of detection (LLOD) was set at 1 ng/mL by international standardization guidelines, with a coefficient of variation maintained below 2%, ensuring accuracy and consistency in the measurements.

Documentation of relevant data

Data on demographics, clinical history, laboratory values, Tac trough levels (C0), and treatment regimens were collected from medical records and stored securely in a password-protected Excel spreadsheet.

Statistical analysis

Relevant statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) software version 24 (SPSS Inc., IL, USA). A p-value of less than 0.05 was considered statistically significant.

• Comparison of continuous variables (e.g., age, height, weight, BMI) among different groups was performed using one-way Analysis of Variance (ANOVA), followed by post hoc Tukey's test for pairwise comparisons.

• Categorical variables (e.g., gender, skin color, HLA mismatch) were analysed using the appropriate Chi-square test (χ² test) or Fisher’s exact test.

Results

The demographic and clinical characteristics of SKTP and healthy volunteers are summarized in Table 3. All patients tested negative for HBsAg, HCV, and HIV, while being CMV-G positive and CMV-M negative. Skin color distribution showed that 96.9% of SKTP had light skin, while only 3.1% had dark skin. Regarding HLA mismatch, 41.9% of patients had one mismatch, 37.2% had two, 17.8% had three, 2.3% had zero mismatches, and 0.8% had four mismatches.

Table 3.

Comparative Demographic Characteristics of Saudi Kidney Transplant Patients (SKTP) and Healthy Volunteers

Variable	SKTP Patients (n = 129)	Healthy Volunteers (n = 122)
Gender
Male	73 (56.6%)	77 (63.1%)
Female	56 (43.4%)	45 (36.9%)
Age (years)
Mean ± SD	38.1 ± 12.5	29.5 + 7.5
Range	18–60	19–49

Table 4 presents the genotypic frequencies of CYP3A5 and CYP3A4 polymorphisms in Saudi healthy volunteers and SKTPs (n=251). Table 5 presents the allelic frequencies and minor allele frequencies (MAFs) of relevant CYP3A5 and CYP3A4 polymorphisms (n=251).

Table 4.

The genotype frequencies of CYP3A5 and CYP3A4 polymorphisms (n = 251)

Gene/Genotype	Healthy (n = 122)	Patients (n = 129)
CYP3A5*2 (rs28365083)
*1/*1 (G/G)	122 (100%)	129 (100%)
CYP3A5*3 (rs776746)
*1/*1 (T/T)	2 (1.6%)	2 (1.6%)
*3/*3 (C/C)	120 (98.4%)	127 (98.4%)
CYP3A5*4 (rs56411402)
*1/*1 (T/T)	122 (100%)	129 (100%)
CYP3A4*1B (rs2740574)
*1/*1 (T/T)	106 (86.9%)	113 (87.6%)
*1/*1B (T/C)	14 (11.5%)	15 (11.6%)
*1B/*1B (C/C)	2 (1.6%)	1 (0.8%)
CYP3A4*6 (rs4646438)
*1/*1 (–/–)	122 (100%)	129 (100%)
CYP3A4*18 (rs28371759)
*1/*1 (A/A)	122 (100%)	129 (100%)
CYP3A4*22 (rs35599367)
*1/*1 (G/G)	107 (89.2%)	119 (92.2%)
*1/*22 (G/A)	11 (9.2%)	10 (7.8%)
*22/*22 (A/A)	2 (1.6%)	0 (0.0%)
Missing	2	0

Table 5.

The allelic frequencies and minor allele frequencies (MAFs) of relevant CYP3A5 and CYP3A4 polymorphisms (n = 251)

rs No	Gene genotypes	Allelic status	Allelic frequency	Allelic %	MAF
rs28365083	CYP3A5*2	*1 (G)	502	100
		*2 (T)	0	0
rs776746	CYP3A5*3	*1 (T)	8	1.6	0.984
		*3 (C)	494	98.4
rs56411402	CYP3A5*4	*1 (T)	502	100
		*4 (C)	0	0
rs2740574	CYP3A4*1B	*1 (T)	467	93	0.070
		*1B (C)	35	7
rs4646438	CYP3A4*6	*1 (D)	502	100
		*6 (T)	0	0
rs28371759	CYP3A4*18	*1 (A)	502	100
		*18 (G)	0	0
rs35599367	CYP3A4*22 (2 Missing values)	*1 (G)	473	95	0.050
		*22 (A)	25	5

Results were consistent across both groups unless stated otherwise. CYP3A5*2, CYP3A5*4, CYP3A4*6, and CYP3A4*18 were exclusively wild types (100%). For CYP3A5*3, the wild allele (*1/*1) was observed in 1.6% of participants, while the mutant allele (*3/*3) predominated at 98.4%, highlighting its high prevalence in the study population.

CYP3A4*1B showed wild-type alleles (*1/*1) in 87.2% of participants, heterozygous (*1/*1B) in 11.6%, and mutant alleles (*1B/*1B) in 1.2%, with a minor allele frequency (MAF) of 0.070. For CYP3A4*22, the wild allele (*1/*1) was present in 90.8%, heterozygous (*1/*22) in 8.4%, and mutant (*22/*22) in only 0.8%, observed exclusively in healthy volunteers, with an MAF of 0.050. These findings highlight the predominance of wild-type alleles in most polymorphisms, with significant variations noted for CYP3A5*3, CYP3A4*1B, and CYP3A4*22.

CYP3A5*3 Genotypes and Tac Requirements in Kidney Transplant Patients

The impact of CYP3A5*3 (rs776746) genotypes on tacrolimus (Tac) mean dose requirements and mean trough concentrations (C0) in SKTPs (n = 129) is depicted in Figs. 1 and 2. Figure 1 shows the variations in Tac mean dose requirements over time, highlighting that carriers of the wild allele (*1/*1 or *1/*3) required significantly higher doses, particularly during the early post-transplantation period, to achieve therapeutic Tac levels. Figure 2 illustrates the corresponding mean C0 levels, revealing that wild allele carriers consistently had lower C0 levels than mutant allele carriers (3/3), despite receiving the same initial dosing regimen. The findings demonstrate a statistically significant correlation between **CYP3A53* genotypes, Tac dose requirements, and C0 levels at almost all assessed time points. These data underscore the influence of CYP3A5*3 polymorphism on Tac pharmacokinetics in SKTPs. [Supplementary data tables provide detailed results.]

Fig. 1.

Tac mean dose requirement through different time points according to CYP3A5*3 (rs776746) genotypes in Saudi Kidney Transplant Patients (SKTPs) (n = 129)

Fig. 2.

Tac mean C0 through different time points according to CYP3A5*3 (rs776746) genotypes in Saudi Kidney Transplant Patients SKTPs (n = 129)

CYP3A4*1B Genotypes and Tac Requirements in Kidney Transplant Patients

Figures 3 and 4 illustrate the impact of CYP3A4*1B (rs2740574) genotypes on tacrolimus (Tac) mean dose requirements, mean trough concentrations (C₀), and dose-adjusted C₀ levels at 12 time points in a cohort of 129 SKTP. Figure 3 demonstrates that patients with the mutant genotype (*1B/*1B) and heterozygous genotype (*1/*1B) required significantly higher initial Tac doses, particularly in the early post-transplant period, to achieve therapeutic C₀ levels. This higher dose requirement was most pronounced in those with the mutant genotype. Figure 4 reveals that mutant carriers (*1B/*1B) consistently exhibited lower C₀ levels compared to wild allele carriers (*1/*1), although they received the same initial dosage of Tac. This trend was not as evident in heterozygous patients, except during the first week of post-transplantation. Overall, the data emphasize the need for higher Tac doses in patients with the mutant genotype to achieve therapeutic C₀ levels.

Fig. 3.

Tac mean dose requirement through different time points according to CYP3A4*1B (rs2740574) genotypes in Saudi Kidney Transplant Patients SKTPs (n = 129)

Fig. 4.

Tac mean C0 through different time points according to CYP3A4*1B (rs2740574) genotypes in Saudi Kidney Transplant Patients SKTPs (n = 129)

CYP3A4*22 Genotypes and Tac Requirements in Kidney Transplant Patients

Figures 5 and 6 highlight the effects of CYP3A4*22 genotypes on tacrolimus (Tac) dose requirements and trough concentrations (C₀) in SKTP. Figure 5 indicates that patients with the heterozygous genotype (*1/*22) required a lower initial Tac dose compared to those with the wild allele (*1/*1), particularly in the early post-transplant period. Figure 6 shows that despite receiving the same initial dose of 0.1 mg/kg/day, as well as subsequent dose adjustments, heterozygous patients exhibited higher Tac C₀ levels than their wild allele counterparts. These findings suggest that the heterozygous *1/*22 genotype is associated with enhanced Tac exposure, necessitating careful dose optimization.

Fig. 5.

Tac mean dose requirement through different time points according to CYP3A4*22 (rs35599367) genotypes in Saudi Kidney Transplant Patients (SKTPs) (n = 129)

Fig. 6.

Tac mean C0 through different time points according to CYP3A4*22 (rs35599367) genotypes in Saudi Kidney Transplant Patients (SKTPs) (n = 129)

Discussion

Tacrolimus exhibits high inter-patient pharmacokinetic variability, and pharmacogenetic factors are pivotal in explaining these differences (Chen and Prasad 2018). Our study is among the first from Saudi Arabia to address tacrolimus pharmacogenetics in kidney transplant recipients, helping to fill a regional knowledge gap. Data on immunosuppressant pharmacogenetics in Middle Eastern populations have been scarce, so our findings provide valuable local insight. Notably, the allele distribution in our Saudi cohort mirrors patterns seen in nearby populations – for example, a Lebanese study found that most individuals carried the non-functional CYP3A5*3 allele, similar to European populations (Milane et al. 2021). Such regional evidence underlines the importance of studying these genetic influences locally, as population-specific frequencies and effects can guide more accurate, personalized tacrolimus dosing.

CYP3A5 is a key enzyme in tacrolimus metabolism, and the common loss-of-function variant CYP3A53 (6986A > G) has a profound impact on dose requirements. In our Saudi transplant cohort, the CYP3A53 allele was highly prevalent, consistent with prior reports in Saudi population (Goljan et al. 2022b). This means the majority of our patients are CYP3A5 non-expressers (homozygous *3/*3), who produce a non-functional enzyme. Clinically, these patients tend to achieve target tacrolimus concentrations with lower doses, whereas carriers of at least one functional allele (1) – so-called CYP3A5 “expressers” – metabolize tacrolimus faster and typically require significantly higher doses. In fact, CYP3A5 expressers often need roughly 1.5- to twofold higher tacrolimus dosages than CYP3A53/3 patients to reach comparable trough (Bayanova et al. 2024). In our study, this genotype effect was evident: patients with CYP3A53/*3 achieved therapeutic tacrolimus levels with smaller doses, while those with *1 alleles needed escalated dosing to avoid subtherapeutic troughs, mirroring findings in other populations.

Our results reinforce the extensive body of literature identifying CYP3A5 genotype as the single most influential determinant of tacrolimus pharmacokinetics. A meta-analysis by Rojas et al. found that kidney transplant patients carrying a CYP3A5*1 allele (expressers) had significantly lower dose-normalized tacrolimus blood concentrations at multiple time points post-transplant (weeks 1–2 and months 1, 3, 6, 12) compared to CYP3A53/*3 patients. Consequently, without genotype-guided dose adjustments, CYP3A5 expressers may be at higher risk of under-immunosuppression: the meta-analysis suggested these patients had an elevated incidence of acute rejection, presumably due to subtherapeutic drug exposure. Conversely, non-expressers (*3/*3) can more readily accumulate tacrolimus, which may predispose them to tacrolimus-related nephrotoxicity if standard dosing is applied (Bayanova et al. 2024). Thus, CYP3A5 variability has tangible clinical consequences. By identifying a patient’s metabolizer status, we can adjust initial tacrolimus dosing to avoid extremes of drug exposure. Several studies – including a recent prospective study in an Egyptian cohort – emphasize that preemptive CYP3A5 genotyping can help optimize tacrolimus therapy, allowing doses to be tailored so that each patient more quickly reaches therapeutic levels without toxicity (Ebid et al. 2022). Our findings strongly concur: incorporating CYP3A5-guided dosing in clinical practice would be expected to improve therapeutic outcomes by reducing the trial-and-error period of dose titration, ultimately minimizing the risks of rejection (from under-dosing) or adverse effects (from over-dosing).

Although CYP3A5 garners most attention, variability in the CYP3A4 gene can also affect tacrolimus metabolism. The two principal CYP3A4 variants of interest are 22 (an intronic SNP, rs35599367) and 1B (a promoter region variant, rs2740574). CYP3A4*22 is a minor allele associated with reduced CYP3A4 enzyme expression. It is relatively rare in Middle Eastern populations (for example, only ~ 3% of Egyptian patients carried a 22 allele in one cohort) (Ebid et al. 2022), but its presence has a measurable impact. Carriers of CYP3A4*22 have slower tacrolimus clearance, leading to higher blood concentrations per dose. In the Egyptian study, for instance, patients with at least one 22 allele showed a significantly higher dose-adjusted trough concentration (median C_0/D) than wild-type (1/1) patients by the third month post-transplant (Ebid et al. 2022). Even after accounting for CYP3A5 genotype, the effect of 22 remains evident: a combined analysis of multiple studies demonstrated that CYP3A4*22 carriers (in the absence of functional CYP3A5) had about 0.67 ng/mL/mg higher tacrolimus trough levels per mg of dose, and required on average 1.8 mg/day lower tacrolimus doses, compared to CYP3A41/1 (Bayanova et al. 2024). In practical terms, a patient carrying CYP3A4*22 is an inherently slower metabolizer of tacrolimus and may be prone to drug accumulation; recognizing this variant can alert clinicians to reduce the starting dose and vigilantly monitor trough levels to prevent toxicity. Notably, our study observed that the few patients with CYP3A422 achieved target concentrations with smaller doses than typical, aligning with these reports. Furthermore, the combination of CYP3A422 and CYP3A5*3 can compound the effect: one report classified patients with *3/*3 plus 22 as “poor metabolizers” who exhibited substantially higher tacrolimus exposure than those lacking either variant (Bayanova et al. 2024). Although numbers were small, it underlines that CYP3A422 – while uncommon – contributes additively to tacrolimus disposition and merits consideration in a comprehensive pharmacogenetic assessment.

In contrast, CYP3A4*1B tends to have the opposite effect. This variant (predominant in some ethnic groups, such as those of African descent) is associated with increased CYP3A4 activity or expression, though findings have been somewhat inconsistent across studies. A meta-analysis by Shi et al. examined CYP3A4*1B in adult renal transplant recipients and found that patients with the 1B allele generally required higher tacrolimus doses than those with the wild-type genotype (Rojas et al. 2015). Specifically, in predominantly European cohorts, CYP3A4*1B carriers had lower tacrolimus concentration-to-dose ratios and needed significantly larger weight-adjusted doses at 7 days, 6 months, and 12 months post-transplant. Thus, CYP3A4*1B can be viewed as a “fast-metabolizer” marker. In our Saudi population, CYP3A4*1B is expected to be relatively infrequent (given its low frequency in non-African groups), and indeed we did not find a strong signal of its effect in our cohort. Nevertheless, its potential influence should be acknowledged: a CYP3A4*1B carrier might clear tacrolimus more rapidly, risking subtherapeutic levels if managed with standard dosing. Overall, while CYP3A4 polymorphisms exert a more modest influence on tacrolimus pharmacokinetics than CYP3A5, they can still meaningfully alter dose requirements in certain patients. For clinicians, awareness of a patient’s CYP3A4*22 or *1B status – when available – could further refine tacrolimus dosing decisions (e.g., flagging *22 carriers for dose reduction or *1B carriers for closer monitoring of troughs). Our data suggest that incorporating CYP3A4 genotyping alongside CYP3A5 could enhance the personalization of tacrolimus therapy, especially in borderline cases where unexplained PK variability persists.

Our findings underscore the clinical relevance of incorporating pharmacogenetic testing—particularly for CYP3A5*3 and CYP3A4*22—into routine tacrolimus dosing strategies. Early identification of CYP3A5 non-expressers allows for reduced initial dosing, minimizing the risk of tacrolimus overexposure and nephrotoxicity (Lee et al. 2022; Rojas et al. 2015; Abdel-Kahaar et al. 2019). Conversely, expressers may require prompt dose escalation to avoid under-immunosuppression and acute rejection (Nguyen et al. 2023; Abdel-Kahaar et al. 2019; Coto et al. 2011). Integrating genotyping into the pre-transplant workup can facilitate personalized dosing from the outset, improving time in therapeutic range and reducing early dose titration errors (Ebid et al. 2022; Coto et al. 2011; Song et al. 2019). This aligns with precision medicine goals and may enhance both graft outcomes and patient safety—particularly in high-prevalence regions like Saudi Arabia where CYP3A5*3 predominates (Ebid et al. 2022; Almeman 2021; Al Nasser 2020).

Study Limitations

Despite the insights gained, several limitations of our study should be acknowledged. First, this was a single-center study with a relatively limited sample size; thus, our patient cohort may not fully represent the broader kidney transplant population in Saudi Arabia, limiting the generalizability of the findings. Second, the study’s retrospective design imposed inherent constraints on controlling confounding variables. Factors such as patients’ diet, concurrent medications, and unmeasured genetic differences (beyond the variants we genotyped) could all influence tacrolimus levels and clinical outcomes, but we could only account for those variables documented in the charts. Third, some genetic subgroups were under-represented in our cohort. For example, certain minor alleles and categories of HLA mismatch were found in only a few patients. The low frequency of these groups reduces our statistical power to detect significant associations or differences attributable to them. It is possible that real genotype–phenotype effects exist for these variants, but our study was not large enough to demonstrate them conclusively. Finally, we did not examine long-term clinical outcomes in depth (beyond acute trough level achievement and early post-transplant events), so the ultimate impact of genotype-adjusted dosing on graft survival or chronic rejection remains to be confirmed.

Looking forward, our findings need to be validated and expanded upon by larger studies. We recommend future research in the form of multi-center trials or consortiums with more diverse and sizable patient cohorts, ideally using a prospective design where dosing is guided by genotype from the outset. Such studies would provide more robust evidence on the benefits of pharmacogenetic-driven tacrolimus therapy and could address any population-specific effects in Arab or Middle Eastern patients that our single-center study could not fully capture. By overcoming the aforementioned limitations, subsequent work can build on our results and further pave the way for implementing personalized immunosuppression in clinical practice.

Conclusion

Our study highlights the strong impact of pharmacogenetics on tacrolimus therapy in Saudi kidney transplant patients. The CYP3A5*3 variant, highly prevalent in this population, is associated with reduced enzyme activity and lower tacrolimus dose requirements. Identifying patients’ CYP3A5 genotype can guide personalized dosing to improve efficacy and reduce toxicity. Additionally, considering CYP3A4 polymorphisms (*1B and *22) can further refine dosing strategies. Incorporating genetic testing into routine practice may enhance outcomes by preventing rejection and minimizing adverse effects.

Supplementary Information

Below is the link to the electronic supplementary material.

Authors’ contribution

Marzog S Al Nasser, Mai A A Sattar Ahmad, Sherif Edris, Ezz Abdelfattah, Ahmed S Ali, Mohammad F. Zaitoun, and Zoheir Damanhouri contributed to the study conception and design. Material preparation and analysis, data collection and analysis were performed by Marzog S Al Nasser, Ezz Abdelfattah, Futoon H Alharbi, and Hadiah B Al Mahdi. The first draft of the manuscript was written by Marzog S Al Nasser and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

The study was self-funded.

Data availability

The data is available upon offical request and institutional approval, without disclosure of patients’ identifiers.

Declarations

Ethics

All Patients were consented before samples withdrawal, the study was approved by the Armed Forces Hospital Southern Region (H-06-KM-001).

Competing interests

Nothing to disclose.