Impact of CYP3A5 Gene Polymorphisms on Tacrolimus Pharmacokinetics and Renal Allograft Rejection in Kidney Transplant Recipients: A Meta-Analysis Across Ethnic Populations

Impact of CYP3A5 Gene Polymorphisms on Tacrolimus Pharmacokinetics and Renal Allograft Rejection in Kidney Transplant Recipients: A Meta-Analysis Across Ethnic Populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of CYP3A5 Gene Polymorphisms on Tacrolimus Pharmacokinetics and Renal Allograft Rejection in Kidney Transplant Recipients: A Meta-Analysis Across Ethnic Populations Abdul Khan, Syed Zaidi, Shafaq Hassan, Erum Hanif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7490941/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tacrolimus is a cornerstone immunosuppressant in kidney transplantation, but its pharmacokinetics show significant interindividual variability, primarily due to CYP3A5 polymorphisms. This meta-analysis evaluated the impact of CYP3A5 expresser (*1/*1, *1/*3) versus non-expresser (*3/*3) genotypes on tacrolimus concentration-to-dose (Co/D) ratios and renal allograft rejection. Fifty-one studies were included: 24 reported Co/D ratios, 17 addressed rejection episodes, and 10 provided both. CYP3A5 expressers had significantly lower Co/D ratios at all post-transplant time points, reflecting faster metabolism and higher dose requirements. Ethnicity-stratified analysis revealed stronger effects in Asians (SMD: − 1.35 to − 1.50) than in Europeans (SMD: − 0.37 to − 1.05). Although overall rejection risk was not significantly higher in expressers (OR: 1.16, p = 0.13), a significant association was found in Asian populations (OR: 1.56, p = 0.0061). These findings support genotype-guided dosing of tacrolimus to improve clinical outcomes in kidney transplant recipients. Biological sciences/Computational biology and bioinformatics/Predictive medicine Biological sciences/Genetics/Gene expression Biological sciences/Genetics/Genetic association study CYP3A5 polymorphism tacrolimus kidney transplantation pharmacogenetics meta-analysis renal allograft rejection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Tacrolimus, isolated from Streptomyces tsukubaensis in 1984, is an immunosuppressant widely used to prevent organ rejection in transplant recipients, particularly among the recipients of liver, kidney, or heart transplants [ref. 1, 2]. The primary target of tacrolimus is the calcineurin, a calcium-dependent serine/threonine protein phosphatase predominantly expressed in T cells [ref. 3]. Calcineurin is necessary for the activation of several genes for cytokines required for T cell activation and proliferation, which include Interleukin (IL)-2 [ref. 4]. It inhibits calcineurin-mediated signal transduction by binding immunophilins, thus preventing T cell activation and IL-2 synthesis [ref. 4–6]. This suppresses both the cell-mediated immunity as well as the humoral immunity to the allograft. However, tacrolimus has a narrow therapeutic window with regard to dosage and concentration, which means that dosing is particularly difficult due to the large inter-individual variability in dose requirements and concentrations [ref. 7]. Tacrolimus requires careful monitoring and dosing adjustment to achieve optimal drug levels. Standard practice involves regular measurement of tacrolimus trough levels, the concentration of the drug in the blood just before the next dose to guide dosing adjustments. Despite this approach, achieving and maintaining the appropriate tacrolimus concentration can be challenging due to its complex pharmacokinetics [ref. 8]. Tacrolimus is extensively metabolized by the CYP3A enzyme subfamily [9]. The CYP3A enzyme subfamily is mainly made up of CYP3A4 and CYP3A5. Specifically, CYP3A5 is involved in the metabolism of tacrolimus in the liver and the small intestine [ref. 10]. A SNP at the position 6986 (6986A > G) in intron 3 of the CYP3A5 gene produces a splice variant leading to the production of the non-functional CYP3A5*3 protein, which is associated with a ‘CYP3A5 nonexpresser’ phenotype [ref. 11]. Patients who are CYP3A5*3 (G allele) homozygotes have a lower expression of the CYP3A5 enzyme and higher dose-normalized tacrolimus concentrations. Expressers with at least one CYP3A5*1 allele have lower blood concentration due to faster metabolism, thereby being more susceptible to rejection of transplants [ref. 12]. Therefore, the CYP3A5 genotype has been identified as the main predictor of tacrolimus exposure and the pharmacogenomic mechanism of the inter-patient dosing difference. This genetic information could be incorporated to enhance the first dose determination and first-time achievement of the therapeutic range [ref. 13]. Studies have shown that various factors, including ethnic background, liver and kidney disease, other medications, and gene variations affect the pharmacokinetics of tacrolimus [ref. 14]. Of these, genetic variations in the expression and function of the metabolizing enzymes like the cytochrome P450 (CYP) 3A5 have been identified as the major predictors of dose-standardized tacrolimus exposure. Systematic reviews have shown significantly higher tacrolimus trough concentration divided by daily dose per body weight (Co/D) in patients carrying CYP3A5 (GG or *3/*3) polymorphism i.e., nonexpresser, than expresser CYP3A5 (AA or *1/*1) at various posttransplant time. These analyses have certain limitations as they lack pooled studies with similar clinical covariates, ethnicity, and posttransplantation duration. In this meta-analysis we aim to assess the impact of CYP3A5 genetic polymorphism on tacrolimus pharmacokinetics and outcomes in kidney transplant recipients. Tacrolimus Co/D ratio and the incidence of graft rejection are compared between expressers and nonexpressers with regard to ethnicity (European/Asian) and time post-transplantation. Our data compilation approach combines pharmacokinetic data within homogeneous subgroups to understand genotype-specific tacrolimus exposure during multiple transplant phases and concentration dynamics. This can inform time-dependent dosing adjustments through the initial critical exposure phase. Assessing differential rejection risk will also help to evaluate the possible clinical benefits of genotype-based dosing. In sum, this meta-analysis will give ethnicity-specific estimates of the probable size and time course of the effect of the CYP3A5 polymorphism on the pharmacokinetics of tacrolimus and its important effects in transplantation among kidney transplant recipients. These results could enhance the usage of preemptive genotyping, which allows for personalized dose adjustments from the start rather than relying solely on weight-based dosing. By personalizing immunosuppression through pharmacogenetics, the issues of efficiency, side effects, and long-term consequences can be addressed. Methodology Literature Search Strategy The literature search was performed from the electronic databases of PubMed, Google Scholar, EMBASE, and the Cochrane Library published up to July 2024. The MeSH terms utilized to identify relevant studies were “Cytochrome P-450 CYP3A,” “Tacrolimus,” “Kidney Transplantation,” “Meta-Analysis,” “NOT (Systematic Review [Publication Type])” and NOT (Review [Publication Type]).”. For further data, the references cited in the selected studies were also explored. The database search was conducted by two reviewers independently. Data Inclusion/Exclusion Criteria Studies published between 2000 and 2024 were included. In total, 104 studies were eligible for further analysis [Figure 1 ]. These selected studies were screened for the following data according to the eligibility criteria: the first author's name, year of publication, ethnicity, CYP3A5 genotyping (CYP3A5*1/*1, CYP3A5*1/*3, and CYP3A5*3/*3) among renal transplant patients, total number of events/total number of patients in expressers and non-expressers, and mean and standard deviation of tacrolimus dosage and trough concentration levels at week 1, 2, and 3 and month 1, 3, 6, and 12 after transplantation. The review articles were excluded. When additional data was required, the corresponding authors were contacted via email; however, the responses received were not relevant and those studies were omitted. Data Extraction and Management After completing the literature search, data extraction was carried out from all 104 studies. The extracted information was organized into a Microsoft Office Excel file with two separate sheets: ( 1 ) Graft Rejection Episodes: Collected data on the incidence of graft rejection in relation to CYP3A5 gene polymorphisms. ( 2 ) Tacrolimus Dosage and Trough Concentration Levels: This data was further categorized into different time intervals: week 1, week 2, week 3, month 1, month 3, month 6, and month 12. Statistical Analysis All statistical analyses were performed by using R language, specifically utilizing the meta package. The following analyses were conducted: ( 1 ) Forest plots were generated to visualize the impact of CYP3A5 gene polymorphisms on (a) graft rejection rates in the Asian, European, and both populations. (b) Tacrolimus dosage and trough concentration levels for the Asian, European, and both populations at week 1, week 2, week 3, month 1, month 3, month 6, and month 12. ( 2 ) Funnel plots were created to assess publication bias for each dataset mentioned above. ( 3 ) Begg's and Egger's Tests: For each forest plot, Begg's and Egger's tests were conducted to statistically evaluate the presence of publication bias. p-values were calculated for each forest plot to check the significance of potential biases. A p-value < 0.05 was considered as significant publication bias, and p-value ≥ 0.05 was considered as no evidence of publication bias. In this meta-analysis, continuous outcomes (association of tacrolimus Co/D with CYP3A5 gene polymorphism) were determined by standardized mean difference (SMD), and dichotomous outcomes (graft rejection episodes) were determined by the odds ratio (OR). Heterogeneity (I 2 ) of the studies was calculated by using the I 2 statistic test. The range of heterogeneity was 0–100%. A fixed-effects model was applied when heterogeneity was absent, while a random-effects model was used when I 2 > 50% or p ≤ 0.05. Tools and Software Literature was searched from the electronic databases of PubMed, Google Scholar, EMBASE, and the Cochrane Library. Microsoft Office Excel was employed for data management and organization. For statistical analysis, R language (version 4.4.1) was used to generate the forest and funnel plots, as well as for the execution of Begg's and Egger's tests. The meta package was used for performing the meta-analysis. RESULTS Study Selection and Characteristics A comprehensive literature search was conducted from electronic databases of PubMed, Google Scholar, EMBASE, and Cochrane Library for studies published up to July 2024, focusing on the role of CYP3A5 gene polymorphism in kidney transplantation with tacrolimus (TAC) treatment. The search employed MeSH terms related to tacrolimus, meta-analysis, and cytochrome P450 CYP3A in kidney transplantation. For the broader inclusion of relevant studies, the references cited in the retrieved articles were also explored. A total of 104 studies were retrieved from the literature search. After a thorough screening, 53 studies were excluded based on data insufficiency or failure to meet the inclusion criteria. The corresponding authors were contacted via email for additional information. Finally, 51 studies were selected for inclusion, 24 of which focused on the relationship between CYP3A5 polymorphisms and tacrolimus pharmacokinetics (Co/D ratio), 17 on rejection episodes in renal transplant patients, and 10 studies contained both data [Table 1,2]. Tacrolimus Co/D Studies in Renal Transplant Patients Across all time intervals (Week 1, Week 2, Week 3, Month 1, Month 3, Month 6, and Month 12), the Standardized Mean Differences (-0.68, -1.17, -1.10, -1.09, -1.27, -0.77, and − 0.79) at each time point indicate a decrease in the tacrolimus concentration-to-dose (Co/D) ratio for CYP3A5 expressers compared to non-expressers. The results showed no significant heterogeneity at Week 2 and Week 3 (0% and 4%) and showed high significant heterogeneity at Week 1, Month 1, Month 3, Month 6, and Month 12 (77.4%, 63.5%, 80%, 66%, and 86%) among the mean differences of the Tacrolimus Co/D [Figure 2 ]. The meta-analysis found no significant evidence of publication bias as shown by Begg’s rank test (0.86, 0.76, 0.59, 0.43, 0.45, 0.95, and 0.37), and there was no significant publication bias in Egger’s test as well (0.10, 0.86, 0.83, 0.48, 0.39, and 0.30), except for Month 12 (0.04). Tacrolimus Co/D Ratio with Time Intervals among Asian Transplant Patients For the Asian population, the SMD values (-1.35, -1.39, -1.16, -1.13, -1.50, -0.94, and − 0.98) at each time point indicate a substantial decrease in the Co/D ratio for CYP3A5 expressers. The results showed high significant heterogeneity (40%, 68%, 80.7%, 88.6%, 42.3%, and 59.2%) at all intervals except Week 2 (0%) among the mean differences of Tacrolimus Co/D [Figure 3 ]. The meta-analysis consistently shows no evidence of publication bias based on Begg’s test (0.81, 1.00, 1.00, 0.13, 1.00, 0.76, and 0.71) and Egger’s tests (0.39, 0.74, 0.70, 0.24, 0.54, 0.75, and 0.59). Tacrolimus Co/D Ratio with Time Intervals among European Transplant Patients In the European population, the SMD values (-0.37, -1.01, -1.05, -1.04, -1.02, -0.96, and − 0.77) consistently indicate a decrease in the Co/D ratio for CYP3A5 expressers, though the magnitude of the decrease is smaller compared to the Asian population. Results showed that there was no significant heterogeneity (0%, 0%, 0%, 0%, 20.3%, and 0%) among the studies except at Month 12 (90%) [Figure 4 ]. The meta-analysis found no significant evidence of publication bias as shown by Begg’s rank test (0.06, 0.31, 1.00, 1.00, 1.00, 1.00, and 0.22), and there was no significant publication bias in Egger’s test (0.33, 0.83, 0.52, 0.87, 0.67, and 0.25) as well, except at Week 1 (0.0007). Tacrolimus-Based Rejection Episodes The association of CYP3A5 genotypes with renal allograft rejection episodes was determined in 27 studies. The selected studies contained 3,507 patients. Results revealed an odds ratio (OR) of 1.16 with a 95% confidence interval (CI) ranging from 0.96 to 1.40. The heterogeneity was low (I 2 = 3.9%), indicating minimal variability between the included studies. The overall effect test yielded a z-value of 1.50 with a p-value of 0.13 [Figure 5 ]. Begg’s rank test (p = 0.79) and Egger’s test (p = 0.32) showed no evidence of publication bias. Association between CYP3A5 Expresser and Non-expresser Genotypes: Association between CYP3A5 Expresser and Non-expresser Genotypes with Renal Allograft Rejection in Asian Populations The analysis of 10 studies of Asian populations included 1,014 patients with the CYP3A5 genotype and showed a statistically significant association with renal allograft rejection. The calculated OR was 1.56 with a 95% CI of 1.14 to 2.15. There was no heterogeneity (0%) among the studies. The test for overall effect showed a z-value of 2.74 and a p-value of 0.0061 [Figure 6 :A]. No publication bias was detected, as indicated by Begg’s rank test (p = 0.59) and Egger’s test (p = 0.72). Association between CYP3A5 Expresser and Non-expresser Genotypes with Renal Allograft Rejection in European Populations The analysis of 14 studies of European populations comprised 1,674 patients of CYP3A5 genotype and showed an OR of 1.14 with a 95% CI of 0.84 to 1.55. The heterogeneity was very low at 2%. The overall effect test produced a z-value of 0.81 and a p-value of 0.42 [Figure 6 :B]. Begg’s rank test (p = 0.83) and Egger’s test (p = 0.79) did not reveal any evidence of publication bias. DISCUSSION This meta-analysis highlights the significant influence of CYP3A5 gene polymorphisms on tacrolimus pharmacokinetics and transplant outcomes. CYP3A5 expressers (*1/*1 and *1/*3) were found to metabolize tacrolimus more rapidly than non-expressers (*3/*3), leading to lower drug exposure for the same dose. These pharmacokinetic differences were consistently observed across all post-transplant intervals, underscoring the sustained impact of CYP3A5 genotype on tacrolimus clearance and the potential clinical consequences of unadjusted dosing. The large sample size and stratified subgroup analyses allowed for a clearer understanding of how CYP3A5 expression patterns vary across ethnicities and influence treatment response. Our results reinforce the importance of genotype-guided tacrolimus dosing, particularly in populations with a higher frequency of the expresser allele. Tacrolimus Concentration-to-Dose (Co/D) Ratio CYP3A5 expressers consistently exhibited significantly lower Co/D ratios at all measured time points from Week 1 to Month 12. These findings confirm that expressers require higher tacrolimus doses to achieve therapeutic levels. The persistence of this pattern across time intervals — with Standardized Mean Differences ranging from − 0.68 to − 1.27 — indicates that this is not a transient post-transplant effect but rather a consistent pharmacogenetic influence. The results showed low heterogeneity at certain intervals (e.g., Week 2 and Week 3), suggesting consistency across studies. However, high heterogeneity was noted at other intervals, particularly Week 1, Month 3, and Month 12. This variation may stem from differences in clinical protocols, population characteristics, or sample sizes across studies, especially in ethnically diverse groups. Importantly, despite variability in effect size, the direction of association was consistent — always indicating reduced tacrolimus exposure in expressers. This trend is clinically important, especially in the early post-transplant phase when underexposure to tacrolimus can increase the risk of acute rejection. The findings underscore the need for preemptive dose adjustments based on genotype to avoid subtherapeutic levels in expressers. Renal Allograft Rejection Episodes Although the overall association between CYP3A5 expression and rejection risk was not statistically significant (OR 1.16, p = 0.13), the trend toward increased rejection in expressers is noteworthy. The slight elevation in odds ratio may reflect clinically meaningful underexposure to tacrolimus due to faster metabolism in this group, particularly when standard dosing is applied without consideration of genotype. Importantly, heterogeneity among these studies was very low (3.9%), indicating that the trend was relatively consistent. This strengthens the suggestion that even though the result did not meet statistical significance, CYP3A5 expression could be an independent risk factor for rejection in some contexts, especially if therapeutic drug monitoring is delayed or inadequate during the early post-transplant period. Ethnic Differences The impact of CYP3A5 expression was more pronounced in Asian populations than in Europeans, both in terms of Co/D ratios and rejection risk. In Asian cohorts, expressers exhibited a significantly greater reduction in Co/D ratios compared to non-expressers (SMDs as low as − 1.50), which likely reflects both higher allele frequency and greater metabolic impact. In this group, CYP3A5 expression was significantly associated with increased rejection risk (OR 1.56, p = 0.0061), indicating that tacrolimus underexposure due to rapid metabolism may have tangible clinical consequences if not accounted for in dosing strategies. In contrast, the European subgroup showed a weaker reduction in Co/D ratios and no statistically significant association with rejection (OR 1.14, p = 0.42). These differences may reflect both genetic and clinical practice variations, such as more frequent drug monitoring or lower baseline rejection risk. The heterogeneity was notably low in European studies, further suggesting that the CYP3A5 effect in this group is less variable and perhaps less clinically impactful. These findings emphasize the importance of population-specific pharmacogenetic protocols. In regions with high expresser allele prevalence, such as Asia, genotype-guided dose optimization may be particularly critical for preventing rejection. Clinical Implications This study supports the clinical utility of CYP3A5 genotyping in tailoring tacrolimus therapy. Expressers metabolize the drug more rapidly and are at greater risk of underexposure if standard dosing protocols are used. Genotype-guided therapy enables early dose adjustments, reducing the risk of rejection and improving patient outcomes. The findings have particular relevance for Asian populations, where the frequency and impact of the expresser genotype are higher. The variability in SMDs and higher heterogeneity observed in Asian cohorts underscore the need for more individualized and proactive therapeutic drug monitoring (TDM) in these patients. By identifying patients at risk for fast metabolism before treatment begins, clinicians can implement higher initial dosing, closer monitoring, and more dynamic adjustments. This approach not only improves safety but may also reduce hospital readmissions related to rejection or toxicity. Limitations This meta-analysis has several limitations. First, heterogeneity was high in some subgroups, particularly among Asian Co/D studies, which may reduce the precision of pooled estimates. Second, many studies lacked detailed reporting on confounders such as patient weight, age, or comorbidities, which could influence tacrolimus metabolism. Third, this analysis focused solely on CYP3A5, while other genes like CYP3A4 and ABCB1 also play a role in tacrolimus pharmacokinetics and were not considered here. Additionally, data on long-term graft outcomes beyond 12 months were limited, making it difficult to assess the sustained clinical impact of genotype-guided dosing. The underrepresentation of some ethnic groups, such as South Asians and Africans, limits the generalizability of these findings to global transplant populations. Future Directions Future studies should focus on multi-gene pharmacogenomic models that include CYP3A4, ABCB1, and other relevant genes to better predict tacrolimus response. There is also a need for larger, prospective, and ethnically diverse trials to validate genotype-guided dosing algorithms in real-world clinical settings. Inclusion of long-term follow-up data will be crucial for evaluating the effect of personalized dosing on graft survival, chronic rejection, and overall patient health. Additionally, implementation research — including cost-effectiveness studies and integration into electronic health records — is needed to translate pharmacogenetic insights into routine care. Conclusion This meta-analysis confirms that CYP3A5 expressers metabolize tacrolimus more rapidly, resulting in consistently lower Co/D ratios across all post-transplant intervals. This metabolic difference is more pronounced in Asian populations, who also showed a significantly increased risk of graft rejection. These findings support the use of genotype-guided tacrolimus dosing to improve therapeutic precision, particularly in high-risk populations. Despite certain limitations, the consistent pharmacokinetic patterns and population-specific differences provide a strong rationale for integrating CYP3A5 testing into pre-transplant evaluations. Future research should expand to multi-gene models, diverse populations, and long-term clinical outcomes to fully realize the benefits of precision medicine in kidney transplantation. Declarations Acknowledgements The authors gratefully acknowledge the support of the Department of Biotechnology, University of Karachi, for providing the academic environment necessary to carry out this research. We are also thankful to the Centre for Human Genetics and Molecular Medicine, Sindh Institute of Urology and Transplantation (SIUT) for facilitating access to relevant literature and clinical insights that greatly contributed to the development of this meta-analysis. Author Contributions Syed Mustafa Zaidi : Contributed to data extraction, statistical analysis, creation of figures (PRISMA flowchart, forest and funnel plots), and writing of the manuscript under supervision. Dr. Shafaq Aiyaz Hassan : Supervised the project, provided critical feedback, ensured methodological integrity, and reviewed the manuscript for accuracy and academic rigor. Dr. Erum Hanif: Assisted in study design, guided interpretation of results, and provided critical revisions to the manuscript. Abdul Rafay Khan : Led the research planning and design, guided the literature review process, supervised data collection, assisted in statistical visualization (forest and funnel plots), and played a key role in structuring and writing the manuscript. Also served as the corresponding author. All authors have reviewed and approved the final manuscript. Competing Interests The authors declare that they have no competing financial or personal interests that could have appeared to influence the work reported in this paper. Data Availability Statement The data supporting the findings of this study are available within the article and its supplementary materials. All relevant raw data used for the meta-analysis, including extracted study characteristics, effect sizes, and statistical outputs, can be made available by the corresponding author upon reasonable request for non-commercial research purposes, without breaching participant confidentiality. 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Tacrolimus dose requirements and CYP3A5 genotype and the development of calcineurin inhibitor-associated nephrotoxicity in renal allograft recipients. Therapeutic Drug Monitoring , 32 (4), 394–404. https://doi.org/10.1097/ftd.0b013e3181e06818 Min, S., Kim, S. Y., Ahn, S. H., Min, S., Kim, S. H., Kim, Y. S., Moon, K. C., Oh, J. M., Kim, S. J., & Ha, J. (2010). CYP3A5 *1 allele: Impacts on early acute rejection and graft function in tacrolimus-based renal transplant recipients. Transplantation, 90 (12), 1394–1400. https://doi.org/10.1097/tp.0b013e3181fa93a4 Niioka, T., Kagaya, H., Saito, M., Inoue, T., Numakura, K., Yamamoto, R., Habuchi, T., Satoh, S., & Miura, M. (2017). Impact of the CYP3A5 genotype on the distributions of dose-adjusted trough concentrations and incidence of rejection in Japanese renal transplant recipients receiving different tacrolimus formulations. 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Higher number of tacrolimus dose adjustments in kidney transplant recipients who are extensive and intermediate CYP3A5 metabolizers. Clinical Transplantation , 37 (4). https://doi.org/10.1111/ctr.14893 Xuan, N. T., Hop, V. Q., Kien, T. Q., Toan, P. Q., Thang, L. V., Binh, H. T., Van Tran, P., Minh, H. T., Man, P. T., Cuong, H. X., Ben, N. H., Phuong, N. M., Linh, N. T., Linh, N. T., Dung, V. D., Quyen, L. T. B., Hang, D. T. T., & Su, H. X. (2022). Frequencies and association of CYP3A5 polymorphism with tacrolimus concentration among renal transplant recipients in Vietnam. Transplantation Proceedings, 54 (8), 2140–2146. https://doi.org/10.1016/j.transproceed.2022.07.009 Kim, I., Moon, Y. J., Ji, E., Kim, K. I., Han, N., Kim, S. J., Shin, W. G., Ha, J., Yoon, J., Lee, H. S., & Oh, J. M. (2011b). Clinical and genetic factors affecting tacrolimus trough levels and drug-related outcomes in Korean kidney transplant recipients. European Journal of Clinical Pharmacology , 68 (5), 657–669. https://doi.org/10.1007/s00228-011-1182-5 Li, L., Li, C., Zheng, L., Zhang, Y., Jiang, H., Si-Tu, B., & Li, Z. (2011b). Tacrolimus dosing in Chinese renal transplant recipients: a population-based pharmacogenetics study. European Journal of Clinical Pharmacology , 67 (8), 787–795. https://doi.org/10.1007/s00228-011-1010-y Li, J., Wang, X., Chen, S., Liu, L., Fu, Q., Chen, X., Teng, L., Wang, C., & Huang, M. (2010b). Effects of diltiazem on pharmacokinetics of tacrolimus in relation to CYP3A5 genotype status in renal recipients: from retrospective to prospective. The Pharmacogenomics Journal , 11 (4), 300–306. https://doi.org/10.1038/tpj.2010.42 Zhang, X., Liu, Z., Zheng, J., Chen, Z., Tang, Z., Chen, J., & Li, L. (2005b). Influence of CYP3A5 and MDR1 polymorphisms on tacrolimus concentration in the early stage after renal transplantation. Clinical Transplantation , 19 (5), 638–643. https://doi.org/10.1111/j.1399-0012.2005.00370.x Zhang, J., Zhang, X., Liu, L., & Tong, W. (2010b). Value of CYP3A5 genotyping on determining initial dosages of tacrolimus for Chinese renal transplant recipients. Transplantation Proceedings, 42 (9), 3459–3464. https://doi.org/10.1016/j.transproceed.2010.06.028 Ferraresso, M., Turolo, S., Ghio, L., Tirelli, A. S., Belingheri, M., Villa, R., Groppali, E., & Edefonti, A. (2011b). Association between CYP3A5 polymorphisms and blood pressure in kidney transplant recipients receiving calcineurin inhibitors. Clinical and Experimental Hypertension , 33 (6), 359–365. https://doi.org/10.3109/10641963.2011.561896 Galiana, M., Jos, M., Bos, V., Bea, S., Ros, E., Sánchez-Plumed, J., Luis, J., & Fernández, S. (2012b). Pharmacogenetics of immunosuppressive drugs in renal transplantation. In InTech eBooks . https://doi.org/10.5772/26325 Kurzawski, M., Dąbrowska, J., Dziewanowski, K., Domański, L., Perużyńska, M., & Droździk, M. (2014). CYP3A5 and CYP3A4, but not ABCB1 polymorphisms affect tacrolimus dose-adjusted trough concentrations in kidney transplant recipients. Pharmacogenomics , 15 (2), 179–188. https://doi.org/10.2217/pgs.13.199 Mourad, M., Wallemacq, P., De Meyer, M., Brandt, D., Van Kerkhove, V., Malaise, J., Eddour, D. C., Lison, D., & Haufroid, V. (2006). The influence of genetic polymorphisms of cytochrome P450 3A5 and ABCB1 on starting dose- and weight-standardized tacrolimus trough concentrations after kidney transplantation in relation to renal function. Clinical Chemistry and Laboratory Medicine (CCLM) , 44 (10). https://doi.org/10.1515/cclm.2006.229 Tavira, B., García, E. C., Díaz-Corte, C., Ortega, F., Arias, M., Torres, A., Díaz, J. M., Selgas, R., López-Larrea, C., Campistol, J. M., & Alvarez, V. (2011). Pharmacogenetics of tacrolimus after renal transplantation: analysis of polymorphisms in genes encoding 16 drug metabolizing enzymes. Clinical Chemistry and Laboratory Medicine (CCLM) , 49 (5), 825–833. https://doi.org/10.1515/cclm.2011.143 Turolo, S., Tirelli, A. S., Ferraresso, M., Ghio, L., Belingheri, M., Groppali, E., Torresani, E., & Edefonti, A. (2010). Frequencies and roles of CYP3A5, CYP3A4 and ABCB1 single nucleotide polymorphisms in Italian teenagers after kidney transplantation. Pharmacological Reports , 62 (6), 1159–1169. https://doi.org/10.1016/s1734-1140(10)70378-9 Shilbayeh, S., Zmeili, R., & Almardini, R. (2013). The impact of CYP3A5 and MDR1 polymorphisms on tacrolimus dosage requirements and trough concentrations in pediatric renal transplant recipients. Saudi Journal of Kidney Diseases and Transplantation , 24 (6), 1125. https://doi.org/10.4103/1319-2442.121268 De Wildt, S. N., Van Schaik, R. H. N., Soldin, O. P., Soldin, S. J., Brojeni, P. Y., Van Der Heiden, I. P., Parshuram, C., Nulman, I., & Koren, G. (2011). The interactions of age, genetics, and disease severity on tacrolimus dosing requirements after pediatric kidney and liver transplantation. European Journal of Clinical Pharmacology , 67 (12), 1231–1241. https://doi.org/10.1007/s00228-011-1083-7 Buijsch, R. A. O. D., Christiaans, M. H., Stolk, L. M., De Vries, J. E., Cheung, C. Y., Undre, N. A., Van Hooff, J. P., Van Dieijen-Visser, M. P., & Bekers, O. (2007). Tacrolimus pharmacokinetics and pharmacogenetics: influence of adenosine triphosphate‐binding cassette B1 (ABCB1) and cytochrome (CYP) 3A polymorphisms. Fundamental and Clinical Pharmacology , 21 (4), 427–435. https://doi.org/10.1111/j.1472-8206.2007.00504.x Miura, M., Satoh, S., Kagaya, H., Saito, M., Inoue, T., Tsuchiya, N., Suzuki, T., & Habuchi, T. (2009). No impact of age on dose-adjusted pharmacokinetics of tacrolimus, mycophenolic acid and prednisolone 1 month after renal transplantation. European Journal of Clinical Pharmacology , 65 (10), 1047–1053. https://doi.org/10.1007/s00228-009-0721-9 Provenzani, A. (2011). Influence of CYP3A5 and ABCB1 gene polymorphisms and other factors on tacrolimus dosing in Caucasian liver and kidney transplant patients. International Journal of Molecular Medicine. https://doi.org/10.3892/ijmm.2011.794 Thervet, E., Anglicheau, D., King, B., Schlageter, M., Cassinat, B., Beaune, P., Legendre, C., & Daly, A. K. (2003). Impact of cytochrome P450 3A5 genetic polymorphism on tacrolimus doses and concentration-to-dose ratio in renal transplant recipients. Transplantation , 76 (8), 1233–1235. https://doi.org/10.1097/01.tp.0000090753.99170.89 Chandel, N., Aggarwal, P. K., Minz, M., Sakhuja, V., Kohli, K. K., & Jha, V. (2009). CYP3A5*1/*3 genotype influences the blood concentration of tacrolimus in response to metabolic inhibition by ketoconazole. Pharmacogenetics and Genomics , 19 (6), 458–463. https://doi.org/10.1097/fpc.0b013e32832bd085 Loh, P., Lou, H., Zhao, Y., Chin, Y., & Vathsala, A. (2008). Significant impact of gene polymorphisms on tacrolimus but not cyclosporine dosing in Asian renal transplant recipients. Transplantation Proceedings, 40 (5), 1690–1695. https://doi.org/10.1016/j.transproceed.2008.04.010 De Jonge, H., De Loor, H., Verbeke, K., Vanrenterghem, Y., & Kuypers, D. R. (2012). In vivo CYP3A4 activity, CYP3A5 genotype, and hematocrit predict tacrolimus dose requirements and clearance in renal transplant patients. Clinical Pharmacology & Therapeutics , 92 (3), 366–375. https://doi.org/10.1038/clpt.2012.109 Jun, K. R., Lee, W., Jang, M. S., Chun, S., Song, G., Park, K. T., Lee, S. G., Han, D. J., Kang, C., Cho, D., Kim, J. Q., & Min, W. (2009). Tacrolimus concentrations in relation to CYP3A and ABCB1 polymorphisms among solid organ transplant recipients in Korea. Transplantation , 87 (8), 1225–1231. https://doi.org/10.1097/tp.0b013e31819f117e Hirano, K., Naito, T., Mino, Y., Takayama, T., Ozono, S., & Kawakami, J. (2012). Impact of CYP3A5 genetic polymorphism on cross-reactivity in tacrolimus chemiluminescent immunoassay in kidney transplant recipients. Clinica Chimica Acta , 414 , 120–124. https://doi.org/10.1016/j.cca.2012.07.018 Zhao, Y., Song, M., Guan, D., Bi, S., Meng, J., Li, Q., & Wang, W. (2005). Genetic polymorphisms of CYP3A5 genes and concentration of the cyclosporine and tacrolimus. Transplantation Proceedings, 37 (1), 178–181. https://doi.org/10.1016/j.transproceed.2005.01.077 Ferraris, J. R., Argibay, P. F., Costa, L., Jimenez, G., Coccia, P. A., Ghezzi, L. F. R., Ferraris, V., Belloso, W. H., Redal, M. A., & Larriba, J. M. (2011). Influence of CYP3A5 polymorphism on tacrolimus maintenance doses and serum levels after renal transplantation: Age dependency and pharmacological interaction with steroids. Pediatric Transplantation , 15 (5), 525–532. https://doi.org/10.1111/j.1399-3046.2011.01513.x Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Table1.xlsx Table 1 Table2.xlsx Table 2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7490941","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":510911616,"identity":"f61238ec-d58c-4fac-ac0d-e7c87eb41026","order_by":0,"name":"Abdul Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACdiCuYGAwMGBgYHwAZPPwEdTCDMRnIFqYDUBa2EjRwiYBEiCohb+Z9+GHAzWHjc3Zzx6r/JpjJ8PGwPzw0Q08WiQOsxtLHDh22MyyJy/ttuy2ZKDD2IyNc/BZc5iNQfoD22EbgwM5ZrcltzEDtfCwSePTIn+YjfnHgX9ALeffmBVLbqsnrMXgMBubxMG2w2YGN3LMGD9uO0xYiyFQi8XBvnRjgxtvjKUZtx3nYWMm4Be5423MNw58szbccD7H8OPPbdX2/OzNDx/j9T4ENINJZh4wSVg5CNSBScYfxKkeBaNgFIyCEQYAPrhGtQDEyiwAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Khan","suffix":""},{"id":510911617,"identity":"7508edc7-3565-4b91-887c-56c90fa15fcf","order_by":1,"name":"Syed Zaidi","email":"","orcid":"","institution":"University of Karachi","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"","lastName":"Zaidi","suffix":""},{"id":510911618,"identity":"6a46b5d5-f625-4b56-986e-8debefb5e232","order_by":2,"name":"Shafaq Hassan","email":"","orcid":"","institution":"University of Karachi","correspondingAuthor":false,"prefix":"","firstName":"Shafaq","middleName":"","lastName":"Hassan","suffix":""},{"id":510911619,"identity":"7fdca5c9-5d1e-4003-afeb-70a24f982388","order_by":3,"name":"Erum Hanif","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Erum","middleName":"","lastName":"Hanif","suffix":""}],"badges":[],"createdAt":"2025-08-29 18:25:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7490941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7490941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91363451,"identity":"b3893bfc-3ea5-4c48-803e-5c8ff0796082","added_by":"auto","created_at":"2025-09-15 16:53:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":539620,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram of study selection for the meta-analysis. A total of 104 studies were identified through database searches, 75 were screened, and 51 met the inclusion criteria. Abbreviations: n = number of studies.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/5bad1bba85b954f49cfda0ac.jpg"},{"id":91363450,"identity":"ca89fda1-7750-4289-ae5c-c629818b13ed","added_by":"auto","created_at":"2025-09-15 16:53:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1784022,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of tacrolimus concentration-to-dose (Co/D) ratios in renal transplant recipients (combined populations). The pooled standardized mean difference (SMD) was estimated using a random-effects model. Squares represent individual study effects with 95% confidence intervals (CI), and the diamond indicates the overall pooled effect. The vertical line at zero represents the null hypothesis of no genotype effect. Abbreviations: CI, confidence interval; IV, inverse variance; df., degrees of freedom; I², heterogeneity index; Std, standardized.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/f3f4ad0ddfbb97f4e646874e.png"},{"id":91363442,"identity":"60f079b4-944d-4249-bcc6-a2d799304c6e","added_by":"auto","created_at":"2025-09-15 16:53:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4681903,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of tacrolimus Co/D ratios in renal transplant recipients among Asian population. The pooled SMD was calculated using a random-effects model. Squares represent study-specific effects with 95% CIs, and the diamond denotes the overall pooled estimate. The vertical line at zero corresponds to the null hypothesis. Abbreviations as in Figure 2.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/2fbd0cc99c3262f0e440c626.png"},{"id":91363453,"identity":"357fb7ca-ea52-4f9d-a833-5c172dd58497","added_by":"auto","created_at":"2025-09-15 16:53:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1183030,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of tacrolimus Co/D ratios in renal transplant recipients among European population. The pooled SMD was derived using a random-effects model. Squares represent study-level estimates with 95% CIs, and the diamond indicates the overall pooled effect. The vertical line at zero corresponds to the null hypothesis. Abbreviations as in Figure 2.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/3efaa369cb09dddbef2ef9ec.png"},{"id":91363454,"identity":"cec400fe-4b42-42cf-b989-cc7ad7899df4","added_by":"auto","created_at":"2025-09-15 16:53:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":645486,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between CYP3A5 genotype and renal allograft rejection. Pooled odds ratios (OR) were estimated using a random-effects model. Squares represent individual study effects with 95% CIs, and the diamond indicates the overall pooled estimate. The vertical line at one corresponds to the null hypothesis. Abbreviations: CI, confidence interval; OR, odds ratio; I², heterogeneity index.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/b89a039fa9034b1773dafa9a.jpg"},{"id":91363438,"identity":"721df6f7-cf60-484f-b5f8-d762656b151e","added_by":"auto","created_at":"2025-09-15 16:53:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":278399,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between CYP3A5 genotype and renal allograft rejection among the (a) Asian and (b) European populations. Subgroup analyses were performed using a random-effects model. Squares represent study-specific odds ratios with 95% CIs, and diamonds indicate pooled subgroup effects. The vertical line at one corresponds to the null hypothesis. Abbreviations as in Figure 5.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/6ff2a367b00557d210d19403.png"},{"id":92878866,"identity":"112a7474-82aa-4ca3-8bb3-1b2520dcba95","added_by":"auto","created_at":"2025-10-06 15:14:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9706791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/41da954c-d77f-4aa3-8aa3-c7360c2215fe.pdf"},{"id":91363440,"identity":"0d4bd6d9-9850-41b1-bf35-e13791193208","added_by":"auto","created_at":"2025-09-15 16:53:36","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14351,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/58ef62add35cad54cf905912.xlsx"},{"id":91363461,"identity":"dc02f633-1e6e-4bf7-bc47-fb07e0df5611","added_by":"auto","created_at":"2025-09-15 16:53:38","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14096,"visible":true,"origin":"","legend":"Table 2","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7490941/v1/65984c59befcfc5fb3e1fe3f.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Impact of CYP3A5 Gene Polymorphisms on Tacrolimus Pharmacokinetics and Renal Allograft Rejection in Kidney Transplant Recipients: A Meta-Analysis Across Ethnic Populations","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTacrolimus, isolated from Streptomyces tsukubaensis in 1984, is an immunosuppressant widely used to prevent organ rejection in transplant recipients, particularly among the recipients of liver, kidney, or heart transplants [ref. 1, 2]. The primary target of tacrolimus is the calcineurin, a calcium-dependent serine/threonine protein phosphatase predominantly expressed in T cells [ref. 3]. Calcineurin is necessary for the activation of several genes for cytokines required for T cell activation and proliferation, which include Interleukin (IL)-2 [ref. 4]. It inhibits calcineurin-mediated signal transduction by binding immunophilins, thus preventing T cell activation and IL-2 synthesis [ref. 4\u0026ndash;6]. This suppresses both the cell-mediated immunity as well as the humoral immunity to the allograft.\u003c/p\u003e\u003cp\u003eHowever, tacrolimus has a narrow therapeutic window with regard to dosage and concentration, which means that dosing is particularly difficult due to the large inter-individual variability in dose requirements and concentrations [ref. 7]. Tacrolimus requires careful monitoring and dosing adjustment to achieve optimal drug levels. Standard practice involves regular measurement of tacrolimus trough levels, the concentration of the drug in the blood just before the next dose to guide dosing adjustments. Despite this approach, achieving and maintaining the appropriate tacrolimus concentration can be challenging due to its complex pharmacokinetics [ref. 8].\u003c/p\u003e\u003cp\u003eTacrolimus is extensively metabolized by the CYP3A enzyme subfamily [9]. The CYP3A enzyme subfamily is mainly made up of CYP3A4 and CYP3A5. Specifically, CYP3A5 is involved in the metabolism of tacrolimus in the liver and the small intestine [ref. 10]. A SNP at the position 6986 (6986A\u0026thinsp;\u0026gt;\u0026thinsp;G) in intron 3 of the CYP3A5 gene produces a splice variant leading to the production of the non-functional CYP3A5*3 protein, which is associated with a \u0026lsquo;CYP3A5 nonexpresser\u0026rsquo; phenotype [ref. 11]. Patients who are CYP3A5*3 (G allele) homozygotes have a lower expression of the CYP3A5 enzyme and higher dose-normalized tacrolimus concentrations. Expressers with at least one CYP3A5*1 allele have lower blood concentration due to faster metabolism, thereby being more susceptible to rejection of transplants [ref. 12]. Therefore, the CYP3A5 genotype has been identified as the main predictor of tacrolimus exposure and the pharmacogenomic mechanism of the inter-patient dosing difference. This genetic information could be incorporated to enhance the first dose determination and first-time achievement of the therapeutic range [ref. 13].\u003c/p\u003e\u003cp\u003eStudies have shown that various factors, including ethnic background, liver and kidney disease, other medications, and gene variations affect the pharmacokinetics of tacrolimus [ref. 14]. Of these, genetic variations in the expression and function of the metabolizing enzymes like the cytochrome P450 (CYP) 3A5 have been identified as the major predictors of dose-standardized tacrolimus exposure. Systematic reviews have shown significantly higher tacrolimus trough concentration divided by daily dose per body weight (Co/D) in patients carrying CYP3A5 (GG or *3/*3) polymorphism i.e., nonexpresser, than expresser CYP3A5 (AA or *1/*1) at various posttransplant time. These analyses have certain limitations as they lack pooled studies with similar clinical covariates, ethnicity, and posttransplantation duration.\u003c/p\u003e\u003cp\u003eIn this meta-analysis we aim to assess the impact of CYP3A5 genetic polymorphism on tacrolimus pharmacokinetics and outcomes in kidney transplant recipients. Tacrolimus Co/D ratio and the incidence of graft rejection are compared between expressers and nonexpressers with regard to ethnicity (European/Asian) and time post-transplantation. Our data compilation approach combines pharmacokinetic data within homogeneous subgroups to understand genotype-specific tacrolimus exposure during multiple transplant phases and concentration dynamics. This can inform time-dependent dosing adjustments through the initial critical exposure phase. Assessing differential rejection risk will also help to evaluate the possible clinical benefits of genotype-based dosing.\u003c/p\u003e\u003cp\u003eIn sum, this meta-analysis will give ethnicity-specific estimates of the probable size and time course of the effect of the CYP3A5 polymorphism on the pharmacokinetics of tacrolimus and its important effects in transplantation among kidney transplant recipients. These results could enhance the usage of preemptive genotyping, which allows for personalized dose adjustments from the start rather than relying solely on weight-based dosing. By personalizing immunosuppression through pharmacogenetics, the issues of efficiency, side effects, and long-term consequences can be addressed.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLiterature Search Strategy\u003c/h2\u003e\u003cp\u003eThe literature search was performed from the electronic databases of PubMed, Google Scholar, EMBASE, and the Cochrane Library published up to July 2024. The MeSH terms utilized to identify relevant studies were \u0026ldquo;Cytochrome P-450 CYP3A,\u0026rdquo; \u0026ldquo;Tacrolimus,\u0026rdquo; \u0026ldquo;Kidney Transplantation,\u0026rdquo; \u0026ldquo;Meta-Analysis,\u0026rdquo; \u0026ldquo;NOT (Systematic Review [Publication Type])\u0026rdquo; and NOT (Review [Publication Type]).\u0026rdquo;. For further data, the references cited in the selected studies were also explored. The database search was conducted by two reviewers independently.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Inclusion/Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eStudies published between 2000 and 2024 were included. In total, 104 studies were eligible for further analysis [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]. These selected studies were screened for the following data according to the eligibility criteria: the first author's name, year of publication, ethnicity, CYP3A5 genotyping (CYP3A5*1/*1, CYP3A5*1/*3, and CYP3A5*3/*3) among renal transplant patients, total number of events/total number of patients in expressers and non-expressers, and mean and standard deviation of tacrolimus dosage and trough concentration levels at week 1, 2, and 3 and month 1, 3, 6, and 12 after transplantation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe review articles were excluded. When additional data was required, the corresponding authors were contacted via email; however, the responses received were not relevant and those studies were omitted.\u003c/p\u003e\n\u003ch3\u003eData Extraction and Management\u003c/h3\u003e\n\u003cp\u003eAfter completing the literature search, data extraction was carried out from all 104 studies. The extracted information was organized into a Microsoft Office Excel file with two separate sheets: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Graft Rejection Episodes: Collected data on the incidence of graft rejection in relation to CYP3A5 gene polymorphisms. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Tacrolimus Dosage and Trough Concentration Levels: This data was further categorized into different time intervals: week 1, week 2, week 3, month 1, month 3, month 6, and month 12.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed by using R language, specifically utilizing the meta package. The following analyses were conducted: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Forest plots were generated to visualize the impact of CYP3A5 gene polymorphisms on (a) graft rejection rates in the Asian, European, and both populations. (b) Tacrolimus dosage and trough concentration levels for the Asian, European, and both populations at week 1, week 2, week 3, month 1, month 3, month 6, and month 12. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Funnel plots were created to assess publication bias for each dataset mentioned above. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Begg's and Egger's Tests: For each forest plot, Begg's and Egger's tests were conducted to statistically evaluate the presence of publication bias. p-values were calculated for each forest plot to check the significance of potential biases. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant publication bias, and p-value\u0026thinsp;\u0026ge;\u0026thinsp;0.05 was considered as no evidence of publication bias.\u003c/p\u003e\u003cp\u003eIn this meta-analysis, continuous outcomes (association of tacrolimus Co/D with CYP3A5 gene polymorphism) were determined by standardized mean difference (SMD), and dichotomous outcomes (graft rejection episodes) were determined by the odds ratio (OR).\u003c/p\u003e\u003cp\u003eHeterogeneity (I\u003csup\u003e2\u003c/sup\u003e) of the studies was calculated by using the I\u003csup\u003e2\u003c/sup\u003e statistic test. The range of heterogeneity was 0\u0026ndash;100%. A fixed-effects model was applied when heterogeneity was absent, while a random-effects model was used when I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50% or p\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTools and Software\u003c/h3\u003e\n\u003cp\u003eLiterature was searched from the electronic databases of PubMed, Google Scholar, EMBASE, and the Cochrane Library. Microsoft Office Excel was employed for data management and organization. For statistical analysis, R language (version 4.4.1) was used to generate the forest and funnel plots, as well as for the execution of Begg's and Egger's tests. The meta package was used for performing the meta-analysis.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStudy Selection and Characteristics\u003c/h2\u003e\u003cp\u003eA comprehensive literature search was conducted from electronic databases of PubMed, Google Scholar, EMBASE, and Cochrane Library for studies published up to July 2024, focusing on the role of CYP3A5 gene polymorphism in kidney transplantation with tacrolimus (TAC) treatment. The search employed MeSH terms related to tacrolimus, meta-analysis, and cytochrome P450 CYP3A in kidney transplantation. For the broader inclusion of relevant studies, the references cited in the retrieved articles were also explored.\u003c/p\u003e\u003cp\u003eA total of 104 studies were retrieved from the literature search. After a thorough screening, 53 studies were excluded based on data insufficiency or failure to meet the inclusion criteria. The corresponding authors were contacted via email for additional information. Finally, 51 studies were selected for inclusion, 24 of which focused on the relationship between CYP3A5 polymorphisms and tacrolimus pharmacokinetics (Co/D ratio), 17 on rejection episodes in renal transplant patients, and 10 studies contained both data [Table\u0026nbsp;1,2].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTacrolimus Co/D Studies in Renal Transplant Patients\u003c/h3\u003e\n\u003cp\u003eAcross all time intervals (Week 1, Week 2, Week 3, Month 1, Month 3, Month 6, and Month 12), the Standardized Mean Differences (-0.68, -1.17, -1.10, -1.09, -1.27, -0.77, and \u0026minus;\u0026thinsp;0.79) at each time point indicate a decrease in the tacrolimus concentration-to-dose (Co/D) ratio for CYP3A5 expressers compared to non-expressers. The results showed no significant heterogeneity at Week 2 and Week 3 (0% and 4%) and showed high significant heterogeneity at Week 1, Month 1, Month 3, Month 6, and Month 12 (77.4%, 63.5%, 80%, 66%, and 86%) among the mean differences of the Tacrolimus Co/D [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe meta-analysis found no significant evidence of publication bias as shown by Begg\u0026rsquo;s rank test (0.86, 0.76, 0.59, 0.43, 0.45, 0.95, and 0.37), and there was no significant publication bias in Egger\u0026rsquo;s test as well (0.10, 0.86, 0.83, 0.48, 0.39, and 0.30), except for Month 12 (0.04).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTacrolimus Co/D Ratio with Time Intervals among Asian Transplant Patients\u003c/h2\u003e\u003cp\u003eFor the Asian population, the SMD values (-1.35, -1.39, -1.16, -1.13, -1.50, -0.94, and \u0026minus;\u0026thinsp;0.98) at each time point indicate a substantial decrease in the Co/D ratio for CYP3A5 expressers. The results showed high significant heterogeneity (40%, 68%, 80.7%, 88.6%, 42.3%, and 59.2%) at all intervals except Week 2 (0%) among the mean differences of Tacrolimus Co/D [Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe meta-analysis consistently shows no evidence of publication bias based on Begg\u0026rsquo;s test (0.81, 1.00, 1.00, 0.13, 1.00, 0.76, and 0.71) and Egger\u0026rsquo;s tests (0.39, 0.74, 0.70, 0.24, 0.54, 0.75, and 0.59).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTacrolimus Co/D Ratio with Time Intervals among European Transplant Patients\u003c/h2\u003e\u003cp\u003eIn the European population, the SMD values (-0.37, -1.01, -1.05, -1.04, -1.02, -0.96, and \u0026minus;\u0026thinsp;0.77) consistently indicate a decrease in the Co/D ratio for CYP3A5 expressers, though the magnitude of the decrease is smaller compared to the Asian population. Results showed that there was no significant heterogeneity (0%, 0%, 0%, 0%, 20.3%, and 0%) among the studies except at Month 12 (90%) [Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe meta-analysis found no significant evidence of publication bias as shown by Begg\u0026rsquo;s rank test (0.06, 0.31, 1.00, 1.00, 1.00, 1.00, and 0.22), and there was no significant publication bias in Egger\u0026rsquo;s test (0.33, 0.83, 0.52, 0.87, 0.67, and 0.25) as well, except at Week 1 (0.0007).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTacrolimus-Based Rejection Episodes\u003c/h2\u003e\u003cp\u003eThe association of CYP3A5 genotypes with renal allograft rejection episodes was determined in 27 studies. The selected studies contained 3,507 patients. Results revealed an odds ratio (OR) of 1.16 with a 95% confidence interval (CI) ranging from 0.96 to 1.40. The heterogeneity was low (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.9%), indicating minimal variability between the included studies. The overall effect test yielded a z-value of 1.50 with a p-value of 0.13 [Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBegg\u0026rsquo;s rank test (p\u0026thinsp;=\u0026thinsp;0.79) and Egger\u0026rsquo;s test (p\u0026thinsp;=\u0026thinsp;0.32) showed no evidence of publication bias.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAssociation between CYP3A5 Expresser and Non-expresser Genotypes:\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003eAssociation between CYP3A5 Expresser and Non-expresser Genotypes with Renal Allograft Rejection in Asian Populations\u003c/h2\u003e\u003cp\u003eThe analysis of 10 studies of Asian populations included 1,014 patients with the CYP3A5 genotype and showed a statistically significant association with renal allograft rejection. The calculated OR was 1.56 with a 95% CI of 1.14 to 2.15. There was no heterogeneity (0%) among the studies. The test for overall effect showed a z-value of 2.74 and a p-value of 0.0061 [Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e:A].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo publication bias was detected, as indicated by Begg\u0026rsquo;s rank test (p\u0026thinsp;=\u0026thinsp;0.59) and Egger\u0026rsquo;s test (p\u0026thinsp;=\u0026thinsp;0.72).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAssociation between CYP3A5 Expresser and Non-expresser Genotypes with Renal Allograft Rejection in European Populations\u003c/h2\u003e\u003cp\u003eThe analysis of 14 studies of European populations comprised 1,674 patients of CYP3A5 genotype and showed an OR of 1.14 with a 95% CI of 0.84 to 1.55. The heterogeneity was very low at 2%. The overall effect test produced a z-value of 0.81 and a p-value of 0.42 [Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e:B].\u003c/p\u003e\u003cp\u003eBegg\u0026rsquo;s rank test (p\u0026thinsp;=\u0026thinsp;0.83) and Egger\u0026rsquo;s test (p\u0026thinsp;=\u0026thinsp;0.79) did not reveal any evidence of publication bias.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis meta-analysis highlights the significant influence of CYP3A5 gene polymorphisms on tacrolimus pharmacokinetics and transplant outcomes. CYP3A5 expressers (*1/*1 and *1/*3) were found to metabolize tacrolimus more rapidly than non-expressers (*3/*3), leading to lower drug exposure for the same dose. These pharmacokinetic differences were consistently observed across all post-transplant intervals, underscoring the sustained impact of CYP3A5 genotype on tacrolimus clearance and the potential clinical consequences of unadjusted dosing.\u003c/p\u003e\u003cp\u003eThe large sample size and stratified subgroup analyses allowed for a clearer understanding of how CYP3A5 expression patterns vary across ethnicities and influence treatment response. Our results reinforce the importance of genotype-guided tacrolimus dosing, particularly in populations with a higher frequency of the expresser allele.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eTacrolimus Concentration-to-Dose (Co/D) Ratio\u003c/h2\u003e\u003cp\u003eCYP3A5 expressers consistently exhibited significantly lower Co/D ratios at all measured time points from Week 1 to Month 12. These findings confirm that expressers require higher tacrolimus doses to achieve therapeutic levels. The persistence of this pattern across time intervals \u0026mdash; with Standardized Mean Differences ranging from \u0026minus;\u0026thinsp;0.68 to \u0026minus;\u0026thinsp;1.27 \u0026mdash; indicates that this is not a transient post-transplant effect but rather a consistent pharmacogenetic influence.\u003c/p\u003e\u003cp\u003eThe results showed low heterogeneity at certain intervals (e.g., Week 2 and Week 3), suggesting consistency across studies. However, high heterogeneity was noted at other intervals, particularly Week 1, Month 3, and Month 12. This variation may stem from differences in clinical protocols, population characteristics, or sample sizes across studies, especially in ethnically diverse groups. Importantly, despite variability in effect size, the direction of association was consistent \u0026mdash; always indicating reduced tacrolimus exposure in expressers.\u003c/p\u003e\u003cp\u003eThis trend is clinically important, especially in the early post-transplant phase when underexposure to tacrolimus can increase the risk of acute rejection. The findings underscore the need for preemptive dose adjustments based on genotype to avoid subtherapeutic levels in expressers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eRenal Allograft Rejection Episodes\u003c/h2\u003e\u003cp\u003eAlthough the overall association between CYP3A5 expression and rejection risk was not statistically significant (OR 1.16, p\u0026thinsp;=\u0026thinsp;0.13), the trend toward increased rejection in expressers is noteworthy. The slight elevation in odds ratio may reflect clinically meaningful underexposure to tacrolimus due to faster metabolism in this group, particularly when standard dosing is applied without consideration of genotype.\u003c/p\u003e\u003cp\u003eImportantly, heterogeneity among these studies was very low (3.9%), indicating that the trend was relatively consistent. This strengthens the suggestion that even though the result did not meet statistical significance, CYP3A5 expression could be an independent risk factor for rejection in some contexts, especially if therapeutic drug monitoring is delayed or inadequate during the early post-transplant period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eEthnic Differences\u003c/h2\u003e\u003cp\u003eThe impact of CYP3A5 expression was more pronounced in Asian populations than in Europeans, both in terms of Co/D ratios and rejection risk. In Asian cohorts, expressers exhibited a significantly greater reduction in Co/D ratios compared to non-expressers (SMDs as low as \u0026minus;\u0026thinsp;1.50), which likely reflects both higher allele frequency and greater metabolic impact. In this group, CYP3A5 expression was significantly associated with increased rejection risk (OR 1.56, p\u0026thinsp;=\u0026thinsp;0.0061), indicating that tacrolimus underexposure due to rapid metabolism may have tangible clinical consequences if not accounted for in dosing strategies.\u003c/p\u003e\u003cp\u003eIn contrast, the European subgroup showed a weaker reduction in Co/D ratios and no statistically significant association with rejection (OR 1.14, p\u0026thinsp;=\u0026thinsp;0.42). These differences may reflect both genetic and clinical practice variations, such as more frequent drug monitoring or lower baseline rejection risk. The heterogeneity was notably low in European studies, further suggesting that the CYP3A5 effect in this group is less variable and perhaps less clinically impactful.\u003c/p\u003e\u003cp\u003eThese findings emphasize the importance of population-specific pharmacogenetic protocols. In regions with high expresser allele prevalence, such as Asia, genotype-guided dose optimization may be particularly critical for preventing rejection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eThis study supports the clinical utility of CYP3A5 genotyping in tailoring tacrolimus therapy. Expressers metabolize the drug more rapidly and are at greater risk of underexposure if standard dosing protocols are used. Genotype-guided therapy enables early dose adjustments, reducing the risk of rejection and improving patient outcomes.\u003c/p\u003e\u003cp\u003eThe findings have particular relevance for Asian populations, where the frequency and impact of the expresser genotype are higher. The variability in SMDs and higher heterogeneity observed in Asian cohorts underscore the need for more individualized and proactive therapeutic drug monitoring (TDM) in these patients.\u003c/p\u003e\u003cp\u003eBy identifying patients at risk for fast metabolism before treatment begins, clinicians can implement higher initial dosing, closer monitoring, and more dynamic adjustments. This approach not only improves safety but may also reduce hospital readmissions related to rejection or toxicity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis meta-analysis has several limitations. First, heterogeneity was high in some subgroups, particularly among Asian Co/D studies, which may reduce the precision of pooled estimates. Second, many studies lacked detailed reporting on confounders such as patient weight, age, or comorbidities, which could influence tacrolimus metabolism. Third, this analysis focused solely on CYP3A5, while other genes like CYP3A4 and ABCB1 also play a role in tacrolimus pharmacokinetics and were not considered here.\u003c/p\u003e\u003cp\u003eAdditionally, data on long-term graft outcomes beyond 12 months were limited, making it difficult to assess the sustained clinical impact of genotype-guided dosing. The underrepresentation of some ethnic groups, such as South Asians and Africans, limits the generalizability of these findings to global transplant populations.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eFuture studies should focus on multi-gene pharmacogenomic models that include CYP3A4, ABCB1, and other relevant genes to better predict tacrolimus response. There is also a need for larger, prospective, and ethnically diverse trials to validate genotype-guided dosing algorithms in real-world clinical settings.\u003c/p\u003e\u003cp\u003eInclusion of long-term follow-up data will be crucial for evaluating the effect of personalized dosing on graft survival, chronic rejection, and overall patient health. Additionally, implementation research \u0026mdash; including cost-effectiveness studies and integration into electronic health records \u0026mdash; is needed to translate pharmacogenetic insights into routine care.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis meta-analysis confirms that CYP3A5 expressers metabolize tacrolimus more rapidly, resulting in consistently lower Co/D ratios across all post-transplant intervals. This metabolic difference is more pronounced in Asian populations, who also showed a significantly increased risk of graft rejection. These findings support the use of genotype-guided tacrolimus dosing to improve therapeutic precision, particularly in high-risk populations.\u003c/p\u003e\u003cp\u003eDespite certain limitations, the consistent pharmacokinetic patterns and population-specific differences provide a strong rationale for integrating CYP3A5 testing into pre-transplant evaluations. Future research should expand to multi-gene models, diverse populations, and long-term clinical outcomes to fully realize the benefits of precision medicine in kidney transplantation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support of the Department of Biotechnology, University of Karachi, for providing the academic environment necessary to carry out this research. We are also thankful to the\u0026nbsp;Centre for Human Genetics and Molecular Medicine,\u0026nbsp;Sindh Institute of Urology and Transplantation (SIUT) for facilitating access to relevant literature and clinical insights that greatly contributed to the development of this meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSyed Mustafa Zaidi\u003c/strong\u003e: Contributed to data extraction, statistical analysis, creation of figures (PRISMA flowchart, forest and funnel plots), and writing of the manuscript under supervision.\u003cbr\u003e\u003cstrong\u003eDr. Shafaq Aiyaz Hassan\u003c/strong\u003e: Supervised the project, provided critical feedback, ensured methodological integrity, and reviewed the manuscript for accuracy and academic rigor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Erum Hanif:\u003c/strong\u003e Assisted in study design, guided interpretation of results, and provided critical revisions to the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbdul Rafay Khan\u003c/strong\u003e: Led the research planning and design, guided the literature review process, supervised data collection, assisted in statistical visualization (forest and funnel plots), and played a key role in structuring and writing the manuscript. Also served as the corresponding author.\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial or personal interests that could have appeared to influence the work reported in this paper.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available within the article and its supplementary materials. All relevant raw data used for the meta-analysis, including extracted study characteristics, effect sizes, and statistical outputs, can be made available by the corresponding author upon reasonable request for non-commercial research purposes, without breaching participant confidentiality.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarreiro C, Prieto CAV, Sola-Landa A, Solera E, Mart\u0026iacute;nez-Castro M, P\u0026eacute;rez‐Redondo R, et al. Draft genome of Streptomyces tsukubaensis NRRL 18488, the producer of the clinically important immunosuppressant tacrolimus (FK506). 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Impact of cytochrome P450 3A5 genetic polymorphism on tacrolimus doses and concentration-to-dose ratio in renal transplant recipients. \u003cem\u003eTransplantation\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(8), 1233\u0026ndash;1235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.tp.0000090753.99170.89\u003c/span\u003e\u003cspan address=\"10.1097/01.tp.0000090753.99170.89\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChandel, N., Aggarwal, P. K., Minz, M., Sakhuja, V., Kohli, K. K., \u0026amp; Jha, V. (2009). CYP3A5*1/*3 genotype influences the blood concentration of tacrolimus in response to metabolic inhibition by ketoconazole. \u003cem\u003ePharmacogenetics and Genomics\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(6), 458\u0026ndash;463. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/fpc.0b013e32832bd085\u003c/span\u003e\u003cspan address=\"10.1097/fpc.0b013e32832bd085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoh, P., Lou, H., Zhao, Y., Chin, Y., \u0026amp; Vathsala, A. (2008). Significant impact of gene polymorphisms on tacrolimus but not cyclosporine dosing in Asian renal transplant recipients. \u003cem\u003eTransplantation Proceedings, 40\u003c/em\u003e(5), 1690\u0026ndash;1695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.transproceed.2008.04.010\u003c/span\u003e\u003cspan address=\"10.1016/j.transproceed.2008.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Jonge, H., De Loor, H., Verbeke, K., Vanrenterghem, Y., \u0026amp; Kuypers, D. R. (2012). 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Impact of CYP3A5 genetic polymorphism on cross-reactivity in tacrolimus chemiluminescent immunoassay in kidney transplant recipients. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e, \u003cem\u003e414\u003c/em\u003e, 120\u0026ndash;124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cca.2012.07.018\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2012.07.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, Y., Song, M., Guan, D., Bi, S., Meng, J., Li, Q., \u0026amp; Wang, W. (2005). Genetic polymorphisms of CYP3A5 genes and concentration of the cyclosporine and tacrolimus. \u003cem\u003eTransplantation Proceedings, 37\u003c/em\u003e(1), 178\u0026ndash;181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.transproceed.2005.01.077\u003c/span\u003e\u003cspan address=\"10.1016/j.transproceed.2005.01.077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerraris, J. R., Argibay, P. F., Costa, L., Jimenez, G., Coccia, P. A., Ghezzi, L. F. R., Ferraris, V., Belloso, W. H., Redal, M. A., \u0026amp; Larriba, J. M. (2011). Influence of CYP3A5 polymorphism on tacrolimus maintenance doses and serum levels after renal transplantation: Age dependency and pharmacological interaction with steroids. \u003cem\u003ePediatric Transplantation\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(5), 525\u0026ndash;532. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1399-3046.2011.01513.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1399-3046.2011.01513.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CYP3A5 polymorphism, tacrolimus, kidney transplantation, pharmacogenetics, meta-analysis, renal allograft rejection","lastPublishedDoi":"10.21203/rs.3.rs-7490941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7490941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTacrolimus is a cornerstone immunosuppressant in kidney transplantation, but its pharmacokinetics show significant interindividual variability, primarily due to CYP3A5 polymorphisms. This meta-analysis evaluated the impact of CYP3A5 expresser (*1/*1, *1/*3) versus non-expresser (*3/*3) genotypes on tacrolimus concentration-to-dose (Co/D) ratios and renal allograft rejection. Fifty-one studies were included: 24 reported Co/D ratios, 17 addressed rejection episodes, and 10 provided both. CYP3A5 expressers had significantly lower Co/D ratios at all post-transplant time points, reflecting faster metabolism and higher dose requirements. Ethnicity-stratified analysis revealed stronger effects in Asians (SMD: \u0026minus;\u0026thinsp;1.35 to \u0026minus;\u0026thinsp;1.50) than in Europeans (SMD: \u0026minus;\u0026thinsp;0.37 to \u0026minus;\u0026thinsp;1.05). Although overall rejection risk was not significantly higher in expressers (OR: 1.16, p\u0026thinsp;=\u0026thinsp;0.13), a significant association was found in Asian populations (OR: 1.56, p\u0026thinsp;=\u0026thinsp;0.0061). These findings support genotype-guided dosing of tacrolimus to improve clinical outcomes in kidney transplant recipients.\u003c/p\u003e","manuscriptTitle":"Impact of CYP3A5 Gene Polymorphisms on Tacrolimus Pharmacokinetics and Renal Allograft Rejection in Kidney Transplant Recipients: A Meta-Analysis Across Ethnic Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 16:53:23","doi":"10.21203/rs.3.rs-7490941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb6aa9ac-4b39-4e39-b50b-80ec7124382a","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54274155,"name":"Biological sciences/Computational biology and bioinformatics/Predictive medicine"},{"id":54274156,"name":"Biological sciences/Genetics/Gene expression"},{"id":54274157,"name":"Biological sciences/Genetics/Genetic association study"}],"tags":[],"updatedAt":"2025-10-06T15:06:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 16:53:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7490941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7490941","identity":"rs-7490941","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}