Tacrolimus remains the cornerstone of immunosuppressive therapy following organ transplant because of its superiority in preventing acute cellular rejection. Despite this advantage, precision in tacrolimus dosing remains an enigma, dogged by its narrow therapeutic window and high interpatient and intrapatient variability. As a result of this, tacrolimus requires therapeutic drug monitoring, which helps limit the time a patient is underexposed or overexposed. However, many transplant practitioners would argue that therapeutic drug monitoring alone is reactionary, and the majority of dose manipulations continue to be trial and error. Bodyweight has traditionally been recommended as an aid in personalizing initial tacrolimus doses, but, unfortunately, it is a poor predictor of drug clearance. It is well documented that a low tacrolimus concentration-to-dose ratio is indicative of rapid tacrolimus clearance and is a risk factor for death-censored graft loss.1 In recent years, pharmacogenetic analyses have evaluated tacrolimus response to human genetic variations and genotype assessment has become an important tool for determining appropriate doses. Tacrolimus is metabolized by CYP3A4 and CYP3A5 enzymes in the gut and liver and variants in these metabolic genes have been strongly associated with tacrolimus metabolism and clearance. The presence of at least 1 CYP3A5*1 allele results in patients being considered expressers or rapid metabolizers. This is seen in only approximately 15% of Caucasians, but its prevalence exceeds 50% in patients of Asian, Hispanic, and African descent.2 These expressers require higher tacrolimus doses, >1.5–2 times that of nonexpressers, to achieve therapeutic concentrations. CYP3A4*22 and CYP3A4*20 expression, as well as genetic variations in ABCB1, POR, PPARA, and NR1I2 genes all appear to impact tacrolimus pharmacokinetics, but seem to be less clinically relevant than variants in CYP3A4 and CYP3A5.3 With available data regarding the impact of CYP3A5 genotype variants on tacrolimus, the Clinical Pharmacogenetics Implementation Consortium group has published specific dosing recommendations based on individual patient genotyping.4
In this edition of Transplantation, Yoon et al5 present an important analysis outlining the impact of pharmacogenetic variants on tacrolimus in >1100 Korean patients. The significant findings of this article include the impact of the CYP3A5*3 allele, the loss-of-function variant, as the most potent single-nucleotide polymorphism affecting variability in tacrolimus pharmacokinetics among Koreans. The authors also demonstrated a negligible impact of the POR*28 genotype on tacrolimus variability in comparison to clinical variables or the CYP3A5*3 genotype. The authors discovered rare variants of CYP3A5 and CYP3A4 and noted that the impact of the CYP3A5 rare variant was dependent on its isomerization. Four CYP3A4 rare variants were noted, including 1 novel rare variant (S500Kfs*20) that resulted in decreased tacrolimus clearance with a resultant enhanced drug exposure. Finally, the authors noted 10 rare variants in the CYP1A1 gene. In patients that carried the CYP1A1 rare variant and were considered poor metabolizers, tacrolimus trough levels were lower. The authors theorized that this may be secondary to induction of CYP3A4 expression due to the presence of CPY1A1.5 At first glance, the results of this study seem to limit application just to the Korean population, but given the prevalence of CYP3A5*3 allele among other ethnic groups, including Caucasians, these data should provide the impetus to more closely study other patient populations for these rare variants and resultant tacrolimus handling. It is clear that this article advances our understanding of tacrolimus pharmacogenetics.
However, I would be remiss if I did not point out that many have hoped that pharmacogenetic-guided tacrolimus dosing would be a panacea that would maximize tacrolimus efficacy and safety. The clinical outcomes of pharmacogenetic-guided tacrolimus dosing have been evaluated in 3 randomized-controlled trials.6-8 Unfortunately, none of these trials demonstrated a clinical benefit to pharmacogenetic-guided dosing in short- or long-term outcomes. This underpins the reason why genotype-based tacrolimus dosing is not commonplace among transplant centers. The Clinical Pharmacogenetics Implementation Consortium group recommends the use of pharmacogenetics-based tacrolimus dosing only when an individual’s genotype is already available.4
Numerous studies have outlined factors other than genetic phenotype that are associated with variation in tacrolimus exposure among transplant recipients. These include patient age, gender, kidney or liver function, hematocrit, plasma albumin concentrations, body surface area, and concomitant medications.3 Population pharmacokinetic models, an approach combining the above-listed factors with known genetic information, have been used to create dosing algorithms to more precisely forecast a patient’s tacrolimus dose requirements. However, these models require external validation and prospective trialing. Several studies are currently investigating the efficacy of these dosing algorithms in renal transplant recipients.9
When evaluating the impact that pharmacogenetics has on transplant outcomes, one should also address that most of the current research has focused primarily on calculating the initial tacrolimus dose. However, keeping transplant recipients within therapeutic range for an extended period of time posttransplant remains a challenge. Long-term management with tacrolimus can be significantly impacted by intentional and nonintentional medication nonadherence. Also, there are mounting data regarding the impact that the human microbiome has on drug metabolism and clearance.10,11
Further understanding of tacrolimus pharmacogenetics is essential to solving the mystery of truly personalized medicine in organ transplantation. The incorporation of genetic information, in conjunction with patient-specific and environmental profiles, will allow transplant practitioners to assess individual risks and tailor immunosuppressive strategies. Yoon et al have discovered an important piece in the tacrolimus-dosing puzzle, but now it is up to the transplant community to determine where this piece fits.
1. Jouve T, Fonrose X, Noble J, et al. The TOMATO study (tacrolimus metabolization in kidney transplantation): impact of the concentration-dose ratio on death-censored graft survival. Transplantation. 2020;104:1263–1271.
2. Barry A, Levine M. A systematic review of the effect of CYP3A5 genotype on the apparent oral clearance of tacrolimus in renal transplant recipients. Ther Drug Monit. 2010;32:708–714.
3. Salvadori M, Tsalouchos A. Pharmacogenetics of immunosuppressant drugs: a new aspect for individualized therapy. World J Transplant. 2020;10:90–103.
4. Birdwell KA, Decker B, Barbarino JM, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for CYP3A5 genotype and tacrolimus dosing. Clin Pharmacol Ther. 2015;98:19–24.
5. Yoon J, Song SH, Choi S, et al. Unraveling the genomic architecture of the CYP3A Locus and ADME genes for personalized tacrolimus dosing. Transplantation. 2020.
6. Min S, Papaz T, Lafreniere-Roula M, et al. A randomized clinical trial of age and genotype-guided tacrolimus dosing after pediatric solid organ transplantation. Pediatr Transplant. 2018;22:e13285.
7. Shuker N, Bouamar R, van Schaik RH, et al. A randomized controlled trial comparing the efficacy of Cyp3a5 genotype-based with body-weight-based tacrolimus dosing after living donor kidney transplantation. Am J Transplant. 2016;16:2085–2096.
8. Thervet E, Loriot MA, Barbier S, et al. Optimization of initial tacrolimus dose using pharmacogenetic testing. Clin Pharmacol Ther. 2010;87:721–726.
9. US National Library of Medicine. Studies NCT03465410; NCT03020589; NCT03527238. Available at https://clinicaltrials.gov/ct2/home
. Accessed December 22, 2020.
10. Guo Y, Crnkovic CM, Won KJ, et al. Commensal gut bacteria convert the immunosuppressant tacrolimus to less potent metabolites. Drug Metab Dispos. 2019;47:194–202.
11. Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, et al. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570:462–467.