
==== Front
Pharmacogenomics
Pharmacogenomics
Pharmacogenomics
1462-2416
1744-8042
Taylor & Francis

39109483
10.1080/14622416.2024.2370761
2370761
Version of Record
Review Article
Review
Pharmacogenetics of Calcineurin inhibitors in kidney transplant recipients: the African gap. A narrative review
https://orcid.org/0009-0008-3789-5326
Hussaini Sadiq Aliyu * a b c
https://orcid.org/0000-0001-6291-6610
Waziri Bala b
https://orcid.org/0000-0002-4080-624X
Dickens Caroline a
https://orcid.org/0000-0003-2750-2062
Duarte Raquel a
a Department of Internal Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
b Department of Internal Medicine, Ibrahim Badamasi Babangida Specialist Hospital, Minna, Nigeria
c Department of Pharmacology, Ibrahim Badamasi Babangida University, Lapai, Nigeria
* CONTACT: Tel.: +27 83 718 1174; sadiqaliyuhussaini@gmail.com
7 8 2024
2024
7 8 2024
25 7 329341
Aptara21 6 2024
18 7 2024
16 3 2024
18 6 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Calcineurin inhibitors (CNIs) are the mainstay of immunosuppression in kidney transplantation. Interpatient variability in the disposition of calcineurin inhibitors is a well-researched phenomenon and has a well-established genetic contribution. There is great diversity in the makeup of African genomes, but very little is known about the pharmacogenetics of CNIs and transplant outcomes. This review focuses on genetic variants of calcineurin inhibitors' metabolizing enzymes (CYP3A4, CYP3A5), related molecules (POR, PPARA) and membrane transporters involved in the metabolism of calcineurin inhibitors. Given the genetic diversity across the African continent, it is imperative to generate pharmacogenetic data, especially in the era of personalized medicine and emphasizes the need for studies specific to African populations. The study of allelic variants in populations where they have greater frequencies will help answer questions regarding their impact. We aim to fill the knowledge gaps by reviewing existing research and highlighting areas where African research can contribute.

Tweetable Abstract

Research on the pharmacogenetics of calcineurin inhibitors in kidney transplant recipients is truly wanting in data from the African continent. Given Africa's vast genetic diversity, it is necessary to intensify efforts to generate data from Africa in this field.

Executive summary

Interpatient variability of calcineurin inhibitors is well researched and has a well-established genetic component although very little is known in the African context.

This review provides an overview of currently available data on the pharmacogenetics of CNI in kidney transplant recipients and highlights research gaps in general and in Africa specifically.

Genetic variants of the calcineurin metabolizing enzymes (CYP3A4 and CYP3A5), related molecules (POR, PPARA) and membrane transporters are described.

Calcineurin inhibitors' metabolizing enzymes

The frequency of genetic variants of calcineurin metabolizing enzymes differed significantly in African populations, compared with other populations. Investigating the role of these enzymes in populations with higher frequencies may provide better and unique insights into how they affect CNI disposition and kidney transplant outcomes.

Other Calcineurin inhibitors' metabolism-related molecules

Inter-individual genetic variance in calcineurin inhibitor metabolism related molecules can affect the main calcineurin metabolizing enzymes differently. The pattern of distribution of these molecules also differs in the African population compared with other populations; thus, their influence may not be directly extrapolated.

Membrane transporters

Research on the effects of genetic variation on membrane transporters has been extensive, but conflicting. The relative uniformity in the occurrence of some common variants in the African population may provide an opportunity to reduce the confounding effects of these variants in research on other CNI pharmacogenomic variants.

Keywords: 

cyclosporine
CYP450 variants
metabolizing enzymes
pharmacodynamics
pharmacokinetics
SNPs
tacrolimus
transporters
==== Body
pmc1. Background

An essential factor in the success of kidney transplantation is the effectiveness of pharmacological immunosuppression. Among the medications used for the achievement of this immunomodulation, Calcineurin inhibitors (CNIs), such as Cyclosporine and Tacrolimus, are commonly utilized [1], and are presently prescribed to more than 94% of kidney transplant recipients in the United States [2]. Despite their usefulness, CNIs are far from ideal as they have a narrow therapeutic index, thus exhibiting a thin line between effectiveness and toxicity. Further complicating this narrow therapeutic index is the wide intra- and interpatient variability of the concentration of these drugs. This variability necessitates the use of therapeutic drug monitoring (TDM), which has remained the gold standard [3,4]. The problem with TDM is that it depends on first administering the drug, which exposes patients to potential unwanted effects, and thereafter measuring the in vivo concentration of the drug to arrive at the correct dose for each patient based on multiple iterations.

Pharmacogenetics studies the relationship between genetic variation and drug disposition. Interindividual variability in the disposition of CNIs is shown to be genetically influenced [5] and has been extensively researched. Central to the investigation of the genetic component of variability is the impact of single-nucleotide polymorphisms (SNPs) in genes that encode metabolizing enzymes and membrane transporters.

Practically, the genetic variability is inferred by measuring the whole blood concentration of these medications, or cellular and tissue concentrations. Pharmacodynamic parameters such as the incidence of acute rejection, nephrotoxicity and other toxicities known to result from the effect of CNIs can also be measured.

Africa is unique in both the epidemiology of kidney disease and the genetic makeup of its population. There is an unparalleled genetic diversity across Africa, leading to increased interest in genomic research with a reasonable representation of several ethnicities in projects like HapMap, gnomAD and the 1000 genomes project [6–8]. However, despite Africa's genetic diversity and the research interest in its population's genetic makeup, there is a paucity of data on the relationship between genetic variants and pharmacological substances generally, and for CNIs particularly, in relation to transplantation.

Chronic kidney disease is more prevalent in individuals of African ancestry, and there is greater progression to end-stage kidney disease (ESKD) [9–11]. For patients who have developed ESKD, the best treatment option is kidney transplantation. In Africa, kidney transplantation faces many challenges, including financial constraints, the paucity of donors, transplant specialists and well-formed transplant programs [10]. Moreover, racial disparities are documented regarding the outcome of transplantation and studies from outside the continent, but including people of African ancestry, showed more incidences of transplant failure [12,13].

This review aims to provide an overview of currently available data on the pharmacogenetics of CNI in kidney transplant recipients and highlights the need for research specifically on African transplant recipients to fill the gap in the literature and provide a basis for comparison with other well-studied populations.

We performed a literature search around the following keywords: “Calcineurin inhibitors”, “Tacrolimus”, “Cyclosporine”, “Kidney transplant”, “Solid organ transplant”, “Single nucleotide polymorphism”, “Genetic variants”, “Pharmacogen*”, “Blood concentration”, “Genome-wide association study” and “Africa*”.

Articles relevant to the review's aim, and references cited in them, were reviewed and analyzed. Additional information regarding variants and their allele frequencies in different populations was obtained from the National Centre for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) and the European Molecular Biology Laboratory – European Bioinformatics Institute (EBML-EBI) (https://www.ebi.ac.uk/) databases.

We selected the most studied or relevant variants for the main membrane transporters and metabolizing enzymes of Tacrolimus and Cyclosporine [4,14]. We also reviewed other variants that influence these proteins. The chief metabolizing enzymes for CNIs are the CYP3A4/5 subfamily of CYP P450. The list of studied transporters includes the extensively studied P-glycoprotein encoded by the ABCB1 gene and, to a lesser extent, the solute carrier proteins [14]. Finally, we provide a summary of the variants and highlight future avenues for exploration of these variant(s) effects.

2. Calcineurin inhibitor metabolizing enzymes

2.1. CYP P450 3A (CYP3A)

Among biotransformation systems involved in metabolism and subsequent elimination of medications and endogenous molecules, the human CYP P450 3A subfamily is the most robust, versatile and abundant in the liver and small intestine [15]. Its members are important and relevant to the first-pass metabolism and systemic bioavailability of up to 50% of clinically utilized medications [16]. This subfamily has four main isoforms; CYP3A4, CYP3A5, CYP3A7 and CYP3A43 and are variably expressed at the individual and racial/ethnic level [17]. The two most important and studied isoforms are CYP3A4 and CYP3A5, with both showing significant overlapping substrate specificity [18,19]. CYP3A7 is a fetal isoform, only found in some adults in a negligible amount [20] while CYP3A43 contributes only 0.1–0.2% of CYP3A transcripts in adults.

CYP3A4 and CYP3A5, are the primary metabolizers of Cyclosporine and Tacrolimus. The degree to which these isoforms metabolize the drugs varies significantly at both individual and racial levels [21–23].

Both CYP3A4 and CYP3A5 have a large and growing number of genetic variants in the form of SNPs, insertions, deletions (indels) and other types of variation. As of June 2024, a ClinVar (https://www.ncbi.nlm.nih.gov/clinvar) [24] search returns 43 variants for the CYP3A4 gene. Thirteen of these are deletions, twelve duplications, two insertions and sixteen SNPs. The search also returned fourteen deletions, thirteen duplications, three insertions and thirty-seven SNPs for the CYP3A5 gene, a total of 66 variants in all. The occurrence and combinations of these variants are subject to individual, ethnic and racial variation [4,21,25]. In relation to CNI metabolism, a previous review identified CYP3A4 -392A>G (rs2740574) and CYP3A5 6986A>G (rs776746) as the most extensively studied variants of the CYP3A4 and CYP3A5 isoforms, respectively [4].

The function of these CNI-metabolizing isoforms is also intricately connected to other proteins, notably POR [26,27], PPARA [28] and membrane transport proteins [14].

2.2. CYP3A4*1.001 (CYP3A4*1B)

The distribution of CYP3A4 SNPs has been investigated in different populations with varying allele frequencies. CYP3A4*1.001 (rs2740574), formerly CYP3A4*1B, for example, is reported in 4.2% of European-Americans and 27.1% of African-Americans but was not found in either Koreans, Han Chinese or Japanese [29]. CYP3A4*1.001 is the most extensively studied CYP3A4 SNP and involves the replacement of the C nucleotide by a T in the 5′-prime promoter region of the gene, first described by Rebbeck et al. (1998) [30]. In samples of African ancestry, the ancestral C nucleotide shows the highest allele frequency (64–76%) compared with populations of European or Asian ancestry where the T nucleotide is most common (Table 1).

Table 1. Allele frequency of the most studied single nucleotide polymorphisms in relation to pharmacogenetics of calcineurin inhibitors in transplant recipients.

Allele/SNP	1000 Genome Project Phase III (32)	GnomAD genomes V3.1.2 (11)	NCBI ALFA	
Population (percentage in the database)	Allele frequency %	Population (percentage in the database)	Allele frequency	Population (percentage in the database)	Allele frequency	
Ancestral nucleotide	Variant nucleotide	Ancestral nucleotide	Variant nucleotide	Ancestral nucleotide	Variant nucleotide	
CYP3A4*1B	 	C	T	 	C	T	 	C	T	
rs2740574	ALL	23.1 (1156)	76.9 (3852)	ALL	20.9 (31728)	79.1 (120052)	ALL	11.0 (7668)	89.0 (61974)	
Increased function variant	AFR (26.4)	76.6 (1012)	23.4 (310)	AFR (27.2)	64.1 (26478)	35.9 (14832)	AFR (11.1)	63.8 (4919)	36.2 (2787)	
 	AMR (13.9)	10.5 (73)	89.5 (621)	AMR (10.1)	11.3 (1723)	88.7 (13535)	AMRb (6.9)	8.2 (395)	91.8 (4441)	
 	EAS (20.1)	0.4 (4)	99.6 (1004)	EAS (3.4)	0.2 (10)	99.8 (5186)	EAS (0.6)	0.0 (0)	100 (388)	
 	EUR (20.1)	2.8 (28)	97.2 (978)	EURa (44.7)	3.5 (2383)	96.5 (65483)	EUR (78.0)	3.6 (1972)	96.4 (52372)	
 	SAS (19.5)	4.0 (39)	96.0 (939)	SAS (3.2)	3.0 (146)	97.0 (4664)	SAS (0.2)	2.6 (4)	97.4 (150)	
CYP3A4*22	 	G	A	 	G	A	 	G	A	
rs35599367	ALL	98.5 (4933)	1.5 (75)	ALL	96.8 (147184)	3.2 (4824)	ALL	95.8 (40503)	4.2 (1773)	
Reduced function variant	AFR (26.4)	99.9 (1321)	0.1 (1)	AFR (27.2)	99.1 (41020)	0.9 (364)	AFR (8.0)	99.1 (3349)	0.9 (31)	
 	AMR (13.9)	97.4 (676)	2.6 (18)	AMR (10.0)	97.5 (14871)	2.5 (389)	AMRb (1.4)	97.4 (594)	2.6 (16)	
 	EAS (20.1)	100 (1008)	0.0 (0)	EAS (3.4)	100 (5190)	0.0 (0)	EAS (0.4)	100 (164)	0.0 (0)	
 	EUR (20.1)	95.0 (956)	5.0 (50)	EURa (44.7)	95.1 (64651)	4.9 (3347)	EUR (86.6)	95.4 (34879)	4.6 (1689)	
 	SAS (19.5)	99.4 (972)	0.6 (6)	SAS (3.2)	99.1 (4770)	0.9 (44)	SAS (0.2)	99.0 (103)	1.0 (1)	
CYP3A5*3	 	T	C	 	T	C	 	T	C	
rs776746	ALL	37.9 (1896)	62.1 (3112)	ALL	27.1 (41185)	72.9 (110783)	ALL	11.3 (26286)	88.7 (207070)	
Loss of function variant	AFR (26.4)	82.0 (1084)	18.0 (238)	AFR (27.2)	69.5 (28687)	30.5 (12603)	AFR (4.6)	69.7 (7402)	30.3 (3226)	
 	AMR (13.9)	20.3 (141)	79.7 (553)	AMR (10.0)	22.1 (3364)	77.9 (11892)	AMRb (3.9)	20.4 (1838)	79.6 (7168)	
 	EAS (20.1)	28.7 (289)	71.3 (719)	EAS (3.4)	27.6 (1432)	72.4 (3748)	EAS (0.3)	27.8 (179)	72.2 (465)	
 	EUR (20.1)	5.7 (57)	94.3 (949)	EURa (44.8)	6.9 (4691)	93.1 (63351)	EUR (85.2)	7.0 (13929)	93.0 (184967)	
 	SAS (19.5)	33.2 (325)	66.8 (653)	SAS (3.2)	30.1 (1455)	69.9 (3371)	SAS (2.2)	25.0 (1266)	75.0 (3796)	
CYP3A5*6	 	C	T	 	C	T	 	C	T	
rs10264272	ALL	95.5 (4785)	4.5 (223)	ALL	96.3 (146503)	3.7 (5627)	ALL	99.4 (293209)	0.6 (1759)	
Loss of function variant	AFR (26.4)	84.6 (1118)	15.4 (204)	AFR (27.2)	87.6 (36280)	12.4 (5134)	AFR (3.7)	88.5 (9578)	11.5 (1248)	
 	AMR (13.9)	97.7 (678)	2.3 (16)	AMR (10.0)	97.7 (14923)	2.3 (349)	AMRb (0.6)	99.1 (1829)	0.09 (17)	
 	EAS (20.1)	100 (1008)	0.0 (0)	EAS (3.4)	100 (5202)	0.0 (0)	EAS (1.1)	100 (3112)	0.0 (0)	
 	EUR (20.1)	99.7 (1003)	0.3 (3)	EURa (44.7)	99.9 (67948)	0.01 (64)	EUR (87.5)	99.9 (257954)	0.01 (284)	
 	SAS (19.5)	100 (978)	0.0 (0)	SAS (3.2)	99.9 (4823)	0.01 (3)	SAS (1.8)	100 (5218)	0.0 (0)	
CYP3A5*7	 	A	AA	 	A	AA	 	A	AA	
rs41303343	ALL	96.8 (4850)	3.2 (158)	ALL	97.1 (147741)	2.9 (4407)	ALL	99.5 (94482)	0.5 (450)	
INDEL	AFR (26.4)	88.2 (1166)	11.8 (156)	AFR (27.2)	89.9 (37214)	10.1 (4192)	AFR (4.5)	90.8 (3909)	9.2 (397)	
Loss of function variant	AMR (13.9)	99.7 (692)	0.3 (2)	AMR (10.0)	99.1 (15129)	0.9 (143)	AMRb (1.0)	99.4 (922)	0.6 (6)	
 	EAS (20.1)	100 (1008)	0.0 (0)	EAS (3.4)	99.98 (5199)	0.02 (1)	EAS (2.8)	100 (2674)	0.0 (0)	
 	EUR (20.1)	100 (1006)	0.0 (0)	EURa (44.7)	99.96 (68016)	0.04 (24)	EUR (86.0)	99.98 (81622)	0.02 (20)	
 	SAS (19.5)	100 (978)	0.0 (0)	SAS (3.2)	99.96 (4824)	0.04 (2)	SAS (0.3)	100 (274)	0.0 (0)	
POR*28	 	C	T	 	C	T	 	C	T	
rs1057868	ALL	71.4 (3575)	28.6 (1433)	ALL	72.8 (110687)	27.3 (41467)	ALL	71.4 (97339)	28.6 (39019)	
Reduced function variant.	AFR (26.4)	83.0 (1097)	17.0 (225)	AFR (27.2)	81.7 (33840)	18.3 (7600)	AFR (2.7)	80.3 (2932)	19.7 (720)	
 	AMR (13.9)	72.0 (500)	28.0 (194)	AMR (10.0)	70.7 (10806)	29.3 (4478)	AMRb (0.5)	72.2 (469)	27.8 (181)	
 	EAS (20.1)	62.6 (631)	37.4 (377)	EAS (3.4)	61.8 (3205)	38.2 (1979)	EAS (1.4)	60.0 (1142)	40.0 (762)	
 	EUR (20.1)	70.5 (709)	29.5 (297)	EURa (44.7)	71.4 (48576)	28.6 (19442)	EUR (84.0)	71.5 (81835)	28.5 (32703)	
 	SAS (19.5)	65.2 (638)	34.8 (340)	SAS (3.2)	64.2 (3104)	35.8 (1730)	SAS (0.1)	62.0 (62)	38.0 (38)	
PPARA	 	G	A	 	G	A	 	G	A	
rs4253728	ALL	89.5 (4481)	10.5 (527)	ALL	81.5 (123995)	18.5 (28101)	ALL	74.9 (155616)	25.1 (52076)	
Loss of function variant	AFR (26.4)	97.1 (1284)	2.9 (38)	AFR (27.2)	93.8 (38877)	6.2 (2557)	AFR (3.9)	93.5 (7490)	6.5 (518)	
 	AMR (13.9)	84.4 (586)	15.6 (108)	AMR (10.0)	81.4 (12423)	18.6 (2847)	AMRb (4.3)	85.2 (7632)	14.8 (1326)	
 	EAS (20.1)	99.8 (1006)	0.2 (2)	EAS (3.4)	99.9 (5194)	0.01 (6)	EAS (0.3)	100 (606)	0.0 (0)	
 	EUR (20.1)	72.2 (726)	27.8 (280)	EURa (44.7)	73.2 (49788)	26.8 (18200)	EUR (85.0)	73.0 (128793)	27.0 (47663)	
 	SAS (19.5)	89.9 (879)	10.1 (99)	SAS (3.2)	87.6 (4236)	12.4 (602)	SAS (2.4)	84.5 (4275)	15.5 (785)	
PPARA	 	A	G	 	A	G	 	A	G	
rs4823613	ALL	72.6 (3638)	27.4 (1370)	ALL	70.1 (106383)	29.9 (45395)	ALL	71.8 (145464)	28.2 (57218)	
Loss of function variant	AFR (26.4)	59.8 (790)	40.2 (532)	AFR (27.2)	62.5 (25798)	37.5 (15480)	AFR (3.7)	61.5 (4627)	38.5 (2897)	
 	AMR (13.9)	72.2 (501)	27.8 (193)	AMR (10.0)	69.9 (10637)	30.1 (4585)	AMRb(3.4)	75.1 (5170)	24.9 (1716)	
 	EAS (20.1)	80.1 (807)	19.9 (201)	EAS (3.4)	77.6 (4004)	22.4 (1158)	EAS (0.3)	78.8 (465)	21.2 (125)	
 	EUR (20.1)	71.0 (714)	29.0 (292)	EURa (44.8)	72.3 (49111)	27.7 (18849)	EUR (86.3)	71.8 (125571)	28.2 (49291)	
 	SAS (19.5)	84.5 (826)	15.5 (152)	SAS (3.2)	82.8 (3981)	17.2 (829)	SAS (2.5)	80.7 (4072)	19.3 (976)	
ABCB1 3435	 	G	A	 	G	A	 	G	A	
rs1045642	ALL	60.5 (3029)	39.5 (1979)	ALL	57.2 (86843)	42.8 (65009)	ALL	49.3 (166518)	50.7 (171132)	
Normal/Reduced function variant	AFR (26.4)	85.0 (1124)	15.0 (198)	AFR (27.2)	79.7 (32932)	20.3 (8388)	AFR (2.7)	77.8 (7201)	22.2 (2049)	
 	AMR (13.9)	57.2 (397)	42.8 (297)	AMR (10.0)	56.8 (8663)	43.2 (6589)	AMRb (2.5)	54.8 (4659)	45.2 (3843)	
 	EAS (20.1)	60.2 (607)	39.8 (401)	EAS (3.4)	62.0 (3207)	38.0 (1969)	EAS (0.9)	62.3 (1964)	37.7 (1188)	
 	EUR (20.1)	48.2 (485)	51.8 (521)	EURa (44.7)	46.9 (31853)	53.1 (36095)	EUR (86.8)	48.0 (140854)	52.0 (152286)	
 	SAS (19.5)	42.5 (416)	57.5 (562)	SAS (3.2)	41.1 (1984)	58.9 (2838)	SAS (1.5)	38.7 (2026)	61.3 (3204)	
ABCB1 2677	 	C	A/T	 	C	A/T	 	C	A/T	
rs2032582	ALL	61.7 (3090)	33.4 (1674)/4.9 (244)	ALL	64.1 (97433)	33.5 (50937)/2.4 (3636)	ALL	55.5 (148748)	44.4 (118921)/0.1 (336)	
Normal/Reduced function variant	AFR (26.4)	98.0 (1295)	2.0 (26)/0.1 (1)	AFR (27.2)	91.0 (37690)	8.6 (3544)/0.4 (184)	AFR (2.0)	87.8 (4691)	12.0 (639)/0.2 (10)	
 	AMR (13.9)	57.2 (397)	36.9 (256)/5.9 (41)	AMR (10.0)	60.4 (9198)	36.3 (5536)/3.4 (520)	AMRb (2.3)	54.4 (3293)	45.6 (2765)/0.0 (0)	
 	EAS (20.1)	46.8 (472)	39.8 (401)/13.4 (135)	EAS (3.4)	47.4 (2454)	39.1 (2028)/13.5 (700)	EAS (1.8)	43.8 (2056)	56.2 (2640)/0.04 (2)	
 	EUR (20.1)	57.3 (576)	41.0 (412)/1.8 (18)	EURa (44.7)	54.6 (37091)	43.3 (29424)/2.1 (1451)	EUR (86.6)	55.2 (128206)	44.7 (103783)/0.01 (266)	
 	SAS (19.5)	35.8 (350)	59.2 (579)/5.0 (49)	SAS (3.2)	35.5 (1714)	59.5 (2869)/5.0 (241)	SAS (0.1)	28.8 (88)	71.2 (218)/0.0 (0)	
ABCB1 1236	 	G	A	 	G	A	 	G	A	
rs1128503	ALL	58.4 (2924)	41.6 (2084)	ALL	61.9 (93943)	38.1 (57913)	ALL	57.3 (173774)	42.7 (129284)	
Normal/reduced function variant	AFR (26.4)	86.4 (1142)	13.6 (180)	AFR (27.2)	80.6 (33348)	19.4 (8012)	AFR (3.8)	79.3 (9179)	20.7 (2391)	
 	AMR (13.9)	59.7 (414)	40.3 (280)	AMR (10.0)	57.3 (8736)	42.7 (6506)	AMRb (2.4)	52.4 (3798)	47.6 (3444)	
 	EAS (20.1)	37.3 (376)	62.7 (632)	EAS (3.4)	36.5 (1888)	63.4 (3278)	EAS (2.1)	38.9 (1236)	61.1 (1940)	
 	EUR (20.1)	58.4 (588)	41.6 (418)	EURa (44.7)	56.4 (38347)	43.6 (29617)	EUR (85.6)	57.2 (148318)	42.8 (110992)	
 	SAS (19.5)	41.3 (404)	58.7 (574)	SAS (3.2)	40.0 (1925)	60.0 (2889)	SAS (1.7)	39.1 (2040)	61.0 (3184)	
a EUR: Described as non-Finnish European as against Finnish European.

b AMR: Described as Latin American 2 – Latin American individuals with mostly European and native American ancestry as against Latin American 1, who are Latin Americans with Afro-Caribbean ancestry.

ALL: All populations put together; AFR: African; AMR: American; EAS: East Asian; EUR: European; SAS: South Asian.

Cyclosporine metabolism is mainly by the CYP3A4 isoform, although CYP3A5 contributes to a lesser degree. Results of the few studies that have examined the influence of the CYP3A4*1.001 SNP on Cyclosporine's pharmacokinetics have been mixed. Some studies could not establish evidence for a significant influence of the CYP3A4*1.001 SNP on Cyclosporine pharmacokinetics [31–33]. The CYP3A4*1.001 ancestral C allele occurs at a very low frequency in the main populations (European and Asian) it has been investigated in [34]. This may also explain why only a few studies have been conducted regarding the influence of this allele on Cyclosporine pharmacokinetics. A meta-analysis by Wang et el al. [35] in 2018, found only four studies that could be included. Their meta-analysis, however, points to a higher dose-adjusted trough concentration in carriers of the T allele of CYP3A4*1 compared with those carrying the CYP3A4*1.001 C allele but not in the dose or trough concentrations alone. They further advised caution in interpreting their results as only a few studies were analyzed, and subgroup analysis at different time points failed to point to significant differences between ancestral and variant allele carriers [35]. A small sample-sized study of 14 healthy volunteers, 11 of whom were of African ancestry, demonstrated a lower dose-adjusted area under the concentration curve (AUC) and higher oral clearance in carriers of the C allele [36]. The frequent occurrence of the C allele in people of African ancestry provides an opportunity for evaluating the effect of this SNP on Cyclosporine pharmacokinetics. To date, however, no such study has been done.

Even fewer studies have been conducted looking at the association of the ancestral CYP3A4*1.001 C allele and Tacrolimus pharmacokinetics. Reasons for this might be because Tacrolimus is mainly metabolized by CYP3A5, a well-established relationship has been observed between Tacrolimus pharmacokinetics and some CYP3A5 SNPs, and the low frequency of the C allele in populations from other racial groups. Moreover, studies have shown a significant linkage between the CYP3A4*1.001 SNP and the functional SNP of the CYP3A5 isoform (CYP3A5*1). The effects noticed on Tacrolimus pharmacokinetics are thought to be due to this linkage and not an isolated effect of this allele [22]. Here, studies in African ancestry populations provide a good opportunity for a better understanding of this relationship due to the occurrence of both alleles in the population.

Studies of transplant outcomes with both Cyclosporine and Tacrolimus use in relation to CYP3A4*1.001 are quite few, mostly among people of European ancestry, with the inclusion of a few African Americans. With Cyclosporine, no relationship could be established between CYP3A4*1.001 and acute rejection, kidney function or incidence of biopsy-proven acute rejection (BPAR) within 12 months post-transplant [37]. Similarly, no relationship could be established between CYP3A4*1.001 and Tacrolimus in relation to acute rejection, creatinine clearance, graft loss, or 1-year survival [38,39].

2.3. CYP3A4*22

CYP3A4*22 (rs35599367) is another significantly investigated SNP in relation to CNIs. It involves a G>A transition and occurs at a relatively low frequency in all races (Table 1). The minor allele (A) frequency is highest among people of European ancestry (5–8%) and much lower in people of African and Asian ancestry (0–4.3%) [23]. The effect of the minor allele was previously only reported in the European population, but a recent Egyptian study investigated the role of this SNP together with the CYP3A5*3 allele although, only five patients (3.2%) were carriers of the CYP3A4*22.

CYP3A4*22 was shown to affect Cyclosporine pharmacokinetics in a sample of Norwegian renal transplant recipients, with those carrying the A allele having a significantly higher dose-adjusted 2-hour post-dose (C2) level in both a univariable analysis and a multivariable analysis with other gene variants. Overall, having the A allele independently explained approximately 12% of the variability in Cyclosporine dose-adjusted C2 concentration.

In Tacrolimus disposition, CYP3A4*22 is shown to permit better prediction of dose requirement and trough concentration when combined with CYP3A5*3 [23]. Among four ancestry groups investigated by Mohamed et al. (2019), namely Europeans, African-Americans, Native Americans and Asians, a gene-wide analysis, showed a significant influence of CYP3A4*22 on dose-normalized Tacrolimus trough concentration in recipients of European ancestry [40]. In the above-mentioned Egyptian study, the small sample size of the carriers of this SNP precludes analysis of its effect independent of the CYP3A5*3 SNP.

Further investigation of both the distribution of CYP3A4*22, its effect on the disposition of Cyclosporine and Tacrolimus and its influence when studied with other variants in resident/indigenous African populations is needed.

2.4. CYP3A5*3

CYP3A5*3 (rs776746) is one of the multiple variants of the polymorphic CYP3A5 gene and is an A>G transition at position 6986 on the minus strand (C to T on the plus strand, Table 1). This variant leads to a cryptic splice site and produces a non-functional enzyme. Homozygous/heterozygous carriers of the A allele are referred to as CYP3A5 expressors because they form a functional protein. Homozygous carriers of the G allele do not produce a functional protein and are thus referred to as non-expressors.

Researchers have extensively studied this variant and how it alters CNI disposition in various populations. There have been several studies on people of European ancestry [41–52], Asian ancestry [21,53–64] and in mixed populations consisting majority of people of European ancestry and very few individuals of African extraction [38,39,65–67].

Studies evaluating the influence of CYP3A5*3 on the pharmacokinetics of Cyclosporine showed contradictory findings [22]. This may be because the main metabolizing enzyme for Cyclosporine is CYP3A4, with CYP3A5 only making a small contribution. Most of these studies were conducted in people of European and Asian ancestry, both populations with a lower frequency of the CYP3A5*1 A allele (expressors) as well as a low frequency of the CYP3A4*1.001 C allele. Hopefully, a clearer relationship can be deciphered in an African population with a good genotypic mixture of these alleles.

A well-established relationship exists between the CYP3A5 polymorphism and Tacrolimus pharmacokinetics. This relationship has been shown in terms of dose-adjusted concentration, dose requirements, trough concentrations and time to achieve target concentration [38,39,45,46,53,58,65,68,69]. The carriers of the CYP3A5*1 A allele (expressors) show normal functionality of the resulting enzyme and lead to higher metabolism of Tacrolimus compared with the non-expressors and thus require higher dosing [39,45,48]. Some studies found lower trough or dose-adjusted trough concentrations in expressors compared with non-expressors [43,58,65], while other studies reported higher dose requirements [39,58,70]. This influence is also reflected in the time taken to achieve the target concentration, which was found to be shorter in the non-expresser group compared with expressors [39,71,72].

A multi-ethnic South African study that evaluated the influence of the CYP3A5 polymorphism in relation to Tacrolimus pharmacokinetics in 43 kidney transplant recipients consisting of people of black, white and mixed ancestry backgrounds found no CYP3A5*3 non-expressors (homozygous GG) among the black transplant recipients but found 88% non-expressors among the white transplant population [70]. The mean total daily dose differed among the racial groups, and a twofold increase in dose requirement was noted in CYP3A5 expressors compared with non-expressors. Mean Tacrolimus trough (C0) concentration did not differ across the racial groups and over time when analyzed in relation to genotypes, but this may be related to the practice of TDM and appropriate dose adjustments. The study points to the need for more indigenous African studies with larger sample sizes.

Studies on outcomes such as acute rejection, serum creatinine, hypertension, eGFR and nephrotoxicity attempting to find an association between CYP3A5*3 and Cyclosporine are limited, and most failed to find any association [37,73,74]. Similarly, the occurrence of various outcome parameters could not be correlated with the existence of the CYP3A5*3 G allele despite the significant correlation with Tacrolimus pharmacokinetic parameters [22,75].

2.5. CYP3A5*6 & CYP3A5*7

CYP3A5*6 (rs10264272) is a C>T transition of the CYP3A5 gene present in 12–25% of people of African ancestry but relatively absent in Europeans and Asians [38]. CYP3A5*6 also codes for a splicing defect, leading to a defective CYP3A5 enzyme [17]. CYP3A5*7 (rs41303343), another allelic variant of CYP3A5, is found in about 10% of people of African ancestry but is largely unreported in Europeans and Asians [38]. It has a single nucleotide (A) insertion, resulting in a frameshift mutation and thus a non-functional enzyme due to truncation [76]. Both CYP3A5*6 and CYP3A5*7 contribute to variations in Tacrolimus trough levels in African American kidney transplant recipients [77]. Moreover, an increase in the number of loss of function (LoF) alleles (CYP3A5*3, CYP3A5*6 and CYP3A5*7) in an individual, correlates with increased eGFR, hinting at the possibility of a protective effect with reduced Tacrolimus metabolism. However, no association has been shown with clinical acute rejection.

There is a paucity of studies evaluating the effect of these variants (CYP3A5*6 and CYP3A5*7) on inter-individual variability of Cyclosporine pharmacokinetic or pharmacodynamic parameters. With the apparent exclusivity of these variants in Africans, further investigating their effects in African populations will be worthwhile.

3. Other Calcineurin inhibitors' metabolism-related molecules

3.1. POR

Human POR is a 75 kDa protein comprising 680 amino acids and is on chromosome 7 with 16 exons [78]. POR transfers electrons from NADPH to microsomal CYP450 enzymes, resulting in their activity. POR is essential in CYP450-mediated metabolism, suggesting that functional variants of POR could have an effect. Multiple variants have been described for POR, the commonest being POR*28 (rs1057868), associated with various CYP450 enzymes' activity with diverse effects for different CYP450s [79–81]. The POR*28 C>T transition alters the conformation of the protein, modifying the POR – CYP450 interaction [82,83].

De Jong et al. (2011) [26] reported that in CYP3A5 expressors, the presence of at least one POR*28 T allele is associated with a 25% higher Tacrolimus dose requirement in the first year post-transplant compared with homozygous CC individuals (POR*1/*1). Furthermore, expressors with the T allele require significantly more time to reach the predefined Tacrolimus target trough concentrations (C0). No effect was demonstrated in nonexpressors. In another study, Elens et al. (2014) [27] reported an overall increase in CYP3A4 and CYP3A5 activity with the POR*28 T allele. For Tacrolimus, in CYP3A5 expressors, there was a 16.9% decrease in dose-adjusted C0 in POR*28 T carriers when compared with expressors with the homozygous CC (POR*1/*1). Similarly, nonexpressors of CYP3A5, although requiring two POR*28 T alleles, showed a 24.1% decrease in dose-adjusted C0, suggesting higher CYP3A4 activity. Similarly, patients on Cyclosporine, homozygous for POR*28 TT (POR*28/*28) and expressing neither CYP3A5 nor the reduced function CYP3A4*22 allele, showed 15% lower Cyclosporine dose-adjusted C0. For both Cyclosporine and Tacrolimus cohorts, no pharmacodynamic parameters, namely delayed graft function (DGF) and BPAR, were associated with the POR*28 T allele. These findings were confirmed in two independent cohorts [27].

Kurzawski et al. (2014) [28] investigated both POR*28 and two SNPs of PPARA (rs4253728 and rs4823613) but were unable to find any significant difference with Tacrolimus-related pharmacokinetic parameters and new-onset diabetes mellitus after transplant (NODAT).

A recent study by Booyse et al. (2022) characterized POR haplotype distribution in African populations compared with other global populations and found that, although no significant differences in allele frequency exist between African populations, the POR*28 T allele occurs at a higher frequency in individuals of non-African ancestry [84]. To our knowledge, the POR genetic variants have yet to be studied in relation to CNIs in the context of kidney, or other organ, transplantation in Africa.

4. PPARA

PPARA is a nuclear receptor encoded by the PPARA gene. It is highly expressed in the liver, brown adipose tissue, and skeletal muscles and contributes to interpatient variability in CYP3A expression [85]. Klien et al. (2012) first demonstrated the strong impact of PPARA on both the expression and function of CYP3A4 via CYP3A4 mRNA, protein and 2-OH-atorvastatin metabolism [85]. Following this lead, Lunde et al. (2014) investigated the effect of two highly linked PPARA SNPs (rs4253728 and rs4823613) together with those of CYP3A5*3, CYP3A4*22 and POR*28, on Cyclosporine and Tacrolimus dose-adjusted concentrations in 177 Norwegian kidney transplant recipients. They demonstrated a 19% higher dose-adjusted Tacrolimus trough concentration in patients with PPARA variants. No effect was demonstrated in patients on Cyclosporine [49]. In contrast, when Kurzawski et al. 2014 investigated the impact of POR*28 and the above-mentioned PPARA SNPs in 241 white kidney transplant recipients, neither dose-adjusted Tacrolimus concentration nor occurrence of NODAT showed significant differences [86]. As shown in Table 1, the allele frequency of these SNPs differs in Africans compared with other populations, and thus, independent validation is warranted.

5. Membrane transporters

The main transporters investigated with CNIs are P-glycoprotein/ABCB1, but others are ABCC, SLCO1 and SLCO2 [4,14,21,22]. P-glycoprotein is present on the outer membrane of various cell types and is responsible for the influx and outflow of various chemicals, drugs and other xenobiotics [87]. It is an ATP-dependent transporter encoded by what was formerly referred to as the MDR1 gene, with its most studied SNPs being ABCB1 3435 C>T (rs1045642), ABCB1 1236 C>T (rs1128503) and ABCB1 2677 G>T/A (rs2032582) which are in high linkage disequilibrium [4,88,89].

A majority of studies involving ABCB1 SNPs failed to find any association between these SNPs and Cyclosporine pharmacokinetics, as extensively reviewed by Staatz et al. (2010) [4], although a few studies pointed toward higher Cyclosporine levels in patients with variant alleles. A meta-analysis of 14 studies involving 1036 individuals failed to resolve the relationship of ABCB1 3435 with either dose-adjusted Cmax or C0 [90]. Studies for ABCB1 1236 and ABCB1 2677 showed higher levels of Cyclosporine in patients with the variant alleles (T and T/A respectively), pointing to a less functional glycoprotein that should otherwise pump the drug out of cells and thus limit exposure [4]. A similar trend of conflicting results was noted with studies investigating the influence of ABCB1 polymorphisms on the metabolism of Tacrolimus, even in studies evaluating the three most studied ABCB1 variants in combination [4,22].

The effect of ABCB1 SNPs may be difficult to evaluate in isolation due to the influence of the SNPs of metabolizing enzymes, especially CYP3A5 with Tacrolimus, and indeed, some studies point to this [21,53]. However, one study was able to demonstrate that even after removing the confounding effect of the CYP3A5*3 C allele by using the complete wild-type (TT) combination, the ABCB1 reference SNPs have higher dose-adjusted Tacrolimus levels at various time points compared with a completely variant haplotype combination [91].

Pharmacodynamically, the results of studies on the relationship between ABCB1 SNPs and Cyclosporine are generally conflicting and show no clear direction of influence [22]. While some showed an increased incidence of BPAR in patients carrying the homozygous alleles of the three most studied ABCB1 variants, others showed that the increased risk is in carriers of the heterozygous C-G-T haplotype versus the two homozygous haplotypes of T-T-T (all variant) and C-G-C (all wild type) [92]. Most other studies do not show any association between the three most studied ABCB1 SNPs and acute rejection in kidney transplant recipients on Cyclosporine. Similarly, studies on ABCB1 SNPs and their influence on nephrotoxicity are conflicting [22]. These studies did not factor in the influence of metabolizing enzyme SNPs, and studies assessing other outcome parameters in relation to the SNPs of ABCB1 are quite scarce. Woillard et al. (2010), however, demonstrated an adverse effect of the T-T-T haplotype of the donor on graft survival and renal function in comparison with the other haplotypes [93]. Most studies failed to establish a relationship between ABCB1 SNPs and Tacrolimus pharmacodynamics in terms of BPAR, nephrotoxicity and graft function, and the few studies that indicate some influence, are both conflicting and occur in other transplants, such as lung and liver [22,94,95].

Several issues confound the understanding of the influence of P-glycoprotein genetic variants. The main pharmacokinetic parameters used to evaluate their effects are related to blood level concentrations, but these variants also affect intracellular concentration, which, although being a better marker theoretically, is practically challenging, poorly investigated and thus difficult to derive conclusions from [4,14]. Moreover, drug metabolites including metabolites of CNIs are also transported back into the gut lumen by the same P-glycoprotein [22,96]. Another factor is that, whereas the activity of hepatic and intestinal CNIs-metabolizing enzyme (CYP3A4) is shown to change after kidney transplantation, that of hepatic and intestinal P-glycoprotein does not change significantly.

The African population provides a fertile ground to investigate the impact of P-glycoprotein because people of African ancestry show the lowest variant allele frequencies of the three most widely studied ABCB1 SNPs with the highest wild-type haplotype (C-G-C) frequency of 43.6%–79.3% [89]. This will isolate the confounding effect of genetic variability and thus provide a better perspective on other factors.

Several other transporters and their genetic variants, such as ABCC2, ABCC8 and SLCO1B1/3, have been studied in terms of CNI pharmacokinetics and pharmacodynamics. As extensively reviewed by Tron et al. (2019) [14], the findings are both diverse and controversial and lack investigation in large cohorts. These other transporters have not been studied as much as ABCB1; some are only studied in vitro, while others have been inferred by results of large genome-wide association studies (GWAS) that have not yet been validated or replicated.

6. Conclusion

The heavy burden of kidney disease in Africa and challenges with access to healthcare, including transplantation, against the backdrop of Africa's unique and unparalleled genetic diversity, calls for more research into pharmacogenetics. In the context of CNIs, further research is important as CNIs are highly variably disposed at both an individual and ethnicity level and their utilization is complicated by their narrow therapeutic index.

In the resource constrained setting of African clinical research, it may be more prudent to look at well characterized variants that have shown mechanistic significance at the protein or RNA level (in vitro or in vivo), even if in another population, rather than looking for novel genetic variants with unknown significance. Recommendations for further study of known variants are shown in Table 2. These studies will help better rationalize the use of medications that are both lifesaving and life-changing.

7. Future perspective

The paucity of pharmacogenetic data from Africa in relation to calcineurin inhibitors and other pharmacological substances is a glaring phenomenon. In view of resource constraints, clinical research on these populations should leverage exploring known and well-studied variants in other populations for comparisons, correlations and relevant implementation. In the context of basic research, wide exploration in the form of Genome-wide association studies, whole exome sequencing and other large-scale exploration studies will yield a considerable amount of novel data with high potential for informative insights.

Table 2. Summary of findings and recommendations for further studies.

SNP/allele(s)	Summary	Further research/action recommendation	
CYP3A4*1.001	This variant is less common in Europeans and Asians but more commonly found in Africans, the population in which it is studied less. A small pilot study of mostly Africans has shown some influence	A large-scale study on Africans is recommended and likely to yield better insight, leveraging the higher allele frequency. It will also provide an opportunity to understand better the mechanistic linkage between this SNP and the CYP3A5*1/3 allele	
CYP3A4*22	This is a low-frequency variant, but its frequency is higher in European ancestry and has shown some influence, especially when investigated in conjunction with other influential variants	Although investigating this variant singly may not be worthwhile, it has shown some influence when studied with other variants. Large-scale studies, especially genome-wide association studies in conjunction with other variants, can help further elucidate this variant's distribution and effect	
CYP3A5*3	This has shown an influence on Tacrolimus PK but not on outcomes. Its influence on Cyclosporine has not been demonstrated, probably because CYP3A4 is the main metabolizing isoform	Large-scale studies and/or clinical trials will be worthwhile. More studies will also provide room for systematic reviews and meta-analyses that can properly clarify effect/influence.
More and well-designed outcome studies will also help delineate the relationship between this SNP and outcome parameters	
CYP3A5*6 and CYP3A5*7	These variants show apparent exclusivity in people of African ancestry and have also demonstrated some influence on Tacrolimus PK	More studies on these variants are better targeted to the African populations. The Influence on Tacrolimus PK needs replication, and data is needed regarding Tacrolimus versus outcomes, and Cyclosporine's PK and outcomes	
POR*28	The distribution of this variant differs among Africans compared with other populations. It influences CNI's PK in conjunction with other CYP3A4/5 variants	Considering the differences in distribution, studies in the African population will provide more data and clarity on the effects of this variant and how it interrelates with variants of the major metabolizing enzymes	
PPARA	The discussed variants have been investigated in conjunction with other CNI metabolism-related variants in other populations. The results were conflicting but interesting	More studies on these variants concerning CNI metabolism are generally needed. Considering the potential multivariant nature these studies will require, population specific studies, including in Africans, is highly recommended	
ABCB1 3435, ABCB1 2677 and ABCB1 1236.	These have been extensively studied with conflicting findings for CNI and other medications. They are in high linkage disequilibrium with each other and show homogeneity in the African ancestry population	Including these variants in large-scale studies exploring the effect of variants of metabolizing enzymes will provide data for comparisons and assessment of confounders. These variants are also good candidates for studies utilizing cellular concentration measurements rather than blood concentration measurements to assess drug disposition	

Author contributions

SA Hussaini – conception, writing and editing of the work. B Waziri – conception, review and editing of the work. C Dickens – conception, review and editing of the work. R Duarte – conception, review and editing of the work.

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.
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References

1. Bentata Y. Tacrolimus: 20 years of use in adult kidney transplantation. What we should know about its nephrotoxicity. Artif Organs. 2020;44 (2 ):140–152. doi:10.1111/aor.13551 31386765
2. Andreoni KA, Brayman K, Guidinger MK, Sommers CM, Sung RS. Kidney and pancreas transplantation in the United States, 1996–2005. Am J Transplant. 2007;7 :1359–1375. doi:10.1111/j.1600-6143.2006.01781.x 17428285
3. Schiff J, Cole E, Cantarovich M. Therapeutic monitoring of calcineurin inhibitors for the nephrologist. Clin J Am Soc Nephrol. 2007;2 (2 ):374–384. doi:10.2215/CJN.03791106 17699437
4. Staatz CE, Goodman LK, Tett SE. Effect of CYP3A and ABCB1 single nucleotide polymorphisms on the pharmacokinetics and pharmacodynamics of calcineurin inhibitors: part I. Clin Pharmacokinet. 2010;49 (3 ):141–175. doi:10.2165/11317350-000000000-00000 20170205
5. Staatz CE, Tett SE. Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2004;43 :623–653. doi:10.2165/00003088-200443100-00001 15244495
6. Barnes MR. Navigating the HapMap. Briefings in Bioinformatics. 2006;7 (3 ):211–224. doi:10.1093/bib/bbl021 16877472
7. Fairley S, Lowy-Gallego E, Perry E, Flicek P. The International Genome Sample Resource (IGSR) collection of open human genomic variation resources. Nucleic Acids Res. 2019;48 (D1 ):D941–D947. doi:10.1093/nar/gkz836
8. Chen S, Francioli LC, Goodrich JK, et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024;625 (7993 ):92–100. doi:10.1038/s41586-023-06045-0 38057664
9. Naicker S. End-stage renal disease in sub-Saharan and South Africa. Kidney Int. 2003;63 :S119–S122. doi:10.1046/j.1523-1755.63.s83.25.x
10. Akinsola A. Kidney diseases in Africa: aetiological considerations, peculiarities and burden. Afr J Med Med Sci. 2012;41 (2 ):119–133.23185909
11. Pollak MR. Kidney disease and African ancestry. Nat Genet. 2008;40 (10 ):1145–1146. doi:10.1038/ng1008-1145 18818713
12. Eckhoff DE, Young CJ, Gaston RS, et al. Racial disparities in renal allograft survival: a public health issue? J Am Coll Surg. 2007;204 (5 ):894–902. doi:10.1016/j.jamcollsurg.2007.01.024 17481506
13. Wilkins LJ, Nyame YA, Gan V, et al. A contemporary analysis of outcomes and modifiable risk factors of ethnic disparities in kidney transplantation. J Natl Med Assoc. 2019;111 (2 ):202–209. doi:10.1016/j.jnma.2018.10.011 30409716
14. Tron C, Lemaitre F, Verstuyft C, Petitcollin A, Verdier M-C, Bellissant E. Pharmacogenetics of membrane transporters of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2019;58 (5 ):593–613. doi:10.1007/s40262-018-0717-7 30415459
15. Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Therapeut. 2013;138 (1 ):103–141. doi:10.1016/j.pharmthera.2012.12.007
16. Eichelbaum M, Burk O. CYP3A genetics in drug metabolism. Nat Med. 2001;7 (3 ):285–287. doi:10.1038/85417 11231620
17. Kuehl P, Zhang J, Lin Y, et al. Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Genet. 2001;27 (4 ):383–391. doi:10.1038/86882 11279519
18. Dai Y, Iwanaga K, Lin YS, et al. In vitro metabolism of cyclosporine A by human kidney CYP3A5. Biochem Pharmacol. 2004;68 (9 ):1889–1902. doi:10.1016/j.bcp.2004.07.012 15450954
19. Kamdem LK, Streit F, Zanger UM, Brockmöller J, et al. Contribution of CYP3A5 to the in vitro hepatic clearance of tacrolimus. Clin Chem. 2005;51 (8 ):1374–1381. doi:10.1373/clinchem.2005.050047 15951320
20. Sim SC, Edwards RJ, Boobis AR, Ingelman-Sundberg M. CYP3A7 protein expression is high in a fraction of adult human livers and partially associated with the CYP3A7*1C allele. Pharmacogenet Genom. 2005;15 (9 ):625–631. doi:10.1097/01.fpc.0000171516.84139.89
21. Cheung C, Op den Buijsch RA, Wong KM, et al. Influence of different allelic variants of the CYP3A and ABCB1 genes on the tacrolimus pharmacokinetic profile of Chinese renal transplant recipients. Pharmacogenomics. 2006;7 (4 ):563–574. doi:10.2217/14622416.7.4.563 16753004
22. Staatz CE, Goodman LK, Tett SE. Effect of CYP3A and ABCB1 single nucleotide polymorphisms on the pharmacokinetics and pharmacodynamics of calcineurin inhibitors: Part II. Clin Pharmacokinet. 2010;49 (4 ):207–221. doi:10.2165/11317550-000000000-00000 20214406
23. Yamada T, Zhang M, Masuda S. Significance of ethnic factors in immunosuppressive therapy management after organ transplantation. Ther Drug Monit. 2020;42 (3 ):369–380. doi:10.1097/FTD.0000000000000748 32091469
24. Landrum MJ, Lee JM, Benson M, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46 (D1 ):D1062–D1067. doi:10.1093/nar/gkx1153 29165669
25. Guttman Y, Nudel A, Kerem Z. Polymorphism in cytochrome P450 3A4 is ethnicity related. Front Genet. 2019;10 :224. doi:10.3389/fgene.2019.00224 30941162
26. De Jonge H, Metalidis C, Naesens M, Lambrechts D, Kuypers DRJ. The P450 oxidoreductase*28 SNP is associated with low initial tacrolimus exposure and increased dose requirements in CYP3A5-expressing renal recipients. Pharmacogenomics. 2011;12 (9 ):1281–1291. doi:10.2217/pgs.11.77 21770725
27. Elens L, Hesselink DA, Bouamar R, et al. Impact of POR*28 on the pharmacokinetics of tacrolimus and cyclosporine A in renal transplant patients. Ther Drug Monit. 2014;36 (1 ):71–79. doi:10.1097/FTD.0b013e31829da6dd 24061445
28. Kurzawski M, Dabrowska J, Dziewanowski K, Domanski L, Peruynska M, Drozdzik M. CYP3A5 and CYP3A4, but not ABCB1 polymorphisms affect tacrolimus dose-adjusted trough concentrations in kidney transplant recipients. Pharmacogenomics. 2014;15 (2 ):179–188. doi:10.2217/pgs.13.199 24444408
29. Lee JS, Cheong HS, Kim LH, et al. Screening of genetic polymorphisms of CYP3A4 and CYP3A5 genes. Korean J Physiol Pharmacol. 2013;17 (6 ):479. doi:10.4196/kjpp.2013.17.6.479 24381495
30. Rebbeck TR, Jaffe JM, Walker AH, Wein AJ, Malkowicz SB. Modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. JNCI: Journal of the National Cancer Institute. 1998;90 (16 ):1225–1229. doi:10.1093/jnci/90.16.1225 9719084
31. Hesselink DA, Van Schaik RH, Van Der Heiden IP, et al. Genetic polymorphisms of the CYP3A4, CYP3A5, and MDR-1 genes and pharmacokinetics of the calcineurin inhibitors cyclosporine and tacrolimus. Clin Pharmacol Therapeut. 2003;74 (3 ):245–254. doi:10.1016/S0009-9236(03)00168-1
32. Von Ahsen N, Richter M, Grupp C, Ringe B, Oellerich M, Armstrong VW. No influence of the MDR-1 C3435T polymorphism or a CYP3A4 promoter polymorphism (CYP3A4-V allele) on dose-adjusted cyclosporin A trough concentrations or rejection incidence in stable renal transplant recipients. Clin Chem. 2001;47 (6 ):1048–1052. doi:10.1093/clinchem/47.6.1048 11375290
33. Rivory L, Qin H, Clarke S, et al. Frequency of cytochrome P 450 3A4 variant genotype in transplant population and lack of association with cyclosporin clearance. Eur J Clin Pharmacol. 2000;56 :395–398. doi:10.1007/s002280000166 11009048
34. Lamba JK, Lin YS, Schuetz EG, Thummel KE. Genetic contribution to variable human CYP3A-mediated metabolism. Advan Drug Deliv Rev. 2002;54 (10 ):1271–1294. doi:10.1016/S0169-409X(02)00066-2 12406645
35. Wang C-E, Lu K-P, Chang Z, Guo M-L, Qiao H-L. Association of CYP3A4*1B genotype with Cyclosporin A pharmacokinetics in renal transplant recipients: a meta-analysis. Gene. 2018;664 :44–49. doi:10.1016/j.gene.2018.04.043 29678659
36. Min DI, Ellingrod VL. Association of the CYP3A4*1B 5′-flanking region polymorphism with cyclosporine pharmacokinetics in healthy subjects. Ther Drug Monit. 2003;25 (3 ):305–309. doi:10.1097/00007691-200306000-00010 12766558
37. Grinyo J, Vanrenterghem Y, Nashan B, et al. Association of four DNA polymorphisms with acute rejection after kidney transplantation. Transpl Int. 2008;21 (9 ):879–891. doi:10.1111/j.1432-2277.2008.00679.x 18444945
38. Roy JN, Barama A, Poirier C, Vinet B, Roger M. Cyp3A4, Cyp3A5, and MDR-1 genetic influences on tacrolimus pharmacokinetics in renal transplant recipients. Pharmacogenet Genom. 2006;16 (9 ):659–665. doi:10.1097/01.fpc.0000220571.20961.dd
39. Hesselink DA, Van Schaik RHN, Van Agteren M, et al. CYP3A5 genotype is not associated with a higher risk of acute rejection in tacrolimus-treated renal transplant recipients. Pharmacogenet Genom. 2008;18 (4 ):339–348. doi:10.1097/FPC.0b013e3282f75f88
40. Mohamed ME, Schladt DP, Guan W, et al. Tacrolimus troughs and genetic determinants of metabolism in kidney transplant recipients: a comparison of four ancestry groups. Am J Transplant. 2019;19 (10 ):2795–2804. doi:10.1111/ajt.15385 30953600
41. Anglicheau D, Thervet E, Etienne I, et al. CYP3A5 and MDR1 genetic polymorphisms and cyclosporine pharmacokinetics after renal transplantation. Clin Pharmacol Therapeut. 2004;75 (5 ):422–433. doi:10.1016/j.clpt.2004.01.009
42. Hesselink DA, Van Gelder T, Van Schaik RH, et al. Population pharmacokinetics of cyclosporine in kidney and heart transplant recipients and the influence of ethnicity and genetic polymorphisms in the MDR-1, CYP3A4, and CYP3A5 genes. Clin Pharmacol Therapeut. 2004;76 (6 ):545–556. doi:10.1016/j.clpt.2004.08.022
43. Haufroid V, Mourad M, Van Kerckhove V, et al. The effect of CYP3A5 and MDR1 (ABCB1) polymorphisms on cyclosporine and tacrolimus dose requirements and trough blood levels in stable renal transplant patients. Pharmacogenetics. 2004;14 (3 ):147–154. doi:10.1097/00008571-200403000-00002 15167702
44. Crettol S, Venetz J-P, Fontana M, Aubert J-D, Pascual M, Eap CB. CYP3A7, CYP3A5, CYP3A4, and ABCB1 genetic polymorphisms, cyclosporine concentration, and dose requirement in transplant recipients. Ther Drug Monit. 2008;30 (6 ):689–699. doi:10.1097/FTD.0b013e31818a2a60 18978522
45. Op Den Buijsch RA, Christiaans MH, Stolk LM, et al. Tacrolimus pharmacokinetics and pharmacogenetics: influence of adenosine triphosphate-binding cassette B1 (ABCB1) and cytochrome (CYP) 3A polymorphisms. Fundament Clin Pharmacol. 2007;21 (4 ):427–435. doi:10.1111/j.1472-8206.2007.00504.x
46. Tirelli S, Ferraresso M, Ghio L, et al. The effect of CYP3A5 polymorphisms on the pharmacokinetics of tacrolimus in adolescent kidney transplant recipients. Med Sci Monitor. 2008;14 (5 ):CR251–CR254.
47. Renders L, Frisman M, Ufer M, et al. CYP3A5 genotype markedly influences the pharmacokinetics of tacrolimus and sirolimus in kidney transplant recipients. Clin Pharmacol Therapeut. 2007;81 (2 ):228–234. doi:10.1038/sj.clpt.6100039
48. Ferraresso M, Tirelli A, Ghio L, et al. Influence of the CYP3A5 genotype on tacrolimus pharmacokinetics and pharmacodynamics in young kidney transplant recipients. Pediatr Transplant. 2007;11 (3 ):296–300. doi:10.1111/j.1399-3046.2006.00662.x 17430486
49. Lunde I, Bremer S, Midtvedt K, et al. The influence of CYP3A, PPARA, and POR genetic variants on the pharmacokinetics of tacrolimus and cyclosporine in renal transplant recipients. Eur J Clin Pharmacol. 2014;70 (6 ):685–693. doi:10.1007/s00228-014-1656-3 24658827
50. Mendes J, Martinho A, Simoes O, Mota A, Breitenfeld L, Pais L. Genetic polymorphisms in CYP3A5 and MDR1 genes and their correlations with plasma levels of tacrolimus and cyclosporine in renal transplant recipients. Transplantation Proceedings. 2009;41 (3 ):840–842. doi:10.1016/j.transproceed.2009.01.050 19376366
51. Moes D, Swen JJ, Den Hartigh J, et al. Effect of CYP3A4*22, CYP3A5*3, and CYP3A combined genotypes on cyclosporine, everolimus, and tacrolimus pharmacokinetics in renal transplantation. CPT. 2014;3 (2 ):1–12. doi:10.1038/psp.2013.78
52. Provenzani A, Notarbartolo M, Labbozzetta M, et al. Influence of CYP3A5 and ABCB1 gene polymorphisms and other factors on tacrolimus dosing in Caucasian liver and kidney transplant patients. Inter J Mol Med. 2011;28 (6 ):1093–1102. doi:10.3892/ijmm.2011.794
53. Loh P, Lou H, Zhao Y, Chin Y, Vathsala A. Significant impact of gene polymorphisms on tacrolimus but not cyclosporine dosing in Asian renal transplant recipients. Transplantation Proceedings. 2008;40 (5 ):1690–1695. doi:10.1016/j.transproceed.2008.04.010 18589174
54. Zhao Y, Guan DL, Song MS, Liu J, Fu G. Genetic polymorphisms of CYP3A5 genes and concentration of the cyclosporine and tacrolimus. Transplantation. 2004;78 (2 ):615. doi:10.1097/00007890-200407271-01654 15446323
55. Chu XM, Hao HP, Wang GJ, Guo LQ, Min PQ. Influence of CYP3A5 genetic polymorphism on cyclosporine A metabolism and elimination in Chinese renal transplant recipients. Acta Pharmacologica Sinica. 2006;27 (11 ):1504–1508. doi:10.1111/j.1745-7254.2006.00428.x 17049128
56. Hu YF, Qiu W, Liu ZQ, et al. Effects of genetic polymorphisms of CYP3A4, CYP3A5 and MDR1 on cyclosporine pharmacokinetics after renal transplantation. Clin Exp Pharmacol Physiol. 2006;33 (11 ):1093–1098. doi:10.1111/j.1440-1681.2006.04492.x 17042920
57. Qiu X-Y, Jiao Z, Zhang M, et al. Association of MDR1, CYP3A4*18B, and CYP3A5*3 polymorphisms with cyclosporine pharmacokinetics in Chinese renal transplant recipients. Eur J Clin Pharmacol. 2008;64 :1069–1084. doi:10.1007/s00228-008-0520-8 18636247
58. Zhang X, Liu ZH, Zheng JM, et al. Influence of CYP3A5 and MDR1 polymorphisms on tacrolimus concentration in the early stage after renal transplantation. Clin Transplant. 2005;19 (5 ):638–643. doi:10.1111/j.1399-0012.2005.00370.x 16146556
59. Satoh S, Kagaya H, Saito M, et al. Lack of tacrolimus circadian pharmacokinetics and CYP3A5 pharmacogenetics in the early and maintenance stages in Japanese renal transplant recipients. Br J Clin Pharmacol. 2008;66 (2 ):207–214. doi:10.1111/j.1365-2125.2008.03188.x 18429967
60. Satoh S, Saito M, Inoue T, et al. CYP3A5*1 allele associated with tacrolimus trough concentrations but not subclinical acute rejection or chronic allograft nephropathy in Japanese renal transplant recipients. Eur J Clin Pharmacol. 2009;65 (5 ):473–481. doi:10.1007/s00228-008-0606-3 19125240
61. Tada H, Tsuchiya N, Satoh S, et al. Impact of CYP3A5 and MDR1 (ABCB1) C3435T polymorphisms on the pharmacokinetics of tacrolimus in renal transplant recipients. Transplantation Proceedings. 2005;37 (4 ):1730–1732. doi:10.1016/j.transproceed.2005.02.073 15919447
62. Ro H, Min SI, Yang J, et al. Impact of tacrolimus intraindividual variability and CYP3A5 genetic polymorphism on acute rejection in kidney transplantation. Ther Drug Monit. 2012;34 (6 ):680–685. doi:10.1097/FTD.0b013e3182731809 23149441
63. Liu LS, Li J, Chen XT, et al. Comparison of tacrolimus and cyclosporin A in CYP3A5 expressing Chinese de novo kidney transplant recipients: a 2-year prospective study. Int J Clin Pract. 2015;69 :43–52. doi:10.1111/ijcp.12666
64. Vannaprasaht S, Reungjui S, Supanya D, et al. Personalized tacrolimus doses determined by CYP3A5 genotype for induction and maintenance phases of kidney transplantation. Clin Ther. 2013;35 (11 ):1762–1769. doi:10.1016/j.clinthera.2013.08.019 24120259
65. Macphee IaM, Fredericks S, Mohamed M, et al. Tacrolimus pharmacogenetics: the CYP3A5*1 allele predicts low dose-normalized tacrolimus blood concentrations in Whites and South Asians. Transplantation. 2005;79 (4 ):499–502. doi:10.1097/01.TP.0000151766.73249.12 15729180
66. Fredericks S, Moreton M, Reboux S, et al. Multidrug resistance gene-1 (MDR-1) haplotypes have a minor influence on tacrolimus dose requirements. Transplantation. 2006;82 (5 ):705–708. doi:10.1097/01.tp.0000234942.78716.c0 16969296
67. Mourad M, Wallemacq P, De Meyer M, et al. 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. Clin Chem Lab Med. 2006;44 (10 ):1192–1198. doi:10.1515/CCLM.2006.229 17032130
68. Ebid A, Ismail DA, Lotfy NM, Mahmoud MA, Elsharkawy M. Influence of CYP3A422 and CYP3A53 combined genotypes on tacrolimus dose requirements in Egyptian renal transplant patients. J Clin Pharm Ther. 2022;47 (12 ):2255–2263. doi:10.1111/jcpt.13804 36379901
69. Rojas L, Neumann I, Herrero MJ, et al. Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: a systematic review and meta-analysis of observational studies. The Pharmacogenomics Journal. 2015;15 (1 ):38–48. doi:10.1038/tpj.2014.38 25201288
70. Muller WK, Dandara C, Manning K, et al. CYP3A5 polymorphisms and their effects on tacrolimus exposure in an ethnically diverse South African renal transplant population. SAMJ S Afr Med J. 2020;110 (2 ):159–166. doi:10.7196/SAMJ.2020.v110i2.13969 32657689
71. Macphee IA, Fredericks S, Tai T, et al. The influence of pharmacogenetics on the time to achieve target tacrolimus concentrations after kidney transplantation. Am J Transplant. 2004;4 (6 ):914–919. doi:10.1111/j.1600-6143.2004.00435.x 15147425
72. Zhang X, Liu Z-H, Zheng J-M, et al. Influence of CYP3A5 and MDR1 polymorphisms on tacrolimus concentration in the early stage after renal transplantation. Clin Transplant. 2005;19 (5 ):638–643. doi:10.1111/j.1399-0012.2005.00370.x 16146556
73. Eng H-S, Mohamed Z, Calne R, et al. The influence of CYP3A gene polymorphisms on cyclosporine dose requirement in renal allograft recipients. Kidney Int. 2006;69 (10 ):1858–1864. doi:10.1038/sj.ki.5000325 16612333
74. Hauser IA, Schaeffeler E, Gauer S, et al. ABCB1 genotype of the donor but not of the recipient is a major risk factor for cyclosporine-related nephrotoxicity after renal transplantation. J Am Soc Nephrol. 2005;16 (5 ):1501–1511. doi:10.1681/ASN.2004100882 15772250
75. Brunet M, Shipkova M, Van Gelder T, et al. Barcelona consensus on biomarker-based immunosuppressive drugs management in solid organ transplantation. Ther Drug Monit. 2016;38 :S1–S20. doi:10.1097/FTD.0000000000000287 26977997
76. Hustert E, Haberl M, Burk O, et al. The genetic determinants of the CYP3A5 polymorphism. Pharmacogenet Genom. 2001;11 (9 ):773–779. doi:10.1097/00008571-200112000-00005
77. Oetting WS, Schladt DP, Guan W, et al. Genomewide Association Study of Tacrolimus Concentrations in African American Kidney Transplant Recipients Identifies Multiple CYP3A5 Alleles. Am J Transplant. 2016;16 (2 ):574–582. doi:10.1111/ajt.13495 26485092
78. Yamano S, Aoyama T, Mcbride O, Hardwick J, Gelboin H, Gonzalez F. Human NADPH-P450 oxidoreductase: complementary DNA cloning, sequence and vaccinia virus-mediated expression and localization of the CYPOR gene to chromosome 7. Mol Pharmacol. 1989;36 (1 ):83–88.2501655
79. Agrawal V, Huang N, Miller WL. Pharmacogenetics of P450 oxidoreductase: effect of sequence variants on activities of CYP1A2 and CYP2C19. Pharmacogenet Genom. 2008;18 (7 ):569–576. doi:10.1097/FPC.0b013e32830054ac
80. Gong L, Zhang C-M, Lv J-F, Zhou H-H, Fan L. Polymorphisms in cytochrome P450 oxidoreductase and its effect on drug metabolism and efficacy. Pharmacogenet Genom. 2017;27 (9 ):337–346. doi:10.1097/FPC.0000000000000297
81. Miller WL, Agrawal V, Sandee D, et al. Consequences of POR mutations and polymorphisms. Mol Cell Endocrinol. 2011;336 (1–2 ):174–179. doi:10.1016/j.mce.2010.10.022 21070833
82. Huang N, Agrawal V, Giacomini KM, Miller WL. Genetics of P450 oxidoreductase: sequence variation in 842 individuals of four ethnicities and activities of 15 missense mutations. Proc Natl Acad Sci USA. 2008;105 (5 ):1733–1738. doi:10.1073/pnas.0711621105 18230729
83. Hubbard PA, Shen AL, Paschke R, Kasper CB, Kim J-JP. NADPH-cytochrome P450 oxidoreductase: structural basis for hydride and electron transfer. J Biol Chem. 2001;276 (31 ):29163–29170. doi:10.1074/jbc.M101731200 11371558
84. Booyse RP, Twesigomwe D, Hazelhurst S. Characterization of POR haplotype distribution in African populations and comparison with other global populations. Pharmacogenomics. 2022;23 (14 ):771–782. doi:10.2217/pgs-2022-0082 36043428
85. Klein K, Thomas M, Winter S, et al. PPARA: a novel genetic determinant of CYP3A4 in vitro and in vivo. Clin Pharmacol Therapeut. 2012;91 (6 ):1044–1052. doi:10.1038/clpt.2011.336
86. Kurzawski M, Malinowski D, Dziewanowski K, Drozdzik M. Impact of PPARA and POR polymorphisms on tacrolimus pharmacokinetics and new-onset diabetes in kidney transplant recipients. Pharmacogenet Genom. 2014;24 (8 ):397–400. doi:10.1097/FPC.0000000000000067
87. Cheng Y, El-Kattan A, Zhang Y, Ray AS, Lai Y. Involvement of drug transporters in organ toxicity: the fundamental basis of drug discovery and development. Chem Res Toxicol. 2016;29 (4 ):545–563. doi:10.1021/acs.chemrestox.5b00511 26889774
88. Hesselink DA, Bouamar R, Elens L, Van Schaik RH, Van Gelder T. The role of pharmacogenetics in the disposition of and response to tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2014;53 (2 ):123–139. doi:10.1007/s40262-013-0120-3 24249597
89. Pauli-Magnus C, Kroetz DL. Functional implications of genetic polymorphisms in the multidrug resistance gene MDR1 (ABCB1). Pharm Res. 2004;21 (6 ):904–913. doi:10.1023/B:PHAM.0000029276.21063.0b 15212152
90. Jiang ZP, Wang YR, Xu P, Liu RR, Zhao XL, Chen FP. Meta-analysis of the effect of MDR1 C3435T polymorphism on cyclosporine pharmacokinetics. Basic Clin Pharmacol Toxicol. 2008;103 (5 ):433–444. doi:10.1111/j.1742-7843.2008.00300.x 18801030
91. Wang J, Zeevi A, Mccurry K, et al. Impact of ABCB1 (MDR1) haplotypes on tacrolimus dosing in adult lung transplant patients who are CYP3A5*3/*3 nonexpressors. Transplant Immunol. 2006;15 (3 ):235–240. doi:10.1016/j.trim.2005.08.001
92. Bandur S, Petrasek J, Hribova P, Novotna E, Brabcova I, Viklicky O. Haplotypic structure of ABCB1/MDR1 gene modifies the risk of the acute allograft rejection in renal transplant recipients. Transplantation. 2008;86 (9 ):1206–1213. doi:10.1097/TP.0b013e318187c4d1 19005401
93. Woillard JB, Rerolle JP, Picard N, et al. Donor P-gp polymorphisms strongly influence renal function and graft loss in a cohort of renal transplant recipients on cyclosporine therapy in a long-term follow-up. Clin Pharmacol Therapeut. 2010;88 (1 ):95–100. doi:10.1038/clpt.2010.62
94. Debette-Gratien M, Woillard J-B, Picard N, et al. Influence of donor and recipient CYP3A4, CYP3A5, and ABCB1 genotypes on clinical outcomes and nephrotoxicity in liver transplant recipients. Transplantation. 2016;100 (10 ):2129–2137. doi:10.1097/TP.0000000000001394 27653228
95. Woillard J-B, Gatault P, Picard N, Arnion H, Anglicheau D, Marquet P. A donor and recipient candidate gene association study of allograft loss in renal transplant recipients receiving a tacrolimus-based regimen. Am J Transplant. 2018;18 (12 ):2905–2913. doi:10.1111/ajt.14894 29689130
96. Yokogawa K, Takahashi M, Tamai I, et al. P-glycoprotein-dependent disposition kinetics of tacrolimus: studies in mdr la knockout mice. Pharm Res. 1999;16 (8 ):1213–1218. doi:10.1023/A:1018993312773 10468022
