Effect of CYP3A4*22, POR*28, and PPARA rs4253728 on sirolimus in vitro metabolism and trough concentrations in kidney transplant recipients.
Recently, a newly discovered polymorphism in CYP3A4 (cytochrome P450,
family 3, subfamily A, polypeptide 4) intron 6 (rs35599367 C>T; CYP3A4*22), associated with decreased mRNA hepatic expression and enzymatic activity, was found to be significantly associated with increased cyclosporine and tacrolimus doseadjusted concentrations in kidney transplant recipients (12,13). The variant allele also resulted in a higher risk of decreasing glomerular function and worse creatinine clearance in cyclosporine-treated patients (14).
A single-nucleotide polymorphism (SNP) in the POR [P450 (cytochrome) oxidoreductase] gene (rs1057868 C>T; POR*28 allele) was also recently shown to result in higher tacrolimus dose requirements in CYP3A5 (cytochrome P450, family 3, subfamily 5) nonexpressers (15). Consistently, this SNP was associated with a 1.6-fold increase in CYP3A activity as measured by the midazolam phenotyping test in 2 independent population datasets (16).
Finally, a comprehensive study associating in vitro and clinical data identified an SNP in PPARA (peroxisome proliferator-activated receptor alpha) (rs4253728) as another candidate to explain the variability in CYP3A4 activity (17).
Altogether, these results suggest that pharmacogenetic testing might allow the classification of individuals according to their CYP3A metabolic capability. None of these newly discovered genetic variations have been studied with regard to their impact on SRL metabolism, exposure, and adverse events.
Therefore, our aim was to assess the individual and combined impact of CYP3A4*22, CYP3A5*3, POR*28, and PPARA-rs4253728 alleles on (a) SRL in vitro hepatic metabolism, (b) SRL trough concentrations ([C.sub.0]), and (c) SRL adverse events in kidney transplant recipients.
Material and Methods
IN VITRO EXPERIMENTS
SRL (100 [micro]g/L) was incubated with individually prepared human liver microsomes (HLM) (n = 31) (donor characteristics are given in Table 1 in the Data Supplement that accompanies the online version of this report at http://www.clinchem.org/content/vol59/ issue 12) and pooled HLM as previously described (18). Briefly, the incubation mixture consisted of 0.1 mol/L Tris buffer, pH 7.4, 10 mmol/L Mg[Cl.sub.2], 2 mmol/LNADPH, HLM (0.1 mg protein/mL), and SRL prepared in acetonitrile/water (25/1.25 [micro]L) (1.25% acetonitrile in final incubation medium). Incubations (100 iL) were performed at 37 [degrees]C after a 5-min pre-incubation of the reagents. The reaction was initiated by adding NADPH. After 10 min, the reaction was stopped using ice-cold acetonitrile (100 [micro]L). The remaining concentration of SRL in the incubation mixture was determined by using liquid chromatographytandem mass spectrometry (LC-MS/MS) as described previously (18).
One hundred and thirteen adult kidney transplant patients (80 males and 33 females) switched from a CNI to SRL at least 3 months after transplantation [median (25th-75th percentiles) 3.8 (1.2-9.3) years; minimum, 0.3, maximum, 25.9 years] were included in this retrospective study. Sixty-five percent of the patients were switched from cyclosporine and 35% from tacrolimus. At the time of the switch to SRL, the mean (SD) patient age was 52 (14) years. This study complied with the Declaration of Helsinki and was approved by an ethics committee, and written informed consent was obtained from each patient enrolled. None of the patients discontinued SRL within the first 6 months post-conversion for uncontrolled adverse events. SRL [C.sub.0] determination was chosen as a marker of exposure because of the excellent correlation between [C.sub.0] and interdose area under the concentration curve previously reported ([r.sup.2] = 0.83-0.95) (19-21). All patients had a [C.sub.0] determination once a week until they obtained the target value and then every month. For each patient, clinical data were recorded [date of transplantation and date of SRL introduction, age, sex, presence and date of occurrence of cutaneous adverse effects (AEs), lymphedemas, and infections]. Infections were defined as severe infections at any location that required hospitalization. Cutaneous AEs were defined as cutaneous lesions, folliculites, aphthous stomatitis, macula-papular and squamous lesions, and acne. Edemas and pneumonitis were also collected from the clinical files. Additionally, the doses of all immunosuppressants, hemoglobin concentrations, plasma total cholesterol, plasma triglyceride concentrations, and the dose and type of statins were retrospectively collected from patient charts at 1, 3, and 6 months after the initiation of SRL. Dose reduction was defined as reduction of SRL dose occurring in any context at 3 or 6 months compared to the dose at 1 month postswitch to SRL. SRL dose changes occurring within the period were not recorded. No period earlier than 1 month post-SRL introduction was considered, to take into account the time required to reach steady-state. Statin administration was categorized in 4 strata: no statin (statins = 0), low dose (statins = 1; 10mg/day of pravastatin, simvastatin, or atorvastatin), middle dose (statins = 2; 20, or 30 mg/day of pravastatin and 20-40 mg/day of simvastatin or atorvastatin), and high dose (statins = 3; 40 mg/day of pravastatin and 50-80 mg/ day of simvastatin or atorvastatin). Patient demographics and laboratory test values were previously reported (22).
IDENTIFICATION OF GENOTYPES
Microsomes and patients were genotyped for CYP3A5 rs776746 A>G (CYP3A5*3), CYP3A4 rs35599367 C>T (CYP3A4*22), POR rs1057868 C>T (POR*28), and PPARA rs4253728 G>A by use of Taqman[R] allelic discrimination assays on an ABI PRISM 7000 sequence detection system (Applied Biosystems). Assays were ordered from Applied-Biosystems as custom (CYP3A5 assay ID, AHD0879) or inventoried TaqMan SNP genotyp ing assays (POR assay, C_8890131_30; CYP3A4 assay, C_59013445_10; PPARA assay, C_31052401_10).
SRL LC-MS/MS ASSAY
SRL was determined in blood and in vitro samples by using turbulent-flow chromatography-tandem mass spectrometry using a validated, previously described method (18).
Statistical analyses were performed using R software version 2.13.1 (R foundation for statistical computing, http://www.r-project.org). Conformity of genotyping data with Hardy-Weinberg equilibrium was verified with the Fisher's exact test in the "SNPassoc" package. The Kruskal-Wallis or Mann-Whitney tests were used to compare the SRL in vitro disappearance rate between genotype groups. Genetic models were chosen on the basis of genotype frequencies and the literature. The normality of the distribution of continuous phenotypes was assessed by both visual examination of the probability density function and the Shapiro-Wilk test. SRL [C.sub.0] concentrations, SRL dose, and SRL [C.sub.0]/ dose were log-transformed before analysis to obtain normal distributions.
In a first step, associations between nongenetic covariates and phenotypes were investigated using a backward stepwise process. Intermediate models including the significant covariates were selected based on the Akaike criterion. Then, SNPs were included in these adjusted models and a backward selection based on the Bayesian information criterion was performed to obtain the final models.
We investigated the effects of genotypes on SRL dose, [C.sub.0] and [C.sub.0]/dose, and hemoglobin, cholesterol, and triglyceride plasma concentrations using linear mixed-effect models ("lme4" package), which allowed correlations between observations per patient. The effects of genotypes on infections, cutaneous AEs, edema, pneumonitis, and dose reduction were investigated by using logistic regression with only the first event taken into account. SRL exposure was studied in these analyses as the area under the curve of all the available [C.sub.0]/dose data in the follow-up period [cumulative exposure = equivalent to (mean [C.sub.0]/dose) X follow-up duration]. The Benjamini-Hochberg false discovery rate was applied to the final models (23) to account for multiple testing. All statistical analyses were 2-sided except in vitro comparisons. P < 0.05 was considered statistically significant.
IN VITRO DATA
Microsomes carrying the CYP3A4*22 allele (n = 3 heterozygotes and 1 homozygote) metabolized SRL at significantly lower rates than noncarriers (n = 27) [65.5 (55.2-80.7) vs 86.1 (53.4-105.2) pmol/mg/min; P = 0.0411]. In contrast, no significant effect of POR*28 (P = 0.4606) or PPARA rs4253728 (P = 0.2766) was observed (Fig. 1).
Patient genotypes are described in online Supplemental Table 2. Genotyping results were in HardyWeinberg equilibrium. An important linkage disequilibrium was found between rs35599367 (CYP3A4*22) and rs776746 (CYP3A5*3) (D' = 0.78, [r.sup.2] = 0.58). Patients carrying the CYP3A4*22 defective allele (n = 11) were more frequently carriers of the inactive CYP3A5*3 (n = 9/11) than the CYP3A5*1 allele (n = 2/11). According to genotype frequencies and to the literature, all the genotypes were studied using a dominant genetic model.
IMPACT OF GENETIC VARIATIONS ON SRL EXPOSURE
Effects of genotypes. No significant association was found between CYP3A4 and CYP3A5 genotypes and SRL dose, [C.sub.0], or [C.sub.0]/dose (Table 1). After correction for multiple testing, only the POR*28 allele (CT/TT vs CC) was associated with a significant decrease in SRL log transformed [C.sub.0] values over the follow-up period [[beta] = -0.15 (0.05); P = 0.0197] but it had no effect on SRL log-transformed dose or [C.sub.0]/dose (Table 1). The evolution of SRL [C.sub.0], [C.sub.0]/dose, and dose in carriers and non-carriers of POR*28 is depicted in Fig. 2. When combined with the CYP3A genotypes, this influence of the POR*28 polymorphism concerned only carriers of CYP3A4*1 (CYP3A4*1/POR*28 vs CYP3A4*1/POR*1 alleles; P = 0.0150). No significant differences were observed for patients carrying other allele combinations compared to the CYP3A4*1/POR*1 or CYP3A5*1/POR*1 combinations chosen as references (data not shown). No effect of the POR*28 allele was observed on log-transformed SRL [C.sub.0]/dose (Table 1), regardless of the combination of genotypes (Fig. 3).
Effect of predicted CYP3A metabolic status. The effect of combined CYP3A genotypes on log-transformed [C.sub.0] and [C.sub.0]/dose was investigated at each period. Patients were classified on the basis of their expected metabolic status. Poor metabolizers were defined as CYP3A4*22 and homozygous CYP3A5*3 carriers (n = 9); intermediate metabolizers as CYP3A4*22 and CYP3A5*1 carriers (n = 2) or homozygous CYP3A4*1 and homozygous CYP3A5*3 carriers (n = 83); and extensive metabolizers as homozygous CYP3A4*1 and CYP3A5*1 carriers (n = 19). No significant association between the metabolic status and log-transformed [C.sub.0] or [C.sub.0]/dose was found (see online Supplemental Fig. 1).
IMPACT OF GENETIC VARIATIONS ON SRL ADVERSE EVENTS
Table 2 shows the nongenetic covariates selected in the intermediate model of each phenotype. After adjustment for nongenetic covariates and correction for false discovery finding, none of the SNPs tested showed a significant association with phenotypes (Table 3).
Moreover, no association was found between SNPs and phenotypes using crude analysis (i.e., without adjustment on nongenetic covariates) (data not shown).
In this study we investigated whether genetic variations in CYP3A and 2 associated regulators (POR and PPARA) contributed to the variability of SRL disposition or adverse effects in renal transplant patients. The effect of gene polymorphisms on SRL metabolism was investigated in vitro using human liver microsomes, and potential associations with SRL blood concentrations and AEs were explored in parallel in 113 kidney transplant recipients.
In vitro, only the newly discovered CYP3A4 intron 6 C>T SNP (CYP3A4*22) was identified as a significant determinant of the variability in SRL metabolism. In contrast, only the POR*28 allele influenced SRL [C.sub.0] concentrations in kidney transplant patients. None of the genotypes tested were associated with SRL AEs in our patient population.
The CYP3A4*22 allele was previously found to decrease CYP3A4 mRNA hepatic expression (24) and to significantly reduce the CYP3A4-catalyzed hydroxylation of testosterone in liver microsomes. In addition, this allele resulted in higher cyclosporine and tacrolimus dose-adjusted [C.sub.0] in kidney transplant recipients (12,13). Here the CYP3A4*22 allele was associated in vitro with a significant decrease of SRL metabolic clearance by human liver microsomes, but this effect did not translate into a difference in SRL blood exposure in patients. Given the limited effect observed in vitro (approximately 20% lower metabolic rates in microsomes carrying the CYP3A4*22 allele), it is likely that other environmental or genetic sources of variability have tempered the effect of this genotype in vivo. It is noteworthy that, based on our in vitro results, our population sample set had a sufficient size to detect the expected effect of the CYP3A4*22 allele; a power calculation indicated a power of 0.92 [[micro]1 = 80.8 (13.3), n1 = 102, [micro]2 = 66.7 (13.1), n2 = 11].
We did not find any association between the CYP3A5*1/*3 genotype and SRL blood concentrations. We previously provided in vitro evidence that CYP3A5 genotype had no influence on SRL metabolism by HLM (18). We indeed observed similar intrinsic clearance ([CL.sub.int]) for SRL hydroxylation, demethylation, and depletion between HLM carrying a CYP3A5*1 (active) allele and HLM not expressing CYP3A5. However, previous reports on such an association in patients are controversial. Two studies found an effect of the CYP3A5*1 allele on SRL exposure. However, it was specifically in patients without CNI cotreatment (5, 6), but this was not confirmed in a third study (7). In the study we report here, in which the number of patients was relatively large compared to those in previous studies, the results are not in favor of this effect either, despite the fact that patients received no CNI cotreatment, which may have concealed the genetic effect. When the CYP3A metabolic status (integrating the CYP3A4 and 3A5 genotypes) was taken into account, as previously done for tacrolimus (13), once again no significant association was observed with SRL exposure or adverse effects. Taken together, these results suggest that CYP3A genetic variability has a very limited impact on SRL concentrations in kidney transplant recipients.
The POR gene modulates the activity of CYP3A via an electron transfer from NADPH to microsomal P450 enzymes (25) and is thus another candidate to explain the variability in CYP3A activity. The POR*28 allele, associated with an amino acid substitution, was found to increase CYP3A in vivo activity, as assessed by the midazolam metabolic ratio in 2 population datasets (16). Although this allele had no effect on SRL in vitro metabolic clearance, we observed here a significant, although weak, decrease of SRL [C.sub.0] in carriers of the POR*28 allele (-0.15 p,g/L for carriers of at least 1 T allele). Subgroup analysis showed that this effect concerned only the patients carrying a CYP3A4*1 functional allele, which seems logical because decreased POR activity may not show up if CYP3A4 activity is also decreased. However, the POR*28 allele had no significant effect on SRL [C.sub.0]/dose or dose, whether in the overall study population or in subgroups. It is thus likely that the clinical relevance of this finding is limited.
De Jonge et al. (15) showed that the POR*28 allele (CT/TT vs CC) was associated with a decrease in tacrolimus [C.sub.0] in the first 3 days after tacrolimus initiation and then with a significant increase in tacrolimus daily dose to achieve similar [C.sub.0] compared to POR*28 noncarriers. These results concerned only CYP3A5 ex pressors. The authors provided no clear explanation for this finding, but again it is likely that a functional CYP3A is required for the influence of the POR genotype to become apparent. In our study the POR*28 allele had no significant influence on SRL concentrations or dose regardless of the CYP3A5 genotype, but we previously demonstrated using recombinant enzymes that CYP3A5 activity toward SRL is much more limited than that of CYP3A4 (CLint = 0.64 vs 2.36 [micro]L/pmol P450 per min) (18). Accordingly, as mentioned above, the CYP3A5*1 allele had no influence on SRL concentrations in this study.
Another potential source of SRL pharmacokinetic variability was the rs4253728G>A polymorphism in PPARA. Klein et al. showed that homozygous carriers of the rs4253728 expressed significantly less PPAR-a protein in the liver (17). This nuclear receptor is believed to influence CYP3A4 activity by (a) indirect regulation of CYP3A4 gene transcription through PXR, another nuclear receptor; (b) its anti-inflammatory properties, resulting in the inhibition of the down-regulation of CYP3A4 by inflammation; or (c) a direct activation of CYP3A4 gene transcription. In any case, based on the metabolism of atorvastatin in vitro and in vivo, Klein et al. concluded that the rs4253728 SNP influenced CYP3A4 gene expression and its activity (17). Here, we found no effect of this SNP on SRL metabolism in vitro using HLM or in kidney transplant recipients. Since this study is the first to investigate the effect of this SNP on SRL, further investigations are required.
The inclusion of in vitro data was useful to assess the genetic determinants of SRL hepatic metabolism, but this part of the work has limitations because non-genetic factors may influence HLM enzyme activity. We found no correlation between HLM donor age and SRL metabolism (Spearman correlation test, P = 0.6125), and the rate of SRL metabolism did not differ between HLM derived from women and men (MannWhitney test, P = 0.5058). Notably, other donor characteristics not recorded in the present study such as donor medication before the surgery might influence CYP3A activity and be potential confounding factors.
We found no significant association between SRL AEs and CYP3A4*22, CYP3A5*3, POR*28, or PPARArs4253728 SNPs, although as previously observed in the same group of patients (22), most of these AEs were influenced by SRL exposure (edemas, cutaneous ad verse events, and cholesterol and hemoglobin concentrations). No association with dose reduction, which could indirectly reflect the occurrence of an adverse event, was found either. The absence of significant influences of the investigated SNPs on SRL exposure (except for the weak effect of POR*28) probably account for their lack of influence on AEs. Other AEs (triglyceride concentrations, infection, and pneumonitis) appeared to be independent from patient exposure to SRL and were not associated with the genetic variations studied here.
In conclusion, we found that the CYP3A4*22 allele was associated with a moderate decrease in SRL hepatic metabolism in vitro but did not contribute significantly to the pharmacokinetic variability of SRL in renal transplant patients. On the other hand, the POR*28 allele was associated with a significant decrease in SRL [C.sub.0] in kidney transplant recipients, but this effect did not show up in vitro, and given its limited extent it is likely that it is not clinically relevant. Contrary to what was recently found for tacrolimus and cyclosporine, these 2 recently described genetic variations do not seem to contribute substantially to the pharmacokinetic variability of SRL in transplant patients and would not be useful for dose individualization.
Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, oranalysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.
Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.
Role of Sponsor: No sponsor was declared.
Acknowledgments: We are grateful toJ.H. Comte forhis contribution to laboratory analyses and K. Poole for manuscript editing.
(1.) Lampen A, Zhang Y, Hackbarth I, Benet LZ, Sewing KF, Christians U. Metabolism and transport of the macrolide immunosuppressant sirolimus in the small intestine. J Pharmacol Exp Ther 1998; 285:1104-12.
(2.) Lamoureux F, Picard N, Boussera B, Sauvage FL, Marquet P. Sirolimus and everolimus intestinal absorption and interaction with calcineurin inhibitors: a differential effect between cyclosporine and tacrolimus. Fundam Clin Pharmacol 2011; 26:463-72.
(3.) Crowe A, Lemaire M. In vitro and in situ absorption of SDZ-RAD using a human intestinal cell line (Caco-2) and a single pass perfusion model in rats: comparison with rapamycin. Pharm Res 1998;15:1666-72.
(4.) Picard N, Marquet P. The influence of pharmacogenetics and cofactors on clinical outcomes in kidney transplantation. Expert Opin Drug Metab Toxicol 2012;7:731-43.
(5.) Anglicheau D, Le Corre D, Lechaton S, LaurentPuig P, Kreis H, Beaune P, et al. Consequences of genetic polymorphisms for sirolimus requirements after renal transplant in patients on primary sirolimus therapy. Am J Transplant 2005;5:595-603.
(6.) Le Meur Y, Djebli N, Szelag JC, Hoizey G, Toupance O, Rerolle JP, Marquet P. CYP3A5*3 influences sirolimus oral clearance in de novo and stable renal transplant recipients. Clin Pharmacol Ther 2006;80:51-60.
(7.) Mourad M, Mourad G, Wallemacq P, Garrigue V, Van Bellingen C, Van Kerckhove V, et al. Sirolimus and tacrolimus trough concentrations and dose requirements after kidney transplantation in relation to CYP3A5 and MDR1 polymorphisms and steroids. Transplantation 2005;80:977-84.
(8.) Kuehl P, Zhang J, Lin Y, Lamba J, Assem M, Schuetz J, et al. Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Genet 2001;27:383-91.
(9.) Djebli N, Rousseau A, Hoizey G, Rerolle JP, Toupance O, Le Meur Y, Marquet P. Sirolimus population pharmacokinetic/pharmacogenetic analysis and Bayesian modelling in kidney transplant recipients. Clin Pharmacokinet 2006; 45:1135-48.
(10.) Miao LY, Huang CR, Hou JQ, Qian MY. Association study of ABCB1 and CYP3A5 gene polymorphisms with sirolimus trough concentration and dose requirements in Chinese renal transplant recipients. Biopharm Drug Dispos 2008;29:1-5.
(11.) Renders L, Frisman M, Ufer M, Mosyagin I, Haenisch S, Ott U, et al. CYP3A5 genotype markedly influences the pharmacokinetics of tacrolimus and sirolimus in kidney transplant recipients. Clin Pharmacol Ther 2007;81:228-34.
(12.) Elens L, van Schaik RH, Panin N, de Meyer M, Wallemacq P, Lison D, et al. Effect of a new functional CYP3A4 polymorphism on calcineurin inhibitors' dose requirements and trough blood levels in stable renal transplant patients. Pharmacogenomics 2011;12:1383-96.
(13.) Elens L, Bouamar R, Hesselink DA, Haufroid V, van der Heiden IP, van Gelder T, van Schaik RH. A new functional CYP3A4 intron 6 polymorphism significantly affects tacrolimus pharmacokinetics in kidney transplant recipients. Clin Chem 2011; 57:1574-83.
(14.) Elens L, Bouamar R, Hesselink DA, Haufroid V, van Gelder T, van Schaik RH. The new CYP3A4 intron 6 C>T polymorphism (CYP3A4*22) is associated with an increased risk of delayed graft function and worse renal function in cyclosporine-treated kidney transplant patients. Pharmacogenet Genomics 2012;22:373-80.
(15.) de Jonge H, Metalidis C, Naesens M, Lambrechts D, Kuypers DR. The P450 oxidoreductase *28 SNP is associated with low initial tacrolimus exposure and increased dose requirements in CYP3A5-expressing renal recipients. Pharmacogenomics 2011;12:1281-91.
(16.) Oneda B, Crettol S, Jaquenoud Sirot E, Bochud M, Ansermot N, Eap CB. The P450 oxidoreductase genotype is associated with CYP3A activity in vivo as measured by the midazolam phenotyping test. Pharmacogenet Genomics 2009;19:877-83.
(17.) Klein K, Thomas M, Winter S, Nussler AK, Niemi M, Schwab M, Zanger UM. PPARA: a novel genetic determinant of CYP3A4 in vitro and in vivo. Clin Pharmacol Ther 2012;91:1044-52.
(18.) Picard N, Djebli N, Sauvage FL, Marquet P. Metabolism of sirolimus in the presence or absence of cyclosporine by genotyped human liver microsomes and recombinant cytochromes P450 3A4 and 3A5. Drug Metab Dispos 2007;35:350-5.
(19.) Zimmerman JJ, Kahan BD. Pharmacokinetics of sirolimus in stable renal transplant patients after multiple oral dose administration. J Clin Pharmacol 1997;37:405-15.
(20.) Kahan BD, Napoli KL. Role of therapeutic drug monitoring of rapamycin. Transplant Proc 1998; 30:2189-91.
(21.) Kahan BD, Napoli KL, Kelly PA, Podbielski J, Hussein I, Urbauer DL, et al. Therapeutic drug monitoring of sirolimus: correlations with efficacy and toxicity. Clin Transplant 2000;14:97-109.
(22.) Woillard JB, Kamar N, Rousseau A, Rostaing L, Marquet P, Picard N. Association of sirolimus adverse effects with m-TOR, p70S6K or Raptor polymorphisms in kidney transplant recipients. Pharmacogenet Genomics 2012;22:725-32.
(23.) Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc 1995;57:289-300.
(24.) Wang D, Guo Y, Wrighton SA, Cooke GE, Sadee W. Intronic polymorphism in CYP3A4 affects hepatic expression and response to statin drugs. Pharmacogenomics J 2011;11:274-86.
(25.) Hubbard PA, Shen AL, Paschke R, Kasper CB, Kim JJ. NADPH-cytochrome P450 oxidoreductase. Structural basis for hydride and electron transfer. J Biol Chem 2001;276:29163-70.
Jean-Baptiste Woillard, [1,2,3] Nassim Kamar, [4,5,6] Sandra Coste,  Lionel Rostaing, [4,5,6] Pierre Marquet, [1,2,3] and Nicolas Picard [1,2,3] *
 INSERM, UMR S-850, Limoges, France;  Univ Limoges, Limoges, France;  CHU Limoges, Department of Pharmacology and Toxicology, Limoges, France;  CHU Toulouse, Department of Nephrology-Dialysis and Multi-Organ Transplantation, Toulouse, France;  INSERM, U563, IFR-BMT, CHU Purpan, Toulouse, France;  Univ Paul Sabatier, Toulouse, France
 Nonstandard abbreviations: SRL, sirolimus; CYP3A, cytochrome P450 3A; P-gp, P-glycoprotein; CNI, calcineurin inhibitor; SNP, single-nucleotide polymorphism; POR: P450 oxydo-reductase; [C.sub.0], trough concentrations; AE, adverse effects; HLM, human liver microsomes; LC-MS/MS, liquid chromatography-tandem mass spectrometry; CLint, intrinsic clearance.
 Human genes: ABCB1, ATP-binding cassette, sub-family B (MDR/TAP), member 1; CYP3A, cytochrome P450, family 3, subfamily A; CYP3A4, cytochrome P450, family 3, subfamily A, polypeptide 4; POR, P450 (cytochrome) oxidoreductase; CYP3A5, cytochrome P450, family 3, subfamily 5; PPARA, peroxisome proliferatoractivated receptor alpha.
* Address correspondence to this author at: INSERM UMR-S850, 2 rue du Dr. Marcland, 87025 Limoges, France. Fax +33(0)555435936; e-mail nicolas. firstname.lastname@example.org.
Received February 8, 2013; accepted August 1, 2013.
Previously published online at DOI: 10.1373/clinchem.2013.204990
Table 1. Linear mixed effect regression analyses of SNP effects on exposure phenotypes. Variable Category Phenotype Mean difference CYP3A4*22 CT/TT vs CC log [C.sub.0]/dose 0.0812 rs35599367 log dose 0.0004 log [C.sub.0] 0.0343 CYP3A5*3 rs776746 GG vs GA log [C.sub.0]/dose -0.0186 log dose 0.0595 log [C.sub.0] 0.0853 POR*28 rs1057868 CT/TT vs CC log [C.sub.0]/dose -0.2002 log dose 0.0639 log [C.sub.0] -0.1503 PPARA rs4253728 GG vs GA/AA log [C.sub.0]/dose -0.2155 log dose 0.2148 log [C.sub.0] 0.0249 Variable Category Phenotype SD P (a) CYP3A4*22 CT/TT vs CC log [C.sub.0]/dose 0.1602 0.8810 rs35599367 log dose 0.1335 0.9976 log [C.sub.0] 0.0932 0.8278 CYP3A5*3 rs776746 GG vs GA log [C.sub.0]/dose 0.1235 0.8810 log dose 0.1036 0.9976 log [C.sub.0] 0.0692 0.6605 POR*28 rs1057868 CT/TT vs CC log [C.sub.0]/dose 0.0940 0.1419 log dose 0.0798 0.9976 log [C.sub.0] 0.0524 0.0197 PPARA rs4253728 GG vs GA/AA log [C.sub.0]/dose 0.1972 0.8305 log dose 0.1642 0.7744 log [C.sub.0] 0.1142 0.8278 (a) P values were subjected to the Benjamini/Hochberg correction to account for the analysis of 3 phenotypes (log [C.sub.0]/dose, log dose, and log [C.sub.0]). Table 2. Intermediate models with significant nongenetic covariates for each phenotype used for adjustment of SNP analysis. Phenotype (case/total) Covariates Category Infection (32/113) Sex M vs F Pneumonitis (4/113) Age Per year increase Time between Per year increase transplantation and switch Cutaneous adverse SRL cumulative Per unit increase events (41/113) concentrations Edemas (20/113) Age Per year increase Time between Per year increase transplantation and switch SRL cumulative exposure Per unit increase Dose reduction (37/113) SRL cumulative exposure Per unit increase Statin High dosing vs no Phenotype Covariates Category Hemoglobin concentrations SRL [C.sub.0]/dose Per unit increase Time between Per unit increase transplantation and switch Total cholesterol plasma SRL [C.sub.0]/dose Per unit increase concentrations Corticosteroids intake Yes vs no Statin High dosing vs no Triglyceride plasma Time between Per unit increase concentrations transplantation and switch Corticosteroids intake Yes vs no Statin High dosing vs no Phenotype (case/total) Covariates Odds ratio (95% CI) Infection (32/113) Sex 0.47 (0.20-1.13) Pneumonitis (4/113) Age 1.19(1.04-1.46) Time between 1.02 (1.00-1.04) transplantation and switch Cutaneous adverse SRL cumulative 1.16 (1.02-1.35) events (41/113) concentrations Edemas (20/113) Age 1.07 (1.02-1.14) Time between 1.00 (0.99-1.01) transplantation and switch SRL cumulative exposure 1.31 (1.12-1.60) Dose reduction (37/113) SRL cumulative exposure 1.21 (1.05-1.45) Statin 7.10 (2.09-26.82) Phenotype Covariates P (SD) Hemoglobin concentrations SRL [C.sub.0]/dose 0.060 (0.039) Time between 0.0022 (0.0014) transplantation and switch Total cholesterol plasma SRL [C.sub.0]/dose -0.039 (0.027) concentrations Corticosteroids intake 0.697 (0.225) Statin 0.495 (0.228) Triglyceride plasma Time between -0.002 (0.001) concentrations transplantation and switch Corticosteroids intake 0.373 (0.225) Statin 0.585 (0.214) Phenotype (case/total) Covariates P Infection (32/113) Sex 0.0892 Pneumonitis (4/113) Age 0.0388 Time between 0.0225 transplantation and switch Cutaneous adverse SRL cumulative 0.0261 events (41/113) concentrations Edemas (20/113) Age 0.00444 Time between 0.08960 transplantation and switch SRL cumulative exposure 0.00253 Dose reduction (37/113) SRL cumulative exposure 0.01669 Statin 0.00233 Phenotype Covariates P Hemoglobin concentrations SRL [C.sub.0]/dose 0.1291 Time between 0.133 transplantation and switch Total cholesterol plasma SRL [C.sub.0]/dose 0.161 concentrations Corticosteroids intake 0.002 Statin 0.032 Triglyceride plasma Time between 0.082 concentrations transplantation and switch Corticosteroids intake 0.101 Statin 0.007 (a) SRL cumulative exposure = equivalent to (mean [C.sub.0]-dose) x follow-up duration. Table 3. Univariate analysis of SNPs using the model adjusted for covariates selected at the first step. Phenotype Variable Category Total cholesterol plasma CYP3A4 CT/TT vs CC concentration (g/L) adjusted on [C.sub.0]/dose SRL, corticosteroids, and statin comedication CYP3A5 GG vs AG POR CT/TT vs CC PPARA GG vs GA/AA Triglyceride plasma concentration CYP3A4 CT/TT vs CC (g/L) adjusted on time between transplantation and switch to SRL, corticosteroids, and statin comedication CYP3A5 GG vs AG POR CT/TT vs CC PPARA GG vs GA/AA Hemoglobin concentration (g/dL) CYP3A4 CT/TT vs CC adjusted on [C.sub.0]/dose SRL, time between transplantation and switch to SRL CYP3A5 GG vs AG POR CT/TT vs CC PPARA GG vs GA/AA Phenotype Variable Category Cutaneous adverse events adjusted CYP3A4 CT/TT vs CC on SRL cumulative exposure (b) CYP3A5 GA vs GG POR CT/TT vs CC PPARA GA/AA vs GG Pneumonitis adjusted on age and CYP3A4 CT/TT vs CC time between transplantation and switch to SRL CYP3A5 GA vs GG POR CT/TT vs CC PPARA GA/AA vs GG Infections adjusted on sex CYP3A4 CT/TT vs CC CYP3A5 GA vs GG POR CT/TT vs CC PPARA GA/AA vs GG Edemas adjusted on age, time CYP3A4 CT/TT vs CC between transplantation, and switch to SRL and SRL cumulative exposure CYP3A5 GA vs GG POR CT/TT vs CC PPARA GA/AA vs GG Dose reduction adjusted on statin CYP3A4 CT/TT vs CC comedication and SRL cumulative exposure CYP3A5 GA vs GG POR CT/TT vs CC PPARA GA/AA vs GG Phenotype [beta] SD P coefficient Total cholesterol plasma -0.1088 0.2848 1.0000 concentration (g/L) adjusted on [C.sub.0]/dose SRL, corticosteroids, and statin comedication -0.26338 0.22741 0.5352 0.1291 0.1773 0.9126 0.0352 0.1737 0.8395 Triglyceride plasma concentration -0.3273 0.2719 1.0000 (g/L) adjusted on time between transplantation and switch to SRL, corticosteroids, and statin comedication -0.2048 0.2250 0.5352 0.1917 0.1668 0.9126 0.2041 0.17059 0.8395 Hemoglobin concentration (g/dL) -0.291 0.312 1.0000 adjusted on [C.sub.0]/dose SRL, time between transplantation and switch to SRL -0.529 0.280 0.4822 -0.0213 0.203 0.9126 0.1211 0.1814 0.8395 Phenotype Odds ratio 95% CI P (a) Cutaneous adverse events adjusted 1.40 0.33-5.30 1.0000 on SRL cumulative exposure (b) 0.49 0.14-1.49 0.5352 0.57 0.24-1.28 0.9126 0.46 0.19-1.05 0.5651 Pneumonitis adjusted on age and NAc NA NA time between transplantation and switch to SRL 0.42 0.02-4.82 0.5352 0.72 0.03-8.06 0.9126 2.25 0.22-51.6 0.8395 Infections adjusted on sex 0.41 0.06-1.81 1.0000 0.59 0.16-1.81 0.5352 1.26 0.55-2.94 0.9126 0.81 0.35-1.86 0.8395 Edemas adjusted on age, time 1.00 0.03-3.97 1.0000 between transplantation, and switch to SRL and SRL cumulative exposure 0.61 0.11-2.62 0.5352 0.84 0.23-2.91 0.9126 0.62 0.16-2.25 0.8395 Dose reduction adjusted on statin 5.12 1.10-28.8 0.3536 comedication and SRL cumulative exposure 2.16 0.70-6.73 0.5352 0.60 0.24-1.46 0.9126 0.63 0.26-1.55 0.8395 (a) P values indicated are those corrected by the Benjamini-Hochberg correction for 8 phenotypes. (b) SRL cumulative exposure = equivalent to (mean [C.sub.0]/dose) x follow-up duration. (c) NA, not applicable.
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|Title Annotation:||Drug Monitoring and Toxicology|
|Author:||Woillard, Jean-Baptiste; Kamar, Nassim; Coste, Sandra; Rostaing, Lionel; Marquet, Pierre; Picard, Ni|
|Date:||Dec 1, 2013|
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