Prediction of Free from Total Mycophenolic Acid Concentrations in Stable Renal Transplant Patients: A Population-Based Approach.

BACKGROUND: A population pharmacokinetic (PK) protein-binding model was developed to (1) predict free mycophenolic acid (fMPA) based on total MPA (tMPA) concentrations in renal transplant patients, to establish the therapeutic range of fMPA through pharmacokinetic-pharmacodynamic studies; and (2) provide a guideline for dosing mycophenolate mofetil (MMF). METHODS: Full PK profiles of 56 patients (from five different occasions) during the first year after transplantation who were treated with oral MMF and cyclosporine, or macrolides (either tacrolimus or sirolimus), were analysed. fMPA protein-binding was modelled using nonlinear mixed effects modelling (NONMEM). The influence of physiological factors and coadministered immunosupressant was studied. RESULTS: A two-compartment model with first-order absorption and elimination, linear protein binding and enterohepatic circulation (EHC) best described the PK of MPA. Different recycling rate constants were considered depending on the coadministered immunosuppressant. The protein-binding rate constant (KB [relative standard error, RSE%]) increased nonlinearly with renal function according to K B = 43.1 (3.13)·(CLCR/59.51)0.394(10.66) h-1. Furthermore, fMPA plasma clearance, given by clearance of the free mycophenolic acid (CLfMPA), CLfMPA = 410 (RSE%3.00)·(1+CsA·0.594 (22.39)) L/h, was 59.4% greater in cyclosporine-treated patients than in macrolide-treated patients, leading to lower MPA exposures. External evaluation proved acceptable area under the plasma concentration-time curve and trough concentration predictions. CONCLUSIONS: A reliable protein-binding population PK model was developed for prediction of fMPA or tMPA from each other and for dose guiding in stable renal transplant recipients.

The combination of CYP3A4*22 and CYP3A5*3 single-nucleotide polymorphisms determines tacrolimus dose requirement after kidney transplantation.

INTRODUCTION: Tacrolimus (Tac) has a narrow therapeutic window and shows large between-patient pharmacokinetic variability. As a result, over-immunosuppression and under-immunosuppression are frequently encountered in daily clinical practice. Unraveling the impact of genetic polymorphisms on Tac pharmacokinetics may help to refine therapy. In this study, the associations of single-nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes (CYP3A) with Tac pharmacokinetics were investigated in renal transplant recipients. PARTICIPANTS AND METHODS: In a cohort of 272 kidney transplant recipients, associations between functional genetic variants (CYP3A4*22 and CYP3A5*3) and dose-adjusted predose Tac concentrations (C0) and daily doses of Tac at days 5-7 and 15 and 1, 3, 6 and 12 months after renal transplantation were evaluated. Patients were genotyped and clustered according to both CYP3A4*22 and CYP3A5*3 allelic status: poor (PM) (CYP3A4*22 carriers with CYP3A5*3/*3), intermediate (IM) (CYP3A4*1/*1 with CYP3A5*3/*3 or CYP3A4*22 carriers with CYP3A5*1 carriers) and extensive CYP3A-metabolizers (EM) (CYP3A4*1/*1 and CYP3A5*1 carriers). RESULTS: EM had an 88% lower dose-adjusted C0 compared with IM. PM had a 26% higher dose-adjusted C0 compared with IM. The percentage of patients with supratherapeutic Tac exposure (C0>15 ng/ml) was significantly higher in PM (43.5%) compared with EM (0%) at days 5-7 after transplantation (P=0.01). About 30% of EM had subtherapeutic exposure (C0<5 ng/ml) at days 5-7 after transplantation (P=0.001). CONCLUSION: The combined CYP3A4 and CYP3A5 genotype of renal transplant recipients has a major influence on the Tac dose required to reach the target exposure.

A New CYP3A5*3 and CYP3A4*22 Cluster Influencing Tacrolimus Target Concentrations: A Population Approach.

BACKGROUND: Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients. METHODS: Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM® version 7.2. RESULTS: The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers. CONCLUSIONS: Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.

Development of a population PK model of tacrolimus for adaptive dosage control in stable kidney transplant patients.

BACKGROUND: Tacrolimus pharmacokinetics (PK) presents a high variability that hampers its therapeutic use. The aims of this study are to: (1) develop a population pharmacokinetic (PPK) model for tacrolimus and to identify the factors that contribute to the variability of tacrolimus PK in renal transplant patients; and (2) to establish a new Bayesian estimator that can easily and routinely be applied in the hospital. A new PPK model may allow efficacy to be optimized, improve dose regimens, minimize side effects, and decrease the cost of extensive area under the curve (AUC) monitoring. METHODS: PPK analysis of the full PK profiles of 16 patients on 5 occasions was performed with NONMEM 7.2. Biochemical variables (hematocrit, hemoglobin, aspartate aminotransferase, and others) were analyzed. RESULTS: A 2-open-compartment model with interoccasion variability best described the PK of tacrolimus. Three transit compartments provided the best description of the absorption process. The hematocrit, aspartate aminotransferase, and alanine aminotransferase were not significant in the covariate analysis. External validation with 91 patients proved the good predictability of the model with a bias and precision of 0.37 mcg/L (CI 95%, -0.11 to 1.20 mcg/L) and 0.38 mcg/L (CI 95%, 0.02 to 1.21 mcg/L), respectively. A limited sampling strategy using 1 sampling point at predose (trough concentrations) showed a good performance in AUC0-12h estimation with a correlation between AUCfull and AUCLSS, bias and imprecision of r = 0.75, 6.78% (range, -16.26% to 30.06%) and 1.42% (IC 95%, 0.14%-3.61%), respectively. CONCLUSIONS: The PPK model developed provides reliable prior information for Bayesian adaptive control of dosage regimens of tacrolimus to achieve the desired AUC goals in stable renal transplant patients.

Pharmacokinetic modeling of enterohepatic circulation of mycophenolic acid in renal transplant recipients.

Several factors contribute to mycophenolic acid (MPA) between-patient variability. Here we characterize the metabolic pathways of MPA and quantify the effect of combining genetic polymorphism of multidrug-resistant-associated protein-2, demographics, biochemical covariates, co-medication (cyclosporine (CsA) vs. macrolides), and renal function on MPA, 7-O-MPA-glucuronide (MPAG), and acyl-glucuronide (AcMPAG) disposition, in renal transplant recipients, after mycophenolate mofetil. Complete pharmacokinetic profiles from 56 patients (five occasions) were analyzed. Enterohepatic circulation was modeled by transport of MPAG to the absorption site. This transport significantly decreased with increasing CsA trough concentrations (CtroughCsA). MPAG and AcMPAG plasma clearances significantly decreased with renal function. No significant influence of multidrug-resistant-associated protein-2 C24T single-nucleotide polymorphism was found. The model adequately predicted the increase in MPAG/AcMPAG exposures in CsA and macrolide patients with decreased renal function. This resulted in higher MPA exposures in macrolide patients versus CsA patients, and increased MPA exposures with renal function from 25 to 10 ml/min, in macrolide patients, owing to enhanced MPAG enterohepatic circulation. Lower-percentage enterohepatic circulation occurred with higher CtroughCsA and renal function values. The lack of MPA protein-binding modeling did not permit evaluation of the impact of renal function and CtroughCsA on MPA exposures in CsA patients. Thus, dose tailoring of covariates is recommended for target MPA exposure.

Influence of MRP2 on MPA pharmacokinetics in renal transplant recipients-results of the Pharmacogenomic Substudy within the Symphony Study.

BACKGROUND: The aim of this study was to determine the relationship between single-nucleotide polymorphisms (SNPs) in MRP2 genes and mycophenolic acid (MPA) pharmacokinetics in renal transplant recipients of the Symphony Pharmacogenomic substudy. METHODS: Sixty-six renal transplant recipients of eight Spanish centres were randomized into four branches of immunosuppressive regimen: low dose of cyclosporine, standard dose of cyclosporine, tacrolimus and sirolimus, all in addition to mycophenolate mofetil and steroids. Fifty-five patients were genotyped for SNPs in MRP2, C24T and C3972T. Pharmacokinetic sampling was done before MPA administration and up to 12 h post-dose at Day 7, 1 month and 3 months post-transplant. Relationships of area under the curve (AUC) of MPA and MPAG plasma sampling with the presence of MRP2 SNPs and with the immunosuppressive regimens were studied. RESULTS: At steady-state conditions, MPA-reduced exposure was observed in C24T variant allele in MRP2 (CC: 68.73 ± 6.78; *T: 48.12 ± 4.90, P = 0.023); no significant differences linked to C3972T SNP were observed. Taking into account groups of treatment, lower MPA AUC in variant allele of C24T was only found under macrolides treatment with statistically significant differences at Month 3 (Tac and SRL, CC: 86.52 ± 10.98 versus *T: 41.99 ± 4.82, P = 0.001; CsA, CC: 52.31 ± 5.30 versus *T: 54.24 ± 8.30, P = 0.772); for C3972T, the same tendency was found but differences at steady state did not reach statistical significance. CONCLUSIONS: Renal transplant recipients T carriers of C24T MRP2 with macrolides treatment were associated with reduced MPA AUC in steady-state conditions. Patients treated with cyclosporine lost the effect of this polymorphism.