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The CYP3A biomarker 4?-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation.

ABSTRACT: AIMS:Tacrolimus is a cornerstone in modern immunosuppressive therapy after kidney transplantation. Tacrolimus dosing is challenged by considerable pharmacokinetic variability, both between patients and over time after transplantation, partly due to variability in cytochrome P450 3A (CYP3A) activity. The aim of this study was to assess the value of the endogenous CYP3A marker 4?-hydroxycholesterol (4?OHC) for tacrolimus dose individualization early after kidney transplantation. METHODS:Data were obtained from 79 adult kidney transplant recipients who contributed a total of 625 4?OHC measurements and 1999 tacrolimus whole blood concentrations during the first 2 months after transplantation. The relationships between 4?OHC levels and individual estimates of tacrolimus apparent plasma clearance (CL/Fplasma ) at different time points after transplantation were investigated using scatterplots and population pharmacokinetic modelling. RESULTS:There was no significant correlation between pre-transplant 4?OHC levels and tacrolimus CL/Fplasma the first week (r = 0.19 [95% CI -0.03-0.40]) or between 4?OHC and tacrolimus CL/Fplasma 1 week (r = 0.20 [-0.11-0.47]), 4 weeks (r = 0.21 [-0.07-0.46]) or 2 months (r = 0.24 [-0.03-0.48]) after transplantation (P ? 0.06). In the population analysis, time-varying 4?OHC was not a statistically significant covariate on tacrolimus CL/Fplasma , neither in terms of absolute values (P = 0.11) nor in terms of changes from baseline (P = 0.17). 4?OHC values increased between 1 week and 2 months after transplantation (median change +57% [IQR +22-83%], P < 0.001), indicating increasing CYP3A activity. Contradictorily, tacrolimus CL/Fplasma decreased over the same period (median change -13% [IQR -3 to -26%], P < 0.001). CONCLUSIONS:4?OHC does not appear to have a clinical potential to improve individualization of tacrolimus doses early after kidney transplantation.

SUBMITTER: Storset E

PROVIDER: S-EPMC5465334 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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The CYP3A biomarker 4β-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation.

Størset Elisabet E Hole Kristine K Midtvedt Karsten K Bergan Stein S Molden Espen E Åsberg Anders A

British journal of clinical pharmacology 20170227 7

<h4>Aims</h4>Tacrolimus is a cornerstone in modern immunosuppressive therapy after kidney transplantation. Tacrolimus dosing is challenged by considerable pharmacokinetic variability, both between patients and over time after transplantation, partly due to variability in cytochrome P450 3A (CYP3A) activity. The aim of this study was to assess the value of the endogenous CYP3A marker 4β-hydroxycholesterol (4βOHC) for tacrolimus dose individualization early after kidney transplantation.<h4>Methods ...[more]

PMID: 28146606

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Project description:Achieving fast immunosuppression blood exposure after kidney transplantation is key to abrogating both preformed and de novo anti-donor humoral and cellular alloresponses. However, while tacrolimus (TAC) is the cornerstone immunosuppressant inhibiting adaptive alloimmunity, its blood exposure is directly impacted by different single-nucleotide polymorphisms (SNPs) in CYP3A TAC-metabolizing enzymes. Here, we investigated how functional TAC-CYP3A genetic variants (CYP3A4*22/CYP3A5*3) influence the main baseline clinical and immunological risk factors of biopsy-proven acute rejection (BPAR) by means of preformed donor-specific antibodies (DSAs) and donor-specific alloreactive T cells (DSTs) in a large European cohort of 447 kidney transplants receiving TAC-based immunosuppression. A total of 70 (15.7%) patients developed BPAR. Preformed DSAs and DSTs were observed in 12 (2.7%) and 227 (50.8%) patients, respectively. According to the different CYP3A4*22 and CYP3A5*3 functional allele variants, we found 4 differential new clusters impacting fasting TAC exposure after transplantation; 7 (1.6%) were classified as high metabolizers 1 (HM1), 71 (15.9%) as HM2, 324 (72.5%) as intermediate (IM), and 45 (10.1%) as poor metabolizers (PM1). HM1/2 showed significantly lower TAC trough levels and higher dose requirements than IM and PM (p < 0.001) and more frequently showed TAC underexposure (<5 ng/ml). Multivariate Cox regression analyses revealed that CYP3A HM1 and IM pharmacogenetic phenotypes (hazard ratio (HR) 12.566, 95% CI 1.99–79.36, p = 0.007, and HR 4.532, 95% CI 1.10–18.60, p = 0.036, respectively), preformed DSTs (HR 3.482, 95% CI 1.99–6.08, p < 0.001), DSAs (HR 4.421, 95% CI 1.63–11.98, p = 0.003), and delayed graft function (DGF) (HR 2.023, 95% CI 1.22–3.36, p = 0.006) independently predicted BPAR. Notably, a significant interaction between T-cell depletion and TAC underexposure was observed, showing a reduction of the BPAR risk (HR 0.264, 95% CI 0.08–0.92, p = 0.037). Such variables except for DSAs displayed a higher predictive risk for the development of T cell-mediated rejection (TCMR). Refinement of pretransplant monitoring by incorporating TAC CYP3A SNPs with preformed DSAs as well as DSTs may improve current rejection-risk stratification and help induction treatment decision-making.

Project description:BACKGROUND AND OBJECTIVE:Given that a high intrapatient variability (IPV) of tacrolimus whole blood concentration increases the risk for a poor kidney transplant outcome, some experts advocate routine IPV monitoring for detection of high-risk patients. However, attempts to estimate the variance of tacrolimus trough concentrations (TTC) are limited by the need for patients to receive a fixed dose over time and/or the use of linear statistical models. A goal of this study is to overcome the current limitations through the novel application of statistical methodology generalizing the relationship between TTC and dose through the use of nonparametric functional regression modeling. METHODS:With TTC as a response and dose as a covariate, the model employs an unknown bivariate function, allowing for the potentially complex, nonlinear relationship between the two parameters. A dose-adjusted variance of TTC is then derived based on standard functional principal component analysis (FPCA). To assess the model, it was compared against an FPCA-based model and linear mixed-effects models using prediction error, bias, and coverage probabilities for simulated data as well as phase III data from the Astellas new drug application studies for extended-release tacrolimus. RESULTS:Our numerical investigation indicates that the new model better predicts dose-adjusted TTCs compared with the prediction of linear mixed effects models. Estimated coverage probabilities also indicate that the new model accurately accounts for the variance of TTC during the periods of large fluctuation in dose, whereas the linear mixed effects model consistently underestimates the coverage probabilities because of the inaccurate characterization of TTC fluctuation. CONCLUSION:This is the first known application of a functional regression model to assess complex relationships between TTC and dose in a real clinical setting. This new method has applicability in future clinical trials including real-world data sets due to flexibility of the nonparametric modeling approach.

Dataset Information

The CYP3A biomarker 4?-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation.

Publications

The CYP3A biomarker 4β-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation.

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