Dataset Information

Pretransplant 4?-hydroxycholesterol does not predict tacrolimus exposure or dose requirements during the first days after kidney transplantation.

ABSTRACT: AIMS:The CYP3A metric 4?-hydroxycholesterol (4?OHC) has been shown to correlate with tacrolimus steady-state apparent oral clearance (CL/F). Recently, pretransplant 4?OHC was shown not to predict tacrolimus CL/F after transplantation in a cohort of renal recipients (n = 79). The goal of the current study was determine whether these findings could be validated in a substantially larger cohort. METHODS:In a retrospective analysis of 279 renal recipients, tacrolimus trough concentrations (C0), daily dose, haematocrit and other relevant covariates were registered every day for the first 14 days after transplantation. 4?OHC and cholesterol were quantified on plasma collected immediately pretransplant using liquid chromatography tandem-mass spectrometry. Patients were genotyped for CYP3A5*1 and CYP3A4*22. RESULTS:A total of 3551 tacrolimus C0 concentrations were registered. In a linear mixed model for the 14-day period, determinants of tacrolimus C0 were CYP3A5 genotype, haematocrit, age and weight (overall R2 = 0.179). Determinants of daily dose were CYP3A5 genotype, age, methylprednisolone dose, tacrolimus formulation, ALT and estimated glomerular filtration rate (overall R2 = 0.242). Considering each of the first 5 days separately, 4?OHC had a limited effect on tacrolimus C0 on day 3 only (-1.00 ng ml-1 per ln, P = 0.035) but not on any other day, and no effect on dose or C0/dose. During the first 5 days, haematocrit and age, which were previously established as determinants of tacrolimus disposition under steady-state conditions, never explained more than 17.7% of between-subject variability in tacrolimus C0/dose. CONCLUSIONS:The CYP3A metric 4?OHC cannot be used to predict tacrolimus dose requirements in the first days after transplantation.

SUBMITTER: Vanhove T

PROVIDER: S-EPMC5651327 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Pretransplant 4β-hydroxycholesterol does not predict tacrolimus exposure or dose requirements during the first days after kidney transplantation.

Vanhove Thomas T Hasan Mahmoud M Annaert Pieter P Oswald Stefan S Kuypers Dirk R J DRJ

British journal of clinical pharmacology 20170714 11

<h4>Aims</h4>The CYP3A metric 4β-hydroxycholesterol (4βOHC) has been shown to correlate with tacrolimus steady-state apparent oral clearance (CL/F). Recently, pretransplant 4βOHC was shown not to predict tacrolimus CL/F after transplantation in a cohort of renal recipients (n = 79). The goal of the current study was determine whether these findings could be validated in a substantially larger cohort.<h4>Methods</h4>In a retrospective analysis of 279 renal recipients, tacrolimus trough concentrat ...[more]

PMID: 28603840

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Project description:IntroductionPolyomavirus (BKV) infection can lead to major complications and damage to the graft in kidney transplant recipients (KTRs). We investigated whether pretransplant BK serostatus and BK-specific cell-mediated immunity (CMI) predicts post-transplant BK infection.MethodsA total of 93 donor-recipient pairs who underwent kidney transplantation (KT) and 44 healthy controls were examined. Assessment of donor and recipient BKV serostatus and BKV-CMI in recipients was performed prior to transplantation using BKV-IgG ELISA and BKV-specific IFN-g ELISPOT assays against five BK viral antigens (LT, St, VP1, VP2, and VP3). BK viremia was diagnosed when blood BKV-DNA of 104 copies/mL or more was detected during follow-up periods.ResultsAnti-BKV IgG antibody was detected in 74 (79.6%) of 93 KTRs and in 68 (73.1%) of 93 KT donors. A greater percentage of KTRs who received allograft from donors with high levels of anti-BKV IgG had posttransplant BK viremia (+) than KTRs from donors with low anti-BKV IgG (25.5% [12/47] vs. 4.3% [2/46], respectively; P = 0.007). Pretransplant total BKV-ELISPOT results were lower in BK viremia (+) patients than in patients without viremia (-) 20.5 [range 9.9-63.6] vs. 72.0 [43.2 - 110.8]; P = 0. 027). The sensitivity and specificity of the total BKV-ELISPOT assay (cut-off ≤ 53 spots/3×105 cells) for prediction of posttransplant BK viremia were 71.4 (95% CI: 41.9-91.6) and 54.4 (42.8-65.7), respectively. The combination of high donor BKV-IgG, low recipient BKV-IgG, and low total BKV-ELISPOT results improved specificity to 91.1%.DiscussionOur study highlights the importance of pretransplant BKV-IgG serostatus and BKV-specific CMI in predicting posttransplant BKV infection in KTRs. The combination of high donor BKV-IgG, low recipient BKV-IgG, and low total BKV-ELISPOT results predicted BK viremia after KT. Pretransplant identification of patients at highrisk for BK viremia could enable timely interventions and improve clinical outcomes of KTRs.

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.

Project description:Tacrolimus dosing to establish therapeutic levels in recipients of organ transplants is a challenging task because of much interpatient and intrapatient variability in drug absorption, metabolism, and disposition. In view of the reported impact of gut microbial species on drug metabolism, we investigated the relationship between the gut microbiota and tacrolimus dosing requirements in this pilot study of adult kidney transplant recipients. Serial fecal specimens were collected during the first month of transplantation from 19 kidney transplant recipients who either required a 50% increase from initial tacrolimus dosing during the first month of transplantation (Dose Escalation Group, n=5) or did not require such an increase (Dose Stable Group, n=14). We characterized bacterial composition in the fecal specimens by deep sequencing of the PCR amplified 16S rRNA V4-V5 region and we investigated the hypothesis that gut microbial composition is associated with tacrolimus dosing requirements. Initial tacrolimus dosing was similar in the Dose Escalation Group and in the Stable Group (4.2 ± 1.1 mg/day vs. 3.8 ± 0.8 mg/day, respectively, P=0.61, two-way between-group ANOVA using contrasts) but became higher in the Dose Escalation Group than in the Dose Stable Group by the end of the first transplantation month (9.6 ± 2.4 mg/day vs. 3.3 ± 1.5 mg/day, respectively, P<0.001). Our systematic characterization of the gut microbial composition identified that fecal Faecalibacterium prausnitzii abundance in the first week of transplantation was 11.8% in the Dose Escalation Group and 0.8% in the Dose Stable Group (P=0.002, Wilcoxon Rank Sum test, P<0.05 after Benjamini-Hochberg correction for multiple hypotheses). Fecal Faecalibacterium prausnitzii abundance in the first week of transplantation was positively correlated with future tacrolimus dosing at 1 month (R=0.57, P=0.01) and had a coefficient ± standard error of 1.0 ± 0.6 (P=0.08) after multivariable linear regression. Our novel observations may help further explain inter-individual differences in tacrolimus dosing to achieve therapeutic levels.

Dataset Information

Pretransplant 4?-hydroxycholesterol does not predict tacrolimus exposure or dose requirements during the first days after kidney transplantation.

Publications

Pretransplant 4β-hydroxycholesterol does not predict tacrolimus exposure or dose requirements during the first days after kidney transplantation.

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