Dataset Information

A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation.

ABSTRACT: 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.

SUBMITTER: Kim J

PROVIDER: S-EPMC6544741 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation.

Kim Janet J Wilson Sam S Undre Nasrullah A NA Shi Fei F Kristy Rita M RM Schwartz Jason J JJ

Drugs in R&D 20190601 2

<h4>Background and objective</h4>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 th ...[more]

PMID: 31073875

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Project description:PurposeTo explore the relationship between rs2291075 polymorphism in SLCO1B1 gene, which encodes an influx transmembrane protein transporter, and tacrolimus dose-corrected trough concentration (C/D, ng ml-1 mg-1 kg-1) in the early period after liver transplantation.MethodsCYP3A5 rs776746 and SLCO1B1 rs2291075 polymorphisms of 210 liver transplantation patients and their corresponding donor livers were assessed by PCR amplification and DNA sequencing. The influence of gene polymorphisms on C/D values of tacrolimus was analyzed. The early postoperative period after liver transplantation was divided into the convalescence phase (1-14 days) and stationary phase (15-28 days) according to the change of liver function and tacrolimus C/D values.ResultsThe combined analysis of donor and recipient CYP3A5 rs776746 could distinguish the metabolic phenotype of tacrolimus into three groups: fast elimination (FE), intermediate elimination (IE), and slow elimination (SE), which was entitled the FIS classification system. Tacrolimus C/D ratios of recipient SLCO1B1 rs2291075 CT and TT carriers were very close and were significantly lower than those of recipient SLCO1B1 rs2291075 CC genotype carriers in convalescence phase (p = 0.0195) and in stationary phase (p = 0.0152). There were no statistically significant differences between tacrolimus C/D ratios of patients carried with SLCO1B1 rs2291075 CT, TT genotype donors, and those carried with SLCO1B1 rs2291075 CC genotype donors. A model consisting of tacrolimus daily dose, total bilirubin, FIS classification, and recipient SLCO1B1 rs2291075 could predict tacrolimus C/D ratios in the convalescence phase by multivariate analysis. However, recipient SLCO1B1 rs2291075 genotype failed to enter forecast model for C/D ratios in stationary phase. Recipient SLCO1B1 rs2291075 genotype had significant effect on tacrolimus C/D ratios in convalescence phase (p = 0.0300) and stationary phase (p = 0.0400) in subgroup, which excluded the interference come from donor and recipient CYP3A5 rs776746.ConclusionSLCO1B1 rs2291075 could be a novel genetic locus associated with tacrolimus metabolism. The combined analysis of donor and recipient CYP3A5 rs776746, recipient SLCO1B1 rs2291075 genotypes, could be helpful to guide the personalized administration of tacrolimus in early period after liver transplantation.

Dataset Information

A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation.

Publications

A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation.

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