Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach

Abstract

Aims: The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications.

Methods: A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies.

Results: A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation.

Conclusion: A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

Keywords: meta-analysis; pharmacodynamics; pharmacokinetics; pharmacometrics; population analysis; statistics; study design; tacrolimus.

Figure 1

Sigma plot of the scientific literature review of population pharmacokinetic (PKPop) models for tacrolimus. PBPK, physiologically based pharmacokinetics

Figure 2

Goodness‐of‐fit plots of the final population pharmacokinetic model. (A) Observed vs population predicted concentrations; (B) observed vs predicted individual concentrations; (C) conditional weighted residuals vs time post‐transplantation

Figure 3

Normalised prediction distribution error plots (NPDE). (A) Distribution of NPDE. (B) NPDE vs time from the start of the treatment

Figure 4

Prediction‐corrected visual predictive checks (pcVPC) of the model's description of the present data for the final model (A) and stratified by database (B, C, D) for children databases, (E, F, G, H) for adult, (E) for renal transplant patients and (F) for patients on the waiting list for renal transplantation. Red solid line: Median observed concentration; red dashed lines: 5th and 95th percentiles of the observed concentrations. The red and blue shaded areas represent 95% confidence intervals of the prediction percentiles

Figure 5

Goodness‐of‐fit plots of the final population pharmacokinetic model validated in 3 external patient‐level datasets. (A) Observed vs population predicted concentrations for adult kidney transplant; (B) observed vs population predicted concentrations for adult heart transplant; (C) observed vs population predicted concentrations for adult lung transplant

Figure 6

Goodness‐of‐fit plots of the predictions of concentrations for CYP3A5 expressors (CYP3A5*1) and nonexpressors (CYP3A5*3) in the 3 external validation patient‐level datasets (A) observed vs individual predicted concentrations for adult kidney transplant; (B) observed vs individual predicted concentrations for adult heart‐transplant; (C) observed vs individual predicted concentrations for adult lung transplant