External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients

Abstract

AIMSeveral tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors. METHODSPublished models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction-and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated. RESULTSSixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used. CONCLUSIONSThe published models were unsatisfactory in prediction-and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Population pharmacokinetics of tacrolimus in adult renal transplant recipients are continuously published to characterize pharmacokinetics and to identify covariates for dose individualization.• Whether these models can be extrapolated to clinical centres other than those where they were established remains unclear. WHAT THIS STUDY ADDS• For the first time, the predictive performances of all relevant models were evaluated in an independent cohort. • Models provide unsatisfactory results in the prediction-and simulation-based evaluations. However, Bayesian forecasting could improve the predictability considerably.• The factors influencing model predictability included bioassays, involved covariates and whether non-linear kinetics were employed.

Population pharmacokinetic study of tacrolimus in pediatric patients with primary nephrotic syndrome: A comparison of linear and nonlinear Michaelis–Menten pharmacokinetic model

“…It is worth noting that, these studies used a linear compartmental model, a traditional PK modeling strategy for TAC; however, a dose-dependent clearance of TAC was found in our previous study . These findings contradict the traditional viewpoint that TAC is a drug with linear PK, as described by 1-or 2-compartment model Jacobo-Cabral et al, 2015;Prytuła et al, 2016;Andrews et al, 2018;Hao et al, 2018;Wang et al, 2019), but is similar to a recent study in which a superior predictability of nonlinear Michaelis-Menten (MM) model for TAC was found in adult renal transplant recipients (Zhao et al, 2016).…”

“…However, the values of Vmax and Km in pediatric PNS were significantly lower than those in adult renal transplant recipients (Vmax was 5.54 mg/day and Km was 2.36 ng/mL) (Zhao et al, 2016). The discrepancy in these two populations might be due to various changes during the growth of children (Yokoi, 2009) and higher target TAC trough concentrations in renal transplant patients [10][11][12][13][14][15][8][9][10][11][12][13][14][15], and 5-12 ng/mL within 1, 1-3, and 3-12 months after transplantation, respectively (KDIGO Work Group, 2009)] than that in PNS patients [5-10 ng/mL] (Atanda, 2012;Chinese Medical Association, 2017).…”

Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients

“…This suggests that knowledge of typical pharmacokinetic behavior and patient covariate values alone without feedback concentration measurements from individual patients is not sufficient to make precise predictions. This could improve by incorporation of the model in Bayesian forecasting software, which would allow feedback from subsequent measurements and the further individualization of the parameters (43).…”

Effect of Protein Binding on Exposure of Unbound and Total Mycophenolic Acid: A Population Pharmacokinetic Analysis in Chinese Adult Kidney Transplant Recipients

“…The narrow therapeutic window and large PK variability make it necessary to individualise MMF therapy based on therapeutic drug monitoring (TDM). Currently, the maximum a posterior Bayesian (MAPB) method using population PK in combination with Bayesian estimation is recommended for facilitating the optimal pharmacotherapy [13][14][15][16]. This approach is based on a comprehensive understanding of prior information, i.e.…”