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Erschienen in:
01.01.2001 | Original Research Article
Covariate Effects on the Apparent Clearance of Tacrolimus in Paediatric Liver Transplant Patients Undergoing Conversion Therapy
Erschienen in: Clinical Pharmacokinetics | Ausgabe 1/2001
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Objective
To analyse the influence of covariates on the apparent clearance (CL) of tacrolimus in paediatric liver transplant recipients being converted from cyclosporin to tacrolimus.
Design
Retrospective modelling study.
Patients and participants
18 children, 13 girls and 5 boys, aged 4 months to 16 years (median 9.1 years) who required conversion to tacrolimus because of acute or chronic rejection or cyclosporin toxicity.
Methods
287 whole-blood tacrolimus concentrations from therapeutic drug monitoring were used to build a nonlinear mixed-effects population model (NON-MEM program) for the apparent clearance of tacrolimus. Variables considered were age, total bodyweight (TBW), body surface area (BSA), time after initiation of treatment (T), gender, haematocrit (Hct), albumin (Alb), aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase (γGT), alkaline Phosphatase (ALP), bilirubin (BIL), Creatinine clearance (CLcr) and dosage of concomitant corticosteroids (EST).
Results
TBW, T, BIL and ALT were the covariates that displayed a significant influence on CL according to the final regression model: CL (L/h) = 10.4(TBW/70)¾ · e-000032 T· e-0.057 BIL. (1- 0.079 ALT). With this model, the estimates of the coefficients of variation were 24.3% and 29.5% for interpatient variability in CL and residual variability, respectively.
Conclusions
The proposed model for tacrolimus CL can be applied for a priori dosage calculations, although the results should be used with caution because of the unexplained variability in the CL. We therefore recommended close monitoring of tacrolimus whole blood concentrations, especially within the first months of treatment. The best use of the model would be its application in dosage adjustment based on therapeutic drug monitoring and the Bayesian approach.