© 2010 universitair ziekenhuis gent population pharmacokinetics of tacrolimus in stable paediatric...

© 2010 Universitair Ziekenhuis Gent

Population pharmacokinetics of tacrolimus in stable paediatric renal transplant

recipients translated into clinical practice

Agnieszka Prytuła (1), Karlien Cransberg (2), Antonia Bouts (3), Saskia de Wildt (2),

Ron van Schaik (2), Ron Mathot (3)

(1)Ghent University Hospital, Belgium

(2)Erasmus MC, Rotterdam, the Netherlands

(3)AMC, Amsterdam, the Netherlands

© 2010 Universitair Ziekenhuis Gent

Tacrolimus: therapeutic drug monitoring

Pre- dose concentrations (C0)

Pre- dose concentrations (C0)

Area under the concentration time curve (12- hour-AUC)

Area under the concentration time curve (12- hour-AUC)

Abbreviated AUC

C0, C60, C180

concentrations

W Zhao et al. Ther drug monit, 2011

Abbreviated AUC

C0, C60, C180

concentrations

W Zhao et al. Ther drug monit, 2011

© 2010 Universitair Ziekenhuis Gent

Tacrolimus clearance in de novo pediatric kidney recipients related to:

-body weight

-hematocrit

-CYP3A5 polymorphism

Zhao et al. Clinical Pharmacology & Therapeutics 2009

> 1 year

- cortocosteroids

- antiviral prophylaxis

+ antihypertensives

© 2010 Universitair Ziekenhuis Gent

Objectives

to develop a population model for tacrolimus exposure in pediatric renal transplant recipients at least 1 year after transplantation

to identify co-variates contributing to the variability in tacrolimus pharmacokinetics

to develop individualized dosage recommendations

© 2010 Universitair Ziekenhuis Gent

Patients and methods

Retrospective cohort study anthropometric measurements laboratory parameters concomitant medications

Renal transplantation April 1993- June 2011

Genetic analysis: CYP3A5*3 and ABCB1 C3435T polymorphism

Inclusion criteriaInclusion criteria

AUC at least 1 year after renal transplantation

functioning allograft with eGFR> 30 ml/min/1.73m² at baseline

on twice-daily tacrolimus formulation Prograft®

© 2010 Universitair Ziekenhuis Gent

Patients and methods

Calculation of 12-hour AUC using Bayesian analysis

Pharmacokinetic analysisPharmacokinetic analysis

non-linear mixed-effects modelling (NONMEM)structural model

oral clearance, inter-compartment clearance, volume of distribution, the delay between ingestion and start of absorption, absorption rate constant

co-variate analysis

Amsterdam: 2 hour AUC

16 profiles in 9 children

C0 C120

Amsterdam: 2 hour AUC

16 profiles in 9 children

C0 C120

Rotterdam: 4 hour AUC

104 profiles in 54 children

C0 C10 C30 C80 C120 C240

Rotterdam: 4 hour AUC

104 profiles in 54 children

C0 C10 C30 C80 C120 C2401- 5 profiles

per child

© 2010 Universitair Ziekenhuis Gent

Baseline characteristic number(total n= 54)

median(IQR)

range % of total

Gender (boys) 28     52

Age (years)   11.1 (6.2) 3.8 - 18.4  

Time since commencement TAC (months)

  8.1 (7.2) 1.6 - 61.3  

Time since transplantation(months)

  16.2 (24.9) 11.4 – 124  

Donor (deceased) 26     48

Pre-emptive transplantation (yes) 13     24

Weight (kg)   38.6 (26) 15 - 86  

Height (cm)   139 (31) 100 - 176  

Body surface area (m²) 

  1.22 (0.6) 0.7 – 2.1  

7 children weight <20kg

© 2010 Universitair Ziekenhuis Gent

ResultsLaboratory measurements number

(total n= 54)median(IQR)

range % of total

CYP3A5 genotype (n=49)*1/*1*1/*3 *3/*3

 1

1236

     2

2573

ABCB1 C->T genotype (n=49)T/CC/CT/T

 3298

     651916

Creatinine (µmol/l)   87 (61) 24 - 191  

Albumine (g/l)   43 (5) 30 - 49  

γGT (U/l)   13 (9) 4 - 118  

Hematocrit   0.34 (0.05) 0.21 -0. 44  

eGFR Schwartz (ml/min/1.73m²) 

  62 (30) 31 - 194  

© 2010 Universitair Ziekenhuis Gent

Concomitant medications (n= 51)

immunosuppressionimmunosuppression• mycophenolate mofetil n= 30 (80%)

• prednisolone n= 41 (59%)

• azathioprine n= 3 (6%)

antihypertensivesantihypertensives• Ca-channel blockers n= 16 (31%)

• beta- blockers n= 7 (14%)

• Ace-i/ ARB n= 25 (48%)

• thiazide diuretics n= 8 (16%)

• loop diuretics n= 1 (2%)

antibioticsantibiotics• trimethoprim/co-trimoxazole n= 4 (8%)

• nitrofurantoine n= 4 (8%)

growth hormonegrowth hormone n= 6 (12%)

anaemia anaemia • erythropoietin n= 3 (6%)

• ferrous fumarate n= 12 (24%)

vitamin D analoguesvitamin D analogues• cholecalciferol n= 3 (6%)

• 1-alfacalcidol n= 8 (16%)

© 2010 Universitair Ziekenhuis Gent

CYP3A5 and daily tacrolimus dose

baseline

0.16 mg/kg/day 0.12 mg/kg/day

all profiles

P = 0.02 P = 0.004

0.16 mg/kg/day 0.11 mg/kg/day

© 2010 Universitair Ziekenhuis Gent

Tacrolimus oral clearance

Univariate analysisUnivariate analysis•P < 0.001

• recombinant growth hormone

•P < 0.005

• CYP3A5*1 allele • gamma glutamyl transpeptidase • 1-alfacalcidol • ferrous fumarate

•P < 0.05

• male gender• hematocrit• serum creatinine

Final modelFinal model

Structural modelStructural model

• weight (not age)

Co-variate analysisCo-variate analysis

• CYP3A5*1 allele • gamma glutamyl

transpeptidase• Hematocrit

internal validation

© 2010 Universitair Ziekenhuis Gent

Dose simulations

0.025 mg/kg

0.05 mg/kg

0.1 mg/kg

0.2 mg/kg

CYP3A5 *3/*3 CYP3A5 *1/*3

© 2010 Universitair Ziekenhuis Gent

Limitations

No data set for external validation

Pharmacokinetic profiles generated over 10 years

No uniformity in pharmacokinetic profiles between 2 centers

© 2010 Universitair Ziekenhuis Gent

Conclusions

low weight, CYP3A5*1 allele, low gamma glutamyl transpeptidase and low hematocrit are associated with a higher tacrolimus clearance in children > 1 years following kidney transplantation

no association between tacrolimus clearance and age

no association between tacrolimus clearance and concomitant medications

© 2010 Universitair Ziekenhuis Gent

IPTA, San Francisco, California 2015

© 2010 Universitair Ziekenhuis Gent

Model validation

0 1 2 3 4

0

10

20

30

time (h)

tacr

olim

us

con

c (

g/L

)

observed median

predicted median

© 2010 Universitair Ziekenhuis Gent

Pharmacokinetic profiles (AUC) distribution

N= 120

39 - 209 h x ng/ml

25th percentile 80

75th percentile 120

h x ng/ml

h x ng/ml