Dose Prediction of Tacrolimus in de novo Kidney Transplant
Patients with Population Pharmacokinetic Modelling Including
R.R. Press1, B.A. Ploeger2,3, J. den Hartigh1, R.J.H.M. van der Straaten1, J. van Pelt1, M. Danhof2,3, J.W. de Fijter1, and H.J.
1Departments of Clinical Pharmacy and Toxicology, Nephrology and Clinical Chemistry, Leiden University Medical Center, The Netherlands.
2Leiden Amsterdam Center for Drug Research (LACDR), Leiden, The Netherlands.
3LAP&P Consultants BV, Leiden, The Netherlands.
Introduction Methods The pharmacokinetic data were analysed using NON-linear
Mixed Effect Modelling (NONMEM, version V).
De novo kidney transplant patients (n = 33) were treated with A 2 compartment model with first order absorption and
The immunosuppressive drug tacrolimus belongs to the group
basiliximab, mycophenolate mofetil (fixed dose), prednisolone elimination from the central compartment was used to describe
of calcineurin inhibitors together with cyclosporin A.
and tacrolimus. Patients received oral tacrolimus either once or the data. Random effects for interindividual variability on CL
Tacrolimus is responsible for liver toxicity as well as acute and
twice daily. Tacrolimus dose was adjusted according to a and Vc and interoccassion variability on F were identified
chronic nephrotoxicity. Other complications of (chronic)
preset target AUC . PK samples were collected up to 12 assuming a log-normal distribution. The effects of the potential
therapy are cardiovascular- and neurotoxicity, diabetes and
hours after administration on week 2, 4, 6, 8, 10, 12, 17, 21, covariates hematocrit, albumin, age, weight, prednisolon dose
several other clinical disorders . A number of complications
26, 39 and 52 post transplantation. Whole blood and genetic polymorphisms in CYP3A4, CYP3A5,
are related to the blood concentration of tacrolimus.
concentrations were measured with microparticle enzyme P-glycoprotein (P-gp, ABCB1) and the nuclear hormone
Tacrolimus has a narrow therapeutic index and its
immunoassay (MEIA) on an IMx-analyzer. receptor Pregnane-X-receptor on tacrolimus pharmacokinetics
pharmacokinetics shows considerable inter- and intra-
were studied [1, 3, 4].
individual variability, therefore therapeutic drug monitoring
(TDM) in kidney transplant patients is mandatory. The
empirical target was established as the area under the curve
(AUC) of the whole blood concentration time curve of 9
tacrolimus . Individual dose adjustments are made to
Goodness of Fit
achieve target exposure within days after start of the body Results 3
weight based regimen. However, frequent dose adjustments 101
are often required which is still attended with under or
overexposure for a considerable amount of time. As this could
result in either lack of efficacy or toxicity it is important to 2
3 4 5 6 7 8 9 2 3 4 5 6
Individual Prediction (mcg/L) 2
reduce the frequency of dose adjustments by selecting an
individualized optimal starting dose. This requires insight into In the present investigation TRL pharmacokinetics as well as
factors (i.e. covariates) that explain the variability in the the interindividual variability relevant to individualised dosing 8
pharmacokinetics of tacrolimus. is adequately described (Figure 1). 7
As expected bodyweight does not correlate with tacrolimus 3
clearance in the way this is demonstrated for cyclosporin A. In
Aim addition, a clear relationship is observed between bodyweight
3 4 5 6 7 8 9
101 2 3 4 5
and the difference between the observed and target AUC in the Population Prediction (mcg/L)
Selecting an optimal individualised starting dose by first 2 weeks post transplantation (Figure 2), showing that this
identifying mechanistically plausible and clinically relevant difference increases when the difference from the median body Figure 1: Population and individual prediction vs. observed concentrations.
covariates that explain observed variability in the weight increases (weight range: 43-119 kg, median 75 kg). Figure 2: Difference from target exposure (CYP3A5*3*3 only).
pharmacokinetics of tacrolimus. Hence, subjects with a body weight below the median body
weight are under-exposed, potentially resulting in lack of DIFFERENCE FROM TARGET EXPOSURE ON WEEK 2 POST Tx
0.2 mg/kg/day regimen
efficacy (i.e. rejection). On the other hand, heavier subjects are
overdosed thereby increasing the risk for adverse events.
AUC observed - target AUC (mcg*h/l)
GENETIC POLYMORPHISMS IN TACROLIMUS PHARMACOKINETICS
Tacrolimus dosing can be individualised by using biomarkers
such as SNPs in CYP3A5 and PXR or hematocrit. A SNP in
CYP3A5 necessitates a 1.5 fold higher dose than the wild-type 5
tacrolimus clearance (L/h)
Target exposure based on whole blood measurements can
potentially be reached earlier after transplantation in adult
renal transplant patients within the studied bodyweight range 50 60 70 80 90
(weight range: 43-119 kg, median 75 kg) when the bodyweight BODY WEIGHT (kg)
based regimen will be replaced by a dose based on the 2
presently identified effects of genotype and hematocrit. *3*3 (GG) *1*3 (GA)
A relationship between dose and CL/F was observed, which could at
CYP3A5 least partly be attributed to TDM. Patients are selected on basis of their
blood levels, as patients with high blood levels (i.e. low clearance) are
titrated to receive lower doses and vice versa .
Two populations with different values for tacrolimus clearance were
tacrolimus clearance (L/h)
identified. This bimodal distribution could be related to genetic
polymorphisms. Pharmacogenetic differences (Figure 3) were found
Pregnane-X-receptor (PXR) between these populations with genetic polymorphisms (SNPs) in
CYP3A5*3 (CL= 3.4 ± 0.5 vs. 5.3 ± 0.8 L/h) and PXR (CL=3.5 ± 0.7
PXR is a nuclear hormone receptor. It acts as a transcription vs. 4.9 ± 1.0 L/h). SNPs in these proteins are responsible for higher TRL
factor and plays a role in regulation of gene expression for 2 clearance compared to the wild type.
genes involved in drug metabolism and disposition. PXR is a Moreover, an association between the presence of promotor SNPs
CC CT TT
low affinity, high capacity receptor for glucocorticoids and PXR genotype CYP3A4*1B (SNP responsible for increased CL) and ABCB T-129C
could potentially increase tissue specific gene expression of P- (P-gp, SNP responsible for decreased CL) and tacrolimus clearance was
Figure 3. Genetic polymorphisms in CYP3A5 and PXR.
gp and CYP enzymes. Glucocorticoids are substrate for the observed.
Relationship between genotype and tacrolimus clearance.
glucocorticoid receptor at physiological concentrations .
When (high dose) prednisolone is administrated, or high
glucocorticoid levels exist in the body due to for instance References
stress, this low capacity receptor will be saturated and the
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