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Dose Prediction of Tacrolimus in de novo Kidney Transplant
Patients with Population Pharmacokinetic Modelling Including
Genetic Polymorphisms.
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.
Guchelaar1.
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.
The pharmacokinetic data were analysed using NON-linear
Mixed Effect Modelling (NONMEM, version V).
A 2 compartment model with first order absorption and
elimination from the central compartment was used to describe
the data. Random effects for interindividual variability on CL
and Vc and interoccassion variability on F were identified
assuming a log-normal distribution. The effects of the potential
covariates hematocrit, albumin, age, weight, prednisolon dose
and genetic polymorphisms in CYP3A4, CYP3A5,
P-glycoprotein (P-gp, ABCB1) and the nuclear hormone
receptor Pregnane-X-receptor on tacrolimus pharmacokinetics
were studied [1, 3, 4].
De novo kidney transplant patients (n = 33) were treated with
basiliximab, mycophenolate mofetil (fixed dose), prednisolone
and tacrolimus. Patients received oral tacrolimus either once or
twice daily. Tacrolimus dose was adjusted according to a
preset target AUC [2]. PK samples were collected up to 12
hours after administration on week 2, 4, 6, 8, 10, 12, 17, 21,
26, 39 and 52 post transplantation. Whole blood
concentrations were measured with microparticle enzyme
immunoassay (MEIA) on an IMx-analyzer.
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Goodness of Fit
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Results
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Individual Prediction (mcg/L)
In the present investigation TRL pharmacokinetics as well as
the interindividual variability relevant to individualised dosing
is adequately described (Figure 1).
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Observed (mcg/L)
The immunosuppressive drug tacrolimus belongs to the group
of calcineurin inhibitors together with cyclosporin A.
Tacrolimus is responsible for liver toxicity as well as acute and
chronic nephrotoxicity. Other complications of (chronic)
therapy are cardiovascular- and neurotoxicity, diabetes and
several other clinical disorders [1]. A number of complications
are related to the blood concentration of tacrolimus.
Tacrolimus has a narrow therapeutic index and its
pharmacokinetics shows considerable inter- and intraindividual 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
tacrolimus [2]. Individual dose adjustments are made to
achieve target exposure within days after start of the body
weight based regimen. However, frequent dose adjustments
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
reduce the frequency of dose adjustments by selecting an
individualized optimal starting dose. This requires insight into
factors (i.e. covariates) that explain the variability in the
pharmacokinetics of tacrolimus.
Methods
Observed (mcg/L)
Introduction
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Aim
Selecting an optimal individualised starting dose by
identifying mechanistically plausible and clinically relevant
covariates that explain observed variability in the
pharmacokinetics of tacrolimus.
As expected bodyweight does not correlate with tacrolimus
clearance in the way this is demonstrated for cyclosporin A. In
addition, a clear relationship is observed between bodyweight
and the difference between the observed and target AUC in the
first 2 weeks post transplantation (Figure 2), showing that this
difference increases when the difference from the median body
weight increases (weight range: 43-119 kg, median 75 kg).
Hence, subjects with a body weight below the median body
weight are under-exposed, potentially resulting in lack of
efficacy (i.e. rejection). On the other hand, heavier subjects are
overdosed thereby increasing the risk for adverse events.
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Population Prediction (mcg/L)
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Figure 1: Population and individual prediction vs. observed concentrations.
Figure 2: Difference from target exposure (CYP3A5*3*3 only).
DIFFERENCE FROM TARGET EXPOSURE ON WEEK 2 POST Tx
0.2 mg/kg/day regimen
Conclusions
GENETIC POLYMORPHISMS IN TACROLIMUS PHARMACOKINETICS
Target exposure based on whole blood measurements can
potentially be reached earlier after transplantation in adult
renal transplant patients within the studied bodyweight range
(weight range: 43-119 kg, median 75 kg) when the bodyweight
based regimen will be replaced by a dose based on the
presently identified effects of genotype and hematocrit.
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tacrolimus clearance (L/h)
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
constitution.
AUC observed - target AUC (mcg*h/l)
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A relationship between dose and CL/F was observed, which could at
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 [5].
*1*3 (GA)
CYP3A5
Pregnane-X-receptor (PXR)
PXR is a nuclear hormone receptor. It acts as a transcription
factor and plays a role in regulation of gene expression for
genes involved in drug metabolism and disposition. PXR is a
low affinity, high capacity receptor for glucocorticoids and
could potentially increase tissue specific gene expression of Pgp and CYP enzymes. Glucocorticoids are substrate for the
glucocorticoid receptor at physiological concentrations [4].
When (high dose) prednisolone is administrated, or high
glucocorticoid levels exist in the body due to for instance
stress, this low capacity receptor will be saturated and the
glucocorticoid will induce its own metabolism through binding
to PXR which increases transcription of CYP and other
relevant enzymes. Interestingly this could potentially affect the
metabolism of other compounds, such as tacrolimus.
tacrolimus clearance (L/h)
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PXR genotype
Figure 3. Genetic polymorphisms in CYP3A5 and PXR.
Relationship between genotype and tacrolimus clearance.
Two populations with different values for tacrolimus clearance were
identified. This bimodal distribution could be related to genetic
polymorphisms. Pharmacogenetic differences (Figure 3) were found
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
vs. 4.9 ± 1.0 L/h). SNPs in these proteins are responsible for higher TRL
clearance compared to the wild type.
Moreover, an association between the presence of promotor SNPs
CYP3A4*1B (SNP responsible for increased CL) and ABCB T-129C
(P-gp, SNP responsible for decreased CL) and tacrolimus clearance was
observed.
References
[1] Staatz, C.E. et al. Clinical Pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2004: 43 (10): 623-53.
[2] Scholten, E.M. eta la. AUC guided dosing of tacrolimus prevents progressive systemic overexposure in renal transplant patients. Kidney Int. 2005; 67: 2440-47.
[3] Hesselink et al. Genetic polymorphisms of the CYP3A4, CYP3A5 and MDR-1 genes and pharmacokinetics of the calcineurin inhibitors cyclosporine and tacrolimus.
Clin. Pharmacol. Ther. 2003; 74:245-54.
[4] Lambda, J. et al. Genetic variants of PXR and CAR and their implication in drug metabolism and pharmacogenetics. Curr. Drug Metab.2005;6: 369-383.
[5] Ahn, J.E. et al. Inherent correlation between dose and clearance in therapeutic drug monitoring settings: possible misinterpretation in population pharmacokinetic analyses.
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J PKPD 2005; 32 (5-6): 703-18.