Dose Prediction of Tacrolimus in de novo Kidney Transplant Patients with Population Pharmacokinetic Modelling Including Genetic Polymorphisms. R.R. Press 1, B.A. Ploeger 2,3, J. den Hartigh 1, R.J.H.M. van der Straaten 1, J. van Pelt 1, M. Danhof 2,3, J.W. de Fijter 1, and H.J. Guchelaar 1. 1 Departments of Clinical Pharmacy and Toxicology, Nephrology and Clinical Chemistry, Leiden University Medical Center, The Netherlands. 2 Leiden Amsterdam Center for Drug Research (LACDR), Leiden, The Netherlands. 3 LAP&P Consultants BV, Leiden, The Netherlands. Introduction 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 intra- 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 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. Aim Selecting an optimal individualised starting dose by identifying mechanistically plausible and clinically relevant covariates that explain observed variability in the pharmacokinetics of tacrolimus. Conclusions 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. 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: 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. 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 P- gp 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. Methods 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. Results In the present investigation TRL pharmacokinetics as well as the interindividual variability relevant to individualised dosing is adequately described (Figure 1). 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: 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. Figure 3. Genetic polymorphisms in CYP3A5 and PXR. Relationship between genotype and tacrolimus clearance. References [1] Staatz, C.E. et al. Clinical Pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2004: 43 (10): [2] Scholten, E.M. eta la. AUC guided dosing of tacrolimus prevents progressive systemic overexposure in renal transplant patients. Kidney Int. 2005; 67: [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: [4] Lambda, J. et al. Genetic variants of PXR and CAR and their implication in drug metabolism and pharmacogenetics. Curr. Drug Metab.2005;6: [5] Ahn, J.E. et al. Inherent correlation between dose and clearance in therapeutic drug monitoring settings: possible misinterpretation in population pharmacokinetic analyses. J PKPD 2005; 32 (5-6): 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]. 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]. 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. Figure 1: Population and individual prediction vs. observed concentrations. Figure 2: Difference from target exposure (CYP3A5*3*3 only).