European Journal of Pharmaceutical Sciences 52 (2014) 34–40
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Total plasma protein effect on tacrolimus elimination in kidney transplant patients – Population pharmacokinetic approach Bojana Golubovic´ a,⇑, Katarina Vucˇic´evic´ a, Dragana Radivojevic´ b, Sandra Vezmar Kovacˇevic´ a, Milica Prostran c, Branislava Miljkovic´ a a b c
Department of Pharmacokinetics and Clinical Pharmacy, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia Nephrology Clinic, Clinical Centre of Serbia, University of Belgrade, Pasterova 2, 11000 Belgrade, Serbia Departmant of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Dr. Subotic´a 1, 11000 Belgrade, Serbia
a r t i c l e
i n f o
Article history: Received 1 April 2013 Received in revised form 30 August 2013 Accepted 16 October 2013 Available online 30 October 2013 Keywords: Kidney transplantation Plasma proteins Population pharmacokinetics Tacrolimus Therapeutic monitoring
a b s t r a c t Data from routine therapeutic drug monitoring of 105 adult kidney transplant recipients were used for population pharmacokinetic analysis which was performed using a non-linear mixed-effects modeling. The effect of demographic and clinical factors on tacrolimus clearance was evaluated. Following the initiation of treatment with tacrolimus, the results of our study indicate a decrease of the drug clearance on day 15, 1 and 6 months after transplantation for 4.4%, 6.3% and 10.92%, respectively. Our model suggests a negative correlation between tacrolimus clearance and haematocrit. According to final model, clearance decreases with increasing of aspartate aminotransferase. Our results demonstrated that CL/F increases with patients’ weight. This study reveals incensement for 10.4% in tacrolimus clearance with alteration of patients’ minimal measured total protein levels to upper normal range. The findings of this study explore various factors of tacrolimus pharmacokinetic variability and point out a relationship between tacrolimus clearance and total plasma protein. Developed model demonstrates the feasibility of estimation of individual tacrolimus clearance and may allow rational individualization of tacrolimus dosing in kidney transplant patients. Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction Tacrolimus is a potent, calcineurin inhibitor widely used for the prevention of acute and chronic allograft rejections in kidney transplant recipients (Bowman and Brennan, 2008). It has a narrow therapeutic window with wide inter-individual variability in clearance and other pharmacokinetic parameters (Staatz and Tett, 2004). In blood, it is extensively bound to erythrocytes with a mean blood to plasma ratio of about 15, while in plasma, tacrolimus is associated principally with a1-acid glycoprotein (AAG), lipoproteins, globulins and albumin (Staatz and Tett, 2004; Venkataramanan et al., 1995; Warty et al., 1991). Haematocrit is one of the factors that influence tacrolimus blood to plasma ratio (Staatz and Tett, 2004).Tacrolimus is a highly metabolised drug, with only about 0.5% unchanged parent drug appearing in urine or feces (Staatz and Tett, 2004; Venkataramanan et al., 1995). The drug is metabolized mainly by P450 3A isoenzymes (CYP3A) which expression varies widely (Koch et al., 2002; Staatz and Tett, 2004). Additionally, tacrolimus is a substrate of P-glycoprotein (Jeong and Chiou, 2006). The significant relation between the high within-patient variability in the clearance of tacrolimus and ⇑ Corresponding author. Tel.: +381 11 3951373; fax: +381 11 3972840. E-mail address: [email protected] (B. Golubovic´). 0928-0987/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ejps.2013.10.008
long-term graft failure was shown (Borra et al., 2010). There is evidence that low trough blood tacrolimus concentrations correlate with increased risk of rejection, whereas higher trough levels are associated with increased risk of toxicity (Borobia et al., 2009; Kershner and Fitzsimmons, 1996; Staatz et al., 2001; Venkataramanan et al., 2001). Nevertheless, some studies failed to establish a relation between tacrolimus trough concentration and graft rejection (Gaber et al., 1997; Jain et al., 1991). In addition, the correlation between tacrolimus dose and concentration is poor (Venkataramanan et al., 2001). These findings limit optimal titrations of the dosage regimen, and require additional information on the factors that affect the pharmacokinetic characteristics of the drug. The influence of some factors such as time after transplantation, haematocrit, albumin, corticosteroid therapy, liver function, diurnal variation, race and genetic polymorphism were acknowledged (Antignac et al., 2007; Han et al., 2013; Hesselink et al., 2003; Li et al., 2007; Macphee et al., 2002; Staatz and Tett, 2004; Staatz et al., 2002; Undre and Schafer, 1998). However, the results are often contradictory. Some studies correlated increase of clearance with post transplant day, whereas other had opposite results (Antignac et al., 2007, 2005; Han et al., 2013; Passey et al., 2011; Staatz et al., 2002). Furthermore, the effects of a variety of factors on tacrolimus elimination remain inconclusive. Therefore, we studied the effect of demographic and clinical factors such as
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graft origin, dialysis before transplantation, period after transplantation, serum creatinine, haematocrit, total proteins and hepatic enzymes, using data from routine therapeutic drug monitoring (TDM). Our objective was to define the significant factors of tacrolimus pharmacokinetic variability and develop a model for estimation of clearance to be used in transplant patient care. 2. Methods 2.1. Patients and data collection A retrospective analysis of data from 105 adult kidney transplant recipients from the Nephrology Clinic, Clinical Center of Serbia, University of Belgrade, was performed. Patients’ data during TDM were retrospectively collected. Approval for the study was obtained from the Ethics Committee of Clinical Center of Serbia. All data were collected from the patients’ charts, and they included following covariates: gender (GEND), age, body weight (WT), day after transplantation (PDAY), graft origin (GRFT), dialysis before transplantation (DIAL), serum creatinine (SECR), haematocrit (HCT), hemoglobin (HGB), total protein (TP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and concomitant immunosuppressive drug doses. 2.2. Drug administration Patients were on triple immunosuppressive therapy which inÒ cluded tacrolimus (Prograf , Astellas Ireland CO. Ltd.), mycophenolate mofetil (MMF) and corticosteroids (CORT). The recommended initial dose of tacrolimus was 0.3 mg/kg per day. Subsequent doses were adjusted on the basis of achieving tacrolimus trough blood concentrations within target ranges and clinical evidence of efficacy and toxicity. Desired ranges of tacrolimus trough blood concentrations were between 15 and 20 ng/ml in the first two weeks following transplantation, 10–15 ng/ml till the end of the first month, 7–10 ng/ml in the period from the end of the first month to the end of the sixth month after transplantation. 2.3. Blood sampling and bioanalytical assay All collected blood samples were pre-dose and were analyzed in the same laboratory. In the immediate post transplantation period, blood samples were collected two or three times per week until concentrations were stabilized. Thereafter, samples were collected once weekly in the first month after transplantation, and once monthly afterward. If physician suspected the rejection of graft or adverse reactions, drug monitoring was performed more frequently. Concentrations of tacrolimus in whole blood were assessed Ò using Architect system (Abbott Laboratories), a chemiluminescent microparticle immunoassay (CMIA) (ARCHITECT System, 2009). According to the manufacturer’s information the measurement range for assay is 2–30 ng/ml. Blood samples exceeding this range were diluted according to manufacturer’s protocol. 2.4. Population pharmacokinetic analysis Population pharmacokinetic analysis was performed using a Ò non-linear mixed-effects modeling program NONMEM (version 7 level 2, GloboMax LLC, Ellicott City, MD, USA) and Perl speaks Ò NONMEM (version 3.5.3, http://psn.sourceforge.net), Xpose (verÒ sion 4, http://xpose.sourceforge.net/) and R (version 2.15.0, http://r-project.org) were used for graphical presentations. AddiÒ tionally, Pirana (version 2.5.0, http://www.pirana-software.com/) was used for model evaluation and graphical presentation as well.
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Population pharmacokinetic analyses were performed using the first order conditional estimation with interaction (FOCEI) method to improve the estimation of pharmacokinetic parameters and their variability. A one-compartment pharmacokinetic model with first-order absorption and elimination as implemented in NONMEM subroutine ADVAN2 and TRANS2 was used to describe the concentration–time data. Since all data were trough concentrations, estimation of the volume of distribution (V/F) and rate constant of absorption (ka) was not possible and therefore they were fixed at 0.68 l/kg and 1.3 h1 (Summary of Product Characteristics, 2012). Based on the literature value of tacrolimus t1/2 of 15.6 h, and tmax of 2.5 h, ka was estimated using the following equation: tmax = ln(ka/kel)/(ka kel)(Booth and Gobburu, 2003; Vucicevic et al., 2009). In the first step, the base model was derived. The interindividual variability of tacrolimus CL/F (-2) was described by exponential model:
CL=F j ¼ TVCL expðgjCL Þ where CL/Fj is total body clearance for the jth individual, TVCL is typical population value of CL/F and gj is random variable for the jth individual distributed with zero means and respective variances of -2CL. Residual variability of tacrolimus concentration (r2), the additive, the proportional, and the slope-intercept error models were tested as follows:
C ij ¼ C predij þ eij C ij ¼ C predij þ C predij eij C ij ¼ C predij þ C predij e1ij þ e2ij where Cij is the ith observed concentration for the jth individual, Cpredij is predicted concentration for the jth individual and eij is a randomly distributed variable with zero mean and variance r2. Once the base model was established, the effects of covariates on relevant pharmacokinetic parameter variability were explored. Tested covariates were: PDAY, WT, AGE, GRFT, GEND, DIAL, SECR, HCT, UP, ALP, AST, MMF and CORT. Missing covariate data for SECR (0.3%), HGB (1.55%), HCT (2.1%) and TP (6.8%) were treated with multiple imputation of median per day. An imputation method for missing data for ALP (29.46%), AST (28.26%) and ALT (28.51%) was last-observation carried forward (LOCF) (Bonate, 2006; Harrel, 2001). Statistical significance of the covariates was evaluated based on the objective function value (OFV), which is equal to minus twice the log likelihood. Covariates were introduced sequentially into the population models to develop a full model. In each step of the covariate model building the covariate with the highest drop in objective function value (DOFV), at least 3.84 (p < 0.05), was included in the model. The full model was obtained when the effects of all the remaining covariates were insignificant (DOFV < 3.84). The final model was determined by backward elimination of covariates in a stepwise manner. Covariates were kept in the final population pharmacokinetic model when the removal of the covariate resulted in an OFV increase of at least 6.63 (p < 0.01). An additional criterion for the retention of a covariate in the model was reduction in unexplained interindividual variability. The model appropriateness was evaluated by standard diagnostic plots (Karlsson and Savic, 2007), convergence of minimization, number of significant digits more than 3, successful covariance step, gradients in the final iteration being in the range 103 to 102 and absence of substantial g- and e-shrinkage (Savic and Karlsson, 2009). Conditional weighted residuals (CWRES) were calculated for standard diagnostic plots (Hooker et al., 2007). A nonparametric bootstrap of 1000 samples was used for assessing the accuracy and robustness of the final population model (Parke et al., 1999). Additionally, performance of final model was
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Table 1 Patients’ demographic and biochemical characteristics. Characteristics
Number (%)/ Range average ± standard deviation
Gender
62 (59.05) 43 (40.95) 60 ± 47.63
Male Female Period after transplantation (days) Graft origin Living donor Cadaver Dialysis before Yes transplantation No Age (years) Body weight (kg) Serum creatinine (lmol/L) Haematocrit Proteinaemia (g/l) Alkaline phosphatase (IU/l) Aspartate aminotransferase (IU/l) Alanine aminotransferase (IU/l)
75 (71.43) 30 (52.57) 87 (82.86) 18 (17.14) 39.38 ± 10.78 68.41 ± 12.85 207.3 ± 328.02 0.31 ± 0.05 63.23 ± 7.99 70.08 ± 29.54 19.7 ± 23.22 32.92 ± 57.67
Table 3 Summary of covariate effect on tacrolimus clearance (CL/F) during model building (only significant effects are reported). Covariate Base model Forward step
0–206
16–60 38–108 57–1398 0.029–0.75 38–137 6–214 4–414 1–961
Backward step
Haematocrit Body weight Aspartate aminotransferase Post transplant day Total protein Haematocrit Body weight Aspartate aminotransferase Post transplant day Total protein
Objective function value (OFV)
DOFV
8526.87 8251.57 8025.65 8000.25
275.3 225.92 25.4
7979.38 7952.7 8029.63 8175.64 7977.95
20.97 26.68 76.93 222.94 25.25
7988.57 7968.05
35.87 15.35
Table 4 Population pharmacokinetic parameters of tacrolimus and bootstrap validation. Parameter Table 2 Characteristics of immunosuppressive therapy. Drug Tacrolimus Dose (mg/day) Trough concentration (ng/ml) Mycophenolate mofetil Dose (mg/day) Corticosteroids Dose (mg/day)
Average ± standard deviation
hCLa (l/h) hASTb hHCTb hPDAYb hWTb hTPb xCL (%)c r (ng/ml)d
Range
12.09 ± 6.24 11.66 ± 5.02
1–40 2.4–57.2
1207.19 ± 463.56
0–2000
37.63 ± 76.25
2.5–500
Original dataset
Bootstrap
Mean
Median
2.5–97.5th Percentiles
10.017 0.861 0.831 0.0283 0.869 0.161 15.2 4.066
10.016 0.868 0.812 0.0295 0.871 0.156 14.83 4.032
9.688–10.348 1.694–0.369 1.153–0.477 0.0478–0.0136 0.691–1.051 0.009–0.356 13.39–16.87 3.6138–4.519
a
Typical value of tacrolimus clearance. Influential factors for covariates (AST – aspartate aminotransferase, HCT – haematocrit, PDAY – post transplant day, WT – body weight, TP – total protein). c Interindividual variability. d Residual variability. b
6 months after transplantation. Seventy-five kidney grafts were taken from living donors and thirty from cadaveric donors. The mean time to initiation of tacrolimus treatment was 2.6 days. Patient and therapy characteristics are presented in Tables 1 and 2. 3.2. Population pharmacokinetics
Fig. 1. Observed tacrolimus concentrations (ng/ml) versus post transplantation day (PDAY).
evaluated by prediction- and variability-corrected VPC (pvcVPC, n = 1000) (Bergstrand et al., 2011).
3. Results 3.1. Patients characteristics Data from 105 kidney transplant recipients (62 males and 43 females) were collected retrospectively during the maximum
Data for modeling included 1999 trough blood concentrations (Fig. 1). Interindividual variability was evaluated by an exponential model, while residual variability in tacrolimus concentrations was best described by an additive error model. A significant decrease in OFV, in a forward modeling building step, produced the inclusion of HCT, WT, AST, PDAY and TP. In the backward elimination step all of these covariates were retained in the model. Details of a covariate model building are given in Table 3. The final model is described by the following equation:
0:0283 0:869 0:161 PDAY WT TP CL=F ¼ 10:017 47 68 63 86 1 ðAST 15Þ ð1 0:831 ðHCT 0:31ÞÞ 1000 The drop in OFV in the final model compared to the base was 574.171. In the final model, mean interindividual coefficient of variability for CL/F was 15.2%, residual variability was 4.07 ng/ml, and shrinkage of 1.6% was estimated. Parameters of the final model are presented in Table 4. Diagnostics plots are presented in Figs. 2 and 3.
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Fig. 2. Observed tacrolimus concentrations (ng/ml) versus population and individual model-predicted concentrations (ng/ml) for (A) base model and (B) final model. Line of identity (solid); regression line (dashed).
Fig. 3. Conditional weighted residuals (CWRES) versus: (A) mean population predicted concentrations (PRED) and (B) observed concentrations of tacrolimus.
Further step in the analysis included bootstrap analysis of the final model. The mean parameter estimates obtained from the bootstrap process, 998 successful runs out of 1000 scheduled, were not statistically different from the estimates previously obtained with the original dataset (Table 4). Fig. 4 shows pvcVPCs for base and final models confirming the improvement in the model building, while Fig. 5 presents relation between tacrolimus clearance and post transplantation day.
4. Discussion In the present study, we have evaluated tacrolimus population pharmacokinetic parameters in adult kidney transplant recipients, and identified the factors affecting its pharmacokinetics. An onecompartment open model with first-order absorption and elimination was optimal for data modeling, as previously reported (Antignac et al., 2007; Han et al., 2013; Staatz et al., 2002; Zhang et al., 2008).
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Fig. 4. Prediction- and variability-corrected visual predictive check (pvcVPC), prediction- and variability-corrected concentrations (ng/mL) versus post transplant day: (A) base model and (B) final model. Solid and dashed lines represent the median, 5th and 95th percentiles of the observed data with shaded confidence intervals of the prediction intervals for the simulated prediction intervals.
Fig. 5. Tacrolimus clearance (CL/F) versus post transplantation day (PDAY).
The results of the study indicate that tacrolimus CL/F, and consequently average steady-state concentration depend on PDAY, WT, AST, TP and HCT. The final model reveals that CL/F increases with patients’ WT. The value of the exponent for the effect of WT was not significantly different from the theoretic allometric exponent of 0.75. Our analysis showed decreasing of CL/F with increasing number of post transplantation days (Fig. 5). These findings are consistent with the previously published studies (Han et al., 2013; Staatz et al., 2002). Passey et al. found that CL/F decreased by 14% in 6– 10 post transplant days, and by 29% in 11–180 post transplant days, relative to immediate post transplant period (Passey et al., 2011). Compared to initiation of therapy tacrolimus CL/F was decreased on day 15, 1 and 6 months after transplantation for 4.4%, 6.3% and 10.92%, respectively in our study. This reduction in CL/F could be explained by an increase in tacrolimus bioavailability or increase in haematocrit and albumin as the patients’ clinical state improves with time after surgery (Han et al., 2013; Passey et al.,
2011; Undre and Schafer, 1998). Observed differences in the extent of CL/F decrease between our and the results of Passey et al. may be due to inclusion of haematocrit in our final model which influence was not tested on tacrolimus pharmacokinetics in Passey et al. (2011). As tacrolimus is a low-clearance drug with clearance equivalent to 3% of liver blood flow, extensively bound to erythrocytes and highly protein bound (Staatz and Tett, 2004), its clearance depends on the extent of binding (Burton et al., 2005), correspondingly it depends on haematocrit and plasma protein concentration. According to our model CL/F decreased as haematocrit increased which is in compliance with earlier findings (Han et al., 2013; Staatz et al., 2002; Undre and Schafer, 1998; Zahir et al., 2005; Zhang et al., 2008; Zhao et al., 2009). As previously mentioned haematocrit increases with post transplantation days. Other potential reason for differences between our and the results of Passey et al. may be difference of corticosteroid dosage (Passey et al., 2011). Tacrolimus CL/F may also be higher in immediate post transplant period due to relatively high doses of corticosteroid which are co administrated according to the specific protocol of the center for transplantation (Anglicheau et al., 2003; Hesselink et al., 2003; Park et al., 2009). Patients included in our study were on the first day on transplantation on intravenous 500 mg/day methylprednisolone, which was subsequently reduced according the local protocol. Our model suggests negative relation between CL/F and AST as parameter for liver function which is expected for highly metabolized drug and this finding is consistent with previous results (Antignac et al., 2005; Garcia Sanchez et al., 2001; Staatz et al., 2002; Zhang et al., 2008). Albumin was identified as a significant factor of influence on tacrolimus pharmacokinetics in several studies. Previous studies reported that tacrolimus CL/F decrease with increasing albumin (Antignac et al., 2005; Undre and Schafer, 1998; Zahir et al., 2005). Zahir et al. found that the unbound fraction of tacrolimus correlated with AAG and high density lipoprotein cholesterol concentrations (Zahir et al., 2004). Tacrolimus is predominantly bound to AAG, lipoproteins, globulins and albumin (Staatz and Tett, 2004; Warty et al., 1991), hence we evaluated the influence of total plasma protein concentration on CL/F. Our model described increasing of CL/F with increasing total plasma protein concentration. To our
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knowledge, this is the first model that quantifies the influence of plasma proteins on tacrolimus CL/F. This relation may be caused by expected elevation of AAG in the first post operation days when total plasma proteins concentrations are low. As AAG is the acute phase protein, its levels increase in response to systemic tissue injury, inflammation or infection (Fournier et al., 2000). Higher levels of AAG are observed in various disease states, in patients with trauma, severe burns, and recipients of bone marrow and organ transplants (Israili and Dayton, 2001). Additionally, AAG levels may be increased as a result of treatment with high doses of corticosteroid in first post transplantation days (Fournier et al., 2000; Israili and Dayton, 2001; Vannice et al., 1984), as already mentioned for the patients included in this analysis. Accordingly, our model demonstrated incensement for 10.4% in tacrolimus CL/F with alteration of patients’ minimal measured total protein levels to upper normal range. The predictive performance of the model was assessed by standard plots, pvcVPC and bootstrap method. The goodness-of-fit plots (Fig. 2) depict improvement in prediction by final model compared to base. The scatter plots of CWRES vs. predicted concentration (Fig. 3A) showed that the CWRES were mostly randomly distributed and lay within ±3 units of the null ordinate. Nevertheless, Fig. 3A indicates some bias in prediction (Barett, 2002). Values of CWRES higher than 3 were observed in only 0.75% measured concentrations. The bias in residuals observed in Fig. 3A may arise from the study design and the nature of data. As previously discussed by Sheiner and Beal, nonlinear mixed effects modeling of only trough data facilitated precise estimation of CL/F and interindividual variability, but the estimation of residual variability was subject to bias (Sheiner and Beal, 1983). Dependence CWRES of measured concentration (Fig. 3B) indicates less reliable prediction for high values of concentration (>30 ng/ml). According to the manufacturer’s information the measurement range for assay is 2–30 ng/ml while the levels above 30 ng/ml are subject to dilution before quantification (ARCHITECT System, 2009). Therefore, we may consider contribution of bioanalytical aspects to the measurement error observed in higher CWRES for tacrolimus levels above 30 ng/ml. Regardless the aforementioned limitations of study design and the nature of data in modeling process, and negligible bias observed for high tacrolimus concentrations, the influence of defined covariates on tacrolimus CL/F is, with no doubts, precisely described by our model (Sheiner and Beal, 1983). PvcVPC indicate improvement in final model compared to base and bootstrap analysis confirmed accuracy and robustness of the final population model. Certain causes of remaining variability are genetic differences in cytochrome 3A izoenzyme and P-glycoprotein expression (Li et al., 2007; Loh et al., 2008; Macphee et al., 2002; Rosso Felipe et al., 2009; Staatz and Tett, 2004). Accordingly, further work could be assessment of the impact of genetic polymorphism in combination with the clinical factors included in our model. The stable final model, as the model in our study, can be used as a priori information for Bayesian estimation of CL/F in individual patients requiring as few as one blood concentration which is a great advantage in the clinical practice (Proost, 1995). The possibility of estimation of individual pharmacokinetic parameters allows the calculation of the dose needed to achieve a desired steady-state trough concentration, as demonstrated by Antignac et al. (2011).
5. Conclusion The findings of this study explore various factors of tacrolimus pharmacokinetic variability. This is the first study that quantifies the effect of total plasma proteins on tacrolimus elimination. Our
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study demonstrates the feasibility of estimation of individual pharmacokinetics parameters of tacrolimus based on sparse TDM data. Furthermore, development of a Bayesian estimator based on our model would enable the accurate prediction of tacrolimus dose which would offer evidence based guidance to clinicians for dosing this immunosuppressant. Acknowledgements This work was conducted as a part of the project Experimental and Clinical-Pharmacological Investigations of Mechanisms of Drug Action and Interactions in Nervous and Cardiovascular System (No. 175023) funded by Ministry of Education, Science and Technological Development, Belgrade, Republic of Serbia. We are very grateful to Dr. Radmila Blagojevic Lazic and the stuff from Nephrology Clinic, Clinical Centre of Serbia, University of Belgrade, Serbia for their assistance. References Anglicheau, D., Flamant, M., Schlageter, M.H., Martinez, F., Cassinat, B., Beaune, P., Legendre, C., Thervet, E., 2003. Pharmacokinetic interaction between corticosteroids and tacrolimus after renal transplantation. Nephrol. Dial Transplant 18, 2409–2414. Antignac, M., Hulot, J.S., Boleslawski, E., Hannoun, L., Touitou, Y., Farinotti, R., Lechat, P., Urien, S., 2005. Population pharmacokinetics of tacrolimus in full liver transplant patients: modelling of the post-operative clearance. Eur. J. Clin. Pharmacol. 61, 409–416. Antignac, M., Barrou, B., Farinotti, R., Lechat, P., Urien, S., 2007. Population pharmacokinetics and bioavailability of tacrolimus in kidney transplant patients. Br. J. Clin. Pharmacol. 64, 750–757. Antignac, M., Fernandez, C., Barrou, B., Roca, M., Favrat, J.L., Urien, S., Farinotti, R., 2011. Prediction tacrolimus blood levels based on the Bayesian method in adult kidney transplant patients. Eur. J. Drug Metab. Pharmacokinet. 36, 25–33. ARCHITECT System. Tacrolimus package insert. Abbott Laboratories, 2009. Barett, J., 2002. Population pharmacokinetics. In: Schoenwald, R. (Ed.), Pharmacokinetics in Drug Discovery and Development. CRC Press, Boca Raton, pp. 315–356. Bergstrand, M., Hooker, A.C., Wallin, J.E., Karlsson, M.O., 2011. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. Aaps J. 13, 143–151. Bonate, P., 2006. Pharmacokinetic–Pharmacodynamic Modeling and Simulation. Springer, New York. Booth, B.P., Gobburu, J.V., 2003. Considerations in analyzing single-trough concentrations using mixed-effects modeling. J. Clin. Pharmacol. 43, 1307– 1315. Borobia, A.M., Romero, I., Jimenez, C., Gil, F., Ramirez, E., De Gracia, R., Escuin, F., Gonzalez, E., Sansuan, A.J., 2009. Trough tacrolimus concentrations in the first week after kidney transplantation are related to acute rejection. Ther. Drug Monit. 31, 436–442. Borra, L.C., Roodnat, J.I., Kal, J.A., Mathot, R.A., Weimar, W., van Gelder, T., 2010. High within-patient variability in the clearance of tacrolimus is a risk factor for poor long-term outcome after kidney transplantation. Nephrol. Dial Transplant 25, 2757–2763. Bowman, L.J., Brennan, D.C., 2008. The role of tacrolimus in renal transplantation. Expert Opin. Pharmacother. 9, 635–643. Burton, M.E., Shaw, L.M., Schentag, J.J., Evans, W.E., 2005. Applied Pharmacokinetics and Pharmacodynamics: Principles of Therapeutic Drug Monitoring, fourth ed. Lippincot Williams & Wilkins. Fournier, T., Medjoubi, N.N., Porquet, D., 2000. Alpha-1-acid glycoprotein. Biochim. Biophys. Acta 1482, 157–171. Gaber, L.W., Moore, L.W., Reed, L., Russell, W., Alloway, R., Hathaway, D., ShokouhAmiri, M.H., Gaber, A.O., 1997. Renal histology with varying FK506 blood levels. Transplant Proc. 29, 186. Garcia Sanchez, M.J., Manzanares, C., Santos-Buelga, D., Blazquez, A., Manzanares, J., Urruzuno, P., Medina, E., 2001. Covariate effects on the apparent clearance of tacrolimus in paediatric liver transplant patients undergoing conversion therapy. Clin. Pharmacokinet. 40, 63–71. Han, N., Yun, H.Y., Hong, J.Y., Kim, I.W., Ji, E., Hong, S.H., Kim, Y.S., Ha, J., Shin, W.G., Oh, J.M., 2013. Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients. Eur. J. Clin. Pharmacol. 69, 53–63. Harrel, F., 2001. Regression Modeling Strategies. Springer, New York. Hesselink, D.A., Ngyuen, H., Wabbijn, M., Gregoor, P.J., Steyerberg, E.W., van Riemsdijk, I.C., Weimar, W., van Gelder, T., 2003. Tacrolimus dose requirement in renal transplant recipients is significantly higher when used in combination with corticosteroids. Br. J. Clin. Pharmacol. 56, 327–330. Hooker, A.C., Staatz, C.E., Karlsson, M.O., 2007. Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method. Pharm. Res. 24, 2187–2197.
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Israili, Z.H., Dayton, P.G., 2001. Human alpha-1-glycoprotein and its interactions with drugs. Drug Metab. Rev. 33, 161–235. Jain, A.B., Todo, S., Fung, J.J., Venkataramanan, R., Day, R., Bryant, J., Abu-Elmagd, K.M., Alessiani, M., Takaya, S., Tzakis, A., et al., 1991. Correlation of rejection episodes with FK 506 dosage, FK 506 level, and steroids following primary orthotopic liver transplant. Transplant Proc. 23, 3023–3025. Jeong, H., Chiou, W.L., 2006. Role of P-glycoprotein in the hepatic metabolism of tacrolimus. Xenobiotica 36, 1–13. Karlsson, M.O., Savic, R.M., 2007. Diagnosing model diagnostics. Clin. Pharmacol. Ther. 82, 17–20. Kershner, R.P., Fitzsimmons, W.E., 1996. Relationship of FK506 whole blood concentrations and efficacy and toxicity after liver and kidney transplantation. Transplantation 62, 920–926. Koch, I., Weil, R., Wolbold, R., Brockmoller, J., Hustert, E., Burk, O., Nuessler, A., Neuhaus, P., Eichelbaum, M., Zanger, U., Wojnowski, L., 2002. Interindividual variability and tissue-specificity in the expression of cytochrome P450 3A mRNA. Drug Metab. Dispos. 30, 1108–1114. Li, D., Lu, W., Zhu, J.Y., Gao, J., Lou, Y.Q., Zhang, G.L., 2007. Population pharmacokinetics of tacrolimus and CYP3A5, MDR1 and IL-10 polymorphisms in adult liver transplant patients. J. Clin. Pharm. Ther. 32, 505–515. Loh, P.T., Lou, H.X., Zhao, Y., Chin, Y.M., Vathsala, A., 2008. Significant impact of gene polymorphisms on tacrolimus but not cyclosporine dosing in Asian renal transplant recipients. Transplant Proc. 40, 1690–1695. Macphee, I.A., Fredericks, S., Tai, T., Syrris, P., Carter, N.D., Johnston, A., Goldberg, L., Holt, D.W., 2002. Tacrolimus pharmacogenetics: polymorphisms associated with expression of cytochrome p4503A5 and P-glycoprotein correlate with dose requirement. Transplantation 74, 1486–1489. Park, S.I., Felipe, C.R., Pinheiro-Machado, P.G., Garcia, R., Fernandes, F.B., Casarini, D.E., Tedesco-Silva Jr., H., Medina-Pestana, J.O., 2009. Tacrolimus pharmacokinetic drug interactions: effect of prednisone, mycophenolic acid or sirolimus. Fundam. Clin. Pharmacol. 23, 137–145. Parke, J., Holford, N.H., Charles, B.G., 1999. A procedure for generating bootstrap samples for the validation of nonlinear mixed-effects population models. Comput. Methods Programs Biomed. 59, 19–29. Passey, C., Birnbaum, A.K., Brundage, R.C., Oetting, W.S., Israni, A.K., Jacobson, P.A., 2011. Dosing equation for tacrolimus using genetic variants and clinical factors. Br. J. Clin. Pharmacol. 72, 948–957. Proost, J.H., 1995. Adaptive control of drug dosage regimens using maximum a posteriori probability Bayesian fitting. Int. J. Clin. Pharmacol. Ther. 33, 531–536. Rosso Felipe, C., de Sandes, T.V., Sampaio, E.L., Park, S.I., Silva Jr., H.T., Medina Pestana, J.O., 2009. Clinical impact of polymorphisms of transport proteins and enzymes involved in the metabolism of immunosuppressive drugs. Transplant Proc. 41, 1441–1455. Savic, R.M., Karlsson, M.O., 2009. Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions. Aaps J. 11, 558–569. Sheiner, L.B., Beal, S.L., 1983. Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data. J. Pharmacokinet. Biopharm. 11, 303–319.
Staatz, C.E., Tett, S.E., 2004. Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet 43, 623– 653. Staatz, C., Taylor, P., Tett, S., 2001. Low tacrolimus concentrations and increased risk of early acute rejection in adult renal transplantation. Nephrol. Dial. Transplant 16, 1905–1909. Staatz, C.E., Willis, C., Taylor, P.J., Tett, S.E., 2002. Population pharmacokinetics of tacrolimus in adult kidney transplant recipients. Clin. Pharmacol. Ther. 72, 660– 669. Summary of Product Characteristics. Last update on the eMC: 18/09/2012. . Undre, N.A., Schafer, A., 1998. Factors affecting the pharmacokinetics of tacrolimus in the first year after renal transplantation. European Tacrolimus Multicentre Renal Study Group. Transplant Proc. 30, 1261–1263. Vannice, J.L., Taylor, J.M., Ringold, G.M., 1984. Glucocorticoid-mediated induction of alpha 1-acid glycoprotein: evidence for hormone-regulated RNA processing. Proc. Natl. Acad. Sci. USA 81, 4241–4245. Venkataramanan, R., Swaminathan, A., Prasad, T., Jain, A., Zuckerman, S., Warty, V., McMichael, J., Lever, J., Burckart, G., Starzl, T., 1995. Clinical pharmacokinetics of tacrolimus. Clin. Pharmacokinet. 29, 404–430. Venkataramanan, R., Shaw, L.M., Sarkozi, L., Mullins, R., Pirsch, J., MacFarlane, G., Scheller, D., Ersfeld, D., Frick, M., Fitzsimmons, W.E., Virji, M., Jain, A., Brayman, K.L., Shaked, A., 2001. Clinical utility of monitoring tacrolimus blood concentrations in liver transplant patients. J. Clin. Pharmacol. 41, 542–551. Vucicevic, K., Miljkovic, B., Pokrajac, M., Prostran, M., Martinovic, Z., Grabnar, I., 2009. The influence of drug-drug interaction and patients’ characteristics on valproic acid’s clearance in adults with epilepsy using nonlinear mixed effects modeling. Eur. J. Pharm. Sci.: Off. J. Eur. Fed. Pharm. Sci. 38, 512–518. Warty, V., Venkataramanan, R., Zendehrouh, P., McKaveney, T., Chao, J., Todo, S., Starzl, T., 1991. Distribution of FK 506 in plasma lipoproteins in transplant patients. Transplant Proc. 23, 954–955. Zahir, H., McCaughan, G., Gleeson, M., Nand, R.A., McLachlan, A.J., 2004. Factors affecting variability in distribution of tacrolimus in liver transplant recipients. Br. J. Clin. Pharmacol. 57, 298–309. Zahir, H., McLachlan, A.J., Nelson, A., McCaughan, G., Gleeson, M., Akhlaghi, F., 2005. Population pharmacokinetic estimation of tacrolimus apparent clearance in adult liver transplant recipients. Ther. Drug Monit. 27, 422–430. Zhang, G.M., Li, L., Chen, W.Q., Bi, S.S., Liu, X., Zhang, X.L., Lu, W., 2008. Population pharmacokinetics of tacrolimus in Chinese renal transplant patients. Yao Xue Xue Bao 43, 695–701. Zhao, W., Elie, V., Roussey, G., Brochard, K., Niaudet, P., Leroy, V., Loirat, C., Cochat, P., Cloarec, S., Andre, J.L., Garaix, F., Bensman, A., Fakhoury, M., Jacqz-Aigrain, E., 2009. Population pharmacokinetics and pharmacogenetics of tacrolimus in de novo pediatric kidney transplant recipients. Clin. Pharmacol. Ther. 86, 609–618.
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European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps
Total plasma protein effect on tacrolimus elimination in kidney transplant patients – Population pharmacokinetic approach Bojana Golubovic´ a,⇑, Katarina Vucˇic´evic´ a, Dragana Radivojevic´ b, Sandra Vezmar Kovacˇevic´ a, Milica Prostran c, Branislava Miljkovic´ a a b c
Department of Pharmacokinetics and Clinical Pharmacy, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia Nephrology Clinic, Clinical Centre of Serbia, University of Belgrade, Pasterova 2, 11000 Belgrade, Serbia Departmant of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Dr. Subotic´a 1, 11000 Belgrade, Serbia
a r t i c l e
i n f o
Article history: Received 1 April 2013 Received in revised form 30 August 2013 Accepted 16 October 2013 Available online 30 October 2013 Keywords: Kidney transplantation Plasma proteins Population pharmacokinetics Tacrolimus Therapeutic monitoring
a b s t r a c t Data from routine therapeutic drug monitoring of 105 adult kidney transplant recipients were used for population pharmacokinetic analysis which was performed using a non-linear mixed-effects modeling. The effect of demographic and clinical factors on tacrolimus clearance was evaluated. Following the initiation of treatment with tacrolimus, the results of our study indicate a decrease of the drug clearance on day 15, 1 and 6 months after transplantation for 4.4%, 6.3% and 10.92%, respectively. Our model suggests a negative correlation between tacrolimus clearance and haematocrit. According to final model, clearance decreases with increasing of aspartate aminotransferase. Our results demonstrated that CL/F increases with patients’ weight. This study reveals incensement for 10.4% in tacrolimus clearance with alteration of patients’ minimal measured total protein levels to upper normal range. The findings of this study explore various factors of tacrolimus pharmacokinetic variability and point out a relationship between tacrolimus clearance and total plasma protein. Developed model demonstrates the feasibility of estimation of individual tacrolimus clearance and may allow rational individualization of tacrolimus dosing in kidney transplant patients. Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction Tacrolimus is a potent, calcineurin inhibitor widely used for the prevention of acute and chronic allograft rejections in kidney transplant recipients (Bowman and Brennan, 2008). It has a narrow therapeutic window with wide inter-individual variability in clearance and other pharmacokinetic parameters (Staatz and Tett, 2004). In blood, it is extensively bound to erythrocytes with a mean blood to plasma ratio of about 15, while in plasma, tacrolimus is associated principally with a1-acid glycoprotein (AAG), lipoproteins, globulins and albumin (Staatz and Tett, 2004; Venkataramanan et al., 1995; Warty et al., 1991). Haematocrit is one of the factors that influence tacrolimus blood to plasma ratio (Staatz and Tett, 2004).Tacrolimus is a highly metabolised drug, with only about 0.5% unchanged parent drug appearing in urine or feces (Staatz and Tett, 2004; Venkataramanan et al., 1995). The drug is metabolized mainly by P450 3A isoenzymes (CYP3A) which expression varies widely (Koch et al., 2002; Staatz and Tett, 2004). Additionally, tacrolimus is a substrate of P-glycoprotein (Jeong and Chiou, 2006). The significant relation between the high within-patient variability in the clearance of tacrolimus and ⇑ Corresponding author. Tel.: +381 11 3951373; fax: +381 11 3972840. E-mail address: [email protected] (B. Golubovic´). 0928-0987/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ejps.2013.10.008
long-term graft failure was shown (Borra et al., 2010). There is evidence that low trough blood tacrolimus concentrations correlate with increased risk of rejection, whereas higher trough levels are associated with increased risk of toxicity (Borobia et al., 2009; Kershner and Fitzsimmons, 1996; Staatz et al., 2001; Venkataramanan et al., 2001). Nevertheless, some studies failed to establish a relation between tacrolimus trough concentration and graft rejection (Gaber et al., 1997; Jain et al., 1991). In addition, the correlation between tacrolimus dose and concentration is poor (Venkataramanan et al., 2001). These findings limit optimal titrations of the dosage regimen, and require additional information on the factors that affect the pharmacokinetic characteristics of the drug. The influence of some factors such as time after transplantation, haematocrit, albumin, corticosteroid therapy, liver function, diurnal variation, race and genetic polymorphism were acknowledged (Antignac et al., 2007; Han et al., 2013; Hesselink et al., 2003; Li et al., 2007; Macphee et al., 2002; Staatz and Tett, 2004; Staatz et al., 2002; Undre and Schafer, 1998). However, the results are often contradictory. Some studies correlated increase of clearance with post transplant day, whereas other had opposite results (Antignac et al., 2007, 2005; Han et al., 2013; Passey et al., 2011; Staatz et al., 2002). Furthermore, the effects of a variety of factors on tacrolimus elimination remain inconclusive. Therefore, we studied the effect of demographic and clinical factors such as
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graft origin, dialysis before transplantation, period after transplantation, serum creatinine, haematocrit, total proteins and hepatic enzymes, using data from routine therapeutic drug monitoring (TDM). Our objective was to define the significant factors of tacrolimus pharmacokinetic variability and develop a model for estimation of clearance to be used in transplant patient care. 2. Methods 2.1. Patients and data collection A retrospective analysis of data from 105 adult kidney transplant recipients from the Nephrology Clinic, Clinical Center of Serbia, University of Belgrade, was performed. Patients’ data during TDM were retrospectively collected. Approval for the study was obtained from the Ethics Committee of Clinical Center of Serbia. All data were collected from the patients’ charts, and they included following covariates: gender (GEND), age, body weight (WT), day after transplantation (PDAY), graft origin (GRFT), dialysis before transplantation (DIAL), serum creatinine (SECR), haematocrit (HCT), hemoglobin (HGB), total protein (TP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and concomitant immunosuppressive drug doses. 2.2. Drug administration Patients were on triple immunosuppressive therapy which inÒ cluded tacrolimus (Prograf , Astellas Ireland CO. Ltd.), mycophenolate mofetil (MMF) and corticosteroids (CORT). The recommended initial dose of tacrolimus was 0.3 mg/kg per day. Subsequent doses were adjusted on the basis of achieving tacrolimus trough blood concentrations within target ranges and clinical evidence of efficacy and toxicity. Desired ranges of tacrolimus trough blood concentrations were between 15 and 20 ng/ml in the first two weeks following transplantation, 10–15 ng/ml till the end of the first month, 7–10 ng/ml in the period from the end of the first month to the end of the sixth month after transplantation. 2.3. Blood sampling and bioanalytical assay All collected blood samples were pre-dose and were analyzed in the same laboratory. In the immediate post transplantation period, blood samples were collected two or three times per week until concentrations were stabilized. Thereafter, samples were collected once weekly in the first month after transplantation, and once monthly afterward. If physician suspected the rejection of graft or adverse reactions, drug monitoring was performed more frequently. Concentrations of tacrolimus in whole blood were assessed Ò using Architect system (Abbott Laboratories), a chemiluminescent microparticle immunoassay (CMIA) (ARCHITECT System, 2009). According to the manufacturer’s information the measurement range for assay is 2–30 ng/ml. Blood samples exceeding this range were diluted according to manufacturer’s protocol. 2.4. Population pharmacokinetic analysis Population pharmacokinetic analysis was performed using a Ò non-linear mixed-effects modeling program NONMEM (version 7 level 2, GloboMax LLC, Ellicott City, MD, USA) and Perl speaks Ò NONMEM (version 3.5.3, http://psn.sourceforge.net), Xpose (verÒ sion 4, http://xpose.sourceforge.net/) and R (version 2.15.0, http://r-project.org) were used for graphical presentations. AddiÒ tionally, Pirana (version 2.5.0, http://www.pirana-software.com/) was used for model evaluation and graphical presentation as well.
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Population pharmacokinetic analyses were performed using the first order conditional estimation with interaction (FOCEI) method to improve the estimation of pharmacokinetic parameters and their variability. A one-compartment pharmacokinetic model with first-order absorption and elimination as implemented in NONMEM subroutine ADVAN2 and TRANS2 was used to describe the concentration–time data. Since all data were trough concentrations, estimation of the volume of distribution (V/F) and rate constant of absorption (ka) was not possible and therefore they were fixed at 0.68 l/kg and 1.3 h1 (Summary of Product Characteristics, 2012). Based on the literature value of tacrolimus t1/2 of 15.6 h, and tmax of 2.5 h, ka was estimated using the following equation: tmax = ln(ka/kel)/(ka kel)(Booth and Gobburu, 2003; Vucicevic et al., 2009). In the first step, the base model was derived. The interindividual variability of tacrolimus CL/F (-2) was described by exponential model:
CL=F j ¼ TVCL expðgjCL Þ where CL/Fj is total body clearance for the jth individual, TVCL is typical population value of CL/F and gj is random variable for the jth individual distributed with zero means and respective variances of -2CL. Residual variability of tacrolimus concentration (r2), the additive, the proportional, and the slope-intercept error models were tested as follows:
C ij ¼ C predij þ eij C ij ¼ C predij þ C predij eij C ij ¼ C predij þ C predij e1ij þ e2ij where Cij is the ith observed concentration for the jth individual, Cpredij is predicted concentration for the jth individual and eij is a randomly distributed variable with zero mean and variance r2. Once the base model was established, the effects of covariates on relevant pharmacokinetic parameter variability were explored. Tested covariates were: PDAY, WT, AGE, GRFT, GEND, DIAL, SECR, HCT, UP, ALP, AST, MMF and CORT. Missing covariate data for SECR (0.3%), HGB (1.55%), HCT (2.1%) and TP (6.8%) were treated with multiple imputation of median per day. An imputation method for missing data for ALP (29.46%), AST (28.26%) and ALT (28.51%) was last-observation carried forward (LOCF) (Bonate, 2006; Harrel, 2001). Statistical significance of the covariates was evaluated based on the objective function value (OFV), which is equal to minus twice the log likelihood. Covariates were introduced sequentially into the population models to develop a full model. In each step of the covariate model building the covariate with the highest drop in objective function value (DOFV), at least 3.84 (p < 0.05), was included in the model. The full model was obtained when the effects of all the remaining covariates were insignificant (DOFV < 3.84). The final model was determined by backward elimination of covariates in a stepwise manner. Covariates were kept in the final population pharmacokinetic model when the removal of the covariate resulted in an OFV increase of at least 6.63 (p < 0.01). An additional criterion for the retention of a covariate in the model was reduction in unexplained interindividual variability. The model appropriateness was evaluated by standard diagnostic plots (Karlsson and Savic, 2007), convergence of minimization, number of significant digits more than 3, successful covariance step, gradients in the final iteration being in the range 103 to 102 and absence of substantial g- and e-shrinkage (Savic and Karlsson, 2009). Conditional weighted residuals (CWRES) were calculated for standard diagnostic plots (Hooker et al., 2007). A nonparametric bootstrap of 1000 samples was used for assessing the accuracy and robustness of the final population model (Parke et al., 1999). Additionally, performance of final model was
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Table 1 Patients’ demographic and biochemical characteristics. Characteristics
Number (%)/ Range average ± standard deviation
Gender
62 (59.05) 43 (40.95) 60 ± 47.63
Male Female Period after transplantation (days) Graft origin Living donor Cadaver Dialysis before Yes transplantation No Age (years) Body weight (kg) Serum creatinine (lmol/L) Haematocrit Proteinaemia (g/l) Alkaline phosphatase (IU/l) Aspartate aminotransferase (IU/l) Alanine aminotransferase (IU/l)
75 (71.43) 30 (52.57) 87 (82.86) 18 (17.14) 39.38 ± 10.78 68.41 ± 12.85 207.3 ± 328.02 0.31 ± 0.05 63.23 ± 7.99 70.08 ± 29.54 19.7 ± 23.22 32.92 ± 57.67
Table 3 Summary of covariate effect on tacrolimus clearance (CL/F) during model building (only significant effects are reported). Covariate Base model Forward step
0–206
16–60 38–108 57–1398 0.029–0.75 38–137 6–214 4–414 1–961
Backward step
Haematocrit Body weight Aspartate aminotransferase Post transplant day Total protein Haematocrit Body weight Aspartate aminotransferase Post transplant day Total protein
Objective function value (OFV)
DOFV
8526.87 8251.57 8025.65 8000.25
275.3 225.92 25.4
7979.38 7952.7 8029.63 8175.64 7977.95
20.97 26.68 76.93 222.94 25.25
7988.57 7968.05
35.87 15.35
Table 4 Population pharmacokinetic parameters of tacrolimus and bootstrap validation. Parameter Table 2 Characteristics of immunosuppressive therapy. Drug Tacrolimus Dose (mg/day) Trough concentration (ng/ml) Mycophenolate mofetil Dose (mg/day) Corticosteroids Dose (mg/day)
Average ± standard deviation
hCLa (l/h) hASTb hHCTb hPDAYb hWTb hTPb xCL (%)c r (ng/ml)d
Range
12.09 ± 6.24 11.66 ± 5.02
1–40 2.4–57.2
1207.19 ± 463.56
0–2000
37.63 ± 76.25
2.5–500
Original dataset
Bootstrap
Mean
Median
2.5–97.5th Percentiles
10.017 0.861 0.831 0.0283 0.869 0.161 15.2 4.066
10.016 0.868 0.812 0.0295 0.871 0.156 14.83 4.032
9.688–10.348 1.694–0.369 1.153–0.477 0.0478–0.0136 0.691–1.051 0.009–0.356 13.39–16.87 3.6138–4.519
a
Typical value of tacrolimus clearance. Influential factors for covariates (AST – aspartate aminotransferase, HCT – haematocrit, PDAY – post transplant day, WT – body weight, TP – total protein). c Interindividual variability. d Residual variability. b
6 months after transplantation. Seventy-five kidney grafts were taken from living donors and thirty from cadaveric donors. The mean time to initiation of tacrolimus treatment was 2.6 days. Patient and therapy characteristics are presented in Tables 1 and 2. 3.2. Population pharmacokinetics
Fig. 1. Observed tacrolimus concentrations (ng/ml) versus post transplantation day (PDAY).
evaluated by prediction- and variability-corrected VPC (pvcVPC, n = 1000) (Bergstrand et al., 2011).
3. Results 3.1. Patients characteristics Data from 105 kidney transplant recipients (62 males and 43 females) were collected retrospectively during the maximum
Data for modeling included 1999 trough blood concentrations (Fig. 1). Interindividual variability was evaluated by an exponential model, while residual variability in tacrolimus concentrations was best described by an additive error model. A significant decrease in OFV, in a forward modeling building step, produced the inclusion of HCT, WT, AST, PDAY and TP. In the backward elimination step all of these covariates were retained in the model. Details of a covariate model building are given in Table 3. The final model is described by the following equation:
0:0283 0:869 0:161 PDAY WT TP CL=F ¼ 10:017 47 68 63 86 1 ðAST 15Þ ð1 0:831 ðHCT 0:31ÞÞ 1000 The drop in OFV in the final model compared to the base was 574.171. In the final model, mean interindividual coefficient of variability for CL/F was 15.2%, residual variability was 4.07 ng/ml, and shrinkage of 1.6% was estimated. Parameters of the final model are presented in Table 4. Diagnostics plots are presented in Figs. 2 and 3.
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Fig. 2. Observed tacrolimus concentrations (ng/ml) versus population and individual model-predicted concentrations (ng/ml) for (A) base model and (B) final model. Line of identity (solid); regression line (dashed).
Fig. 3. Conditional weighted residuals (CWRES) versus: (A) mean population predicted concentrations (PRED) and (B) observed concentrations of tacrolimus.
Further step in the analysis included bootstrap analysis of the final model. The mean parameter estimates obtained from the bootstrap process, 998 successful runs out of 1000 scheduled, were not statistically different from the estimates previously obtained with the original dataset (Table 4). Fig. 4 shows pvcVPCs for base and final models confirming the improvement in the model building, while Fig. 5 presents relation between tacrolimus clearance and post transplantation day.
4. Discussion In the present study, we have evaluated tacrolimus population pharmacokinetic parameters in adult kidney transplant recipients, and identified the factors affecting its pharmacokinetics. An onecompartment open model with first-order absorption and elimination was optimal for data modeling, as previously reported (Antignac et al., 2007; Han et al., 2013; Staatz et al., 2002; Zhang et al., 2008).
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Fig. 4. Prediction- and variability-corrected visual predictive check (pvcVPC), prediction- and variability-corrected concentrations (ng/mL) versus post transplant day: (A) base model and (B) final model. Solid and dashed lines represent the median, 5th and 95th percentiles of the observed data with shaded confidence intervals of the prediction intervals for the simulated prediction intervals.
Fig. 5. Tacrolimus clearance (CL/F) versus post transplantation day (PDAY).
The results of the study indicate that tacrolimus CL/F, and consequently average steady-state concentration depend on PDAY, WT, AST, TP and HCT. The final model reveals that CL/F increases with patients’ WT. The value of the exponent for the effect of WT was not significantly different from the theoretic allometric exponent of 0.75. Our analysis showed decreasing of CL/F with increasing number of post transplantation days (Fig. 5). These findings are consistent with the previously published studies (Han et al., 2013; Staatz et al., 2002). Passey et al. found that CL/F decreased by 14% in 6– 10 post transplant days, and by 29% in 11–180 post transplant days, relative to immediate post transplant period (Passey et al., 2011). Compared to initiation of therapy tacrolimus CL/F was decreased on day 15, 1 and 6 months after transplantation for 4.4%, 6.3% and 10.92%, respectively in our study. This reduction in CL/F could be explained by an increase in tacrolimus bioavailability or increase in haematocrit and albumin as the patients’ clinical state improves with time after surgery (Han et al., 2013; Passey et al.,
2011; Undre and Schafer, 1998). Observed differences in the extent of CL/F decrease between our and the results of Passey et al. may be due to inclusion of haematocrit in our final model which influence was not tested on tacrolimus pharmacokinetics in Passey et al. (2011). As tacrolimus is a low-clearance drug with clearance equivalent to 3% of liver blood flow, extensively bound to erythrocytes and highly protein bound (Staatz and Tett, 2004), its clearance depends on the extent of binding (Burton et al., 2005), correspondingly it depends on haematocrit and plasma protein concentration. According to our model CL/F decreased as haematocrit increased which is in compliance with earlier findings (Han et al., 2013; Staatz et al., 2002; Undre and Schafer, 1998; Zahir et al., 2005; Zhang et al., 2008; Zhao et al., 2009). As previously mentioned haematocrit increases with post transplantation days. Other potential reason for differences between our and the results of Passey et al. may be difference of corticosteroid dosage (Passey et al., 2011). Tacrolimus CL/F may also be higher in immediate post transplant period due to relatively high doses of corticosteroid which are co administrated according to the specific protocol of the center for transplantation (Anglicheau et al., 2003; Hesselink et al., 2003; Park et al., 2009). Patients included in our study were on the first day on transplantation on intravenous 500 mg/day methylprednisolone, which was subsequently reduced according the local protocol. Our model suggests negative relation between CL/F and AST as parameter for liver function which is expected for highly metabolized drug and this finding is consistent with previous results (Antignac et al., 2005; Garcia Sanchez et al., 2001; Staatz et al., 2002; Zhang et al., 2008). Albumin was identified as a significant factor of influence on tacrolimus pharmacokinetics in several studies. Previous studies reported that tacrolimus CL/F decrease with increasing albumin (Antignac et al., 2005; Undre and Schafer, 1998; Zahir et al., 2005). Zahir et al. found that the unbound fraction of tacrolimus correlated with AAG and high density lipoprotein cholesterol concentrations (Zahir et al., 2004). Tacrolimus is predominantly bound to AAG, lipoproteins, globulins and albumin (Staatz and Tett, 2004; Warty et al., 1991), hence we evaluated the influence of total plasma protein concentration on CL/F. Our model described increasing of CL/F with increasing total plasma protein concentration. To our
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knowledge, this is the first model that quantifies the influence of plasma proteins on tacrolimus CL/F. This relation may be caused by expected elevation of AAG in the first post operation days when total plasma proteins concentrations are low. As AAG is the acute phase protein, its levels increase in response to systemic tissue injury, inflammation or infection (Fournier et al., 2000). Higher levels of AAG are observed in various disease states, in patients with trauma, severe burns, and recipients of bone marrow and organ transplants (Israili and Dayton, 2001). Additionally, AAG levels may be increased as a result of treatment with high doses of corticosteroid in first post transplantation days (Fournier et al., 2000; Israili and Dayton, 2001; Vannice et al., 1984), as already mentioned for the patients included in this analysis. Accordingly, our model demonstrated incensement for 10.4% in tacrolimus CL/F with alteration of patients’ minimal measured total protein levels to upper normal range. The predictive performance of the model was assessed by standard plots, pvcVPC and bootstrap method. The goodness-of-fit plots (Fig. 2) depict improvement in prediction by final model compared to base. The scatter plots of CWRES vs. predicted concentration (Fig. 3A) showed that the CWRES were mostly randomly distributed and lay within ±3 units of the null ordinate. Nevertheless, Fig. 3A indicates some bias in prediction (Barett, 2002). Values of CWRES higher than 3 were observed in only 0.75% measured concentrations. The bias in residuals observed in Fig. 3A may arise from the study design and the nature of data. As previously discussed by Sheiner and Beal, nonlinear mixed effects modeling of only trough data facilitated precise estimation of CL/F and interindividual variability, but the estimation of residual variability was subject to bias (Sheiner and Beal, 1983). Dependence CWRES of measured concentration (Fig. 3B) indicates less reliable prediction for high values of concentration (>30 ng/ml). According to the manufacturer’s information the measurement range for assay is 2–30 ng/ml while the levels above 30 ng/ml are subject to dilution before quantification (ARCHITECT System, 2009). Therefore, we may consider contribution of bioanalytical aspects to the measurement error observed in higher CWRES for tacrolimus levels above 30 ng/ml. Regardless the aforementioned limitations of study design and the nature of data in modeling process, and negligible bias observed for high tacrolimus concentrations, the influence of defined covariates on tacrolimus CL/F is, with no doubts, precisely described by our model (Sheiner and Beal, 1983). PvcVPC indicate improvement in final model compared to base and bootstrap analysis confirmed accuracy and robustness of the final population model. Certain causes of remaining variability are genetic differences in cytochrome 3A izoenzyme and P-glycoprotein expression (Li et al., 2007; Loh et al., 2008; Macphee et al., 2002; Rosso Felipe et al., 2009; Staatz and Tett, 2004). Accordingly, further work could be assessment of the impact of genetic polymorphism in combination with the clinical factors included in our model. The stable final model, as the model in our study, can be used as a priori information for Bayesian estimation of CL/F in individual patients requiring as few as one blood concentration which is a great advantage in the clinical practice (Proost, 1995). The possibility of estimation of individual pharmacokinetic parameters allows the calculation of the dose needed to achieve a desired steady-state trough concentration, as demonstrated by Antignac et al. (2011).
5. Conclusion The findings of this study explore various factors of tacrolimus pharmacokinetic variability. This is the first study that quantifies the effect of total plasma proteins on tacrolimus elimination. Our
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study demonstrates the feasibility of estimation of individual pharmacokinetics parameters of tacrolimus based on sparse TDM data. Furthermore, development of a Bayesian estimator based on our model would enable the accurate prediction of tacrolimus dose which would offer evidence based guidance to clinicians for dosing this immunosuppressant. Acknowledgements This work was conducted as a part of the project Experimental and Clinical-Pharmacological Investigations of Mechanisms of Drug Action and Interactions in Nervous and Cardiovascular System (No. 175023) funded by Ministry of Education, Science and Technological Development, Belgrade, Republic of Serbia. We are very grateful to Dr. Radmila Blagojevic Lazic and the stuff from Nephrology Clinic, Clinical Centre of Serbia, University of Belgrade, Serbia for their assistance. References Anglicheau, D., Flamant, M., Schlageter, M.H., Martinez, F., Cassinat, B., Beaune, P., Legendre, C., Thervet, E., 2003. Pharmacokinetic interaction between corticosteroids and tacrolimus after renal transplantation. Nephrol. Dial Transplant 18, 2409–2414. Antignac, M., Hulot, J.S., Boleslawski, E., Hannoun, L., Touitou, Y., Farinotti, R., Lechat, P., Urien, S., 2005. Population pharmacokinetics of tacrolimus in full liver transplant patients: modelling of the post-operative clearance. Eur. J. Clin. Pharmacol. 61, 409–416. Antignac, M., Barrou, B., Farinotti, R., Lechat, P., Urien, S., 2007. Population pharmacokinetics and bioavailability of tacrolimus in kidney transplant patients. Br. J. Clin. Pharmacol. 64, 750–757. Antignac, M., Fernandez, C., Barrou, B., Roca, M., Favrat, J.L., Urien, S., Farinotti, R., 2011. Prediction tacrolimus blood levels based on the Bayesian method in adult kidney transplant patients. Eur. J. Drug Metab. Pharmacokinet. 36, 25–33. ARCHITECT System. Tacrolimus package insert. Abbott Laboratories, 2009. Barett, J., 2002. Population pharmacokinetics. In: Schoenwald, R. (Ed.), Pharmacokinetics in Drug Discovery and Development. CRC Press, Boca Raton, pp. 315–356. Bergstrand, M., Hooker, A.C., Wallin, J.E., Karlsson, M.O., 2011. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. Aaps J. 13, 143–151. Bonate, P., 2006. Pharmacokinetic–Pharmacodynamic Modeling and Simulation. Springer, New York. Booth, B.P., Gobburu, J.V., 2003. Considerations in analyzing single-trough concentrations using mixed-effects modeling. J. Clin. Pharmacol. 43, 1307– 1315. Borobia, A.M., Romero, I., Jimenez, C., Gil, F., Ramirez, E., De Gracia, R., Escuin, F., Gonzalez, E., Sansuan, A.J., 2009. Trough tacrolimus concentrations in the first week after kidney transplantation are related to acute rejection. Ther. Drug Monit. 31, 436–442. Borra, L.C., Roodnat, J.I., Kal, J.A., Mathot, R.A., Weimar, W., van Gelder, T., 2010. High within-patient variability in the clearance of tacrolimus is a risk factor for poor long-term outcome after kidney transplantation. Nephrol. Dial Transplant 25, 2757–2763. Bowman, L.J., Brennan, D.C., 2008. The role of tacrolimus in renal transplantation. Expert Opin. Pharmacother. 9, 635–643. Burton, M.E., Shaw, L.M., Schentag, J.J., Evans, W.E., 2005. Applied Pharmacokinetics and Pharmacodynamics: Principles of Therapeutic Drug Monitoring, fourth ed. Lippincot Williams & Wilkins. Fournier, T., Medjoubi, N.N., Porquet, D., 2000. Alpha-1-acid glycoprotein. Biochim. Biophys. Acta 1482, 157–171. Gaber, L.W., Moore, L.W., Reed, L., Russell, W., Alloway, R., Hathaway, D., ShokouhAmiri, M.H., Gaber, A.O., 1997. Renal histology with varying FK506 blood levels. Transplant Proc. 29, 186. Garcia Sanchez, M.J., Manzanares, C., Santos-Buelga, D., Blazquez, A., Manzanares, J., Urruzuno, P., Medina, E., 2001. Covariate effects on the apparent clearance of tacrolimus in paediatric liver transplant patients undergoing conversion therapy. Clin. Pharmacokinet. 40, 63–71. Han, N., Yun, H.Y., Hong, J.Y., Kim, I.W., Ji, E., Hong, S.H., Kim, Y.S., Ha, J., Shin, W.G., Oh, J.M., 2013. Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients. Eur. J. Clin. Pharmacol. 69, 53–63. Harrel, F., 2001. Regression Modeling Strategies. Springer, New York. Hesselink, D.A., Ngyuen, H., Wabbijn, M., Gregoor, P.J., Steyerberg, E.W., van Riemsdijk, I.C., Weimar, W., van Gelder, T., 2003. Tacrolimus dose requirement in renal transplant recipients is significantly higher when used in combination with corticosteroids. Br. J. Clin. Pharmacol. 56, 327–330. Hooker, A.C., Staatz, C.E., Karlsson, M.O., 2007. Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method. Pharm. Res. 24, 2187–2197.
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