Chronic complications, such as kidney failure, are seen more frequently in aging HIV patients. Although cART is able to slow progression of HIV-associated nephropathy (HIVAN), it is expected that the number of HIV patients with end-stage renal disease (ESRD) will increase because of an increased life expectancy and subsequently will lead to a higher number of HIV infected patients with ESRD who will require kidney transplantation (1). In addition, antiretroviral drugs, like tenofovir, can also cause nephrotoxicity. During the last 2 decades, more than 200 renal transplants have been performed in HIV-infected patients worldwide (2). Although relatively high rejection rates of 31 % after 1 year which is nearly 3 times higher than data from the U.S. Scientific Registry of Transplant Recipients (SRTR) have been reported (3), mid-term patient and graft survival rates have been similar to that of HIV-negative patients (2, 4, 5). Overall, HIV is no longer considered to be a contraindication for kidney transplantation, and increasing numbers of HIV-infected patients are accepted for kidney transplantation worldwide (6). For a long-term success of transplantation in HIV-infected patients, life-long immunosuppression and virologically effective antiretroviral therapy are necessary. The calcineurin inhibitor (CNI) tacrolimus is one of the most prescribed immunosuppressants in kidney transplantation (7). It has a narrow therapeutic index, which makes therapeutic drug monitoring mandatory. Maintaining therapeutic levels of tacrolimus is essential to prevent rejection caused by underexposure and (nephro)toxicity caused by overexposure (8). Ideally, tacrolimus dosing is based on a 12-hour area under the curve value (AUC0-12). Nevertheless, in clinical practice, dosing is usually guided by monitoring trough levels, because of the assumed correlation between AUC and trough level. Target AUCs of 210 ng.h/mL 1 month and 125 ng.h/mL 1 year after transplantation have been reported in HIV-negative kidney transplant recipients (9). These target AUCs correspond with trough levels of approximately 12.5 and 7.5 ng/mL, respectively. Tacrolimus is primarily metabolized by cytochrome (CYP) 3A in the liver and a substrate of P-glycoprotein (Pgp), an efflux pump, which is primarily localized in the liver and intestines (10, 11). Many of the drugs included in cART have been reported to show clinically relevant interactions with CNIs (12). For instance, nonnucleoside reverse transcriptase inhibitors (NNRTIs) are well-known inducers of CYP3A enzymes that may affect tacrolimus pharmacokinetics and can cause underexposure but also have an overlapping iatrogenic profile regarding hepatoxiticity (13). Furthermore, protease inhibitors (PIs) are very strong inhibitors of CYP3A enzymes and Pgp. Coadministration of PI’s results in a major effect on the pharmacokinetics of tacrolimus and can cause serious overexposure, if the effect of the interaction is not anticipated by strongly reducing the tacrolimus dose (12, 13). Ritonavir, which is added to PIs as a pharmacologic booster to increase bioavailability and reduce clearance, is currently by far the strongest commercially available CYP3A inhibitor. This boosting principle of ritonavir is widely used in the daily clinical practice. Multiple clinical studies in HIV patients using ritonavir-containing cART have shown that to avoid toxicity from tacrolimus overexposure shortly after transplantation, dosage reductions up to 120-fold were necessary as resultant of an increase in half life and major decrease in oral clearance (Cl/F) (12–14). Long-term and extreme overexposure to tacrolimus as a result of the coadministration of ritonavir boosted PIs has resulted in serious clinical toxicity in HIV-infected patients, particularly neuropathy and nephropathy, (14–19). At the time of initiation of our transplantation program for HIV infected patients, there were limited data and no guidelines on dosing tacrolimus in patients on ritonavir containing cART available. Furthermore, the effect of ritonavir-boosted PIs on the pharmacokinetic parameters of tacrolimus has not been quantitatively studied, and the optimal initial dose post transplantation is unclear. Therefore, we incorporated pretransplantation pharmacokinetic curves of tacrolimus in our program to predict tacrolimus dose initiation immediately posttransplantation to avoid tacrolimus overexposure after the initial dose a priori and concomitantly estimate the effect of ritonavir containing cART on the pharmacokinetics of tacrolimus.
Pharmacokinetic Analysis and Model Design
Eleven HIV-infected patients were considered as candidates for kidney transplantation between January 2006 and May 2011. Of these 11 patients, 6 on a ritonavir-based cART were recruited for this study and included after giving informed consent (Table 1). The inclusion period was 5 years as a result of the low prevalence of ESRD in HIV-infected patients in The Netherlands and subsequently the low number of transplantations nationwide. Of these 6 patients 4 were transplanted to date. Test doses ranged from 1 to 7 mg. Figure 1 displays the pharmacokinetic curves of the test dose of tacrolimus in all patients. The pharmacokinetic curves of patients 1 and 2 show peak levels of 57 and 77 ng/mL, respectively and after a rapid decline tacrolimus levels remain at very high tacrolimus levels between 30 to 50 ng/mL for more than 100 hours after administration. Both patients experienced serious neurotoxicity and leukopenia 3 weeks after administration of the test dose, which required temporary withdrawal of ritonavir-boosted lopinavir for 2 weeks to accelerate clearance of tacrolimus. Patients 1, 2, 3, and 6 were monitored for more than 24 hours. In these patients, tacrolimus concentrations exceeded 3 ng/mL for at least 1 week after administration of a single dose of 0.1 mg/kg total body weight.
Because data of only six patients were included pharmacokinetic parameters of the initial model before the bootstrap analysis are displayed as median and range. Comparison of the results obtained from the bootstrap analysis with the original data set resulted in comparable figures, indicating that the model is the model precision is similar to the model of a large kidney transplant recipients population designed by Scholten et al. (Table 2).
Oral clearance of tacrolimus was approximately 40-fold (472 vs. 11.7 mL/min) lower, and elimination half-life was tenfold higher in HIV-infected patients on a ritonavir-containing cART compared with HIV-negative kidney transplant recipients (12.9 vs. 117 hours).
Simulation of Pharmacokinetic Curves
Simulation of a 5 mg BID dosing regimen in our HIV-negative population and a 2-mg loading dose followed by 0.5 mg every 48 hours in patients on ritonavir-containing cART resulted in therapeutic trough levels in both populations directly after transplantation (Figure 2). Trough levels of tacrolimus were 14.6 and 15.1 ng/mL in HIV-negative and HIV-infected patients on ritonavir-containing cART, respectively. The pharmacokinetic curve of tacrolimus in a patient on ritonavir-containing cART lacked an absorption peak every 12 hours; 12 hour area-under-the-curves (AUCs) of patients on ritonavir-containing cART ranged from 205 ng.h/mL directly after tacrolimus intake to 179 ng.h/mL for the 12 h before the next administration. As a result of the former, targeting equivalent steady state trough concentrations requires 44% higher mean 12 h-AUC in HIV-negative recipients compared with patients on ritonavir-containing cART.
Predictive Value of Posttransplantation Loading and Maintenance Dosages
Simulating dosages between 0.5 and 3 mg with the model indicated that five patients would achieve tacrolimus blood levels between 12.5 and 17.5 ng/mL at 24 hours after administration of a 2 mg loading dose and one patient after a loading dose of 1.5 mg. Furthermore, the model predicted that five patients needed a maintenance dose of 0.5 mg every 48 hours and one patient 1 mg every 48 hours to reach therapeutic trough levels of 15 ng/mL during the first week after transplantation. Four patients underwent kidney transplant with a graft from a deceased donor, one patient is still on the waiting list and one died of sepsis before transplantation. The four transplanted patients 1, 2, 4, and 6 received initial doses of tacrolimus of 2, 1.5, 2, and 2 mg, respectively. No significant difference was found between the median predicted and actual posttransplantation 24-hour tacrolimus levels (14.6 vs. 17.8 ng/mL, P=0.19). Patients 4 and 6 experienced a rejection episode at 0.8 and 2.3 years posttransplantation, respectively. To date, all four transplant recipients, respectively, 6.8, 6.2, 2.6, and 2.0 years after transplantation, still have a functioning graft.
This pilot study shows that pretransplantation pharmacokinetic curves of tacrolimus seem promising to prevent posttransplantation overexposure in HIV-infected patients on ritonavir-containing cART. A secondary observation of this study, but nevertheless very important finding, is that the mean 12 h-AUC at steady state of tacrolimus in HIV-negative recipients was 44% higher compared with patients on ritonavir-containing cART, when striving for similar trough levels in both populations. We suggest that in order to reach equivalent exposure of tacrolimus in terms of 12 h-AUCs trough levels in patients on ritonavir-containing cART should be approximately 40 % higher compared to HIV-negative recipients.
The common dosage range for tacrolimus in kidney transplant recipients is 0.10 mg per kg twice daily during the first month after transplantation. In our patients on ritonavir-containing cART, the median advised loading and maintenance dose were 2.0 mg and 0.5 mg every 48 hours respectively, which is in agreement with the results from a other cohorts and case reports of posttransplantation HIV-infected recipients on PI-based cART previously described. (12, 13). Furthermore, from the simulation of the pharmacokinetic curves as displayed by Figure 2, it can be concluded that patients on ritonavir-boosted cART the initial dose of tacrolimus needs to be strongly reduced to approximately 2 mg (0.025 mg/kg total body weight) in our population to reach therapeutic levels directly posttransplantation. Because the pharmacokinetic curve almost resembles a flat line after reaching a peak level, pharmacokinetic parameters related to the distribution of the drug, for example, total body weight, are key parameters to predict the initial dose targeting therapeutic levels directly posttransplantation in these patients. The maintenance dosage is much lower than the loading dose because it is dependent of oral clearance (Cl/F), which is extremely low in patients on a ritonavir-based cART. Finally, simulations based on pretransplantation curves combined with the pharmacokinetic model and bayesian estimation seemed to overestimate tacrolimus clearance compared with a posttransplantation setting, which could be due to a reduction in hepatic metabolic capacity after transplantation. Although posttransplantation data were only available in four patients, the predictive value seems to be fairly good with a slightly higher median first trough level than predicted. Of course, our results deserve to be confirmed by data from other cohorts prior the avocation of broad implementation of pretransplantation pharmacokinetic curves of tacrolimus in HIV-infected patients on a ritonavir containing cART.
Our recommendation of elevating trough levels in these patients is indirectly supported by the clinical finding of Stock et al, (3) that higher trough levels were associated with a decreased risk of a first acute allograft rejection (20). They reported mean tacrolimus trough levels of 9.1 ng/mL one month and 7.2 ng/mL one year after transplantation, and 42% of patients were using PI-based cART. However, no significant correlation could be detected between the use of PIs, tacrolimus trough levels, and rejection. Further studies on the association between tacrolimus trough levels and rejection rates in patients on ritonavir-containing cART should be performed to better understand the change in pharmacokinetics of tacrolimus when administrated together with ritonavir-containing cART and to investigate the increased rejection rates found in studies in HIV-infected kidney transplant recipients. Perhaps focusing on AUC-guided dosing instead of dosing on traditional trough levels could lead to better clinical outcomes. Of course, the potential risk of increased rates of toxicity should not be overlooked upon raising tacrolimus levels.
There were several limitations to this study. Firstly, the number of inclusions was relatively low with pretransplantation pharmacokinetic evaluation in six patients, of whom, four were transplanted, and they used a broad spectrum of antiretroviral drugs. Five patients were simultaneously on an NNRTI (Table 1), and this class of antiretroviral drugs is known to induce CYP3A. Unfortunately, because of large heterogeneity of NNRTIs and small sample size of our cohort, the effect of efavirenz, nevirapine and etravirine on tacrolimus pharmacokinetics could not be quantified. Although a minor remaining inducing effect on CYP3A of NNRTIs cannot be excluded, it is most likely overruled by the extremely strong inhibiting effect of ritonavir. Although the number of inclusions was low, the variability in oral clearance was comparable to pharmacokinetic parameters reported in HIV-negative kidney transplant recipients. Second, pharmacokinetic curves were performed before transplantation, not taking into account the effects of posttransplant medication nor the change in kidney function and the effect of dialysis. Nevertheless, it is to be expected that these factors will not significantly influence the predominately hepatic clearance of tacrolimus after transplantation (8). Third, adherence to ritonavir was not confirmed, which is relevant because of its short half-life. Nevertheless, around the pretransplantation curves, medication was administered by hospital personnel, and none of the patients experienced a virologic failure suggesting a good medication intake. Compliance to cART is of utmost importance in these patients, as once ritonavir is stopped its inhibiting effect on CYP 3A ceases, and the inductive effect on CYP3A of ritonavir may take over (20, 21). However, this inducing effect will probably be tapered by the slow loss of the mechanism-based inhibitory effect on CYP3A of ritonavir (22). In clinical practice, tacrolimus can be expected to reach subtherapeutic levels within 2 days after stopping ritonavir. Finally, the relatively large variability on K12, K21, and Tlag was accepted because the main goal of this study was to predict 24-hour levels of TAC posttransplantation) and not to characterize absorption and distribution parameters in detail.
To our best knowledge, this is the first study describing the use of pretransplantation pharmacokinetic curves of tacrolimus in HIV patients treated with very strongly interacting antiretroviral medication and the first study to suggest the need for adjusted target levels as a result of a pharmacokinetic interaction.
New antiretroviral agents, which do not interact with tacrolimus, such as raltegravir, have been introduced in recent years (23) and have the potential to become the preferred antiretrovirals in patients eligible for transplantation to avoid subsequent interactions. However, knowledge of drug-drug interaction issues and posttransplantation management of patients on ritonavir-containing cART is vital because transplantation-eligible patients whose HIV-resistance profile will not allow them to switch to a non-PI-based regimen will always be present.
MATERIALS AND METHODS
HIV-infected patients on any antiretroviral regimen with ESRD at the University Medical Center Utrecht, The Netherlands, were considered as candidates for kidney transplantation. For this study, patients on a ritonavir-based cART were recruited and included after giving informed consent. Inclusion criteria were older than 18 years of age, HIV infection treated with a combination of antiretroviral agents, which included ritonavir at a minimal dose of 100 mg qd or higher for a minimal period of 6 months, and acceptance for kidney transplantation according to hospital guidelines. HIV-infected transplant candidates had to have an undetectable plasma HIV RNA viral load (<50 copies/mL) for longer than 6 months and CD4+ T-cell count greater than 200/mm3 to be eligible for transplantation. The Medical Ethics Committee of the University Medical Center Utrecht approved the study and all participants provided written informed consent. Patients were given a single oral test dose of tacrolimus (Prograf, Astellas Pharma, Tokyo, Japan) of 0.10 mg/kg total body weight.
Sample Collection and Patient Monitoring
For the pretransplantation curves at least 12 blood samples (at t = 0, 0.5, 1, 2, 3, 4, 6, 8, 10, 12, 18, and 24 hours after administration of the test dose) were drawn from each patient. Patients were monitored for tacrolimus-related toxicity for at least 24 hours. However, if significant overexposure occurred, patients were followed up at least till tacrolimus levels were normalized and/or tacrolimus-related toxicities resolved. According to protocol, additional samples were allowed to be drawn after 24 hours, if the patient consented with prolonged monitoring. Tacrolimus concentrations were determined in whole blood by microparticle enzyme immunoassay (MEIA FK II; Abbott Laboratories, Chicago, IL). First posttransplantation levels were drawn 24 hours after initiation of tacrolimus.
Pharmacokinetic Analysis and Model Design
A pharmacokinetic model was designed using the KinPop module in the pharmacokinetic software package MW/Pharm version 3.80 (Mediware, Zuidhorn, The Netherlands). The MW/Pharm program uses an iterative two-stage Bayesian procedure to estimate the mean and relative standard deviations of the relevant pharmacokinetic parameters. A population two-compartmental model with a lag-time and first-order absorption pharmacokinetics was developed. Because interference of ritonavir with the volume of distribution and absorption rate of tacrolimus was not expected, parameter values for volume of distribution of the central compartment (V1) and absorption rate constant (ka) were fixed according to mean values reported by Scholten et al.(9) Moreover, biovailability (F) was fixed at 1 because of the strong inhibition of intestinal CYP3A enzymes and Pgp by ritonavir. Koolen et al. (24) have shown for example for the CYP3a and PgP substrate docetaxel that the apparent bioavalability (F) increases significantly when ritonavir is coadministered. Model evaluation was performed using the bootstrap option of MW/Pharm. Finally, oral clearance and half-life were estimated and compared with an HIV-negative kidney transplant population.
Simulation of Pharmacokinetic Curves
To visualize the effect of ritonavir-containing cART on tacrolimus pharmacokinetic dosages were simulated, targeting tacrolimus levels between 12.5 and 17.5 ng/mL our HIV-infected population on ritonavir-containing cART were simulated in MW/Pharm. Peak and trough levels, time after administration when the maximum level is reached and AUCs of tacrolimus were estimated for both populations.
Predictive Value of Posttransplantation Loading and Maintenance Dose
The first two patients received a test dose of 0.1 mg/kg. Based on the pharmacokinetic curves retrieved from patients 1 and 2, patients 3 to 6 received reduced test doses of 0.01 to 0.02 mg/kg total body weight. After analyzing the pharmacokinetic curves of tacrolimus, posttransplantation loading and maintenance dosages were established for each patient aiming for a tacrolimus trough level between 12.5 and 17.5 ng/mL according to local transplantation protocol. The predictive value of the pretransplantation curve was assessed by comparing predicted and actual posttransplantation level 24 after administration of tacrolimus by a Wilcoxon signed rank test. A P<0.05 was considered to be statistically significant.
The authors thank Drs. R. Hene, K. Schurink, and T. Ververs from the University Medical Center Utrecht, The Netherlands, for their help with preparation of the protocol. Furthermore, we would like to thank Dr. J. H. Proost from the department of Pharmacokinetics, University of Groningen, The Netherlands, for his help with the pharmacokinetic modeling.
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