|Year : 2021 | Volume
| Issue : 1 | Page : 101-110
|Estimation of Tacrolimus Clearance in Saudi Adult Kidney Transplant Recipients
Saeed Alqahtani1, Maha Alenazi2, Abdullah Alsultan1, Emad Alsarhani1
1 Department of Clinical Pharmacy, College of Pharmacy; Clinical Pharmacokinetics and Pharmacodynamics Unit, King Saud University Medical City, Riyadh, Saudi Arabia
2 Department of Pharmacy, Prince Sultan Cardiac Center, Riyadh, Saudi Arabia
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|Date of Web Publication||16-Jun-2021|
| Abstract|| |
Tacrolimus is commonly used in adult kidney transplant patients. Only few studies have so far described the pharmacokinetics of tacrolimus in the Saudi population. Thus, the goal of this study is to determine the population pharmacokinetics of tacrolimus in Saudi adult kidney transplant recipients and to identify the factors that explain variability. We performed a retrospective chart review of adult patients who received oral tacrolimus at two centers. We developed the population pharmacokinetic models using Monolix 4.4. The factors screened for influence on these parameters were weight, age, gender, liver function tests, and creatinine clearance. The analysis included a total of 149 tacrolimus plasma concentrations from 139 patients. A one-compartment open model with linear absorption and elimination adequately described the data. The average parameter estimates for apparent clearance (CL/F) and apparent volume of distribution (V/F) were 9.1 L/h and 912 L, respectively. The interindividual variabilities (coefficients of variation) in CL/F and V/F were 20% and 18%, respectively. Aspartate aminotransferase was identified to be the main covariate that influences tacrolimus CL/F. In conclusion, the population pharmacokinetic model of tacrolimus was established and a significant covariate of the model was identified. These findings offer a rationale for the personalization of tacrolimus dosing regimens. Further studies are required to understand the factors that may influence the pharmacokinetics of tacrolimus and assist in drug dosage decisions.
|How to cite this article:|
Alqahtani S, Alenazi M, Alsultan A, Alsarhani E. Estimation of Tacrolimus Clearance in Saudi Adult Kidney Transplant Recipients. Saudi J Kidney Dis Transpl 2021;32:101-10
|How to cite this URL:|
Alqahtani S, Alenazi M, Alsultan A, Alsarhani E. Estimation of Tacrolimus Clearance in Saudi Adult Kidney Transplant Recipients. Saudi J Kidney Dis Transpl [serial online] 2021 [cited 2021 Jul 31];32:101-10. Available from: https://www.sjkdt.org/text.asp?2021/32/1/101/318511
| Introduction|| |
Tacrolimus, a macrolide lactone, is isolated from the fermentation broth of Streptomyces tsukubaensis and is an immunosuppressive agent similar to cyclosporine. It belongs to the group of calcineurin inhibitors that has emerged as a valuable therapeutic alternative to cyclosporine as an immunosuppressant following solid organ transplantation. It is very effective at preventing rejection in transplant recipients of heart, liver, kidney, lung, small bowel, pancreas, and bone marrow., In addition, it is used in the treatment of several autoimmune diseases including myasthenia gravis, arthritis, and atopic dermatitis.
After oral administration, tacrolimus is absorbed rapidly, with peak plasma/blood concentrations achieved in 0.5–1 h. The rate of absorption and absolute bioavailability of tacrolimus vary significantly, mean bioavailability is approximately 25% but can range from 5% to 93%. The bioavailability of tacrolimus is similar for pediatric and adult transplant patients. However, lower oral bioavailability has been reported in several types of patients including patients awaiting renal transplantation, in small bowel recipients with open stomas, in African American and non-Caucasian individuals, and in patients with diabetes., Factors responsible for poor or erratic absorption of tacrolimus include poor aqueous solubility and extensive pre-systemic metabolism of tacrolimus by gastrointestinal cytochrome P450 (CYP) 3A iso-enzymes and removal through P-glycoprotein transport. Tacrolimus is highly bound to plasma proteins with protein binding of approximately 99%. It also displays significant binding to red blood cells. Tacrolimus is extensively metabolized in the liver and intestinal walls by CYP3A4 and CYP3A5 through O-demethylation, hydroxylation, and/or oxidative metabolic reactions. Tacrolimus is mainly eliminated by the biliary route, and less than 2.4% is eliminated renally.
Tacrolimus has a narrow therapeutic index and highly variable pharmacokinetic characteristics. This variability is attributed to multiple factors including genetic polymorphisms in CYP3A4 and CYP3A5 metabolizing enzymes and the Pgp transport protein, gender, age, ethnicity, hematocrit and albumin concentrations, and liver dysfunction. In addition, several drug and food interactions may lead to variation in tacrolimus plasma concentration. High plasma concentrations of tacrolimus have been associated with more frequent and more severe nephrotoxicity, neurotoxicity, diabetogenesis, gastrointestinal disturbances, and infection. Thus, close monitoring of tacrolimus concentration is required to achieve optimal efficiency and thus minimize the risk of subtherapeutic or toxic blood concentrations. It has been reported that the efficacy and safety of tacrolimus are highly correlated with the area under the curve. Previous studies with limited sampling strategies that used two or three sample time points demonstrated a high correlation with the area under the curve.
Only a few studies so far have described the pharmacokinetics of tacrolimus in the Saudi population. This study aimed to fill that gap. The goals of this pharmacokinetic study were to estimate the pharmacokinetic parameters of tacrolimus in Saudi adult kidney transplant recipients and identify the factors that explain the variability between patients.
| Methods|| |
Patients and data collection
This was a retrospective chart review study based on data retrieved from health information systems from two centers (King Saud University Medical City and Prince Sultan Military Medical City) between January 2009 and March 2015. Adult kidney transplant recipients were included in the study. Patients were receiving oral tacrolimus as a primary immunosuppressant treatment under standard care during which therapeutic drug monitoring (TDM) was routinely performed. We excluded patients with recent infections or rejections. The following information was collected for each patient: age, weight, gender, concomitant immunosuppressive drug dosage, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total albumin concentration, total bilirubin, and serum creatinine concentration. This study was approved by the Ethics Committee at King Saud Medical City and Prince Sultan Cardiac Center. Informed consent from each patient to participate in this study was not required by the Institutional Review Board, because the study was a retrospective review of clinical records only.
Drug administration and therapeutic drug monitoring data
Patients were administered oral tacrolimus as part of the immunosuppressive therapy following kidney transplantation. The 12-h trough (C0) of tacrolimus levels, every other day, during the immediate postoperative period was used for monitoring the tacrolimus level before target levels are reached. Subsequently, routine TDM for tacrolimus was performed whenever there was a change in medication or patient status that may affect blood levels, or after a decrease in kidney function that may have indicated nephrotoxicity or rejection. Dosage adjustment was based on the biological follow-up and tacrolimus monitoring to maintain C0 in the recommended therapeutic range of 5–15 ng/mL.
All blood samples were analyzed in the same laboratory. The ARCHITECT tacrolimus assay (Abbott Laboratories) was used for the quantification of tacrolimus concentration in whole blood. This assay is a chemiluminescent micro-particle immunoassay for the quantitative determination of tacrolimus in human whole blood on the ARCHITECT i System. The ARCHITECT Tacrolimus assay is designed to have a precision of ≤10% total CV. The procedures provided in the manual of the assay was used for analysis.
Population pharmacokinetic modelling
Pharmacokinetic analysis was computed by using Monolix software (version 4.4). Monolix estimates PK parameters using the stochastic approximation expectation maximization algorithm. First, we developed the base structural model for tacrolimus, and compared one- and two-compartment pharmacokinetic models. Pharmacokinetic parameters were assumed to follow a normal log distribution. For the residual variability, the constant, proportional, and combined error models were tested. The structural models were selected according to the following: (a) the decrease in the minimum of the objective function value (Log-likelihood value); (b) the precision of the parameter estimation expressed as the relative standard error [RSE (%)] and calculated as the ratio between the standard error and the final parameter estimate; (c) physiological plausibility; and (d) goodness of fit (GOF) plots that included the observed versus predicted concentration, residuals plot, and the visual predictive check (VPC). The bioavailability (F) and absorption with a lag time could not be determined because tacrolimus was orally administered and the blood data included many sparse data points; thus, we fixed these parameters. In addition, the pharmacokinetic values of clearance and distribution volume (V) corresponded to the ratios of CL/F (apparent clearance) and V/F (apparent volume of distribution), respectively.
After the appropriate base model was established, 10 covariates were tested, specifically age, gender, weight, serum creatinine, CLCR, total daily dose (TDD), AST, ALT, albumin concentration, and total bilirubin. For covariate testing, we first plotted the individual pharmacokinetic parameters against the covariates to screen for potentially significant correlations. Then, we performed a stepwise regression analysis to test the significant covariates identified in step 1 by using the log-likelihood ratio test. If a trend between a covariate and PK parameter was found, then it was considered for inclusion in the base model.
GOF plots were used as the first indicator of suitability including the representation of model-based individual predictions (IPRED) and population predictions (PRED) versus the observed concentrations. VPC was constructed to study the performance of the final model with the 10th, 50th, and 90th percentiles of the observed data.
| Results|| |
Patients and data collection
A total of 149 blood tacrolimus concentrations were retrieved from the electronic files of 139 Saudi adult kidney transplant recipients; 94 patients (67%) were male. The baseline demographic and general clinical characteristics of the patients used for model building are presented in [Table 1]. The mean age and weight of the population [mean ± standard deviation (SD)] was 43.9 ± 13.5 years and 74.2 ± 19.5 kg, respectively. The mean (±SD) CLCR estimated by the Cockcroft-Gault formula was 77.5 ± 30.4 mL/min. The average TDD of tacrolimus was 2.7 ± 1.7 mg and produced average trough concentration of 9.4 ± 5.5 mg/mL.
After fitting the data against the one- and two-compartment models without any covariates, one-compartment model with first-order absorption and linear elimination was better fitted than two-compartment model. This was noted by a greater reduction of the objective function value (OFV). The pharmacokinetic parameters of tacrolimus that were estimated by Monolix was parameterized in terms of apparent clearance (CL/F) and apparent volume of distribution (V/F). The most accurate error model for residual and interpatient variability was a combined-error model. After testing ten covariates, AST was the only covariate that showed significant influence on tacrolimus CL/F. AST statistically improved the base model, with a 28-point reduction in the OFV and a reduction of approximately 32% in the between-subject variability of CL/F. Consequently, AST covariate was included in the final model, whereas other covariates, which exhibited no significant impact on the pharmacokinetic parameters of tacrolimus were not subject to further investigation. The values of the parameters for the final models are presented in [Table 2].
Diagnostic GOF plots for the tacrolimus final covariate model are shown in [Figure 1]. Both weighted residual (WRES) plots with time post last dose [Figure 1]c and individual predicted concentrations of tacrolimus [Figure 1]d were randomly scattered with most of WRES arranging from -3 to +3. The diagnostic GOF plots and the values of RSE (%) shown in [Table 2], revealed that all parameters were accurately estimated. Moreover, the inspection of the VPC presented in [Figure 2], showed a good correlation between the percentile intervals obtained by simulation in the final model with those of the observed data. Overall, all figures indicate a good predictive capability of the final pharmacokinetic model.
|Figure 1: Goodness-of-fit plots obtained from the final model for tacrolimus. (a) The individual predictions of tacrolimus versus the observed concentrations. (b) The population predictions of tacrolimus vs the observed concentrations. (c) The weighted residuals versus the time since last dose. (d) The weighted residuals versus the individual predicted concentrations.|
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|Figure 2: Visual predictive check for tacrolimus concentration versus time based on 1000 Monte Carlo simulations. The solid green lines represent the 10th, 50th, and 90th percentiles of the observed data. The shaded regions represent the 90% CI around the 10th, 50th, and 90th percentiles of the si mulated data.|
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| Discussion|| |
Tacrolimus is a widely used immunosuppressant with a narrow therapeutic window and extensive side effects; thus, close monitoring is required. The tacrolimus concentrations vary significantly between subjects during the treatment and may lead to an increase either in the rate of rejection or graft loss or in the adverse effects. Therefore, the purpose of this study was to build a population pharmacokinetic model of tacrolimus for Saudi kidney transplant recipients, to generate a basis for the formulation of a more suitable dosing method. Such analysis is important for the development of rational guidelines for accurate individualized dosage prediction for these kidney transplant patients.
To date, several populations’ studies have reported the pharmacokinetic parameters of tacrolimus in various population and ethnic groups.,,, Ethnicity is an important factor that may account for the observed differences in the pharmacokinetics and pharmacodynamics of drugs, which may result in significant variability in response to drug therapy.,,, Ethnic multiplicity in drug response with respect to safety and efficacy, and the resulting differences in recommended doses have been well described for tacrolimus. Mancinelli et al compared the intravenous and oral pharmacokinetics of tacrolimus among subjects of three different ethnic backgrounds –African American, White, and Latin American. They found significant differences in tacrolimus pharmacokinetics between the three different ethnic groups, which may have been attributable to variations in intestinal CYP3A or P-glycoprotein activities. In addition, Felipe et al, investigated the impact of ethnicity on tacrolimus clinical pharmacokinetics and TDM. They aimed to determine the influence of ethnicity on tacrolimus pharmacokinetics and trough concentrations during the first six months after transplantation. Their study showed that the non-White patients had higher tacrolimus variability and lower drug exposure after transplantation than White patients. They concluded that higher initial tacrolimus doses and intensive monitoring were recommended for the administration of tacrolimus-based immunosuppressive therapy to non-White patients of this transplant population. Furthermore, Kim et al investigated the impact of different factors, including ethnicity, on the pharmacokinetics of tacrolimus in pediatric renal transplant patients. They found significant interindividual variability between different ethnic groups. In our region, Shilbayeh investigated whether the presence of CYP3A5 and P-glycoprotein polymorphisms would have an impact on the time required to attain target tacrolimus levels and subsequent pharmacodynamic outcomes in Jordanian pediatric kidney transplant patients. The study found that there were more unwanted side effects in CYP3A5 nonexpresser patients but not with P-glycoprotein polymorphisms. Therefore, the absence of such studies of tacrolimus pharmacokinetics in Saudi kidney transplant populations was the main impetus of the current work. In this work, we determined the population pharmacokinetic parameters of tacrolimus, together with the covariates that cause interindividual variability.
The plasma concentration-time curves for tacrolimus were fitted to one-compartment model, which was in agreement with other studies.,,,,,,,, The estimated pharmacokinetic parameters of tacrolimus were 9.1 L/h and 912 for CL/F and V/F, respectively. Although the values of the lag time and absorption rate (Ka) were fixed, as shown in [Table 2], the sparse data were insufficient to describe these parameters. Our CL/F value was lower than that reported in other studies, most likely because of the differences between study populations. However, our findings were similar to what has been published in a recent study that determined the pharmacokinetics of tacrolimus in liver transplant Saudi patients. Although this study was conducted in patients with liver transplant and, not in kidney transplant, it is the only study that is available in our population. Our multivariate analyses revealed that AST played a significant role in the influence of tacrolimus CL/F in kidney transplant patients. Tacrolimus is mainly subject to metabolic elimination in the liver, and we found that AST negatively influenced its CL/F; though, our study revealed substantial interindividual variability, which was consistent with several other studies., However, no covariates had a significant effect on tacrolimus Vd/F in our study, which was in contrast to other studies that found a correlation between Vd/F and patient weight. Our study showed that only AST could be included in the final covariate model, since none of the other patient characteristics significantly influenced the model and could therefore not be included. In a similar population, Sy et al investigated the impact of CYP3A5 and P-glycoprotein polymorphisms, tacrolimus troughs, and other clinical variables on the time of adverse events associated with tacrolimus therapy in Jordanian pediatric kidney transplant patients. They found that tacrolimus concentration and hematocrit are only the factors that influence the occurrence of these adverse effects.
The current practice at our institute, as well as many others, is to use the measurement of the trough concentration in whole blood for tacrolimus monitoring. The main goal of tacrolimus level monitoring is to personalize the tacrolimus dose, maintain drug efficacy, and minimize the consequences of overexposure. Previous studies have suggested that in order to maximize the efficacy of tacrolimus and prevent rejection in the first month after renal transplantation, tacrolimus trough concentration should be maintained above 10 ng/mL., However, several studies reported a strong correlation between high blood tacrolimus concentration and toxicity, especially renal toxicity.,, Although the area under the concentration versus time curve (AUC) is considered the best marker of tacrolimus exposure, practical utilization is very difficult for several reasons, including logistic and financial constraints and the potential burden on patients. In addition, a tacrolimus AUC target value associated with maximum efficacy and minimum toxicity has not yet been established.
Tacrolimus is a medication with a narrow therapeutic window and its pharmacokinetics is characterized by a high interpatient and intrapatient variability. It has been reported that high tacrolimus intrapatient variability is considered a risk factor for poor long-term outcomes after kidney transplantation including a high risk of graft loss and greater nephrotoxicity.,, Several factors can contribute to this variability. In our study, we found that changes in AST level were the main factor that led to variability in tacrolimus CL/F. In addition, several factors have been reported by different studies., One of these factors is the genetic polymorphism in genes encoding for tacrolimus metabolism (CYP 3A4/5) and transporters [P-gp and anion-transporting polypeptide (OATP) 1B3]., These genetic polymorphisms may also provide an explanation for the significant differences in tacrolimus pharmacokinetics between different ethnic groups, as was evident in our study. It has been reported that there are ethnic distribution differences of CYP3A5 variant alleles with expressers (carriers of the CYP3A5*1) more frequently found in non-Caucasian populations; only approximately 10%–40% of Caucasians are CYP3A5 expressers compared with 33% of Asians and 55% of African Americans.,, Further studies are required to investigate the role of genetic polymorphisms on the tacrolimus pharmacokinetics in our population.
There are certain limitations that exist in the current study. First, the small sample size of adult patients that may cause an absence of some variable (genetic polymorphisms and post operations days) which hindered other covariates from being revealed as significant and predictive of the variability in the pharmacokinetic parameters between subjects. Second, this retrospective study was not designed to evaluate and compare the clinical and safety outcomes associated with current tacrolimus dosing regimens. Nevertheless, this current study provides a good estimation for tacrolimus pharmacokinetics in the absence of other studies in our population. In addition, it showed the most significant factor that should be taken into consideration when designing the dosage regimen of tacrolimus.
| Conclusion|| |
A population pharmacokinetic model of tacrolimus in Saudi adult kidney transplant recipients was established and a significant covariate in the tacrolimus model was identified. These findings offered the rationale for personalization of tacrolimus dosing regimens. Further studies are required to understand the factors that may influence the pharmacokinetics of tacrolimus and may assist in drug dosage decisions.
| Acknowledgments|| |
We thank all the pharmacy and nurse staff at King Khalid University Hospital and Prince Sultan Cardiac Center for their help in facilitating data collection.
| Funding|| |
This work was supported by King Abdulaziz City for Science and Technology (KACST), reference number 1-17-03-0010001.
Conflict of interest: None declared.
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Department of Clinical Pharmacy, College of Pharmacy, King Saud University, P. O. Box 2457, Riyadh 11451
[Figure 1], [Figure 2]
[Table 1], [Table 2]
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