Clinical pharmacokinetics of tacrolimus in heart transplantation: new strategies of monitoring (original) (raw)
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European Journal of Clinical Pharmacology, 2011
To explore the main factors that make difficult the empirical monitoring of Tacrolimus (TAC) in the early period post liver transplantation (LTx). Specifically, those aspects were stressed related with patient idiosyncrasy and clinical status and also those pharmacokinetic (PK) assumptions on which drug individualization is based in clinical practice. Methods Retrospective monitoring data from 75 de novo liver transplant patients treated with twice daily TAC and followed for up to 15 days were analyzed. Extensive laboratory measures were available. Dose adjustment was done empirically using trough levels (C min). The population was separated into two background subgroups for low or high values of AST, Group 1 and Group 2, respectively. Data during the first 4 Days post LTx were used for this grouping. Then, subgroups were identified for albumin (ALB) and hematocrit (HCT) both being either elevated (Group 1A) or reduced (Group 1B) based on cut-off's of 2.5 g/dl and 28%, respectively. Similarly for Group 2 into Group 2A and Group 2B for ALB and HCT being elevated or reduced, respectively. Results The C min /Dose ratio (inversely proportional to CL) had variability (CV% > 80%) that was incongruently higher for the ratio than for C min and Dose separately. This was attributed to that most patients were not at steady state or physiologically stable early post LTx. Group 1 was more predictable than Group 2, which was responsible for the ratio variability. C min was lower across reduced ALB and HCT patient groups, for similar AST conditions (1A vs. 1B and 2A vs. 2B) likely due to increased TAC metabolic clearance (CL) (reduced C min /Dose). The same situation existed for 0-15 Days post LTx and 4-15 Days post LTx observations. Group 2A was the main source of the paradoxical variability in C min /Dose (higher ratio of 2.7; CV=100%) suggesting lower clearance and difficulty in recovery of stability. In contract, Group 2B had the lower ratio (1.4; 47%) but required the highest number of dose adjustments as it was hard to identify clinically. Group 1A was the most predictable empirically. When using observations from 15 patients who entered the clinic in 2007 and 2008, the same subgroups existed in the same proportions. Conclusion The difficulty in empirical dose adjustment of TAC is associated to the inevitable noncompliance of PK assumptions early post LTx and also to the inherent complexity of the clinical condition leading to increased uncertainty for the clinician as to dose selection. Identifying these sub-categories provides a rational means of classifying patients akin to a phenotype. This complexity of the kinetics in LTx and TAC treatment does not invalidate C min as a biomarker, but a Bayes algorithm including a full PK structure and these covariates would be optimal.
A limited sampling strategy for tacrolimus in renal transplant patients
British Journal of Clinical Pharmacology, 2008
Three models were identified, all with R 2 Ն 0.907. Two point models included one with trough (C0) and 1.5 h postdose (C1.5), another with trough and 4 h postdose. Increasing the number of sampling time points to more than two increased R 2 marginally (0.951 to 0.990). After jackknife validation, the two sampling time point (trough and 1.5 h postdose) model accurately predicted AUC0-12. Regression coefficient R 2 = 0.951, intraclass correlation = 0.976, bias [95% confidence interval (CI)] 0.53% (-2.63, 3.69) and precision (95% CI) 6. 35% (4.36, 8.35).
Can initial tacrolimus trough levels be predicted from clinical variables
Transplantation Proceedings, 2004
In eligible patients, cardiac transplantation has become the definitive treatment for end-stage heart failure. The initial posttransplantation course is marked by many potential difficulties, including renal insufficiency, hemodynamic instability, and perioperative bleeding. It is important to prevent early rejection; calcineurin inhibitors, such as tacrolimus or cyclosporine, are integral parts of such management. However, these drugs are associated with renal toxicity in some patients. Previous work suggests that limiting the increase in tacrolimus levels is associated with less renal insufficiency. The hypothesis of the current study was that a combination of clinical or laboratory variables could identify patients at risk for rapid changes in tacrolimus target levels. No single variable was strongly associated with high resultant trough levels following a standard 1-mg oral "test dose" of tacrolimus. However, the combination of 2 indices of liver metabolism (alanine aminotransferase and total bilirubin) along with serum creatinine did identify patients who tended toward elevated levels of tacrolimus (Ն4.5 ng/dL). Other variables, such as demographics, and even functional variables, such as right ventricular function by echocardiography, did not enhance the predictive value of this simple scoring system.
Basic & Clinical Pharmacology & Toxicology, 2010
Monitoring of tacrolimus blood concentration is of utmost importance in the management of renal transplant recipients because of Narrow Therapeutic Index and highly variable pharmacokinetics. The aim of this study was to detect inter-patient pharmacokinetic variability of tacrolimus and to assess the predictability of individual tacrolimus concentrations at various times of the area under the curve (AUC) seeking to find the best sampling time to predict the exposure of tacrolimus in renal transplant recipients with triple therapy. This oral dose tacrolimus pharmacokinetics study was conducted in 18 Serbian renal transplant recipients on triple immunosuppressive therapy, including basiliximab. The first oral dose of tacrolimus (0.05 mg ⁄ kg) was given on day 5 post-transplant; blood concentration was measured by microparticle enzyme immunoassay method. Associations between each sampling time-point of concentrations and AUC 12 were evaluated by Pearson correlation coefficients. Abbreviated sampling equations were derived by multiple, stepwise regression analyses. The variance in the strength of association between predicted AUC (AUC p ) and AUC 12 was reflected by linear regression coefficients. AUC 12 showed remarkable inter-individual variations after the first oral dose of tacrolimus. The area of the maximum AUC was four times higher than that of the minimum AUC. C 4 seems to be an indicator of total body exposure to tacrolimus. Alternatively, the concentrations at 1.5, 4 and 8 hr as an abbreviated AUC were as good a predictor as a full pharmacokinetic study. Our results show a significant difference between men and women. A three-point sampling method seemed to be the best abbreviated AUC for a cost-effective tacrolimus monitoring strategy.
Transplant Patient Classification and Tacrolimus Assays
Therapeutic Drug Monitoring, 2014
Background: A global tacrolimus proficiency study recently showed clinically significant variability between laboratories, the inability of a common calibrator to harmonize methods, and differences in patient classification depending on the test method. The authors evaluated (1) the effect of a change in methodology on patient classification based on tacrolimus blood concentration and (2) the ability of 2 methods to position the concentration in a given specimen within the correct range. Methods: A total of 839 consecutive samples were analyzed at The Rogosin Institute and New York Presbyterian Hospital for routine tacrolimus monitoring over 30 days. Concordance analysis between the methods was performed covering dosage target ranges of 8-10, 6-8, 4-6 ng/mL currently used at our center. Six Sigma Metrics were applied to statistically evaluate the discordance rate. Results: Deming regression comparing liquid chromatographytandem mass spectrometry and immunoassay yielded y = 0.927x 2 0.24; 95% confidence interval, 0.903-0.951; R 2 = 0.875; n = 839. There were 310 pairs (37%) discordant by 1, 21 (2.5%) discordant by 2, and 4 (0.5%) discordant by 3 therapeutic ranges. Surprisingly, 40% of patient samples were discordant when therapeutic ranges were 2 ng/mL wide. This discordant rate is equivalent to 1.7 Sigma and falls far below the minimum acceptable threshold of 3 Sigma. Conclusions: Both methods are capable of measuring tacrolimus in the clinically relevant range between 1 and 10 ng/mL, yet 40% of the samples were discordant with an unacceptable Sigma level. Standardization of tacrolimus assays will mitigate this issue.
Pharmacokinetics of tacrolimus in adult renal transplant recipients
Drug Metabolism and Drug Interactions, 2012
Background: The success of an immunosuppressive drug therapy depends on the extent of exposure to the drugs (the blood levels and duration), which is measured as the area under the curve (AUC). Tacrolimus shows considerable variability in its pharmacokinetics, with poor correlation between the tacrolimus trough level and systemic exposure, as measured by the AUC of concentration time. Monitoring trough levels helps not only in reducing nephrotoxiicity but also in reducing the chances of acute rejection; although there is no international consensus, the trough concentration is used to determine dosing and the AUC for calculating the exposure of the patient to the drug. The major objective of this study was to find the best sampling time for an abbreviated AUC 0-6 (area under the concentration time curve) to predict the total body exposure to tacrolimus in adult renal transplantation recipients. Methods: The study involved retrospective analysis of 14 renal transplant patients (2 female and 12 male) that were on triple immunosuppressive therapy, methyl prednisolone, mycophenolate mofetil and tacrolimus. To determine trough concentrations, blood samples were collected before administration of tacrolimus (0 h) and at fixed time points of 2 h, 4 h and 6 h after administration of oral tacrolimus and analyzed in duplicate by microparticle enzyme immunoassay. AUC 0-6 was determined using the linear trapezoidal rule. The association between the blood concentration and AUC 6 were evaluated by the Pearson correlation coefficient. All statistical analyses were performed using the SPSS software (IBM Corp., NY, USA) program. Results: Trough levels were fairly consistent at 7.9-18 ng. h/mL in all the patients included in this study, and this did not show variation with age or sex. The AUC 0-6 was higher [202-290 ng/mL at 3-8 mg bis-daily (b.d.) dosage] in patients who received kidneys from cadavers compared to recipients from live donors (60.5-171 ng/ mL at 3-8 mg b.d. dosage), but the clinical significance of this is not known. The highest AUC 0-6 was 246 ng/mL, observed at 4.5 mg b.d. dosage. Dosages higher than 2 mg b.d. did not result in a noticeable increase in AUC 0-6. Peak blood levels of tacrolimus were obtained 4 h after administration. Conclusions: Trough level determination and a C2, C4 two-point limited sampling strategy may be useful to plan the dosing strategy and estimate the exposure of renal transplant patients to tacrolimus.
Evaluation of the new EMIT tacrolimus assay in kidney and liver transplant recipients
Transplantation Proceedings, 2004
Tacrolimus (FK506) is a potent macrolide immunosuppressant used for prevention of organ transplant rejection following transplantation. Monitoring of blood tacrolimus concentrations is essential to assess organ rejection and toxicity, because of the agent's narrow therapeutic range, wide inter-and intraindividual pharmacokinetic variability as well as drug interactions mediated by alteration in cytochrome P450. Several methods have been developed to monitor tacrolimus; immunoassays, bioassays, and HPLC/MS. The purpose of this study was to compare two analytical methods: the well-established MEIA II tacrolimus immunoassay using the IMx analyzer and the new EMIT 2000 tacrolimus immunoassay on the Cobas Integra 400 system. Tacrolimus results obtained using the two methods have been compared on 180 whole blood samples from kidney and liver transplant patients. The analytical sensitivities of both methods were defined as 1.2 ng/mL for EMIT and 1.5 ng/mL for MEIA II. The within-run CVs (n ϭ 15) obtained with four-level controls were 9.08%, 9.41%, 5.23% and 4.4% for EMIT 2000. The comparison showed the following relationship between two methods: MEIA ϭ 1.08.EMIT ϩ 0.20 (r ϭ .893). In conclusion, the EMIT 2000 tacrolimus immunoassay is a reliable alternative for the MEIA II method to monitor tacrolimus in organ transplant recipients. It provides a valid quantitative measurement of tacrolimus with comparable % CVs in quality-control as well as patient blood samples. Additionally, the EMIT 2000 method provides a rapid analysis of a large number of samples in one run with a low turnaround time and possibilities to reanalyze critical samples.
Journal of Advanced Pharmacy Research
Objectives: This work was performed to study the pharmacokinetics of tacrolimus in adult liver transplant recipients after optimization of all the known classic factors contributing to inter-patient variability in whole blood tacrolimus levels. Also, to detect if any variability in whole blood tacrolimus levels still exists or this variability is only a function of the classic co-variables so that their optimization will diminish or eliminate it. Methods: Twenty-Six male patients with end-stage liver disease undergoing living donor liver transplantation were selected from the Gastroenterology Department of the International Medical Center, Cairo, Egypt, were enrolled in the study. A patient is initially considered to be a candidate for this study when tacrolimus was indicated as a part of a triple immune suppressive regimen with mycophenolate mofetil and prednisolone. Patients were selected to have non-significant variations in their demographics and pretreatment clinical data. Blood samples were drawn from each patient before the morning dose at specified intervals and the whole blood was assayed for tacrolimus, using Chemiluminescent Microparticle Immunoassay method (CMIA). Six months after liver transplantation, patients were classified into 3 groups based on their tacrolimus trough levels; normalized by its daily dose (C/D ratio), into fast, intermediate and slow metabolizers. Results: The results revealed unpredictable variability in whole blood tacrolimus levels among patients at each sampling time and a marked inter-patient variability in mean whole blood tacrolimus levels among individuals throughout the six months post transplantation period, (P value: <0.0001). Considerable inter-patient variability was also evident in tacrolimus pharmacokinetics. During 1 st month post-transplant, tacrolimus C/D ratio varied from 0.53 to 12.2 (ng/ml*1/mg) and tacrolimus oral clearance (CL/F) varied from 3.4 to 79.4 L/hr. At 3 rd month post-transplant, tacrolimus C/D ratio varied from 0.78 to 8.50 (ng/ml*1/mg) and tacrolimus CL/F varied from 4.9 to 53.2 L/hr. At 6 th month post-transplant, tacrolimus C/D ratio varied from 0.73 to 7.10 (ng/ml*1/mg) and tacrolimus CL/F varied from 5.9 to 56.8 L/hr. The overall mean C/D ratio and oral clearance also showed a great variability among patients with a mean of 2.80±1.89 (CV: 67.5%) and 21.3±12.9 (CV:60.7%), respectively. Conclusion: The variability in whole blood tacrolimus concentrations and tacrolimus pharmacokinetics existed in spite of careful patient selection and optimization of all the classic co-variables known to affect tacrolimus concentrations, suggesting the presence of other unstudied factors; the recently evolving genetic factors might contribute to this variability. It is recommended to still considering therapeutic drug monitoring as an integral part of tacrolimus therapy to control variations in response until the discovery of a model that considers all the expected covariates to predict the response.