Optimal time points for the prediction of the area-under-the-curve in liver transplant patients receiving tacrolimus

Tacrolimus 4-hour monitoring in liver transplant patients is non-inferior to trough monitoring: The randomized controlled FK04 trial

BACKGROUND

After liver transplantation (LT), tacrolimus and ciclosporin treatment can lead to, partially concentration-dependent, chronic kidney disease. Monitoring ciclosporin with two-hour levels reduced overexposure and led to better renal function than trough-monitoring (C0). For tacrolimus, a 4-hour level (C4) can give a reasonable approximation of total drug exposure. We evaluated whether monitoring tacrolimus in stable patients after LT by C4 was superior to C0 regarding renal function, rejection and metabolic parameters.

METHODS

This open label randomized controlled trial compared C4 monitoring of tacrolimus BID (Prograft) to trough (C0) monitoring in stable LT recipients. The target range for C4 of 7.8-16 ng/ml was calculated to be comparable with target C0 of 4-8 ng/ml. Primary endpoint was the effect on renal function and secondary endpoints were the occurrence of treated biopsy-proven acute rejection, blood pressure and metabolic parameters, during 3 months of follow-up.

RESULTS

Fifty patients were randomized to C0 (n = 25) or C4 (n = 25) monitoring. There was no difference in renal function between the C0 and the C4 group (p = .98 and p = .13 for CG and MDRD at 3 months). Also, the amount of proteinuria was similar (p = .59). None of the patients suffered from graft loss or was treated for rejection. Metabolic parameters did not differ between the two groups.

CONCLUSION

Tacrolimus 4-hour monitoring in stable LT patients is not superior to trough monitoring, regarding the effect on renal function, but is safe for use to facilitate tacrolimus monitoring in an afternoon outpatient clinic.

Evaluation of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients

AIMS

To examine the predictive performance of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients.

METHODS

Twenty full tacrolimus area under the concentration-time curve from 0 to 12 h post-dose (AUC(0-12)) profiles (AUCf) were collected from 20 subjects. Predicted tacrolimus AUC(0-12) (AUCp) was calculated using the following: (i) 42 multiple regression-derived limited sampling strategies (LSSs); (ii) five population pharmacokinetic (PK) models in the Bayesian forecasting program TCIWorks; and (iii) a Web-based consultancy service. Correlations (r(2)) between C(0) and AUCf and between AUCp and AUCf were examined. Median percentage prediction error (MPPE) and median absolute percentage prediction error (MAPE) were calculated.

RESULTS

Correlation between C(0) and AUCf was 0.53. Using the 42 LSS equations, correlation between AUCp and AUCf ranged from 0.54 to 0.99. The MPPE and MAPE were <15% for 29 of 42 equations (62%), including five of eight equations based on sampling taken ≤2 h post-dose. Using the PK models in TCIWorks, AUCp derived from only C(0) values showed poor correlation with AUCf (r(2)=0.27-0.54) and unacceptable imprecision (MAPE 17.5-31.6%). In most cases, correlation, bias and imprecision estimates progressively improved with inclusion of a greater number of concentration time points. When concentration measurements at 0, 1, 2 and 4 h post-dose were applied, correlation between AUCp and AUCf ranged from 0.75 to 0.93, and MPPE and MAPE were <15% for all models examined. Using the Web-based consultancy service, correlation between AUCp and AUCf was 0.74, and MPPE and MAPE were 6.6 and 9.6%, respectively.

CONCLUSIONS

Limited sampling methods better predict tacrolimus exposure compared with C(0) measurement. Several LSSs based on sampling taken 2 h or less post-dose predicted exposure with acceptable bias and imprecision. Generally, Bayesian forecasting methods required inclusion of a concentration measurement from >2 h post-dose to adequately predict exposure.

Flexible limited sampling model for monitoring tacrolimus in stable patients having undergone liver transplantation with samples 4 to 6 hours after dosing is superior to trough concentration

Trough (C0) monitoring is not optimal for therapeutic drug monitoring of tacrolimus. To better estimate systemic exposure of tacrolimus and achieve clinical benefit, an improved therapeutic drug monitoring strategy should be developed. The authors examined which single and combination of time points best estimated the empiric "gold standard" AUC0-12h and developed and validated a new, flexible, and accurate limited sampling model for monitoring tacrolimus in patients having undergone liver transplantation. Twenty-three stable patients with full AUC0-12h were divided into two groups based on area under the concentration-time curve/dose. With multiple regression analysis, limited sampling formulae were derived and population-pharmacokinetic-based limited sampling models were developed and validated. A regression analysis was performed between either area under the concentration-time curves calculated with formulae or models with the reference trapezoidal AUC0-12h. Both formulae and models based on single samples C4-C6 (r2 = 0.94 [MPE/MAPE 0/7]-0.90 [2/8] and 0.97 [0/7]-0.97 [1/5]) showed excellent performance. The calculated area under the concentration-time curve target range for tacrolimus was 90 to 130 h*microg/L. Multiple point sampling performed better, especially when using models (r2 > 0.94). C0 was a less precise predictor of AUC0-12h compared with both formulae and models (r2's 0.68 [5/17] and 0.87 [2/14]). In conclusion, trough concentration monitoring is not an accurate method for assessing systemic exposure to tacrolimus in stable patients having undergone liver transplantation. This new limited sampling model, based on single time points C4-C6, shows excellent performance in estimating the AUC0-12h.

Relationships of tacrolimus pharmacokinetic measures and adverse outcomes in stable adult liver transplant recipients

BACKGROUND AND OBJECTIVES

Alternative measures to trough concentrations [non-trough concentrations and limited area under the concentration-time curve (AUC)] have been shown to better predict tacrolimus AUC. The aim of this study was to determine if these are also better predictors of adverse outcomes in long term liver transplant recipients.

METHODS

The associations between tacrolimus trough concentrations (C(0)), non-trough concentrations (C(1), C(2), C(4), C(6/8)), and AUC(0-12) and the occurrence of hypertension, hyperkalaemia, hyperglycaemia and nephrotoxicity were assessed in 34 clinically stable liver transplant patients.

RESULTS AND DISCUSSION

The most common adverse outcome was hypertension, prevalence of 36%. Hyperkalaemia and hyperglycaemia had a prevalence of 21% and 13%, respectively. A sequential population pharmacokinetic/pharmacodynamic approach was implemented. No significant association between predicted C(0), C(1), C(2), C(4), C(6/8) or AUC(0-12) and adverse effects could be found. Tacrolimus concentrations and AUC measures were in the same range in patients with and without adverse effects.

CONCLUSIONS

Measures reported to provide benefit, preventing graft rejection and minimizing acute adverse effects in the early post-transplant period, were not able to predict adverse effects in stable adult liver recipients whose trough concentrations were maintained in the notional target range.

Tacrolimus pharmacokinetics and dose monitoring after lung transplantation for cystic fibrosis and other conditions

In cystic fibrosis (CF), absorption of tacrolimus through the gastrointestinal tract may be impaired due to fat malabsorption. The aim of this pilot study was to compare tacrolimus pharmacokinetics and inter- and intrasubject variability of exposure in stable lung transplant recipients with and without CF, and to determine the best single-time predictors of exposure. The study included 11 lung transplant recipients with CF and 11 without CF who received tacrolimus twice daily. Blood samples were obtained predose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8 and 12 h postdose on 3 separate days within 1 week. Tacrolimus pharmacokinetics and inter- and intrasubject variability of exposure were similar in the two groups, though exposure-per-milligram-dose was approximately 50% lower in CF patients. Tacrolimus trough concentration did not accurately predict the area under the concentration curve (AUC(0-12)), but the concentration measured 3 h postdose (C(3)) was tightly correlated with the AUC(0-12) in both CF (r(2)= 0.86) and non-CF (r(2)= 0.92) patients. In summary, patients with CF have a higher tacrolimus oral clearance, but nonsignificant differences in short-term inter- and intrasubject variability of exposure compared to patients without CF. C(3) is tightly correlated with AUC(0-12) in lung transplant recipients with and without CF.

Immunosuppressive drug monitoring - what to use in clinical practice today to improve renal graft outcome

Therapeutic drug monitoring (TDM) of immunosuppressive therapy is becoming an increasingly complex matter as the number of compounds and their respective combinations are continuously expanding. Unfortunately, in clinical practice, monitoring predose trough blood concentrations is often not sufficient for guiding optimal long-term dosing of these drugs. The excellent short-term results obtained nowadays in renal transplantation confer a misleading feeling of safety despite the fact that long-term results have not substantially improved, definitely not to a point where longer graft survival could counteract the increasing need for transplant organs and less toxicity and side-effects could ameliorate patient survival. It is therefore a challenging task to try to tailor immunosuppressive drug therapy to the individual patient profile and this in a time-dependent manner. For the majority of currently used immunosuppressive drugs, measurement of total drug exposure by determination of the dose-interval area under the concentration curve (AUC) seems to provide more useful information for clinicians in terms of concentration-exposure and exposure-response as well as reproducibility. To simplify this laborious way of measuring drug exposure, several validated abbreviated AUC profiles, accurately predicting the dose-interval AUC, have been put forward. Together with an increasing knowledge of the time-related pharmacokinetic behaviour of immunosuppressive drug and their metabolites, studies are focusing on how to apply abbreviated AUC sampling methods in clinical transplantation, taking into account the numerous factors affecting drug pharmacokinetics. Eventually, TDM using abbreviated AUC profiles has to be prospectively tested against classic methods of drug monitoring in terms of cost-effectiveness, feasibility and clinical relevance with the ultimate goal of improving patient and graft survival.

Sampling Times for Monitoring Tacrolimus in Stable Adult Liver Transplant Recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation

The aim of this review is to analyse critically the recent literature on the clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplant recipients. Dosage and target concentration recommendations for tacrolimus vary from centre to centre, and large pharmacokinetic variability makes it difficult to predict what concentration will be achieved with a particular dose or dosage change. Therapeutic ranges have not been based on statistical approaches. The majority of pharmacokinetic studies have involved intense blood sampling in small homogeneous groups in the immediate post-transplant period. Most have used nonspecific immunoassays and provide little information on pharmacokinetic variability. Demographic investigations seeking correlations between pharmacokinetic parameters and patient factors have generally looked at one covariate at a time and have involved small patient numbers. Factors reported to influence the pharmacokinetics of tacrolimus include the patient group studied, hepatic dysfunction, hepatitis C status, time after transplantation, patient age, donor liver characteristics, recipient race, haematocrit and albumin concentrations, diurnal rhythm, food administration, corticosteroid dosage, diarrhoea and cytochrome P450 (CYP) isoenzyme and P-glycoprotein expression. Population analyses are adding to our understanding of the pharmacokinetics of tacrolimus, but such investigations are still in their infancy. A significant proportion of model variability remains unexplained. Population modelling and Bayesian forecasting may be improved if CYP isoenzymes and/or P-glycoprotein expression could be considered as covariates. Reports have been conflicting as to whether low tacrolimus trough concentrations are related to rejection. Several studies have demonstrated a correlation between high trough concentrations and toxicity, particularly nephrotoxicity. The best predictor of pharmacological effect may be drug concentrations in the transplanted organ itself. Researchers have started to question current reliance on trough measurement during therapeutic drug monitoring, with instances of toxicity and rejection occurring when trough concentrations are within 'acceptable' ranges. The correlation between blood concentration and drug exposure can be improved by use of non-trough timepoints. However, controversy exists as to whether this will provide any great benefit, given the added complexity in monitoring. Investigators are now attempting to quantify the pharmacological effects of tacrolimus on immune cells through assays that measure in vivo calcineurin inhibition and markers of immunosuppression such as cytokine concentration. To date, no studies have correlated pharmacodynamic marker assay results with immunosuppressive efficacy, as determined by allograft outcome, or investigated the relationship between calcineurin inhibition and drug adverse effects. Little is known about the magnitude of the pharmacodynamic variability of tacrolimus.

A longitudinal replicate study of immunosuppressive drugs: a College of American Pathologists study

OBJECTIVE

To identify the sources of analytical variation for cyclosporine and tacrolimus in a 3-year longitudinal study.

DESIGN

Two pools of whole blood were spiked with cyclosporine and tacrolimus, respectively. One aliquot of cyclosporine and 2 of the tacrolimus pool were distributed in the first and last mailing for years 1999 to 2001. For both drugs, the total variance for each method was partitioned into within- and between-laboratory components.

SETTING

The A and C mailings of the 1999, 2000, and 2001 AACC/CAP [American Association for Clinical Chemistry/College of American Pathologists] Immunosuppressive Drugs (CS) Monitoring Survey.

MAIN OUTCOME MEASURES

For each drug, total variance was partitioned into specimen, mailing, year, and interlaboratory effects for each analytical method.

PARTICIPANTS

The 292 laboratories for cyclosporine and 177 laboratories for tacrolimus enrolled in the survey from 1999 to 2001.

RESULTS

For both cyclosporine and tacrolimus, the major source of imprecision came from within-laboratory factors, which accounted for nearly 85% (range, 77% to 90%) of the total variance. For cyclosporine, the major component of within-laboratory variance was between-mailing, within-year effect, whereas for tacrolimus it was the between-year, within-laboratory variation.

CONCLUSION

The major source of long-term survey imprecision for cyclosporine and tacrolimus is within-laboratory factors. The finding that 85% of the total variance was due to within-laboratory variation is similar to other therapeutic drugs.

Immunosuppressive therapy for paediatric transplant patients: pharmacokinetic considerations

Immunosuppressive therapy in paediatric transplant recipients is changing as a consequence of the increasing number of available immunosuppressive agents. Generic and other new formulations are now emerging onto the market, clinical experience is growing, and it is expected that clinicians should tailor immunosuppressive protocols to individual patients by optimising dosages and drugs according to the maturation and clinical status of the child. Most information about the clinical pharmacokinetics of immunosuppressive drugs in paediatrics is centred on cyclosporin, tacrolimus and mycophenolate mofetil in renal and liver transplant recipients; data regarding other immunosuppressants and transplant types are limited. Although the clinical pharmacokinetics of these drugs in paediatric transplant recipients are still under investigation, it is evident that the pharmacokinetic parameters observed in adults may not be applicable to children, especially in younger age groups. In general, patients younger than 5 years old show higher clearance rates irrespective of the organ transplanted or drug used. Another important factor that frequently affects clearance in this patient population is the post-transplant time. In accordance with these findings, and in contrast with the usual under-dosage in children, the need for higher dosages in younger recipients and during the early post-transplant period seems evident. To achieve the best compromise between prevention of rejection and toxicity, dosage individualisation is required and this can be achieved through therapeutic drug monitoring (TDM). This approach is particularly useful to ensure the cost-effective management of paediatric transplant recipients in whom the pharmacokinetic behaviour, target concentrations for clinical use and optimal dosage strategies of a particular drug may not yet be well defined. Although TDM may be a tool for improving immunosuppressive therapy, there is little information concerning its positive contribution to clinical events, including outcomes, for paediatric patients. Substantial information to support the use of TDM exists for cyclosporin and, to a lesser extent, for tacrolimus, but a diversity of options affects their implementation in the clinical setting. The role of TDM in therapy with mycophenolate mofetil and sirolimus has yet to be defined regarding both methods and clinical indications. Pharmacodynamic monitoring appears more suited to other immunosuppressants such as azathioprine, corticosteroids and monoclonal or polyclonal antibodies. If coupled with pharmacokinetic measurements, such monitoring would allow earlier and more precise optimisation of therapy. Very few population pharmacokinetic studies have been carried out in paediatric transplant patients. This type of study is needed so that techniques such as Bayesian forecasting can be applied to optimise immunosuppressive therapy in paediatric transplant patients.