Therapeutic drug monitoring of tacrolimus early after liver transplantation

Cognitive Evaluation in Liver Transplant Patients Under Calcineurin Inhibitor Maintenance Therapy

BACKGROUND

Neurological disorders due to calcineurin inhibitor (CNI) treatment pose a well-known problem after liver transplantation (LTx). In this study, the impact of CNIs on cognitive functioning during maintenance therapy was analyzed. A possible improvement of cognitive functioning, compliance and health-related quality of life (HRQoL) after conversion to a once-daily tacrolimus formulation was prospectively assessed.

METHODS

In a cross-section analysis cognitive functioning of living donors (LD), waiting list patients and LTx patients was tested using a 4 times trail making test (4-TTMT). In a further investigator-initiated trial a possible improvement of cognitive functioning, HRQoL and compliance after conversion to the once-daily tacrolimus formulation was prospectively assessed over 1 year. HRQoL was assessed using an EORTC-QLQ C30 questionnaire and patient's compliance was assessed by the Basel Assessment of Compliance with Immunosuppressive Medication Scales questionnaire. Correlated data were sex, age, time after surgery, liver disease, model of end-stage liver disease score, creatinine, CNI type, and CNI trough levels.

RESULTS

Two hundred eleven patients were included in this cross-section analysis. Twenty-seven patients agreed to participate in the investigator-initiated trial. LTx patients completed the 4-TTMT slower than living donor patients and faster than waiting list patients. Patients with twice daily cyclosporine A (CSA) formulation needed longer to finish the 4-TTMT than patients with the once-daily tacrolimus formulation. After drug conversion of a twice-daily CNI formulation to a once-daily tacrolimus formulation, CSA-treated patients needed longer to improve their cognitive functioning. HRQoL and compliance did not improve after drug conversion.

CONCLUSIONS

Patients with once-daily tacrolimus formulation had a better psychomotor speed than CSA-treated patients. The conversion to once-daily tacrolimus formulation significantly improved cognitive functioning, but had no impact on HRQoL or compliance.

Estimating systemic exposure to levonorgestrel from an oral contraceptive

OBJECTIVE

The gold standard for measuring oral contraceptive (OC) pharmacokinetics is the 24-h steady-state area under the curve (AUC). We conducted this study to assess whether limited sampling at steady state or measurements following use of one or two OCs could provide an adequate proxy in epidemiological studies for the progestin 24-h steady-state AUC of a particular OC.

STUDY DESIGN

We conducted a 13-sample, 24-h pharmacokinetic study on both day 1 and day 21 of the first cycle of a monophasic OC containing 30-mcg ethinyl estradiol and 150-mcg levonorgestrel (LNG) in 17 normal-weight healthy White women and a single-dose 9-sample study of the same OC after a 1-month washout. We compared the 13-sample steady-state results with several steady-state and single-dose results calculated using parsimonious sampling schemes.

RESULTS

The 13-sample steady-state 24-h LNG AUC was highly correlated with the steady-state 24-h trough value [r=0.95; 95% confidence interval (0.85, 0.98)] and with the steady-state 6-, 8-, 12- and 16-h values (0.92≤r≤0.95). The trough values after one or two doses were moderately correlated with the steady-state 24-h AUC value [r=0.70; 95% CI (0.27, 0.90) and 0.77; 95% CI (0.40, 0.92), respectively].

CONCLUSIONS

Single time-point concentrations at steady state and after administration of one or two OCs gave highly to moderately correlated estimates of steady-state LNG AUC. Using such measures could facilitate prospective pharmaco-epidemiologic studies of the OC and its side effects.

IMPLICATIONS

A single time-point LNG concentration at steady state is an excellent proxy for complete and resource-intensive steady-state AUC measurement. The trough level after two single doses is a fair proxy for steady-state AUC. These results provide practical tools to facilitate large studies to investigate the relationship between systemic LNG exposure and side effects in a real-life setting.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing

Tacrolimus is the mainstay immunosuppressant drug used after solid organ and hematopoietic stem cell transplantation. Individuals who express CYP3A5 (extensive and intermediate metabolizers) generally have decreased dose-adjusted trough concentrations of tacrolimus as compared with those who are CYP3A5 nonexpressers (poor metabolizers), possibly delaying achievement of target blood concentrations. We summarize evidence from the published literature supporting this association and provide dosing recommendations for tacrolimus based on CYP3A5 genotype when known (updates at www.pharmgkb.org).

Tacrolimus pharmacokinetics in lung transplantation: new strategies for monitoring

BACKGROUND

Tacrolimus (TAC) dosing in lung transplantation is traditionally based on blood trough levels (C0). The best sampling strategy for the estimation of total drug exposure (area-under-the-curve [AUC]) has not been determined.

METHODS

Thirty-one 12-hour pharmacokinetic profiles were studied in 15 patients (8 men and 7 women, 42.0 +/- 13 years) post-bilateral lung transplantation (7.3 +/- 3.7 months; range, 3-18 months). Twelve-hour AUC (AUC0-12) was calculated by trapezoidal rule. Relationships between individual concentration points or abbreviated kinetics (2-4 concentration points) and AUC0-12 were determined by linear regression analysis (R2; absolute prediction error [APE]).

RESULTS

Pharmacokinetic profiles showed high variability, particularly in the absorption phase. AUC was 221 +/- 47.2 ng/ml (range, 156-329.3 ng/ml) at C0 10 to 15 ng/ml and was independent of TAC dose (R2 = 0.002). C0 was poorly predictive of AUC0-12 (R2 = 0.64; APE, 16.1% +/- 10.9%; range, 1.4%-37.8%). The predictive performance for AUC0-12 was highest with abbreviated kinetics using 4 (C0/C2/C3/C4: R(2) = 0.99; APE, 2.6% +/- 2.0%; range, 0.1%-7%) or 3 concentration points (C0/C2/C4: R2 = 0.98; APE, 2.6% +/- 2.1%; range, 0.1%-9.1%). Of the 2-point kinetics C2/C6 (R2 = 0.96; APE, 5.3% +/- 3.7%; range, 0.1%-12.7%), C2/C4 (R2 = 0.94, APE 6.7% +/- 4.8%; range 0.1%-14.6%) and C0/C4 (R2 = 0.94; APE 4.1% +/- 2.9%; range, 0.5%-11.4%) performed best. Single point strategies (best was C4: R2 = 0.94; APE 7.1% +/- 5.5%, range, 0.2%-24.1%) all had unacceptably high APE (range > 15%).

CONCLUSION

True TAC exposure shows high variability in stable lung transplant patients and is poorly predicted by C0. Abbreviated kinetics covering at least 2 concentration points between 0 and 4 hours post-drug intake are required for an accurate estimation of AUC.

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.

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.

Tacrolimus: a further update of its use in the management of organ transplantation

Extensive clinical use has confirmed that tacrolimus (Prograf) is a key option for immunosuppression after transplantation. In large, prospective, randomised, multicentre trials in adults and children receiving solid organ transplants, tacrolimus was at least as effective or provided better efficacy than cyclosporin microemulsion in terms of patient and graft survival, treatment failure rates and the incidence of biopsy-proven acute and corticosteroid-resistant rejection episodes. Notably, the lower incidence of rejection episodes after renal transplantation in tacrolimus recipients was reflected in improved cost effectiveness. In bone marrow transplant (BMT) recipients, the incidence of tacrolimus grade II-IV graft-versus-host disease was significantly lower with tacrolimus than cyclosporin treatment. Efficacy was maintained in renal and liver transplant recipients after total withdrawal of corticosteroid therapy from tacrolimus-based immunosuppression, with the incidence of acute rejection episodes at up to 2 years' follow-up being similar with or without corticosteroids. Tacrolimus provided effective rescue therapy in transplant recipients with persistent acute or chronic allograft rejection or drug-related toxicity associated with cyclosporin treatment. Typically, conversion to tacrolimus reversed rejection episodes and/or improved the tolerability profile, particularly in terms of reduced hyperlipidaemia. In lung transplant recipients with obliterative bronchiolitis, conversion to tacrolimus reduced the decline in and/or improved lung function in terms of forced expiratory volume in 1 second. Tolerability issues may be a factor when choosing a calcineurin inhibitor. Cyclosporin tends to be associated with a higher incidence of significant hypertension, hyperlipidaemia, hirsutism, gingivitis and gum hyperplasia, whereas the incidence of some types of neurotoxicity, disturbances in glucose metabolism, diarrhoea, pruritus and alopecia may be higher with tacrolimus treatment. Renal function, as assessed by serum creatinine levels and glomerular filtration rates, was better in tacrolimus than cyclosporin recipients at up to 5 years' follow-up.

CONCLUSION

Recent well designed trials have consolidated the place of tacrolimus as an important choice for primary immunosuppression in solid organ transplantation and in BMT. Notably, in adults and children receiving transplants, tacrolimus-based primary immunosuppression was at least as effective or provided better efficacy than cyclosporin microemulsion treatment in terms of patient and graft survival, treatment failure and the incidence of acute and corticosteroid-resistant rejection episodes. The reduced incidence of rejection episodes in renal transplant recipients receiving tacrolimus translated into a better cost effectiveness relative to cyclosporin microemulsion treatment. The optimal immunosuppression regimen is ultimately dependent on balancing such factors as the efficacy of the individual drugs, their tolerability, potential for drug interactions and pharmacoeconomic issues.