Population pharmacokinetic analysis of mycophenolic acid in renal transplant recipients following oral administration of mycophenolate mofetil

Aim To develop a population pharmacokinetic model for mycophenolic acid in adult kidney transplant recipients, quantifying average population pharmacokinetic parameter values, and between- and within-subject variability and to evaluate the influence of covariates on the pharmacokinetic variability. Methods Pharmacokinetic data for mycophenolic acid and covariate information were previously available from 22 patients who underwent kidney transplantation at the Princess Alexandra Hospital. All patients received mycophenolate mofetil 1 g orally twice daily. A total of 557 concentration-time points were available. Data were analysed using the first-order method in NONMEM (version 5 level 1.1) using the G77 FORTRAN compiler. Results The best base model was a two-compartment model with a lag time (apparent oral clearance was 271 h(-1), and apparent volume of the central compartment 981). There was visual evidence of complex absorption and time-dependent clearance processes, but they could not be successfully modelled in this study. Weight was investigated as a covariate, but no significant relationship was determined. Conclusions The complexity in determining the pharmacokinetics of mycophenolic acid is currently underestimated. More complex pharmacokinetic models, though not supported by the limited data collected for this study, may prove useful in the future. The large between-subject and between-occasion variability and the possibility of nonlinear processes associated with the pharmacokinetics of mycophenolic acid raise questions about the value of the use of therapeutic monitoring and limited sampling strategies.

Relationships between sirolimus dosing, concentration and outcomes in renal transplant recipients

Aim To explore relationships between sirolimus dosing, concentration and clinical outcomes. Methods Data were collected from 25 kidney transplant recipients (14 M/11 F), median 278 days after transplantation. Outcomes of interest were white blood cell (WBC) count, platelet (PLT) count, and haematocrit (HCT). A naive pooled data analysis was performed with outcomes dichotomized (Mann-Whitney U-tests). Results Several patients experienced at least one episode when WBC (n = 9), PLT (n = 12), or HCT (n = 21) fell below the lower limits of the normal range. WBC and HCT were significantly lower (P < 0.05) when sirolimus dose was greater than 10 mg day(-1), and sirolimus concentration greater than 12 mu g l(-1). No relationship was shown for PLT and dichotomized sirolimus dose or concentration. Conclusions Given this relationship between sirolimus concentration and effect, linked population pharmacokinetic-pharmacodynamic modelling using data from more renal transplant recipients should now be used to quantify the time course of these relationships to optimize dosing and minimize risk of these adverse outcomes.

Pharmacokinetic considerations relating to tacrolimus dosing in the elderly

Relaxation of the upper age limits for solid organ transplantation coupled with improvements in post-transplant survival have resulted in greater numbers of elderly patients receiving immunosuppressant drugs such as tacrolimus. Tacrolimus is a potent agent with a narrow therapeutic window and large inter- and intraindividual pharmacokinetic variability. Numerous physiological changes occur with aging that could potentially affect the pharmacokinetics of tacrolimus and, hence, patient dosage requirements. Tacrolimus is primarily metabolised by cytochrome P450 (CYP) 3A enzymes in the gut wall and liver. It is also a substrate for P-glycoprotein, which counter-transports diffused tacrolimus out of intestinal cells and back into the gut lumen. Age-associated alterations in CYP3A and P-glycoprotein expression and/or activity, along with liver mass and body composition changes, would be expected to affect the pharmacokinetics of tacrolimus in the elderly. However, interindividual variation in these processes may mask any changes caused by aging. More investigation is needed into the impact aging has on CYP and P-glycoprotein activity and expression. No single-dose, intense blood-sampling study has specifically compared the pharmacokinetics of tacrolimus across different patient age groups. However, five population pharmacokinetic studies, one in kidney, one in bone marrow and three in liver transplant recipients, have investigated age as a co-variate. None found a significant influence for age on tacrolimus bioavailability, volume of distribution or clearance. The number of elderly patients included in each study, however, was not documented and may have been only small. It is likely that inter- and intraindividual pharmacokinetic variability associated with tacrolimus increase in elderly populations. In addition to pharmacokinetic differences, donor organ viability, multiple co-morbidity, polypharmacy and immunological changes need to be considered when using tacrolimus in the elderly. Aging is associated with decreased immunoresponsiveness, a slower body repair process and increased drug adverse effects. Elderly liver and kidney transplant recipients are more likely to develop new-onset diabetes mellitus than younger patients. Elderly transplant recipients exhibit higher mortality from infectious and cardiovascular causes than younger patients but may be less likely to develop acute rejection. Elderly kidney recipients have a higher potential for chronic allograft nephropathy, and a single rejection episode can be more devastating. There is a paucity of information on optimal tacrolimus dosage and target trough concentration in the elderly. The therapeutic window for tacrolimus concentrations may be narrower. Further integrated pharmacokinetic-pharmaco-dynamic studies of tacrolimus are required. It would appear reasonable, based on current knowledge, to commence tacrolimus at similar doses as those used in younger patients. Maintenance dose requirements over the longer term may be lower in the elderly, but the increased variability in kinetics and the variety of factors that impact on dosage suggest that patient care needs to be based around more frequent monitoring in this age group.

Impact of mycophenolate mofetil loading on drug exposure in the early posttransplant period

Achieving adequate therapeutic levels of immunosuppressive medications is important in rejection prevention. This study examined exposure to mycophenolic acid (MPA) in kidney transplant patients within the first 5 days posttransplantation. Methods. This single-center, nonrandomized study of first solitary kidney allograft recipients receiving cyclosporine (n = 116) or tacrolimus (n = 50) included patients who received either 1 g or 1.5 g of mycophenolate mofetil twice daily starting postoperatively. Exposure to MPA was measured at days 3 and 5 posttransplant using published limited sampling time equations. Results. There were no significant differences in exposure in the cyclosporine-treated patients receiving 3-g (n = 22) compared to 2-g (n = 94) daily doses (AUC([0-12]) 33.8 +/- 10.0 mg*h/L versus 30.1 +/- 9.7 mg*h/L, P =.20, respectively). About half the patients in both groups had AUC([0-12]) < 30 mg*h/L on days 3 and 5 posttransplant. On the other hand, there was significantly greater exposure on day 3 in the tacrolimus-treated patients receiving 3 g (n = 21) compared to 2 g (n = 29) daily (AUC([0-12]) 43.1 +/- 9.0 mg*h/L versus 36.8 +/- 11.1 mg*h/L, P =.016, respectively). On day 3 one (4.8%) patient receiving 3 g had an AUC([0-12]) of < 30 mg*h/L; whereas, eight (27.5%) receiving 2 g were below this level (P =.068). The AUC([0-12]) levels were not different on day 5. Conclusions. Loading with higher doses of mycophenolate mofetil results in greater exposure and a trend toward more patients in the therapeutic window within the first week for tacrolimus- but not for cyclosporine-treated patients.

Bayesian population pharmacokinetic analysis of sirolimus

Objective: It is usual that data collected from routine clinical care is sparse and unable to support the more complex pharmacokinetic (PK) models that may have been reported in previous rich data studies. Informative priors may be a pre-requisite for model development. The aim of this study was to estimate the population PK parameters of sirolimus using a fully Bayesian approach with informative priors. Methods: Informative priors including prior mean and precision of the prior mean were elicited from previous published studies using a meta-analytic technique. Precision of between-subject variability was determined by simulations from a Wishart distribution using MATLAB (version 6.5). Concentration-time data of sirolimus retrospectively collected from kidney transplant patients were analysed using WinBUGS (version 1.3). The candidate models were either one- or two-compartment with first order absorption and first order elimination. Model discrimination was based on computation of the posterior odds supporting the model. Results: A total of 315 concentration-time points were obtained from 25 patients. Most data were clustered at trough concentrations with range of 1.6 to 77 hours post-dose. Using informative priors, either a one- or two-compartment model could be used to describe the data. When a one-compartment model was applied, information was gained from the data for the value of apparent clearance (CL/F = 18.5 L/h), and apparent volume of distribution (V/F = 1406 L) but no information was gained about the absorption rate constant (ka). When a two-compartment model was fitted to the data, the data were informative about CL/F, apparent inter-compartmental clearance, and apparent volume of distribution of the peripheral compartment (13.2 L/h, 20.8 L/h, and 579 L, respectively). The posterior distribution of the volume distribution of central compartment and ka were the same as priors. The posterior odds for the two-compartment model was 8.1, indicating the data supported the two-compartment model. Conclusion: The use of informative priors supported the choice of a more complex and informative model that would otherwise have not been supported by the sparse data.

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) (R-2 = 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).

Area-under-the-curve monitoring of prednisolone for dose optimization in a stable renal transplant population

Background: Renal transplant recipients were noted to appear cushingoid while on low doses of steroid as part of a triple therapy immunosuppression of cyclosporin A (CsA), prednisolone, and azathioprine. Methods: The study group comprised adult renal transplant recipients with stable graft function who had received their renal allograft a minimum of 1 year previously (43 studies undertaken in 22 men and 20 women) with median daily prednisone dose of 7 mg (range 3-10). The control group was healthy nontransplant subjects [median dose 10 mg (10-30)]. Prednisolone bioavailability was measured using a limited 6-hour area under the curve (AUC), with prednisolone measured using specific HPLC assay. Results: The median prednisolone AUC/mg dose for all transplant recipients was significantly greater than the control group by approximately 50% (316 nmol(.)h/L/mg prednisolone versus 218). AUC was significantly higher in female recipients (median 415 versus 297 for men) and in recipients receiving cyclospotin (348 versus 285). The highest AUC was in women on estrogen supplements who were receiving cyclosporin (median 595). A significantly higher proportion of patients on triple therapy had steroid side effects compared with those on steroid and azathioprine (17/27 versus 4/15), more women than men had side effects (14/16 versus 7/22), and the AUC/mg prednisone was greater in those with side effects than without (median 377 versus 288 nmol-h/L/mg). Discussion: The results are consistent with the hypothesis that CsA increases the bioavailability of prednisolone, most likely through inhibition of beta-glycoprotein. The increased exposure to steroid increased the side-effect profile of steroids in the majority of patients. Because the major contributor to AUC is the maximum postdose concentration, it may be possible to use single-point monitoring (2 hours postdose) for routine clinical studies.

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

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. 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. 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. 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.