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BACKGROUND Bayesian forecasting-based limited sampling strategies (LSSs) for tacrolimus have not been evaluated for the prediction of subsequent tacrolimus exposure. This study examined the predictive performance of Bayesian forecasting programs/services for the estimation of future tacrolimus area under the curve (AUC) from 0 to 12 hours (AUC0-12) in kidney transplant recipients. METHODS Tacrolimus concentrations were measured in 20 adult kidney transplant recipients, 1 month post-transplant, on 2 occasions one week apart. Twelve samples were taken predose and 13 samples were taken postdose at the specified times on the first and second sampling occasions, respectively. The predicted AUC0-12 (AUCpredicted) was estimated using Bayesian forecasting programs/services and data from both sampling occasions for each patient and compared with the fully measured AUC0-12 (AUCmeasured) calculated using the linear trapezoidal rule on the second sampling occasion. The bias (median percentage prediction error [MPPE]) and imprecision (median absolute prediction error [MAPE]) were determined. RESULTS Three programs/services were evaluated using different LSSs (C0; C0, C1, C3; C0, C1, C2, C4; and all available concentrations). MPPE and MAPE for the prediction of fully measured AUC0-12 were

Background: Prednisolone displays significant pharmacokinetic variability and exposure-outcome relationships in renal transplant recipients, suggesting a role for drug monitoring in some scenarios. It is highly protein-bound, and the free form is pharmacologically active but cumbersome to measure. Saliva concentrations might reflect free plasma prednisolone and present an alternative measurement. The aim of this study was to examine the correlation between total and free plasma and saliva prednisolone in adult renal transplant recipients.Methods: Total and free plasma and saliva prednisolone concentrations were measured in 20 patients receiving oral prednisolone 1-2 months after transplant, between pre-dose and 12 hours post-dose. Prednisolone was determined using high-performance liquid chromatography mass spectrometric detection. The Pearson coefficient was used to assess the association between plasma and salivary prednisolone concentrations and area under the concentration-time curves (AUC(0-12)).Results: When considering all time points, the total and free plasma prednisolone concentrations correlated well (r(2) = 0.81), but there was poor correlation between saliva and free (r(2) = 0.003) and total (r(2) = 0.01) plasma concentrations. When concentrations before the maximum free prednisolone plasma value were excluded, the correlation between free plasma and saliva concentrations improved (r(2) = 0.57). There was a moderate correlation between free and total plasma prednisolone AUC(0-12) (r(2) = 0.62) using all time points, but a poor correlation between free and total plasma prednisolone AUC(0-12) and saliva AUC(0-12) (r(2) = 0.07; r(2) = 0.17).Conclusions: Total and free plasma prednisolone measurements correlated poorly with saliva measurements; however, correlation improved when concentrations early in the dosing interval were excluded.

Although multiple linear regression-based limited sampling strategies (LSSs) have been published for enteric-coated mycophenolate sodium, none have been evaluated for the prediction of subsequent mycophenolic acid (MPA) exposure. This study aimed to examine the predictive performance of the published LSS for the estimation of future MPA area under the concentration-time curve from 0 to 12 hours (AUC0-12) in renal transplant recipients.Total MPA plasma concentrations were measured in 20 adult renal transplant patients on 2 occasions a week apart. All subjects received concomitant tacrolimus and were approximately 1 month after transplant. Samples were taken at 0, 0.33, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 6, and 8 hours and 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2, 3, 4, 6, 9, and 12 hours after dose on the first and second sampling occasion, respectively. Predicted MPA AUC0-12 was calculated using 19 published LSSs and data from the first or second sampling occasion for each patient and compared with the second occasion full MPA AUC0-12 calculated using the linear trapezoidal rule. Bias (median percentage prediction error) and imprecision (median absolute prediction error) were determined.Median percentage prediction error and median absolute prediction error for the prediction of full MPA AUC0-12 were15% for 4 LSSs, using the data from the same (second) occasion. One equation (1.583C1 + 0.765C2 + 0.369C2.5 + 0.748C3 + 1.518C4 + 2.158C6 + 3.292C8 + 3.6690) showed bias and imprecision15% for the prediction of future MPA AUC0-12, where the predicted AUC0-12 from the first occasion was compared with the full AUC0-12 from the second. All LSSs with an acceptable predictive performance included concentrations taken at least 6 hours after the dose.Only one LSS had an acceptable bias and precision for future estimation. Accurate dosage prediction using a multiple linear regression-based LSS was not possible without concentrations up to at least 8 hours after the dose.

Goal: To examine the relationship between mycophenolic acid (MPA) area under the concentration-time curve (AUC) and clinical outcomes in adult kidney transplant recipients.Methods: Forty-nine patients receiving mycophenolate mofetil (MMF) (0.5 - 1g twice daily) and either cyclosporine or tacrolimus co-therapy were recruited. MPA concentration measurements were made at pre-dose, 1, 3 and 6 hours following MMFadministration on day 5 post-transplant. MPA AUC0-12 was estimated using a limited sampling equation which provides estimates with good correlation (r2= 0.836) to full 14 time-point AUC measures1. Patient medical records were reviewed for episodes of rejection, leucopenia and thrombocytopenia in the first 3 months after transplantation. Differences between MPA apparent clearance (CL/F) (calculated as: Dose/AUC0-12) in patients with and without rejection and hematological toxicity were compared using an unpaired t-test or Mann-Whitney U test depending on whether the data were normally distributed.Results: MPA CL/F on day 5 post-transplant ranged from 9.8 to 53.9 L/h (median: 33.3 L/h). MPA CL/F was significantly higher in patients receiving cyclosporine compared to tacrolimus (mean 6 SEM; 34.7 6 1.3 L/h versus 28.2 6 3.4 L/h respectively, p=0.04). Two patients experienced biopsyproven acute rejection of grade 2A or above and 3 had borderline changes suspicious of rejection. There was no significant difference in MPA CL/F between patients who did and did not experience definitive or possible rejection. MPA CL/F was significantly lower in the seven patients who experienced leucopenia (WBC , 4.0 x 109 L-1) compared to those who did not (25.1 6 4.7 L/h versus 34.3 6 1.3 L/h respectively (p= 0.007). There was no significant difference in MPA CL/F between the six patients who experienced thrombocytopenia (PBC , 140 x 109 L-1) and the patients who did not.Conclusion: Estimation of MPA CL/F in the first week post-transplant identified patients whowere more likely to experience leucopenia in this study.

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).

Hydroxychloroquine (HCQ) is an antimalarial drug that is also used as a second-line treatment of rheumatoid arthritis (RA). Clinically, the use of HCQ is characterized by a long delay in the onset of action, and withdrawal of treatment is often a result of inefficacy rather than from toxicity. The slow onset of action can be attributed to the pharmacokinetics (PK) of HCQ, and wide interpatient variability is evident. Tentative relationships between concentration and effect have been made, but to date, no population PK model has been developed for HCQ. This study aimed to develop a population PK model including an estimation of the oral bioavailability of HCQ. In addition, the effects of the coadministration of methotrexate on the PK of HCQ were examined. Hydroxychloroquine blood concentration data were combined from previous pharmacokinetic studies in patients with rheumatoid arthritis. A total of 123 patients were studied, giving the data cohort from four previously published studies. Two groups of patients were included: 74 received hydroxychloroquine (HCQ) alone, and 49 received HCQ and methotrexate (MTX). All data analyses were carried out using the NONMEM program. A one-compartment PK model was supported, rather than a three-compartment model as previously published, probably because of the clustering of concentrations taken at the end of a dosing interval. The population estimate of bioavailability of 0.75 (0.07), n = 9, was consistent with literature values. The parameter values from the final model were: (Cl) over bar = 9.9 +/- 0.4 L/h, (V) over bar 605 +/- 91 L, (k(d)) over bar = 0.77 +/- 0.22 hours(-1), (t(tag)) over bar = 0.44 +/- 0.02 hours. Clearance was not affected by the presence of MTX, and, hence, steady-state drug concentrations and maintenance dosage requirements were similar. A population PK model was successfully developed for HCQ.

This study compared an enzyme-linked immunosorbent assay (ELISA) to a liquid chromatography-tandem mass spectrometry (LC/MS/MS) technique for measurement of tacrolimus concentrations in adult kidney and liver transplant recipients, and investigated how assay choice influenced pharmacokinetic parameter estimates and drug dosage decisions. Tacrolimus concentrations measured by both ELISA and LC/MS/MS from 29 kidney (n = 98 samples) and 27 liver (n = 97 samples) transplant recipients were used to evaluate the performance of these methods in the clinical setting. Tacrolimus concentrations measured by the two techniques were compared via regression analysis. Population pharmacokinetic models were developed independently using ELISA and LC/MS/MS data from 76 kidney recipients. Derived kinetic parameters were used to formulate "typical dosing" regimens for concentration targeting. Dosage recommendations for the two assays were compared. The relation between LC/MS/MS and ELISA measurements was best described by the regression equation ELISA = 1.02. (LC/MS/MS) + 0.14 in kidney recipients, and ELISA = 1.12. (LC/MS/MS) - 0.87 in liver recipients. ELISA displayed less accuracy than LC/MS/MS at lower tacrolimus concentrations. Population pharmacokinetic models based on ELISA and LC/MS/MS data were similar with residual random errors of 4.1 ng/mL and 3.7 ng/mL, respectively. Assay choice gave rise to dosage prediction differences ranging from 0% to 30%. ELISA measurements of tacrolimus are not automatically interchangeable with LC/MS/MS values. Assay differences were greatest in adult liver recipients, probably reflecting periods of liver dysfunction and impaired biliary secretion of metabolites. While the majority of data collected in this study suggested assay differences in adult kidney recipients were minimal, findings of ELISA dosage underpredictions of up to 25% in the long term must be investigated further.

Mycophenolic acid is an immunosuppressant administered as a bioavailable ester, mycophenolate mofetil. The pharmacokinetics of mycophenolic acid have been reported to be variable. Accurate measurement of concentrations of this drug could be important to adjust doses. The aim of this study was to compare the enzyme-multiplied immunoassay technique (EMIT [Dade Behring; San Jose, CA, U.S.A.]) for mycophenolic acid with a high-performance liquid chromatographic (HPLC) assay using samples collected from renal transplant recipients. The HPLC assay used solid phase extraction and a C18 stationary phase with ultraviolet (UV) detection (254 nm). The immunoassay required no manual sample preparation. Plasma samples (n=102) from seven patients, collected at various times after a dose, were analyzed using both methods. Both assays fulfilled quality-control criteria. Higher concentrations were consistently measured in patient samples when using EMIT. The mean (+/-standard deviation [SD]) bias (EMIT-HPLC) was 1.88+/-0.86 mg/L. The differences in concentrations were higher in the middle of a dosage interval, suggesting that a metabolite might have been responsible for overestimation. Measurement of glucuronide concentrations by HPLC demonstrated only a weak correlation between assay differences and glucuronide concentrations. If the crossreacting substance is active, EMIT could provide a superior measure of immunosuppression; if inactive, further work is needed to improve antibody specificity. In conclusion, it was found that EMIT overestimates the concentration of mycophenolic acid in plasma samples from renal transplant recipients compared with HPLC analysis.

Drugs known to inhibit the metabolism of cyclosporine are administered concomitantly to those who undergo cardiothoracic transplantation. The aim of this study was to examine in quantitative terms the relationship between cyclosporine oral dose rate and the trough concentration (Css(trough)) at steady state in patients who undergo cardiothoracic transplantation and are administered cyclosporine alone or in combination with drugs known to inhibit its metabolism. Dose and whole blood cyclosporine Css(trough) observations measured using the enzyme-multiplied immunoassay technique (EMIT) (396 observations) or the TDx assay (435 observations) were collected as part of routine blood concentration monitoring from 182 patients who underwent cardiothoracic transplantation. Data were analyzed using a linear mixed-effects modeling approach to examine the effect of metabolic inhibitors on dose-rate-Css(trough) ratio. The mean (and 95% confidence interval) dose-rate-Css(trough) ratio for cyclosporine generated from concentrations measured using EMIT was 94 (82.5-105.5) Lh(-1) for patients administered cyclosporine alone, 66.7 (58.1-75.3) Lh(-1) for patients administered concomitant diltiazem, 47.9 (15.4 -80.4) Lh(-1) for patients administered concomitant itraconazole, 21.7 (14.8-28.5) Lh(-1) for patients administered concomitant ketoconazole, and 14.9 (11.8-18.1) Lh(-1) for patients concomitantly administered diltiazem and ketoconazole. For patients administered concomitant cyclosporine, ketoconazole, and diltiazem, the dosage of cyclosporine, if it is administered alone, should be 20% to achieve the same blood concentrations. This will allow safer drug concentration targeting of cyclosporine after cardiothoracic transplantation.

The current approach for therapeutic drug monitoring in renal transplant recipients receiving mycophenolate mofetil (MMF) is measurement of total mycophenolic acid (MPA) concentration. Because MPA is highly bound, during hypoalbuminemia the total concentration no longer reflects the free (pharmacologically active) concentration. The authors investigated what degree of hypoalbuminemia causes a significant change in protein binding and thus percentage free MPA. Forty-two renal transplant recipients were recruited for the study. Free and total concentrations of MPA (predose, and 1, 3, and 6 hours post-MMF dose samples) and plasma albumin concentrations were determined on day 5 posttransplantation. Six-hour area under the concentration-time curve (AUC(0-6)) values were calculated for free and total MPA, and percentage free MPA was determined for each patient. The authors found a significant relationship between low albumin concentrations and increased percentage free MPA (Spearman correlation = -0.54, P0.0001). Receiver operating characteristic (ROC) curve analysis was performed on the albumin versus percentage free MPA data. The cutoff value of albumin determined from the ROC analysis that differentiated normal from elevated percentage free MPA (defined asor = 3%) in this patient population was 31 g/L. At this cutoff value albumin was found to be a good predictor of altered free MPA percentage, with a sensitivity and specificity of 0.75 and 0.80, respectively, and an area under the ROC curve of 0.79. To rationalize MMF dosing regimens in hypoalbuminemic patients (plasma albuminor = 31 g/L), clinicians should consider monitoring the free MPA concentration.

To test the predictive capacity of two recently derived population pharmacokinetic models and the usefulness of Bayesian forecasting to predict tacrolimus blood concentrations in pediatric liver and adult kidney transplant recipients.New databases were added to the Abbottbase PKS (Bayesian dosage prediction) program to incorporate the population pharmacokinetic models developed for tacrolimus. Two independent populations of transplant recipients were used to predict tacrolimus trough concentrations. Pharmacokinetic, demographic, and covariate data were collected from patient records. Different time weighting factors were tested (1, 1.005, 1.01) and the influence of excluding data collected in the first 5 days post-transplant examined. Concentrations were predicted until the 10th tacrolimus measurement. Actual tacrolimus concentrations were compared with those predicted by the PKS program and bias and precision determined.Tacrolimus concentrations predicted by the PKS program were, on average, unbiased for the pediatric liver population, but were over-predicted (9%) for the adult renal population. In both populations predictions were not precise (imprecision ranged from 39 to 50%).Due to the imprecision seen in this study, these models could not be used in clinical practice in the immediate post-transplant period. Poor precision may be due to reliance on routine drug monitoring data alone, difficulties with expression of covariates in continuous modeling relationships in the PKS program, lack of accurate quantitative measures of liver function, or large, random intraindividual variability in the bioavailability of tacrolimus.

Therapeutic drug monitoring of cyclosporin (CsA) has been established as part of the routine clinical treatment of patients following organ transplantation for more than 20 years, and based on contemporary knowledge, many consensus guidelines have been published to assist clinics and laboratories attain optimal strategies for patient care. This article addresses the newer directions in CsA monitoring, with particular reference to the Australasian situation that has evolved since the 1993 Australasian guideline. These changes have included the introduction of alternative assay methodologies, changed CsA formulation from Sandimmun to Neoral throughout Australasia, and alternatives to trough concentration (C0) monitoring, especially 2-hour concentration (C2) monitoring and associated validated dilution protocols to accurately quantitate the higher whole blood CsA concentrations. The revision was prepared following a recent survey of all Australasian CsA-monitoring laboratories where discordant practices were evident.

The present study estimated the population pharmacokinetics of lamotrigine in patients receiving oral lamotrigine therapy with drug concentration monitoring, and determined intersubject and intrasubject variability. A total of 129 patients were analyzed from two clinical sites. Of these, 124 patients provided sparse data (198 concentration-time points); nine patients (four from a previous group plus five from the current group) provided rich data (431 points). The population analysis was conducted using P-PHARM (SIMED Scientific Software, Cedex, France), a nonlinear mixed-effect modeling program. A single exponential elimination model (first-order absorption) with heteroscedastic weighting was used. Apparent clearance (CL/F) and volume of distribution (V/F) were the pharmacokinetic parameters estimated. Covariate analysis was performed to determine which factors explained any of the variability associated with lamotrigine clearance. Population estimates of CL/F and V/F for lamotrigine generated in the final model were 2.14 +/- 0.81 L/h and 78.1 +/- 5.1 L/kg. Intersubject and intrasubject variability for clearance was 38% and 38%, respectively. The covariates of concomitant valproate and phenytoin therapy accounted for 42% of the intersubject variability of clearance. Age, gender, clinic site, and other concomitant antiepileptic drugs did not influence clearance. This study of the population pharmacokinetics of lamotrigine in patients using the drug clinically provides useful data and should lead to better dosage individualization for lamotrigine.

Renal drug elimination is determined by glomerular filtration, tubular secretion, and tubular reabsorption. Changes in the integrity of these processes influence renal drug clearance, and these changes may not be detected by conventional measures of renal function such as creatinine clearance. The aim of the current study was to examine the analytic issues needed to develop a cocktail of marker drugs (fluconazole, rac-pindolol, para-aminohippuric acid, sinistrin) to measure simultaneously the mechanisms contributing to renal clearance. High-performance liquid chromatographic methods of analysis for fluconazole, pindolol, para-aminohippuric acid, and creatinine and an enzymatic assay for sinistrin were developed or modified and then validated to allow determination of each of the compounds in both plasma and urine in the presence of all other marker drugs. A pilot clinical study in one volunteer was conducted to ensure that the assays were suitable for quantitating all the marker drugs to the sensitivity and specificity needed to allow accurate determination of individual renal clearances. The performance of all assays (plasma and urine) complied with published validation criteria. All standard curves displayed linearity over the concentration ranges required, with coefficients of correlation greater than 0.99. The precision of the interday and intraday variabilities of quality controls for each marker in plasma and urine were all less than 11.9% for each marker. Recoveries of markers (and internal standards) in plasma and urine were all at least 90%. All markers investigated were shown to be stable when plasma or urine was frozen and thawed. For all the assays developed, there were no interferences from other markers or endogenous substances. In a pilot clinical study, concentrations of all markers could be accurately and reproducibly determined for a sufficient duration of time after administration to calculate accurate renal clearance for each marker. This article presents details of the analytic techniques developed for measuring concentrations of marker drugs for different renal elimination processes administered as a single dose to define the processes contributing to renal drug elimination.

Mycophenolate mofetil, the oral prodrug of mycophenolic acid, is indicated as immunosuppressive therapy after renal transplantation. To aid in the investigation of pharmacokinetic-pharmacodynamic relationships of mycophenolic acid in the clinical setting, limited blood sampling strategies have been proposed, and models from these developed, for the estimation of mycophenolic acid area under the concentration-time curve (AUC). In the current study, the authors investigated the predictive performance of six published models to estimate AUC. A total of 49 profiles from 25 renal transplant patients were used to test each model's performance against a full 14 time-point AUC. A wide range of agreement was found when predicted AUCs were compared with full AUCs using linear regression analysis (range: r2 = 0.499 to 0.836). Model 1, which uses 4 time-points over 6 hours, was found to be superior to all other models. The range of time-points used in this model takes into account patients with variable absorption. This model should be further tested on data sets from other centers. The relatively poor performance of the other models may be caused by their inability to describe the peak concentration in these patients. Caution is warranted when using limited sampling strategies on patients whose absorption of mycophenolic acid is altered, compared with those of the pharmacokinetic profiles from which the model was developed.

Cyclosporin-A (CsA) therapeutic drug monitoring plays an integral role in therapeutic management of immunosuppressed patients, including those with organ transplants. This communication, prepared by an Australian team, presents recommendations for the routine monitoring of CsA, including blood sampling methodological approaches and interpretation of results generated. The consensus approach described is intended to address the diversity of attitudes to CsA monitoring demonstrated in a recent national survey of Australian laboratories that provide CsA analyses.

Therapeutic drug monitoring is used for the immunosuppressant drug, cyclosporin, even though it is often unclear what concentration should be targetted. At St. Vincent's Hospital, Sydney, a polyclonal, whole blood immunoassay is used to measure cyclosporin and metabolites. The objective of the present study was to audit how the concentration results produced by the laboratory were actually used to adjust dosage. Each transplantation unit was asked about their policy for dosage adjustment and which therapeutic range was used. The audit criteria were considered to be passed if the action taken based on a concentration result was (a) predictable based solely on the concentration and its relationship to the stated therapeutic range (e.g., dosage increased if concentration was below therapeutic range) or (b) based on clinical considerations (e.g., dosage decreased because of increased serum creatinine). Data were collected for the actions taken following 347 concentration results over a 6-week period. Audit criteria were fulfilled for 246 (71%) of the results. The majority of the cyclosporin concentrations (75%) were determined for the cardiopulmonary unit; 66% of these fulfilled audit criteria. Approximately two-thirds of all the cyclosporin concentrations requested were followed up by appropriate action based on stated therapeutic ranges or documented clinical reasons.