Evaluation of the practicability of limited sampling strategies for the estimation of mycophenolic acid exposure in Chinese adult renal recipients

The Evaluation of Multiple Linear Regression-Based Limited Sampling Strategies for Mycophenolic Acid in Children with Nephrotic Syndrome

We evaluated mycophenolic acid (MPA) limited sampling strategies (LSSs) established using multiple linear regression (MLR) in children with nephrotic syndrome treated with mycophenolate mofetil (MMF). MLR-LSS is an easy-to-determine approach of therapeutic drug monitoring (TDM). We assessed the practicability of different LSSs for the estimation of MPA exposure as well as the optimal time points for MPA TDM. The literature search returned 29 studies dated 1998–2020. We applied 53 LSSs (n = 48 for MPA, n = 5 for free MPA [fMPA]) to predict the area under the time-concentration curve (AUC_pred) in 24 children with nephrotic syndrome, for whom we previously determined MPA and fMPA concentrations, and compare the results with the determined AUC (AUC_total). Nine equations met the requirements for bias and precision ±15%. The MPA AUC in children with nephrotic syndrome was predicted the best by four time-point LSSs developed for renal transplant recipients. Out of five LSSs evaluated for fMPA, none fulfilled the ±15% criteria for bias and precision probably due to very high percentage of bound MPA (99.64%). MPA LSS for children with nephrotic syndrome should include blood samples collected 1 h, 2 h and near the second MPA maximum concentration. MPA concentrations determined with the high performance liquid chromatography after multiplying by 1.175 may be used in LSSs based on MPA concentrations determined with the immunoassay technique. MPA LSS may facilitate TDM in the case of MMF, however, more studies on fMPA LSS are required for children with nephrotic syndrome.

Limited sampling strategy of mycophenolic acid in adult kidney transplant recipients: influence of the post-transplant period and the pharmacokinetic profile

We aimed to develop an accurate and convenient LSS for predicting MPA-AUC(0-12 hours) in Tunisian adult kidney transplant recipients whose immunosuppressive regimen consisted of MMF and tacrolimus combination with regards to the post-transplant period and the pharmacokinetic profile. Each pharmacokinetic profile consisted of eight blood samples collected during the 12-hour dosing interval. The AUC(0-12 hours) was calculated according to the linear trapezoidal rule. The MPA concentrations at each sampling time were correlated by a linear regression analysis with the measured AUC(0-12). We analyzed all the developed models for their ability to estimate the MPA-AUC(0-12 hours). The best multilinear regression model for predicting the full MPA-AUC(0-12 hours) was found to be the combination of C1, C4, and C6. All the best correlated models and the most convenient ones were verified to be also applicable before 5 months after transplantation and thereafter. These models were also verified to be applicable for patients having or not the second peak in their pharmacokinetic profiles. For practical reasons we recommend a LSS using C0, C1, and C4 that provides a reasonable MPA-AUC(0-12 hours) estimation.

Maximum a posteriori Bayesian estimation of mycophenolic Acid area under the concentration-time curve: is this clinically useful for dosage prediction yet?

This review seeks to summarize the available data about Bayesian estimation of area under the plasma concentration-time curve (AUC) and dosage prediction for mycophenolic acid (MPA) and evaluate whether sufficient evidence is available for routine use of Bayesian dosage prediction in clinical practice. A literature search identified 14 studies that assessed the predictive performance of maximum a posteriori Bayesian estimation of MPA AUC and one report that retrospectively evaluated how closely dosage recommendations based on Bayesian forecasting achieved targeted MPA exposure. Studies to date have mostly been undertaken in renal transplant recipients, with limited investigation in patients treated with MPA for autoimmune disease or haematopoietic stem cell transplantation. All of these studies have involved use of the mycophenolate mofetil (MMF) formulation of MPA, rather than the enteric-coated mycophenolate sodium (EC-MPS) formulation. Bias associated with estimation of MPA AUC using Bayesian forecasting was generally less than 10%. However some difficulties with imprecision was evident, with values ranging from 4% to 34% (based on estimation involving two or more concentration measurements). Evaluation of whether MPA dosing decisions based on Bayesian forecasting (by the free website service https://pharmaco.chu-limoges.fr) achieved target drug exposure has only been undertaken once. When MMF dosage recommendations were applied by clinicians, a higher proportion (72-80%) of subsequent estimated MPA AUC values were within the 30-60 mg · h/L target range, compared with when dosage recommendations were not followed (only 39-57% within target range). Such findings provide evidence that Bayesian dosage prediction is clinically useful for achieving target MPA AUC. This study, however, was retrospective and focussed only on adult renal transplant recipients. Furthermore, in this study, Bayesian-generated AUC estimations and dosage predictions were not compared with a later full measured AUC but rather with a further AUC estimate based on a second Bayesian analysis. This study also provided some evidence that a useful monitoring schedule for MPA AUC following adult renal transplant would be every 2 weeks during the first month post-transplant, every 1-3 months between months 1 and 12, and each year thereafter. It will be interesting to see further validations in different patient groups using the free website service. In summary, the predictive performance of Bayesian estimation of MPA, comparing estimated with measured AUC values, has been reported in several studies. However, the next step of predicting dosages based on these Bayesian-estimated AUCs, and prospectively determining how closely these predicted dosages give drug exposure matching targeted AUCs, remains largely unaddressed. Further prospective studies are required, particularly in non-renal transplant patients and with the EC-MPS formulation. Other important questions remain to be answered, such as: do Bayesian forecasting methods devised to date use the best population pharmacokinetic models or most accurate algorithms; are the methods simple to use for routine clinical practice; do the algorithms actually improve dosage estimations beyond empirical recommendations in all groups that receive MPA therapy; and, importantly, do the dosage predictions, when followed, improve patient health outcomes?

Development and validation of limited sampling strategies for tacrolimus and mycophenolate in steroid-free renal transplant regimens

PURPOSE

1) To develop and validate limited sampling strategies (LSSs) for tacrolimus (TAC) and mycophenolic acid (MPA) in renal transplant recipients not receiving corticosteroids; and 2) to evaluate predictive performance of published LSSs (for steroid-based regimens) in a steroid-free population.

METHODS

On administration of steady-state morning TAC and mycophenolate mofetil doses, 12-hour serial blood samples from 28 stable renal transplant recipients were collected and measured by validated high-performance liquid chromatography methods and area under the curve (AUC) by trapezoidal rule. TAC LSSs were developed and validated by multiple regression analysis by a two-group method (index n = 18; validation n = 10) and MPA LSSs by the jackknife method (n = 28). Potential LSSs were those with r ≥ .8 (TAC) or r ≥ 0.7 (MPA) and < 3 time points within 2 hours (TAC) or 4 hours (MPA) postdose. Predictive performance was calculated and other published TAC and MPA LSSs tested using preset criteria for bias and precision of within ± 15%.

RESULTS

For TAC, three three-concentration, one two-concentration, and one one-concentration model met preset criteria. The best equations were: TAC AUC = 10.338 + 7.739C0 + 3.589C2 (r = 0.956, bias = -3.4%, precision = 4.7%) and TAC AUC = 29.479 + 5.016C2 (r = 0.862, bias = 3.2%, precision = 9.7%). For MPA, only one model was identified: MPA AUC = 9.328 + 1.311C1 + 1.455C2 + 2.901C4 (r = 0.838, bias = -3.8%, precision = 14.9%). One published TAC (and no MPA) LSS in renal transplant recipients on steroid-based regimens met criteria.

CONCLUSIONS

To the authors' knowledge, these LSSs are the first to be developed and validated in steroid-free renal transplant recipients and can be used to accurately predict TAC and MPA AUCs for steroid-free regimens. Because the commonly used MPA LSS is based on a steroid regimen and not predictive for steroid-free patients, the newly derived MPA LSS is being applied at the authors' institution. Other renal transplant centers may also wish to validate this equation in their own patients.

Cost-effectiveness analysis of individualized mycophenolate mofetil dosing in kidney transplant patients in the APOMYGRE trial

BACKGROUND

In the prospective, randomized, multicenter APOMYGRE trial conducted in France, concentration-controlled mycophenolate mofetil (MMF) dosing based on mycophenolic acid (MPA) exposure significantly reduced the treatment failure and acute rejection during the first posttransplantation year compared with fixed-dose MMF. This analysis investigated the cost effectiveness of dose individualization.

METHOD

The study included 65 patients per group (intent-to-treat population). Treatment failure (primary efficacy endpoint) was defined as death, graft loss, acute rejection, or MMF discontinuation because of adverse effects. Data on hospitalizations, drugs prescribed, physicians' fees, laboratory expenses, ambulatory visits, and transportation were retrieved. Costs were calculated from the French National Health System perspective.

RESULTS

The mean (95% confidence interval) total yearly cost per patient was Euro 47,477 (Euro 43,933; Euro 51,020) in the concentration-controlled group and Euro 46,783 ( Euro 44,152; Euro 49,414) in the fixed-dose group (P=0.7). The observed incremental cost-effectiveness ratio was Euro 3757 per treatment failure (Purchasing Power Parities United States/France: $4129). Hospitalization and drug costs accounted for approximately 50% and 25% of total costs, respectively. The cost for MPA area under the concentration-time curve and dose calculation was Euro 452 per patient, less than 1% of the total cost.

CONCLUSION

In the APOMYGRE trial, therapeutic MPA monitoring using a limited sampling strategy reduced the risk of treatment failure and acute rejection in renal allograft recipients during the first 12 months posttransplantation, at neutral cost.

Limited sampling strategies for mycophenolic acid in solid organ transplantation: a systematic review

BACKGROUND

Mycophenolic acid (MPA) is the active metabolite of mycophenolate mofetil, a widely used immunosuppressant. Numerous studies have developed limited sampling strategies (LSSs) to predict MPA AUC in solid organ transplant recipients.

OBJECTIVES

To systematically review and assess quality of literature pertaining to MPA LSSs, evaluate clinical implications and provide suggestions for future research.

METHODS

Literature searches of MEDLINE (1966 - May 2009) and EMBASE (1980 - May 2009) for English articles in solid organ transplantation, along with manual review of article references were conducted. Included articles were categorized according to criteria adapted from levels of evidence of the US Preventative Services Task Force.

RESULTS

Of a total of 29 studies identified, 20 were in kidney, 4 in heart, 4 in liver and 1 in lung transplantation and 7 were in pediatrics. A total of 14 studies were deemed to be Level I evidence studies, 3 were Level II-1, 1 was Level II-2 and 11 were Level III.

CONCLUSIONS

Although various LSSs that are well correlated to MPA AUC while being relatively unbiased and precise to predict MPA AUC have been developed, further research is needed to determine validity of these LSSs in a variety of patient populations and to determine if these LSSs improve patient outcomes.

Limited sampling models and Bayesian estimation for mycophenolic acid area under the curve prediction in stable renal transplant patients co-medicated with ciclosporin or sirolimus

BACKGROUND AND OBJECTIVE

Mycophenolate mofetil is a prodrug of mycophenolic acid (MPA), an immunosuppressive agent used in combination with corticosteroids and calcineurin inhibitors or sirolimus for the prevention of acute rejection after solid organ transplantation. Although MPA has a rather narrow therapeutic window and its pharmacokinetics show considerable intra- and interindividual variability, dosing guidelines recommend a standard dosage regimen of 0.5-1.0 g twice daily in adult renal, liver and cardiac transplant recipients. The main objective of the present study was to develop a method to predict the MPA area under the plasma concentration-time curve during one 12-hour dosing interval (AUC(12)) by using multiple linear regression models and maximum a posteriori (MAP) Bayesian estimation methods in patients co-medicated with ciclosporin or sirolimus, aiming to individualize the dosage regimen of mycophenolate mofetil.

PATIENTS AND METHODS

Pharmacokinetic profiles of MPA and mycophenolic acid glucuronide (MPAG), the main metabolite of MPA, were obtained from 40 stable adult renal allograft recipients on three different occasions: the day before switching from ciclosporin to sirolimus co-medication (+/-7.4 months post-transplantation; period I) and at 60 days and 270 days after the switch (periods II and III). Blood samples for determination of MPA and MPAG concentrations in plasma were taken at 0 hours (pre-dose) and at 0.33, 0.66, 1.25, 2, 4, 6, 8 and 12 hours after oral intake of mycophenolate mofetil. The MPA AUC(12) was calculated by the trapezoidal method (the observed AUC(12)). Patients were randomly divided into (i) a model-building test group (n = 27); and (ii) a model-validation group (n = 13). Multiple linear regression models were developed, based on three sampling times after drug administration. Subsequently, a population pharmacokinetic model describing MPA and MPAG plasma concentrations was developed using nonlinear mixed-effects modelling and a Bayesian estimator based on the population pharmacokinetic model was used to predict the MPA AUC(12) based on three sampling times taken within 2 hours following dosing.

RESULTS

Fifty-two percent of the observed AUC(12) values (three periods) in the 40 patients receiving a fixed dose of mycophenolate mofetil 750 mg twice daily were outside the recommended therapeutic range (30-60 microg x h/mL). The failure of the standard dose to yield an AUC(12) value within the therapeutic range was especially pronounced during the first study period. Of the multiple linear regression models that were tested, the equation based on the 0-hour (pre-dose), 0.66- and 2-hour sampling times showed the best predictive performance in the validation group: r2 = 0.79, relative root mean square error (rRMSE) = 14% and mean relative prediction error (MRPE) = 0.9%. The pharmacokinetics of MPA and MPAG were best described by a two-compartment model with first-order absorption and elimination for MPA, plus a compartment for MPAG, also including a gastrointestinal compartment and enterohepatic cycling in the case of sirolimus co-medication. The ratio of aminotransferase liver enzymes (AST and ALT) and the glomerular filtration rate significantly influenced MPA glucuronidation and MPAG renal excretion, respectively. Bayesian estimation of the MPA AUC(12) based on 0-, 1.25- and 2-hour sampling times predicted the observed AUC(12) values of the patients in the validation group, with the following predictive performance characteristics: r2 = 0.93, rRMSE = 12.4% and MRPE = -0.4%.

CONCLUSION

Use of the developed multiple linear regression equation and Bayesian estimator, both based on only three blood sampling times within 2 hours following a dose of mycophenolate mofetil, allowed an accurate prediction of a patient's MPA AUC(12) for therapeutic drug monitoring and dose individualization. These findings should be validated in a randomized prospective trial.