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Clinical Pharmacokinetics

, Volume 50, Issue 12, pp 759–772 | Cite as

Maximum A Posteriori Bayesian Estimation of Mycophenolic Acid Area Under the Concentration-Time Curve: Is This Clinically Useful for Dosage Prediction Yet?

  • Christine E. Staatz
  • Susan E. Tett
Review Article

Abstract

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 30–60mg · 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?

Keywords

Population Pharmacokinetic Model Bayesian Forecast Dosage Prediction Adult Renal Transplant Recipient Adult Kidney Transplant Recipient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors acknowledge financial support from National Health and Medical Research Council Project Grant ♯511109. C. Staatz is currently supported by a Lions Medical Research Fellowship. The authors thank Drs F. Saint-Marcoux and P. Marquet for sharing information about the Limoges University Hospital website service (https://pharmaco.chu-limoges.fr) for Bayesian forecasting.

S. Tett has no conflict of interest to declare; C. Staatz has been a member of a project team for a Cellcept® Project Research Grant 2009.

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Authors and Affiliations

  1. 1.School of PharmacyUniversity of QueenslandBrisbaneAustralia

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