Optimal Sampling Strategy Development Methodology Using Maximum A Posteriori Bayesian Estimation

Aize Franciscus van der Meer*, Marco A. E. Marcus, Daniel J. Touw, Johannes H. Proost, Cees Neef

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

52 Citations (Web of Science)


Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility.
Original languageEnglish
Pages (from-to)133-146
JournalTherapeutic Drug Monitoring
Issue number2
Publication statusPublished - Apr 2011


  • maximum a posteriori Bayesian estimation
  • optimal sampling strategy development
  • multiple regression analysis

Cite this