Résumé
Combining classical pharmacokinetic principles with statistical models, population pharmacokinetics (popPK) can effectively utilize sparse data for pharmacokinetic analysis. An optimally designed population pharmacokinetic study will balance the efficiency of a popPK study and the precision with which the parameters are estimated to ensure the unbiased estimation of pharmacokinetic parameters and facilitate the development of clinical and non-clinical trials. Sparse sampling methods have been developed for designing population pharmacokinetic experiments including random sampling method, limited sampling strategy, maximum a posteriori Bayesian method, Fisher information matrix method, and informative block randomized design, which have been widely applied in the uni-response and multi-response popPK sampling optimization. In recent years, population pharmacokinetics has been developed rapidly in Chinese materia medica (CMM), but few studies have been conducted to optimize sampling. By comparing the advantages and disadvantages of each sparse point sampling optimization method and the applicable conditions, this work provides a comparative review of optimal design methodologies and gives its application examples, which provides a reference for pharmacokinetic sampling optimization of CMM.