Optimal design of pharmacokinetic studies.
Experimental design is fundamental to successful scientific investigation. Poorly designed experiments lead to the loss of information, which is costly and potentially unethical. Experiments can be designed in an optimal fashion to maximize the amount of information they provide. Optimal design theory uses prior information about the model and parameter estimates to optimize a function of the Fisher information matrix to obtain the best combination of the design factors. In the case of population pharmacokinetic experiments, this involves the selection and a careful balance of a number of design factors, including the number and location of measurement times and the number of subjects to include in the study. It is expected that as the awareness about the benefits of this approach increases, more people will embrace it and ultimately will lead to more efficient population pharmacokinetic experiments and can also help to reduce both cost and time during drug development. This MiniReview provides an introduction to optimal design using examples taken from different pharmacokinetic experiments.
Aarons
Leon
L
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, UK. leon.aarons@manchester.ac.uk
Ogungbenro
Kayode
K
D000818:Animals; D001499:Bayes Theorem; D004347:Drug Interactions; D006801:Humans; D015233:Models, Statistical; D010599:Pharmacokinetics; D010601:Pharmacology, Clinical / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D012107:Research Design / Q000592:standards / Q000706:statistics & numerical data; D018401:Sample Size