Department of Mathematics, Uppsala University, Sweden.
AAPS J. 2011 Dec;13(4):495-507. doi: 10.1208/s12248-011-9291-8. Epub 2011 Jul 15.
We propose a new method for optimal experimental design of population pharmacometric experiments based on global search methods using interval analysis; all variables and parameters are represented as intervals rather than real numbers. The evaluation of a specific design is based on multiple simulations and parameter estimations. The method requires no prior point estimates for the parameters, since the parameters can incorporate any level of uncertainty. In this respect, it is similar to robust optimal design. Representing sampling times and covariates like doses by intervals gives a direct way of optimizing with rigorous sampling and dose intervals that can be useful in clinical practice. Furthermore, the method works on underdetermined problems for which traditional methods typically fail.
我们提出了一种新的方法，用于基于区间分析的全局搜索方法进行群体药代动力学实验的最优实验设计；所有变量和参数都表示为区间，而不是实数。特定设计的评估基于多次模拟和参数估计。该方法不需要参数的先验点估计，因为参数可以包含任何级别的不确定性。在这方面，它类似于鲁棒最优设计。通过区间表示采样时间和协变量（如剂量），可以直接优化严格的采样和剂量区间，这在临床实践中可能很有用。此外，该方法适用于传统方法通常失败的欠定问题。
