Sequential model‐based design of experiments (MBDoE) uses information from previous experiments to select new experimental conditions. Computation of MBDoE objective functions can be impossible due to a noninvertible Fisher information matrix (FIM). Previously, we evaluated a leave‐out (LO) approach that designed experiments by removing problematic model parameters from the design process. Unfortunately, the LO approach can be computationally expensive due to its iterative nature. In this study, we propose a simplified Bayesian approach that makes the FIM invertible by accounting for prior parameter information. We compare the proposed simplified Bayesian approach to the LO approach for sequential A‐optimal design. Results from a pharmaceutical case study show that the proposed approach is superior, on average, for design of experiments. We suggest that simplified Bayesian MBDoE should be combined with a subset‐selection‐based approach for parameter estimation. This combined methodology gave the best results on average for the case study.

中文翻译：

---

在基于模型的药物生产实验设计中使用先验参数知识

基于顺序模型的实验设计（MBDoE）使用来自先前实验的信息来选择新的实验条件。由于不可逆的Fisher信息矩阵（FIM），因此无法进行MBDoE目标函数的计算。以前，我们评估了一种省略法（LO），该方法通过从设计过程中删除有问题的模型参数来设计实验。不幸的是，由于LO方法具有迭代性质，因此在计算上可能会很昂贵。在这项研究中，我们提出一种简化的贝叶斯方法，使FIM通过考虑先前的参数信息可逆。我们将建议的简化贝叶斯方法与LO方法进行顺序A优化设计。药学案例研究的结果表明，所提出的方法在实验设计上平均而言是优越的。我们建议简化贝叶斯MBDoE应该与基于子集选择的方法结合起来进行参数估计。对于案例研究，这种组合方法平均得出最佳结果。