CAPE-Lab-Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova, via Marzolo 9, 35131, Padova, PD, Italy,
J Pharmacokinet Pharmacodyn. 2013 Aug;40(4):451-67. doi: 10.1007/s10928-013-9321-5. Epub 2013 Jun 4.
The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK-PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK-PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.
在药物开发的初步阶段，使用药代动力学（PK）和药效动力学（PD）模型是一种常见且广泛的做法。然而，PK-PD 模型可能会受到与其数学公式内在相关的结构可识别性问题的影响。通常会进行初步的结构可识别性分析，以检查在无噪声数据和无模型不确定性的理想假设下，模型参数是否可以从实验观察中唯一确定。然而，即使对于结构可识别的模型，实际的实验条件和模型不确定性也可能强烈影响以统计合理的方式估计模型参数的实际可能性。本文提出了一种将结构可识别性的数值评估与基于模型的先进实验设计公式相结合的系统方法。其目的是提出一种以最佳方式设计实验的通用方法，通过设计可行且信息丰富的实验，检测一组确保 PK-PD 模型实际可识别性的合适实验设置。本文通过设计可行且信息丰富的实验，展示了基于体外细菌生长和杀伤模型的两个模拟案例研究，证明了该方法在解决模型可识别性问题方面的有效性和通用性。
