School of Pharmacy, University of Otago, 18 Frederick St, North Dunedin, Dunedin, 9016, New Zealand.
J Pharmacokinet Pharmacodyn. 2021 Aug;48(4):509-523. doi: 10.1007/s10928-021-09742-3. Epub 2021 Mar 2.
Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output relationship. In this study, we explore the use of artificial neural networks for approximating an input-output relationship within highly dimensional systems models. We illustrate this approach using a model of blood coagulation. The model consists of two components linked together through a highly dimensional discontinuous interface, which creates a difficulty for model reduction techniques. The proposed approach enables the development of an efficient approximation to complex models with the desired level of accuracy. The technique is applicable to a wide variety of models and provides substantial speed boost for use of such models in simulation and control purposes.
定量系统药理学模型通常非常复杂，不适合进一步的模拟和/或估计分析。模型降阶可用于推导出所需输入-输出关系的机械合理但更简单的模型。在这项研究中，我们探索了使用人工神经网络来近似高维系统模型中的输入-输出关系。我们使用凝血模型来说明这种方法。该模型由两个通过高度不连续界面连接在一起的组件组成，这给模型降阶技术带来了困难。所提出的方法能够以所需的精度有效地开发对复杂模型的逼近。该技术适用于各种模型，并为在模拟和控制目的中使用此类模型提供了实质性的速度提升。
