Department of Pharmaceutics, College of Pharmacy, Center for Pharmacometrics and System Pharmacology at Lake Nona (Orlando), University of Florida, Gainesville, Florida, USA.
Mathematical Institute, Leiden University, Leiden, The Netherlands.
CPT Pharmacometrics Syst Pharmacol. 2023 May;12(5):639-655. doi: 10.1002/psp4.12927. Epub 2023 Mar 23.
The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target-mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well-established, physiologically based model by Li et al. in a step-wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self-guided exploration and hands-on analysis.
本教程的主要目标是为读者提供一条如何建立越来越复杂的单克隆抗体靶介导药物处置（TMDD）模型的路线图。为此，我们以 Mager 和 Jusko 的基本 TMDD 模型为起点，逐步建立了数学模型，每个模型都有详细的可视化效果，最终建立了 Li 等人建立的成熟的基于生理学的模型，以突出影响 mAb 动力学和系统动态的关键生理过程的相对重要性。随着模型变得越来越复杂，结构和参数可识别性的问题就出现了。为了解决这个问题，我们通过曲妥珠单抗的案例研究来指导模型构建者根据使用背景选择模型和参数优化。我们为这篇教程的读者提供了一个简短的总结，介绍了每个模型扩展的优点和局限性，以及模型源代码，以供进一步的自我引导探索和实践分析。
