Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
Drug Metabolism and Pharmacokinetic, Oncology R&D, AstraZeneca, Cambridge, UK.
CPT Pharmacometrics Syst Pharmacol. 2020 Sep;9(9):498-508. doi: 10.1002/psp4.12514. Epub 2020 Jul 8.
Stability analysis, often overlooked in pharmacometrics, is essential to explore dynamical systems. The model developed by Friberg et al. to describe drug-induced hematotoxicity is widely used to support decisions across drug development, and parameter values are often identified from observed blood counts. We use stability analysis to study the parametric dependence of stable and unstable solutions of several Friberg-type models and highlight the risks associated with system instability in the context of nonlinear mixed effects modeling. We emphasize the consequences of unstable solutions on prediction performance by demonstrating nonbiological system behaviors in a real case study of drug-induced thrombocytopenia. Ultimately, we provide simple criteria for identifying parameters associated with stable solutions of Friberg-type models. For instance, in the original Friberg model, we find that stability depends only on the parameter that governs the feedback from peripheral cells to progenitors and provide the exact range of values that results in stable solutions.
稳定性分析在药物代谢动力学中经常被忽视,但对于探索动力学系统至关重要。Friberg 等人开发的用于描述药物引起的血液毒性的模型被广泛用于支持药物开发过程中的决策,并且参数值通常是从观察到的血细胞计数中确定的。我们使用稳定性分析来研究几种 Friberg 型模型的稳定和不稳定解的参数依赖性,并强调在非线性混合效应建模背景下与系统不稳定性相关的风险。我们通过在药物诱导的血小板减少症的实际案例研究中展示非生物学系统行为,强调了不稳定解对预测性能的影响。最终,我们提供了识别与 Friberg 型模型稳定解相关的参数的简单标准。例如,在原始 Friberg 模型中,我们发现稳定性仅取决于从外周细胞到祖细胞的反馈的参数,并提供导致稳定解的精确值范围。