Physiologically based pharmacokinetic (PBPK) models are an important type of systems model used commonly in drug development before commencement of first-in-human studies. Due to structural complexity, these models are not easily utilised for future data-driven population pharmacokinetic (PK) analyses that require simpler models. In the current study we aimed to explore and automate methods of simplifying PBPK models using a proper lumping technique. A linear 17-state PBPK model for fentanyl was identified from the literature. Four methods were developed to search the optimal lumped model, including full enumeration (the reference method), non-adaptive random search (NARS), scree plot plus NARS, and simulated annealing (SA). For exploratory purposes, it was required that the total area under the fentanyl arterial concentration–time curve (AUC) between the lumped and original models differ by 0.002% at maximum. In full enumeration, a 4-state lumped model satisfying the exploratory criterion was found. In NARS, a lumped model with the same number of lumped states was found, requiring a large number of random samples. The scree plot provided a starting lumped model to NARS and the search completed within a short time. In SA, a 4-state lumped model was consistently delivered. In simplify an existing linear fentanyl PBPK model, SA was found to be robust and the most efficient and may be suitable for general application to other larger-scale linear systems. Ultimately, simplified PBPK systems with fundamental mechanisms may be readily used for data-driven PK analyses.

中文翻译：

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自动适当的集总，以简化基于线性生理学的药代动力学系统。

基于生理学的药代动力学（PBPK）模型是在人类首次进行研究之前通常在药物开发中使用的系统模型的一种重要类型。由于结构的复杂性，这些模型不容易用于需要更简单模型的未来数据驱动的群体药代动力学（PK）分析。在当前的研究中，我们旨在探索和自动化使用适当的集总技术简化PBPK模型的方法。从文献中确定了芬太尼的线性17状态PBPK模型。开发了四种方法来搜索最佳集总模型，包括全枚举（参考方法），非自适应随机搜索（NARS），scree图加NARS和模拟退火（SA）。出于探索目的，要求集总模型和原始模型之间的芬太尼动脉浓度-时间曲线下的总面积最大相差0.002％。在完全枚举中，找到了一个满足探索标准的四态集总模型。在NARS中，发现了具有相同数量集总状态的集总模型，需要大量随机样本。卵石图为NARS提供了一个初始集总模型，并在短时间内完成了搜索。在SA中，始终提供4态集总模型。在简化现有的线性芬太尼PBPK模型中，SA被发现是可靠且最有效的，并且可能适用于其他大型线性系统的一般应用。最终，具有基本机制的简化PBPK系统可以很容易地用于数据驱动的PK分析。