论文标题

口服吸入药物的先验药代动力学预测的机械框架

A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs

论文作者

Hartung, Niklas, Borghardt, Jens

论文摘要

口服吸入药物的命运由肺部药代动力学(PK)过程(例如颗粒沉积,肺部药物溶解和粘膜纤毛清除率)确定。尽管每个过程都经过系统地研究,但对其相互作用的定量了解仍然有限,因此确定口服吸入药物的最佳药物和制剂特征仍然具有挑战性。为了研究这种复杂的相互作用,肺部过程可以集成到数学模型中。但是,现有的建模尝试大大简化了这些过程,或者没有系统地评估(临床)数据。在这项工作中,我们开发了一个基于生理结构的人群方程的数学框架,以机械地整合所有相关的肺部过程。选择了量身定制的数值分辨率策略,并针对不同的临床数据集对机械模型进行了系统的评估。没有任何基于单个研究数据的参数估计,开发的模型同时预测了吸入的无溶性颗粒的肺保留谱,((2)吸入的单分散粒子的粒度依赖性PK(3)PK pk的pk pk差异,(3)吸入fluticasone pepionate和budesonetate和Budesonide和Budesonide和(4)PK差异和(4)PK差异均差异。最后,为了确定口服吸入药物的最有影响力的优化标准,我们研究了输入参数对肺和全身暴露的影响。吸入药物的溶解度对局部和全身性PK没有任何相关影响。取而代之的是,肺溶解速率,粒径,组织亲和力和全身清除率是影响力的潜在优化参数。将来,开发的预测框架应被视为识别最佳药物和配方特征的强大工具。

The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic (PK) processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Although each single process has been systematically investigated, a quantitative understanding on their interaction remains limited and hence identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against different clinical datasets. Without any parameter estimation based on individual study data, the developed model simultaneously predicted (1) lung retention profiles of inhaled insoluble particles, (2) particle size-dependent PK of inhaled monodisperse particles, (3) PK differences between inhaled fluticasone propionate and budesonide, and (4) PK differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, we investigated the impact of input parameters on both pulmonary and systemic exposure. Solubility of the inhaled drug did not have any relevant impact on local and systemic PK. Instead, pulmonary dissolution rate, particle size, tissue affinity, and systemic clearance were impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool to identify optimal drug and formulation characteristics.

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