Institute of Mathematics, University of Potsdam, Potsdam, Germany.
Drug Discovery Sciences, Research DMPK, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
PLoS Comput Biol. 2020 Dec 15;16(12):e1008466. doi: 10.1371/journal.pcbi.1008466. eCollection 2020 Dec.
The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore 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 data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.
口服吸入药物的命运取决于肺部药代动力学过程，如颗粒沉积、肺部药物溶解和黏液纤毛清除。尽管每个单一的过程都已被系统地研究过，但对过程之间相互作用的定量理解仍然有限，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，可以将肺部过程整合到数学模型中。然而，现有的建模尝试极大地简化了这些过程，或者没有系统地针对（临床）数据进行评估。在这项工作中，我们开发了一个基于生理结构群体方程的数学框架，以机械地整合所有相关的肺部过程。选择了一种定制的数值分辨率策略，并系统地评估了机械模型与来自不同临床研究的数据。在不适应机械模型或基于个别研究数据估计动力学参数的情况下，所开发的模型能够同时预测（i）吸入不溶性颗粒的肺部保留曲线，（ii）吸入单分散颗粒的粒径依赖性药代动力学，（iii）吸入丙酸氟替卡松和布地奈德之间的药代动力学差异，以及（iv）健康志愿者和哮喘患者之间的药代动力学差异。最后，为了确定口服吸入药物最具影响力的优化标准，将所开发的机械模型应用于研究输入参数对肺部和全身暴露的影响。有趣的是，吸入药物的溶解度对局部和全身药代动力学没有任何相关影响。相反，肺部溶解速率、颗粒大小、组织亲和力和全身清除率是最具影响力的潜在优化参数。在未来，所开发的预测框架应被视为确定最佳药物和制剂特征的有力工具。
