The big crux with drug delivery to human lungs is that the delivered dose at the local site of action is unpredictable and very difficult to measure, even a posteriori. It is highly subject-specific as it depends on lung morphology, disease, breathing, and aerosol characteristics. Given these challenges, computational approaches have shown potential, but have so far failed due to fundamental methodical limitations. We present and validate a novel in silico model that enables the subject-specific prediction of local aerosol deposition throughout the entire lung. Its unprecedented spatiotemporal resolution allows to track each aerosol particle anytime during the breathing cycle, anywhere in the complete system of conducting airways and the alveolar region. Predictions are shown to be in excellent agreement with in vivo SPECT/CT data for a healthy human cohort. We further showcase the model's capabilities to represent strong heterogeneities in diseased lungs by studying an IPF patient. Finally, high computational efficiency and automated model generation and calibration ensure readiness to be applied at scale. We envision our method not only to improve inhalation therapies by informing and accelerating all stages of (pre-)clinical drug and device development, but also as a more-than-equivalent alternative to nuclear imaging of the lungs.

中文翻译：

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计算机高分辨率全肺模型可预测吸入药物的局部输送剂量

向人体肺部输送药物的一大症结在于，局部作用部位的输送剂量是不可预测的，并且非常难以测量，甚至是事后测量也是如此。它具有高度的主题特异性，因为它取决于肺部形态、疾病、呼吸和气溶胶特征。考虑到这些挑战，计算方法已显示出潜力，但由于基本方法的限制，迄今为止尚未成功。我们提出并验证了一种新颖的计算机模型，该模型能够对整个肺部的局部气溶胶沉积进行特定于受试者的预测。其前所未有的时空分辨率允许在呼吸周期中的任何时间、整个气道和肺泡区域系统中的任何位置追踪每个气溶胶颗粒。结果表明，预测结果与健康人群的体内 SPECT/CT 数据非常一致。通过研究 IPF 患者，我们进一步展示了该模型代表患病肺部强烈异质性的能力。最后，高计算效率以及自动化模型生成和校准确保了大规模应用的准备。我们设想我们的方法不仅可以通过通知和加速临床（前）药物和设备开发的所有阶段来改善吸入疗法，而且可以作为肺部核成像的替代方案。高计算效率以及自动化模型生成和校准确保了大规模应用的准备。我们设想我们的方法不仅可以通过通知和加速临床（前）药物和设备开发的所有阶段来改善吸入疗法，而且可以作为肺部核成像的替代方案。高计算效率以及自动化模型生成和校准确保了大规模应用的准备。我们设想我们的方法不仅可以通过通知和加速临床（前）药物和设备开发的所有阶段来改善吸入疗法，而且可以作为肺部核成像的替代方案。