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.

翻译：肺内药物递送的关键难题在于，药物在局部作用位点的递送剂量难以预测且极难测量（即使事后评估亦然）。该过程高度个体特异，受肺部形态、疾病状态、呼吸模式及气溶胶特性共同影响。面对这些挑战，计算方法虽已展现潜力，但因基础方法论局限始终未能突破。我们提出并验证了一种新型在体计算机模型，可实现全肺范围内局部气溶胶沉积的个体化预测。模型具备前所未有的时空分辨率，可在呼吸周期任意时刻追踪每个气溶胶颗粒在完整气道系统及肺泡区域中的运动轨迹。预测结果与健康人群队列的在体SPECT/CT数据高度吻合。通过研究特发性肺纤维化（IPF）患者案例，进一步展示了模型对病变肺内强异质性特征的刻画能力。此外，模型的高计算效率、自动化建模与标定功能确保了其大规模应用潜力。我们预期该方法不仅可通过优化（前）临床药物与器械研发全流程提升吸入治疗水平，更可成为肺部核素成像的等效甚至更优替代方案。