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：将药物递送至人体肺部的关键难点在于，药物在局部作用部位的递送剂量既不可预测又极难测量（甚至事后测量也困难）。该剂量高度依赖于个体特征，受肺形态、疾病状态、呼吸模式及气溶胶特性等因素影响。针对这些挑战，计算方法虽展现出潜力，但受根本性方法学局限至今未能突破。我们提出并验证了一种新型中尺度模型，可实现全肺范围内个体化预测局部气溶胶沉积。该模型具有前所未有的时空分辨率，能够追踪每个气溶胶颗粒在呼吸周期任意时刻、完整导气管系统及肺泡区域各处的运动轨迹。经健康人群队列验证，预测结果与活体SPECT/CT数据高度吻合。我们进一步通过特发性肺纤维化（IPF）患者研究，展示了该模型表征病肺中强烈不均匀性的能力。最后，其高计算效率及自动化的模型生成与校准流程，确保了大规模应用的可行性。我们认为该方法不仅能通过赋能并加速（临床前）药物与器械开发各阶段来改善吸入治疗，还可作为肺部核素成像的超越性替代方案。