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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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 aerosol dosimetry imaging of the lungs.</p>

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In silico high-resolution whole lung model to predict the locally delivered dose of inhaled drugs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Maximilian J. Grill,
  • Jonas Biehler,
  • Karl-Robert Wichmann,
  • Jakob Richter,
  • Maximilian Rixner,
  • David Rudlstorfer,
  • Marie Brei,
  • Julian L. Rehfeldt,
  • Jakob A. Hofer,
  • Joshua Bügel,
  • Nina Pischke,
  • Wolfgang A. Wall,
  • Kei W. Müller

摘要

Background

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.

Methods

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.

Results

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.

Conclusions

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 aerosol dosimetry imaging of the lungs.