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

TL;DR

This paper introduces a novel, high-resolution computational lung model that predicts local inhaled drug deposition with high accuracy, accounting for individual lung differences and disease states, potentially transforming inhalation therapy development.

Contribution

The study presents a subject-specific, in silico lung model with unprecedented resolution and efficiency, validated against in vivo data, enabling better prediction of aerosol deposition in health and disease.

Findings

Model predictions align well with in vivo SPECT/CT data.

The model captures heterogeneity in diseased lungs, such as IPF.

High computational efficiency allows scalable application.

Abstract

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.