Digital lung ‘twins’ could help predict the efficacy of inhaled drugs in patients with respiratory problems. 

Healthcare company Ebenbuild has developed lung digital twin technology, which uses patient-specific models to predict drug deposition. 

According to a study published in Nature Communications Medicine, the validated technology can predict the transportation and regional deposition of inhaled drugs across the entire human lung with high spatial resolution. 

Local drug deposition is strongly influenced by individual lung anatomy, breathing patterns, disease-related structural changes and aerosol properties, but it cannot be measured directly in routine clinical practice or during clinical stages of drug development. As a result, critical development and dosing decisions continue to rely on indirect measurements, population averages or late-stage clinical outcomes. 

Ebenbuild’s technology aims to address this gap by generating patient-specific digital twins of the lung from standard CT data. The technology applies physics-based computational modelling to analyse airflow, tissue mechanics and aerosol transport throughout the entire respiratory system. 

Unlike existing approaches, this lung digital twin represents both the conducting airways and the alveolar region, enabling locally resolved predictions of aerosol deposition at the individual patient level. 

The model could now be used to help treat patients with respiratory problems such as idiopathic pulmonary fibrosis (IPF). 

“This publication provides rigorous, independent, clinical validation of the core modelling engine that underpins our platform,” said Dr Kei Müller, CEO and Co-founder of Ebenbuild.  

“Being able to quantitatively predict regional aerosol deposition at patient level is essential for replacing assumptions with evidence. The validation fuels the further scale-up of our technology, which is already successfully used in revenue-generating projects, proving its value for our customers from pharma, biotech, and medtech who can make better-informed development decisions earlier.”