OBJECTIVE: Drug inhalation is generally accepted as the preferred administration method for treating respiratory diseases. To achieve effective inhaled drug delivery for an individual, it is necessary to use an interdisciplinary approach that can cope with inter-individual differences. The paper aims to present an individualised pulmonary drug deposition model based on Computational Fluid and Particle Dynamics simulations within a time frame acceptable for clinical use.
METHODS: We propose a model that can analyse the inhaled drug delivery efficiency based on the patient\'s airway geometry as well as breathing pattern, which has the potential to also serve as a tool for a sub-regional diagnosis of respiratory diseases. The particle properties and size distribution are taken for the case of drug inhalation by using nebulisers, as they are independent of the patient\'s breathing pattern. Finally, the inhaled drug doses that reach the deep airways of different lobe regions of the patient are studied.
RESULTS: The numerical accuracy of the proposed model is verified by comparison with experimental results. The difference in total drug deposition fractions between the simulation and experimental results is smaller than 4.44% and 1.43% for flow rates of 60 l/min and 15 l/min, respectively. A case study involving a COVID-19 patient is conducted to illustrate the potential clinical use of the model. The study analyses the drug deposition fractions in relation to the breathing pattern, aerosol size distribution, and different lobe regions.
CONCLUSIONS: The entire process of the proposed model can be completed within 48 h, allowing an evaluation of the deposition of the inhaled drug in an individual patient\'s lung within a time frame acceptable for clinical use. Achieving a 48-hour time window for a single evaluation of patient-specific drug delivery enables the physician to monitor the patient\'s changing conditions and potentially adjust the drug administration accordingly. Furthermore, we show that the proposed methodology also offers a possibility to be extended to a detection approach for some respiratory diseases.

Around nine million people have been exposed to toxic humidifier disinfectants (HDs) in Korea. HD exposure may lead to HD-associated lung injuries (HDLI). However, many people who have claimed that they experienced HD exposure were not diagnosed with HDLI but still felt discomfort, possibly due to the unknown effects of HD. Therefore, this study examined HD-exposed subjects with normal-appearing lungs, as well as unexposed subjects, in clusters (subgroups) with distinct characteristics, classified by deep-learning-derived computed-tomography (CT)-based tissue pattern latent traits. Among the major clusters, cluster 0 (C0) and cluster 5 (C5) were dominated by HD-exposed and unexposed subjects, respectively. C0 was characterized by features attributable to lung inflammation or fibrosis in contrast with C5. The computational fluid and particle dynamics (CFPD) analysis suggested that the smaller airway sizes observed in the C0 subjects led to greater airway resistance and particle deposition in the airways. Accordingly, women appeared more vulnerable to HD-associated lung abnormalities than men.