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.

Laryngeal birth defects are considered rare, but they can be life-threatening conditions. The BMP4 gene plays an important role in organ development and tissue remodeling throughout life. Here we examined its role in laryngeal development complementing similar efforts for the lung, pharynx, and cranial base. Our goal was to determine how different imaging techniques contribute to a better understanding of the embryonic anatomy of the normal and diseased larynx in small specimens. Contrast-enhanced micro CT images of embryonic larynx tissue from a mouse model with Bmp4 deletion informed by histology and whole-mount immunofluorescence were used to reconstruct the laryngeal cartilaginous framework in three dimensions. Laryngeal defects included laryngeal cleft, laryngeal asymmetry, ankylosis and atresia. Results implicate BMP4 in laryngeal development and show that the 3D reconstruction of laryngeal elements provides a powerful approach to visualize laryngeal defects and thereby overcoming shortcomings of 2D histological sectioning and whole mount immunofluorescence.

Effective evaluation and prediction of aerosol transport deposition in the human respiratory tracts are critical to aerosol drug delivery and evaluation of inhalation products. Establishment of an in vitro-in vivo correlation (IVIVC) requires the understanding of flow and aerosol behaviour and underlying mechanisms at the microscopic scale. The achievement of the aim can be facilitated via computational fluid dynamics (CFD) based in silico modelling which treats the aerosol delivery as a two-phase flow. CFD modelling research, in particular coupling with discrete phase model (DPM) and discrete element method (DEM) approaches, has been rapidly developed in the past two decades. This paper reviews the recent development in this area. The paper covers the following aspects: geometric models of the respiratory tract, CFD turbulence models for gas phase and its coupling with DPM/DEM for aerosols, and CFD investigation of the effects of key factors associated with geometric variations, flow and powder characteristics. The review showed that in silico study based on CFD models can effectively evaluate and predict aerosol deposition pattern in human respiratory tracts. The review concludes with recommendations on future research to improve in silico prediction to achieve better IVIVC.

The \"microbiome\" is the operative term to refer to a collection of all taxa constituting microbial communities, such as bacteria, archaea, fungi and protists (originally microbiota). The microbiome consists of the indigenous microbial communities and of the host environment that they inhabit. Actually, it has been shown that there is a close relationship between the microbiome and human health and disease condition. Although, initially, the lung was considered sterile, actually, the existence of a healthy lung microbiome is usually accepted. Lung microbiome changes are reported in Chronic Obstructive Pulmonary Disease (COPD) and in its exacerbation. Viral and bacterial infections of the respiratory system are a major cause of COPD exacerbations (AECOPD) leading to increased local and systemic inflammation. Detection rates of virus in AECOPD are variable between 25-62% according to the detection method. The study of human airway and lung disease virome is quite recent and still very limited. The purpose of this review is to summarize recent findings on the lung microbiome composition with a special emphasis on virome in COPD and in AECOPD. Some drugs of natural origins active against resistant bacteria and virus are described.

The scarcity of regional deposition data in distal respiratory airways represents an important challenge for current toxicology and pharmacology research. To bridge this gap, a realistic airway model extending from nasal and oral openings to distal bronchial airways with varying pathway length was built in this study. Transport and deposition characteristics of naturally inhaled ultrafine particles (UFPs) ranging from 1 to 100 nm were numerically investigated, and effects of different inhalation scenarios were considered. To enable intercase particle deposition comparison, an adjusted parameter, unified deposition enhancement factor (UDEF), was proposed for quantifying the localised deposition concentration. Results show that compartment particle deposition peaked around the ultrafine end of the considered size range, and it dropped rapidly with the increase of particle size. Different inhalation modes caused notable deposition changes in the extrathoracic region, while its effects in the TB airway are much less. For UFPs larger than 10 nm, predicted deposition efficiencies in all compartments are all at lowest levels among considered particle size range, implying UFPs ranging from 10 to 100 nm can travel through the whole respiratory airway model and escape to the alveolar region. Furthermore, high enhancement factors were observed at the vicinity of most bifurcation apexes, and more even UDEF distribution was observed from 1-nm particle cases. While for 100-nm cases, the deposited particles tend to concentrate at few \"hot spots\" (areas of high deposition concentration in relation to surrounding surfaces) with greater UDEF in the tracheobronchial airway.