Ching-Long Lin

Accurate prediction of bronchodilator performance remains challenging. We present a coupled computational fluid and particle dynamics (CFPD) and physiologically-based pharmacokinetic /pharmacodynamics (PBPK/PD) framework that leverages subject-specific CT-derived airway trees and airflow distributions to model regional lung exposure and the resulting clinical effect, quantified as the change in Forced Expiratory Volume in 1 second (ΔFEV1). Data from six asthmatic and ten healthy subjects were analyzed. CFPD simulations predicted subject-specific airflow and drug deposition by airway generation for inhalation of 400 μg albuterol using a metered dose inhaler (MDI) and a dry powder inhaler (DPI). The deposited dose was input into the PBPK model at the epithelial lining fluid (ELF) to predict drug concentrations in plasma and tissues. The PD model predicted ΔFEV1 at the subepithelium effect-site. Two key observations emerged. First, MDI use shifted the inhaled dose toward the respiratory region, whereas DPI delivery resulted in greater deposition in generations G2-G5. Second, asthmatic subjects exhibited higher resistance than healthy subjects in generations G5-G10, resulting in larger ΔFEV1. Fitted PD model parameters, yielded a half-maximal effective concentration (EC50 =1.1±0.07 nM) and Hill slope (n =1.6±0.1), with R2 = 0.98. In summary, we developed a subject-specific coupled CFPD-PBPK/PD framework to predict inhaled albuterol exposure in plasma and ELF, as well as the resulting ΔFEV1 clinical response. This approach has the potential to inform inhaler selection, optimize dosing strategies, and enable disease-specific inhalational therapies. The present study was limited to male subjects, and future work should extend the framework to include sex-specific physiological differences.
•Albuterol transport and deposition were simulated in imaging-based subject-specific asthmatic and healthy airways.•Plasma and subepithelial albuterol concentrations were predicted across subject cohorts.•MDI shifts dose to the respiratory zone, whereas DPI deposits more in proximal conducting airways.•Subepithelial effect-site concentrations in generations G5-G10 closely matched observed ΔFEV1.

In computed tomography (CT)-based computational fluid dynamics (CFD) simulations of the human respiratory system, no or few studies have incorporated both realistic upper and lower airways, along with extensions to CT-unresolved higher-generation airways. In this study, we present a CT-based, physiologically consistent CFD model of the human airway that integrates artificial airway extensions down to the transitional bronchioles within the OpenFOAM framework. The model includes a hybrid turbulence approach combining Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES), and a state-of-the-art airway mesh smooth learning (AMSL) technique for constructing accurate airway geometries. Physiologically consistent boundary conditions are applied using airflow data derived from one-dimensional network simulations. We investigate the impact of the hybrid RANS-LES model on airflow characteristics, pressure distribution, and particle deposition by comparing it with conventional turbulence models, including the wall-adapting local eddy-viscosity (WALE) model for LES and the k-ω SST model for RANS. The AMSL method is also evaluated against the traditional Taubin smoothing technique. Our results show that pressure does not monotonically decrease throughout the upper respiratory tract but exhibits a continual decrease in the lower tract, independent of airway generation. The hybrid RANS-LES model demonstrates flow patterns and particle deposition characteristics comparable to those of the LES model and proves an improved fidelity over traditional RANS models. Furthermore, the AMSL technique significantly influences airflow behavior and particle deposition, highlighting the importance of accurate geometry processing. In conclusion, the proposed physiologically consistent CFD model, implemented in the OpenFOAM framework, demonstrates strong potential for clinical and research applications by offering enhanced accuracy and reliability. The use of an integrated airway model, extending from the upper airways to artificially constructed distal airways, facilitates a better understanding of multiscale airflow dynamics in the lungs.

This study investigated asthma phenotypes and their associations with ventilation heterogeneity and particle deposition by utilizing Single-Photon Emission Computed Tomography (SPECT) imaging, quantitative Computed Tomography (qCT) imaging-based subgrouping, and a whole-lung computational model.PURPOSEThis study investigated asthma phenotypes and their associations with ventilation heterogeneity and particle deposition by utilizing Single-Photon Emission Computed Tomography (SPECT) imaging, quantitative Computed Tomography (qCT) imaging-based subgrouping, and a whole-lung computational model.Two datasets were analyzed: one from a combined SPECT and CT (SPECT/CT) study with six asthmatic subjects, and another from the Severe Asthma Research Program (SARP) with 209 asthmatic subjects. Data from 35 previously acquired healthy subjects served as a control group. Each subject underwent CT scans at full inspiration and expiration, along with pulmonary function testing (PFT). The SPECT/CT study included ventilation SPECT imaging. Key qCT variables such as airway diameter, wall thickness, percentage of air trapping (AirT%), and percentage of small airway disease (fSAD%) were assessed. A subject-specific whole-lung computational fluid and particle dynamics (CFPD) model predicted airway resistance, particle deposition fraction, and the coefficient of variation (CV) for ventilation heterogeneity. Subjects were categorized into four predefined asthma imaging subgroups/clusters with increasing severity (C1-C4). CFPD-predicted CVs were validated against SPECT measurements. We compared PFT, qCT, and CFPD variables across SARP clusters and analyzed particle deposition fractions in large conducting, small conducting, and respiratory airways.MATERIALS AND METHODSTwo datasets were analyzed: one from a combined SPECT and CT (SPECT/CT) study with six asthmatic subjects, and another from the Severe Asthma Research Program (SARP) with 209 asthmatic subjects. Data from 35 previously acquired healthy subjects served as a control group. Each subject underwent CT scans at full inspiration and expiration, along with pulmonary function testing (PFT). The SPECT/CT study included ventilation SPECT imaging. Key qCT variables such as airway diameter, wall thickness, percentage of air trapping (AirT%), and percentage of small airway disease (fSAD%) were assessed. A subject-specific whole-lung computational fluid and particle dynamics (CFPD) model predicted airway resistance, particle deposition fraction, and the coefficient of variation (CV) for ventilation heterogeneity. Subjects were categorized into four predefined asthma imaging subgroups/clusters with increasing severity (C1-C4). CFPD-predicted CVs were validated against SPECT measurements. We compared PFT, qCT, and CFPD variables across SARP clusters and analyzed particle deposition fractions in large conducting, small conducting, and respiratory airways.Cluster C4 exhibited a significantly distinct ventilation profile compared to other clusters and health controls. This distinction contrasted with the insignificant differences between ventilation profiles in severity subgroups defined by conventional spirometry-based guidelines. Airway resistance varied significantly across the asthma clusters. Although both C3 and C4 clusters represented severe asthma, only C4 showed a significant increase in AirT%, primarily due to fSAD%. Since inflammatory phenotypes differ - C3 with wall thickening in large and small conducting airways, and C4 with elevated fSAD% and Emph% in small conducting and respiratory airways - fine particles (∼5 μm) and extrafine particles (∼1 μm) are more effective at reaching the respective regions in C3 and C4. Given that C2 and C4 have hyper-responsive phenotypes with narrowed conducting airways, fine particles are more effective in reaching these areas. Airway enlargement in targeted segments of the left lower lobe resulted in improved particle deposition.RESULTSCluster C4 exhibited a significantly distinct ventilation profile compared to other clusters and health controls. This distinction contrasted with the insignificant differences between ventilation profiles in severity subgroups defined by conventional spirometry-based guidelines. Airway resistance varied significantly across the asthma clusters. Although both C3 and C4 clusters represented severe asthma, only C4 showed a significant increase in AirT%, primarily due to fSAD%. Since inflammatory phenotypes differ - C3 with wall thickening in large and small conducting airways, and C4 with elevated fSAD% and Emph% in small conducting and respiratory airways - fine particles (∼5 μm) and extrafine particles (∼1 μm) are more effective at reaching the respective regions in C3 and C4. Given that C2 and C4 have hyper-responsive phenotypes with narrowed conducting airways, fine particles are more effective in reaching these areas. Airway enlargement in targeted segments of the left lower lobe resulted in improved particle deposition.Our cluster-informed CFPD-based approach enhances the understanding of ventilation heterogeneity in asthma and holds potential for refining strategies for inhalational therapies.CONCLUSIONOur cluster-informed CFPD-based approach enhances the understanding of ventilation heterogeneity in asthma and holds potential for refining strategies for inhalational therapies.

IntroductionThe aim of this research is to elucidate chronic obstructive pulmonary disease (COPD) progression by quantifying lung ventilation heterogeneities using single-photon emission computed tomography (SPECT) images and establishing correlations with quantitative computed tomography (qCT) imaging-based metrics. This approach seeks to enhance our understanding of how structural and functional changes influence ventilation heterogeneity in COPD.MethodsEight COPD subjects completed a longitudinal study with three visits, spaced about a year apart. CT scans were performed at each visit and qCT-based variables were derived to measure the structural and functional characteristics of the lungs, while the SPECT-based variables were used to quantify lung ventilation heterogeneity. The correlations between key qCT-based variables and SPECT-based variables were examined.ResultsThe SPECT-based ventilation heterogeneity (CVTotal) showed strong correlations with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung, based on cross-sectional data. Over the 2-year period, changes in SPECT-based hot spots (TCMax) exhibited strong negative correlations with changes in fSAD%Total, Emph%Total, and the average airway diameter in the left upper lobe, as well as a strong positive correlation with alternations in airflow distribution between the upper and lower lobes.DiscussionIn conclusion, this study found strong positive cross-sectional correlations between CVTotal and both fSAD% and Emph%, suggesting that these markers primarily reflect static disease severity at a single time point. In contrast, longitudinal correlations between changes in TCMax and other variables over 2 years may capture the dynamic process of hot spot formation, independent of disease severity. These findings suggest that changes in TCMax may serve as a more sensitive biomarker than changes in CVTotal for tracking the underlying mechanisms of COPD progression.

Recent studies, based on clinical data, have identified sex and age as significant factors associated with an increased risk of long COVID. These two factors align with the two post-COVID-19 clusters identified by a deep learning algorithm in computed tomography (CT) lung scans: Cluster 1 (C1), comprising predominantly females with small airway diseases, and Cluster 2 (C2), characterized by older individuals with fibrotic-like patterns. This study aims to assess the distributions of inhaled aerosols in these clusters.
140 COVID survivors examined around 112 days post-diagnosis, along with 105 uninfected, non-smoking healthy controls, were studied. Their demographic data and CT scans at full inspiration and expiration were analyzed using a combined imaging and modeling approach. A subject-specific CT-based computational model analysis was utilized to predict airway resistance and particle deposition among C1 and C2 subjects. The cluster-specific structure and function relationships were explored.
In C1 subjects, distinctive features included airway narrowing, a reduced homothety ratio of daughter over parent branch diameter, and increased airway resistance. Airway resistance was concentrated in the distal region, with a higher fraction of particle deposition in the proximal airways. On the other hand, C2 subjects exhibited airway dilation, an increased homothety ratio, reduced airway resistance, and a shift of resistance concentration towards the proximal region, allowing for deeper particle penetration into the lungs.
This study revealed unique mechanistic phenotypes of airway resistance and particle deposition in the two post-COVID-19 clusters. The implications of these findings for inhaled drug delivery effectiveness and susceptibility to air pollutants were explored.
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•We propose an airway-mesh-smoothing learning method (AMSL).•AMSL trains two graph convolutional neural networks to smooth vertex positions and face normal vectors.•Compared to the existing methods, the proposed smoothing yields improved results on public datasets.•AMSL reproduces the airway branch diameter accurately, allowing a better estimate of flow properties.
A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations.
The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties.
In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method.
The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.

The simulation of gas flow in the bronchial tree using computational fluid dynamics (CFD) has become a useful tool for the analysis of gas flow mechanics, structural deformation, ventilation, and particle deposition for drug delivery during spontaneous and assisted breathing. CFD allows for new hypotheses to be tested in silico, and detailed results generated without performing expensive experimental procedures that could be potentially harmful to patients. Such computational techniques are also useful for analyzing structure–function relationships in healthy and diseased lungs, assessing regional ventilation at various time points over the course of clinical treatment, or elucidating the changes in airflow patterns over the life span. CFD has also allowed for the development and use of image-based (i.e., patient-specific) models of three-dimensional (3D) airway trees with realistic boundary conditions to achieve more meaningful and personalized data that may be useful for planning effective treatment protocols. This focused review will present a summary of the techniques used in generating realistic 3D airway tree models, the limitations of such models, and the methodologies used for CFD airflow simulation. We will discuss mathematical and image-based geometric models, as well as the various boundary conditions that may be imposed on these geometric models. The results from simulations utilizing mathematical and image-based geometric models of the airway tree will also be discussed in terms of similarities to actual gas flow in the human lung.

This study aims to assess the effects of varying an ethanol co-solvent on the deposition of drug particles in severe asthmatic subjects with distinct airway structures and lung functions using computational fluid dynamics. The subjects were selected from two quantitative computed tomography imaging-based severe asthmatic clusters, differentiated by airway constriction in the left lower lobe. Drug aerosols were assumed to be generated from a pressurized metered-dose inhaler (MDI). The aerosolized droplet sizes were varied by increasing the ethanol co-solvent concentration in the MDI solution. The MDI formulation consists of 1,1,2,2-tetrafluoroethane (HFA-134a), ethanol, and beclomethasone dipropionate (BDP) as the active pharmaceutical ingredient. Since HFA-134a and ethanol are volatile, both substances evaporate rapidly under ambient conditions and trigger condensation of water vapor, increasing the size of aerosols that are predominantly composed of water and BDP. The average deposition fraction in intra-thoracic airways for severe asthmatic subjects with (or without) airway constriction increased from 37%±12 to 53.2%±9.4 (or from 20.7%± 4.6 to 34.7%±6.6) when the ethanol concentration was increased from 1 to 10%wt/wt. However, when the ethanol concentration was further increased from 10 to 20%wt/wt, the deposition fraction decreased. This indicates the importance of selecting appropriate co-solvent amounts during drug formulation development for the treatment of patients with narrowed airway disease. For severe asthmatic subjects with airway narrowing, the inhaled aerosol may benefit from a low hygroscopic effect by reducing ethanol concentration to penetrate the peripheral region effectively. These results could potentially inform the selection of co-solvent amounts for inhalation therapies in a cluster-specific manner.

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.