A quasi-3D model of the whole lung: airway extension to the tracheobronchial limit using the constrained constructive optimization and alveolar modeling, using a sac-trumpet model.

Existing computational models used for simulating the flow and species transport in the human airways are zero-dimensional (0D) compartmental, three-dimensional (3D) computational fluid dynamics (CFD), or the recently developed quasi-3D (Q3D) models. Unlike compartmental models, the full CFD and Q3D models are physiologically and anatomically consistent in the mouth and the upper airways, since the starting point of these models is the mouth-lung surface geometry, typically created from computed tomography (CT) scans. However, the current resolution of CT scans limits the airway detection between the 3rd-4th and 7th-9th generations. Consequently, CFD and the Q3D models developed using these scans are generally limited to these generations. In this study, we developed a method to extend the conducting airways from the end of the truncated Q3D lung to the tracheobronchial (TB) limit. We grew the lung generations within the closed lung lobes using the modified constrained constructive optimization, creating an aerodynamically optimized network aiming to produce equal pressure at the distal ends of the terminal segments. This resulted in a TB volume and lateral area of ∼165 cc and ∼2000 cm2, respectively. We created a "sac-trumpet" model at each of the TB outlets to represent the alveoli. The volumes of the airways and the individual alveolar generations match the anatomical values by design: with the functional residual capacity at 2611 cc. Lateral surface areas were scaled to match the physiological values. These generated Q3D whole lung models can be efficiently used for conducting multiple breathing cycles of drug transport and deposition simulations.

A Quasi-3D compartmental multi-scale approach to detect and quantify diseased regional lung constriction using spirometry data.

Spirometry is a widely used pulmonary function test to detect the airflow limitations associated with various obstructive lung diseases, such as asthma, chronic obstructive pulmonary disease, and even obesity-related complications. These conditions arise due to the change in the airway resistance, alveolar compliance, and inductance values. Currently, zero-dimensional compartmental models are commonly used for calibrating these resistance, compliance, and inductance values, ie, solving the inverse spirometry problem. However, zero-dimensional compartments cannot capture the flow physics or the spatial geometry effects, thereby generating a low fidelity prediction of the diseased lung. Computational fluid dynamics (CFD) models offer higher fidelity solutions but may be impractical for certain applications due to the duration of these simulations. Recently, a novel, fast-running, and robust Quasi-3D (Q3D) wire model for simulating the airflow in the human lung airway was developed by CFD Research Corporation. This Q3D method preserved the 3D spatial nature of the airways and was favorably validated against CFD solutions. In the present study, the Q3D compartmental multi-scale combination is further improved to predict regional lung constriction of diseased lungs using spirometry data. The Q3D mesh is resolved up to the eighth lung airway generation. The remainder of the airways and the alveoli sections are modeled using a compartmental approach. The Q3D geometry is then split into different spatial sections, and the resistance values in these regions are obtained using parameter inversion. Finally, the airway diameter values are then reduced to create the actual diseased lung model, corresponding to these resistance values. This diseased lung model can be used for patient-specific drug deposition predictions and the subsequent optimization of the orally inhaled drug products.

A compartment-quasi-3D multiscale approach for drug absorption, transport, and retention in the human lungs.

Most current models used for modeling the pulmonary drug absorption, transport, and retention are 0D compartmental models where the airways are generally split into the airways and alveolar sections. Such block models deliver low fidelity solutions and the spatial lung drug concentrations cannot be obtained. Other approaches use high fidelity CFD models with limited capabilities due to their exorbitant computational cost. Recently, we presented a novel, fast-running and robust quasi-3D (Q3D) model for modeling the pulmonary airflow. This Q3D method preserved the 3D lung geometry, delivered extremely accurate solutions, and was 25 000 times faster in comparison to the CFD methods. In this paper, we present a Q3D-compartment multiscale combination to model the pulmonary drug absorption, transport, and retention. The initial deposition is obtained from CFD simulations. The lung absorption compartment model of Yu and Rosania is adapted to this multiscale format. The lung is modeled in the Q3D format till the eighth airway generation. The remainder of the lung along with the systemic circulation and elimination processes was modeled using compartments. The Q3D model is further adapted, by allowing for various heterogeneous annular lung layers. This allows us to model the drug transport across the layers and along the lung. Using this multiscale model, the spatiotemporal drug concentrations in the different lung layers and the temporal concentration in the plasma are obtained. The concentration profile in the plasma was found to be better aligned with the experimental findings in comparison with compartmental model for the standard test cases. Thus, this multiscale model can be used to optimize the target-specific drug delivery and increase the localized bioavailability, thereby facilitating applications from the bench to bedside for various patient/lung-disease variations.