Automated bidirectional coupling of multiscale models of aerosol dosimetry: validation with subject-specific deposition data

Assessing the toxicity of airborne particulate matter or the efficacy of inhaled drug depends upon accurate estimates of deposited fraction of inhaled materials. In silico approaches can provide important insights into site- or airway-specific deposition of inhaled aerosols in the respiratory system. In this study, we improved on our recently developed 3D/1D model that simulate aerosol transport and deposition in the whole lung over multiple breath cycles (J. Aerosol Sci 151:105647). A subject-specific multiscale lung model of a healthy male subject using computational fluid-particle dynamics (CFPD) in a 3D model of the oral cavity through the large bronchial airways entering each lobe was bidirectionally coupled with a recently improved Multiple Path Particle Dosimetry (MPPD) model to predict aerosol deposition over the entire respiratory tract over multiple breaths for four conditions matching experimental aerosol exposures in the same subject from which the model was developed. These include two particle sizes (1 and 2.9 μm) and two subject-specific breathing rates of ~300 ml/s (slow breathing) and ~750 ml/s (fast breathing) at a target tidal volume of 1 L. In silico predictions of retained fraction were 0.31 and 0.29 for 1 μm and 0.66 and 0.62 for 2.9 μm during slow and fast breathing, respectively, and compared well with experimental data (1 μm: 0.31±0.01 (slow) and 0.27±0.01 (fast), 2.9 μm: 0.63±0.03 (slow) and 0.68±0.02 (fast)). These results provide a great deal of confidence in the validity and reliability of our approach.

Preventing Respiratory Viral Diseases with Antimicrobial Peptide Master Regulators in the Lung Airway Habitat

The vast surface area of the respiratory system acts as an initial site of contact for microbes and foreign particles. The whole respiratory epithelium is covered with a thin layer of the airway and alveolar secretions. Respiratory secretions contain host defense peptides (HDPs), such as defensins and cathelicidins, which are the best-studied antimicrobial components expressed in the respiratory tract. HDPs have an important role in the human body's initial line of defense against pathogenic microbes. Epithelial and immunological cells produce HDPs in the surface fluids of the lungs, which act as endogenous antibiotics in the respiratory tract. The production and action of these antimicrobial peptides (AMPs) are critical in the host's defense against respiratory infections. In this study, we have described all the HDPs secreted in the respiratory tract as well as how their expression is regulated during respiratory disorders. We focused on the transcriptional expression and regulation mechanisms of respiratory tract HDPs. Understanding how HDPs are controlled throughout infections might provide an alternative to relying on the host's innate immunity to combat respiratory viral infections.

Elevated CO2 modulates airway contractility

Carbon dioxide (CO₂), a primary product of oxidative metabolism, can be sensed by eukaryotic cells eliciting unique responses via specific signalling pathways. Severe lung diseases such as chronic obstructive pulmonary disease are associated with hypoventilation that can lead to the elevation of CO₂ levels in lung tissues and the bloodstream (hypercapnia). However, the pathophysiological effects of hypercapnia on the lungs and specific lung cells are incompletely understood. We have recently reported using combined unbiased molecular approaches with studies in mice and cell culture systems on the mechanisms by which hypercapnia alters airway smooth muscle contractility. In this review, we provide a pathophysiological and mechanistic perspective on the effects of hypercapnia on the lung airways and discuss the recent understanding of high CO₂ modulation of the airway contractility.

Efficient bi-directional coupling of 3D Computational Fluid-Particle Dynamics and 1D Multiple Path Particle Dosimetry lung models for multiscale modeling of aerosol dosimetry

The development of predictive aerosol dosimetry models has been a major focus of environmental toxicology and pharmaceutical health research for decades. One-dimensional (1D) models successfully predict overall deposition averages but fail to accurately predict local deposition. Computational fluid-particle dynamics (CFPD) models provide site-specific predictions but at a computational cost that prohibits whole lung predictions. Thus, there is a need for developing multiscale strategies to provide a realistic subject-specific picture of the fate of inhaled aerosol in the lungs. CT-based 3D/CFPD models of the large airways were bidirectionally coupled with individualized 1D Navier-Stokes airflow and particle transport based upon the widely used Multiple Path Particle Dosimetry Model (MPPD). Distribution of airflows among lobes was adjusted by measured lobar volume changes observed in CT images between FRC and FRC + 1.5 L. As a test of the effectiveness of the coupling procedures, deposition modeling of previous 1 μm aerosol exposure studies was performed. The complete coupled model was run for 3 breaths, with the computation-intense portion being the 3D CFPD Lagrangian particle tracking calculation. The average deposition per breath was 11% in the combined multiscale model with site-specific doses available in the CFPD portion of the model and airway- or region-specific deposition available for the MPPD portion. In conclusion, the key methods developed in this study enable predictions of ventilation heterogeneities and aerosol deposition across the lungs that are not captured by 3D or 1D models alone. These methods can be used as the foundation for multi-scale modeling of the full respiratory system.

Effect of intersubject variability of extrathoracic morphometry, lung airways dimensions and respiratory parameters on particle deposition

OBJECTIVE

The structure of extrathoracic passages, variability of tracheobronchial (TB) airways and alveolar dimensions and individual variations of breathing pattern exhibit significant intersubject variations, which affect extrathoracic deposition and, in further consequence, the fraction of inhaled particles actually reaching the thoracic region. The present study was conducted to quantify the intersubject variability of lung deposition fractions caused by the fluctuations in these three major sources of intersubject variability.

METHODS

To quantify intersubject variability of extrathoracic, thoracic and total deposition fractions (TDF), different combinations of the three sources of variability were simulated to identify the most important factors. Deposition fractions of inhaled particles were computed by the stochastic airway generation model IDEAL. The dimensions of the respiratory airways were scaled in proportion to age and height of the subject to calculate TDFs.

RESULTS

The variability of deposition fractions increased with the stepwise addition of influencing factors and the resulting standard deviations ranged up to 30%. While some combinations enhanced the effects of individual factors on deposition by up to 40%, others seemed to compensate each other with only a minor effect on deposition.

CONCLUSION

The present study attempts to quantify experimentally observed intersubject variability of regional deposition fractions caused by individual variations of nasal and oral geometry, lung airway dimensions and breathing patterns in healthy lungs, serving as a baseline for subsequent calculations for diseased lungs, e.g. asthma, COPD, and emphysema, which may further increase intersubject variabilities of medically relevant depositions.

In vivo lung morphometry with hyperpolarized 3He diffusion MRI: theoretical background

MRI-based study of (3)He gas diffusion in lungs may provide important information on lung microstructure. Lung acinar airways can be described in terms of cylinders covered with alveolar sleeve [Haefeli-Bleuer, Weibel, Anat. Rec. 220 (1988) 401]. For relatively short diffusion times (on the order of a few ms) this geometry allows description of the (3)He diffusion attenuated MR signal in lungs in terms of two diffusion coefficients-longitudinal (D(L)) and transverse (D(T)) with respect to the individual acinar airway axis [Yablonskiy et al., PNAS 99 (2002) 3111]. In this paper, empirical relationships between D(L) and D(T) and the geometrical parameters of airways and alveoli are found by means of computer Monte Carlo simulations. The effects of non-Gaussian signal behavior (dependence of D(L) and D(T) on b-value) are also taken into account. The results obtained are quantitatively valid in the physiologically important range of airway parameters characteristic of healthy lungs and lungs with mild emphysema. In lungs with advanced emphysema, the results provide only "apparent" characteristics but still could potentially be used to evaluate emphysema progression. This creates a basis for in vivo lung morphometry-evaluation of the geometrical parameters of acinar airways from hyperpolarized (3)He diffusion MRI, despite the airways being too small to be resolved by direct imaging. These results also predict a rather substantial dependence of (3)He ADC on the experimentally-controllable diffusion time, Delta. If Delta is decreased from 3 ms to 1 ms, the ADC in normal human lungs may increase by almost 50%. This effect should be taken into account when comparing experimental data obtained with different pulse sequences.

How accurately can the parameters from a model of anisotropic 3He gas diffusion in lung acinar airways be estimated? Bayesian view

In the framework of a recently proposed method for in vivo lung morphometry, acinar lung airways are considered as a set of randomly oriented cylinders covered by alveolar sleeves. Diffusion of (3)He in each airway is anisotropic and can be described by distinct longitudinal and transverse diffusion coefficients. This macroscopically isotropic but microscopically anisotropic model allows estimation of these diffusion coefficients from multi b-value MR experiments despite the airways being too small to be resolved by direct imaging. Herein a Bayesian approach is used for analyzing the uncertainties in the model parameter estimates. The approach allows evaluation of relative errors of the parameter estimates as functions of the "true" values of the parameters, the signal-to-noise ratio, the maximum b-value and the total number of b-values used in the experiment. For a given set of the "true" diffusion parameters, the uncertainty in the estimated diffusion coefficients has a minimum as a function of maximum b-value and total number of data points. Choosing the MR pulse sequence parameters corresponding to this minimum optimizes the diffusion MR experiment and gives the best possible estimates of the diffusion coefficients. The mathematical approach presented can be generalized for models containing arbitrary numbers of estimated parameters.