Patient-specific modeling of aerosol delivery in healthy and asthmatic adults

Computational fluid dynamics model predictions of inhaled corticosteroid deposition in patients with severe asthma

BACKGROUND

Some patients with severe asthma have persistent type-2 inflammation despite being treated with high-dose inhaled corticosteroids (ICS). The variability in ICS deposition between patients with severe asthma is not well-understood and could contribute to this persistence.

OBJECTIVES

To characterise and compare model-predicted deposition of fine-particle and extrafine-particle ICS in patients with severe asthma based on biomarkers of type-2 inflammation, airway morphology and airway function.

METHODS

Twenty-eight patients with severe asthma performed full-inspiration and full-expiration chest CT on the same day that biomarkers of type-2 inflammation were measured. Functional respiratory imaging and computational fluid dynamics were used to simulate and predict intrathoracic, central and peripheral airway deposition, and central-to-peripheral airway deposition (C:P) ratio of fine-particle ICS (fluticasone-propionate HFA) (ICSFP) and extrafine-particle ICS (beclomethasone-dipropionate HFA) (ICSEFP). CT-derived wall area percent (WA%), lumen area (LA) and mucus burden were quantified to characterise airway morphology.

RESULTS

Simulated deposition of ICSEFP was higher than ICSFP in the intrathoracic, central and peripheral airways (all p<0.0001). Greater WA% and smaller LA were correlated with greater C:P ratio of ICSFP (r=0.60, p=0.0068; r=-0.60, p=0.0072) and ICSEFP (r=0.54, p=0.028; r=-0.54, p=0.026). Participants with elevated sputum eosinophils had a greater C:P ratio, irrespective of particle size (ICSFP, p=0.045; ICSEFP, p=0.021).

CONCLUSIONS

In severe asthma patients with thicker airway walls, narrower airway lumens and elevated biomarkers of type-2 inflammation, a smaller ratio of ICSFP reached the peripheral airways. ICSEFP did not fully mitigate this. Patient-specific airway morphology may impact regional ICS deposition and contribute to persistent inflammation.

Subject-Specific Multi-Scale Modeling of the Fate of Inhaled Aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. This paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

Modeling Realistic Geometries in Human Intrathoracic Airways

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

A Critical Analysis of the CFD-DEM Simulation of Pharmaceutical Aerosols Deposition in Upper Intra-Thoracic Airways: Considerations on Aerosol Transport and Deposition

The reliability and accuracy of numerical models and computer simulations to study aerosol deposition in the human respiratory system is investigated for a patient-specific tracheobronchial tree geometry. A computational fluid dynamics (CFD) model coupled with discrete elements methods (DEM) is used to predict the transport and deposition of the aerosol. The results are compared to experimental and numerical data available in the literature to study and quantify the impact of the modeling parameters and numerical assumptions. Even if the total deposition compares very well with the reference data, it is clear from the present work how local deposition results can depend significantly upon spatial discretization and boundary conditions adopted to represent the respiratory act. The modeling of turbulent fluctuations in the airflow is also found to impact the local deposition and, to a minor extent, the flow characteristics at the inlet of the computational domain. Using the CFD-DEM model, it was also possible to calculate the airflow and particles splitting at bifurcations, which were found to depart from the assumption of being equally distributed among branches adopted by some of the simplified deposition models. The results thus suggest the need for further studies towards improving the quantitative prediction of aerosol transport and deposition in the human airways.

In silico investigation of inhalation condition impacts on hygroscopic growth and deposition of salbutamol sulphate in human airways

The objective of this study is to explore the transport, size growth, and deposition of Salbutamol Sulphate (SS) using Computational Fluid Dynamics (CFD). A CT-based realistic model of human airways from the oral cavity to the 5th generation of the lung was utilized as the computational domain. Four Test Cases (TC) with varying temperature and relative humidity (RH) under two inspiratory waveforms were considered to completely evaluate the impact of inhalation conditions on particle growth. Salbutamol Sulphate (SS) is a β2-adrenergic agonist and has been extensively used for asthma treatment. A monodispersed distribution of SS particles with an initial diameter of 167 nm was considered at the mouth inlet based on pharmaceutical data. Results indicated that inhalation of saturated/supersaturated air (RH>100%) leads to significant hygroscopic growth of SS particles with a factor of 10. In addition, the deposition efficiency of SS particles under the Quick and Deep (QD) inhalation profile was enhanced as the flow temperature and humidity increased. However, the implementation of Slow and Deep (SD) inspiratory waveform revealed that the same particle size growth is achieved in the respiratory system with lower deposition efficiency in the mouth-throat (less than 3%) and tracheobronchial airway (less than 2.18%). For the escaped particles form the right lung, in the SD waveform under TC 3, the maximum particle size distribution was for 600 nm particles with 25% probability. In the left lung, 30% of the particles were increased up to 950 nm in size. For the QD waveform in TC 3 and TC4, the most frequent particles were 800 nm with 36% probability. This holds practical significance in the context of deep lung delivery for asthmatic patients with enhanced deposition efficiency and large particle size. The findings of the present study can contribute to the development of targeted drug delivery strategies for the treatment of pulmonary diseases using hygroscopic dry powder formulations.

The utility of hybrid in silico models of airflow and aerosol dosimetry in the lung

The development and application of multi-scale models of the lung has significantly increased in recent years. These hybrid models merge realistic representations of the larger airways with lower-dimensional descriptions of the bronchioles and respiratory airways. Due to recent advancements, it is possible to calculate airflow and dosimetry throughout the entire lung, enabling model validation with human or animal data. Here, we present a hybrid modeling pipeline and corresponding characteristic airflow and particle deposition hotspots. Next, we discuss the limitations of current hybrid models, including the need to update lower-dimensional deposition function descriptions to better represent realistic airway geometries. Future directions should include modeling diseased lungs and use of machine learning to predict whole lung dosimetry maps for a wider population.

Dosimetry Sensitivity in a Lower Dimensional Model of Patient-Specific Asthma Subjects

OBJECTIVE

Experimental uncertainty will impact in silico model calculations of aerosol delivery and deposition. Patient-specific dosimetry models are often parameterized based on medical imaging data, which contain inherent experimental variability.

METHODS

Here, we created and parameterized 1D models of three subject-specific asthmatic subjects and randomly assigned perturbations of up to 15 % on airway diameter, segmental volume, and defected volume. Sensitivity of imaging data experimental variability on dosimetry metrics were quantified.

RESULTS

Lobar particle delivery primarily depended on the distal segmental volumes; 15 % range of noise resulted in delivery to the upper right lobe to vary at most from 15.2 and 18.2 % for one of the severe subjects. Particle deposition was most sensitive to airway diameter; 95 % confidence intervals spanned from 8 to 10.6 % in the mild/moderate subject for 15 % variation on input metrics for 5 [Formula: see text] diameter particles. While these results provide possible ranges of dosimetry calculations for a specific subject, the perturbations were not sufficient to model the large observed inter-subject variability (8.9, 19, and 14.5 % deposition, subjects 1--3, respectively, 5 [Formula: see text] diameter particles).

CONCLUSION

This study highlights that in silico model predictions are robust in the presence of experimental uncertainty and that it continues to be necessary to perform subject-specific simulations, especially within the presence of heterogeneous airway disease.

SIGNIFICANCE

Sensitivity analysis provides confidence in calculating deposition in the airways of asthmatic subjects within the presence of experimental uncertainty.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Imaging drug delivery to the lungs: Methods and applications in oncology

Direct delivery to the lung via inhalation is arguably one of the most logical approaches to treat lung cancer using drugs. However, despite significant efforts and investment in this area, this strategy has not progressed in clinical trials. Imaging drug delivery is a powerful tool to understand and develop novel drug delivery strategies. In this review we focus on imaging studies of drug delivery by the inhalation route, to provide a broad overview of the field to date and attempt to better understand the complexities of this route of administration and the significant barriers that it faces, as well as its advantages. We start with a discussion of the specific challenges for drug delivery to the lung via inhalation. We focus on the barriers that have prevented progress of this approach in oncology, as well as the most recent developments in this area. This is followed by a comprehensive overview of the different imaging modalities that are relevant to lung drug delivery, including nuclear imaging, X-ray imaging, magnetic resonance imaging, optical imaging and mass spectrometry imaging. For each of these modalities, examples from the literature where these techniques have been explored are provided. Finally the different applications of these technologies in oncology are discussed, focusing separately on small molecules and nanomedicines. We hope that this comprehensive review will be informative to the field and will guide the future preclinical and clinical development of this promising drug delivery strategy to maximise its therapeutic potential.

Effect of patient inhalation profile and airway structure on drug deposition in image-based models with particle-particle interactions

For many of the one billion sufferers of respiratory diseases worldwide, managing their disease with inhalers improves their ability to breathe. Poor disease management and rising pollution can trigger exacerbations that require urgent relief. Higher drug deposition in the throat instead of the lungs limits the impact on patient symptoms. To optimise delivery to the lung, patient-specific computational studies of aerosol inhalation can be used. However in many studies, inhalation modelling does not represent situations when the breathing is impaired, such as in recovery from an exacerbation, where the patient's inhalation is much faster and shorter. Here we compare differences in deposition of inhaler particles (10, 4 μm) in the airways of three patients. We aimed to evaluate deposition differences between healthy and impaired breathing with image-based healthy and diseased patient models. We found that the ratio of drug in the lower to upper lobes was 35% larger with a healthy inhalation. For smaller particles the upper airway deposition was similar in all patients, but local deposition hotspots differed in size, location and intensity. Our results identify that image-based airways must be used in respiratory modelling. Various inhalation profiles should be tested for optimal prediction of inhaler deposition.

Mucus Plugs in Asthma at CT Associated with Regional Ventilation Defects at 3He MRI

Background Airway mucus plugs in asthma are associated with exacerbation frequency, increased eosinophilia, and reduced lung function. The relationship between mucus plugs and spatially overlapping ventilation abnormalities observed at hyperpolarized gas MRI has not been assessed quantitatively. Purpose To assess regional associations between CT mucus plugs scored by individual bronchopulmonary segment and corresponding measurements of segmental ventilation defect percentage (VDP) at hyperpolarized helium 3 (³He) MRI. Materials and Methods In this secondary analysis of a Health Insurance Portability and Accountability Act-compliant prospective observational cohort, participants in the Severe Asthma Research Program (SARP) III (NCT01760915) between December 2012 and August 2015 underwent hyperpolarized ³He MRI to determine segmental VDP. Segmental mucus plugs at CT were scored by two readers, with segments scored as plugged only if both readers agreed independently. A linear mixed-effects model controlling for interpatient variability was then used to assess differences in VDP in plugged versus plug-free segments. Results Forty-four participants with asthma were assessed (mean age ± standard deviation, 47 years ± 15; 29 women): 19 with mild-to-moderate asthma and 25 with severe asthma. Mucus plugs were observed in 49 total bronchopulmonary segments across eight of 44 patients. Segments containing mucus plugs had a median segmental VDP of 25.9% (25th-75th percentile, 7.3%-38.3%) versus 1.4% (25th-75th percentile, 0.1%-5.2%; P < .001) in plug-free segments. Similarly, the model estimated a segmental VDP of 18.9% (95% CI: 15.7, 22.2) for mucus-plugged segments versus 5.1% (95% CI: 3.3, 7.0) for plug-free segments (P < .001). Participants with one or more mucus plugs had a median whole-lung VDP of 11.1% (25th-75th percentile, 7.1%-18.9%) versus 3.1% (25th-75th percentile, 1.1%-4.4%) in those without plugs (P < .001). Conclusion Airway mucus plugging at CT was associated with reduced ventilation in the same bronchopulmonary segment at hyperpolarized helium 3 MRI, suggesting that mucus plugging may be an important cause of ventilation defects in asthma. © RSNA, 2021 Online supplemental material is available for this article.

A whole lung in silico model to estimate age dependent particle dosimetry

Anatomical and physiological changes alter airflow characteristics and aerosol distribution in the developing lung. Correlation between age and aerosol dosimetry is needed, specifically because youth are more susceptible to medication side effects. In this study, we estimate aerosol dosages (particle diameters of 1, 3, and 5 [Formula: see text]m) in a 3 month-old infant, a 6 year-old child, and a 36 year-old adult by performing whole lung subject-specific particle simulations throughout respiration. For 3 [Formula: see text]m diameter particles we estimate total deposition as 88, 73, and [Formula: see text] and the conducting versus respiratory deposition ratios as 4.0, 0.5, and 0.4 for the infant, child, and adult, respectively. Due to their lower tidal volumes and functional residual capacities the deposited mass is smaller while the tissue concentrations are larger in the infant and child subjects, compared to the adult. Furthermore, we find that dose cannot be predicted by simply scaling by tidal volumes. These results highlight the need for additional clinical and computational studies that investigate the efficiency of treatment, while optimizing dosage levels in order to alleviate side effects, in youth.

Effects of Anti-T2 Biologic Treatment on Lung Ventilation Evaluated by MRI in Adults With Prednisone-Dependent Asthma

BACKGROUND

The functional consequence of airway obstruction in asthma can be regionally measured using inhaled gas MRI. Ventilation defects visualized by MRI persist post-bronchodilator in patients with severe asthma with uncontrolled sputum eosinophilia and may be due to eosinophil-driven airway pathology that is responsive to "anti-T2" therapy.

RESEARCH QUESTION

Do anti-T2 therapies that clear eosinophils from the airway lumen decrease ventilation defects, measured by inhaled gas MRI, in adults with prednisone-dependent asthma?

STUDY DESIGN AND METHODS

Inhaled hyperpolarized gas MRI was performed before and after bronchodilation in 10 prednisone-dependent patients with asthma with uncontrolled eosinophilic bronchitis (sputum eosinophils ≥3%) at baseline and 558 (100-995) days later when their eosinophilic bronchitis had been controlled (sputum eosinophils <3%) by additional anti-T2 therapy. The effect of anti-T2 therapy on ventilation defects, quantified as the MRI ventilation-defect-percent (VDP), was evaluated before and after bronchodilation for all patients and compared between patients dichotomized based on the median percentage of sputum eosinophils at baseline (15.8%).

RESULTS

MRI VDP was improved pre- (ΔVDP_+anti-T2: -3% ± 4%, P = .02) and post-bronchodilator (ΔVDP_+anti-T2: -3% ± 4%; P = .04) after additional anti-T2 therapy that controlled eosinophilic bronchitis (n = 2 mepolizumab, n = 2 reslizumab, n = 3 benralizumab, n = 1 dupilumab, n = 2 increased daily prednisone). A greater post-bronchodilator ΔVDP_+anti-T2 was observed in those patients with median or higher percentage of sputum eosinophils at baseline (≥15.8%; P = .01). In 7 of 10 patients with asthma, residual ventilation defects persisted despite bronchodilator and anti-T2 therapy.

INTERPRETATION

Controlling sputum eosinophilia with anti-T2 therapies improves ventilation defects, measured by inhaled gas MRI, in adults with prednisone-dependent asthma.

Particle transport and deposition correlation with near-wall flow characteristic under inspiratory airflow in lung airways

Exposure of lung airways to detrimental suspended aerosols in the environment increases the vulnerability of the respiratory and cardiovascular systems. In addition, recent developments in therapeutic inhalation devices magnify the importance of particle transport. In this manuscript, particle transport and deposition patterns in the upper tracheobronchial (TB) tree were studied where the inertial forces are considerable for microparticles. Wall shear stress divergence (WSSdiv) is proposed as a wall-based parameter that can predict particle deposition patterns. WSSdiv is proportional to near-wall normal velocity and can quantify the strength of flow towards and away from the wall. Computational fluid dynamics (CFD) simulations were performed to quantify airflow velocity and WSS vectors for steady inhalation in one case-control and unsteady inhalation in six subject-specific airway trees. Turbulent flow simulation was performed for the steady case using large eddy simulation to study the effect of turbulence. Magnetic resonance velocimetry (MRV) measurements were used to validate the case-control CFD simulation. Inertial particle transport was modeled by solving the Maxey-Riley equation in a Lagrangian framework. Deposition percentage (DP) was quantified for the case-control model over five particle sizes. DP was found to be proportional to particle size in agreement with previous studies in the literature. A normalized deposition concentration (DC) was defined to characterize localized deposition. A relatively strong correlation (Pearson value > 0.7) was found between DC and positive WSSdiv for physiologically relevant Stokes (St) numbers. Additionally, a regional analysis was performed after dividing the lungs into smaller areas. A spatial integral of positive WSSdiv over each division was shown to maintain a very strong correlation (Pearson value > 0.9) with cumulative spatial DC or regional dosimetry. The conclusions were generalized to a larger population in which two healthy and four asthmatic patients were investigated. This study shows that WSSdiv could be used to predict the qualitative surface deposition and relative regional dosimetry without the need to solve a particle transport problem.