The overall goal of this proposal is to develop multiscale computational models that can predict the deposition of inhaled aerosols in all regions of the respiratory system of individuals that are either healthy or suffering from respiratory diseases such as COPD (chronic obstructive pulmonary disease). COPD is generally associated with exposure to toxic/irritant aerosols (e.g. cigarette smoke, occupational dusts/fumes, environmental PM2.5 air pollution, etc.) and adversely affects the quality of life for millions of susceptible individuals. Along with asthma, COPD is the third leading disease-based cause of death in the U.S. In addition, the respiratory system has been exploited as a potential route for local and systemic delivery of therapeutic aerosols for COPD, asthma, or other diseases where drugs may not be as effective by other routes of administration. As a result, the development of predictive aerosol dosimetry models has been a major focus of environmental toxicology and pharmaceutical health research for decades. To date, the challenge of predicting the deposition of inhaled aerosols under disease conditions has been largely unmet. We propose to utilize advancements our established team of investigators and others have made in imaging, aerosol exposure and measurement, and computational modeling to develop, experimentally evaluate, and refine multiscale models that predict site- and region-specific deposition of aerosols throughout the respiratory system and to study how deposition is influenced by disease. Our proposed models will be developed by a step-wise, modular integration of 3D computational fluid dynamic (CFD) airflow and aerosol tracking models that extend from the nose and mouth to the conducting airways of the lung with each 3D pulmonary airway bi-directionally coupled with lower dimensional airflow, aerosol transport, and tissue mechanics models to describe aerosol transport and deposition over the full respiratory system and throughout the complete breathing cycle (Aim 1). Models will initially be developed for healthy individuals (Aim 2) followed by disease (Aim 3) using published airway and tissue mechanics data and, where data do not exist for humans, extracted from our 4D imaging and aerosol deposition data in healthy and diseased rats. Our modular approach to multiscale linkages will allow users to substitute individual model components as new advances are made. The multiscale models will be evaluated and further refined using a rich database of multi-modal 3D imaging and aerosol deposition measurements in human volunteers that include both healthy and COPD cohorts. The expected outcome of our work will be a suite of modular, multiscale models and standardized approaches for new model development that can be used by researchers, risk assessors, or clinicians to predict aerosol deposition in the respiratory systems of humans under healthy and disease conditions in addition to the underlying algorithms and framework for effective linking of user-defined, personalized aerosol dosimetry models in the future.
Along with asthma, COPD is generally associated with exposure to toxic/irritant aerosols (e.g. cigarette smoke, occupational dusts/fumes, environmental PM2.5 air pollution, etc.) and adversely affects the quality of life for millions of susceptible individuals. In addition, the respiratory system has been exploited as a potential route for local and systemic delivery of therapeutic aerosols for COPD, asthma, or other diseases where drugs may not be as effective by other routes of administration. We propose to develop and validate with existing experimental data a state-of-the art multiscale computational modeling approach for predicting the deposition of user-defined aerosols in the respiratory system of humans and rats including those with COPD that will improve assessments of risks for potentially susceptible individuals exposed to airborne aerosols of physical, chemical or biological origin as well as therapeutic interventions via respiratory drug delivery.
