# Multiscale Modeling of Lung Disease-Influenced Aerosol Dosimetry

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $650,000

## Abstract

Abstract
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 fra...

## Key facts

- **NIH application ID:** 9965949
- **Project number:** 5U01ES028669-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** CHANTAL DARQUENNE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $650,000
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9965949

## Citation

> US National Institutes of Health, RePORTER application 9965949, Multiscale Modeling of Lung Disease-Influenced Aerosol Dosimetry (5U01ES028669-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9965949. Licensed CC0.

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