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Physiologically-Based Inhalation Dosimetry Modeling for Nanoparticles: Considerations of Activity, Age, and Gender

Pamela R. Shade and Panos G. Georgopoulos. Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey (UMDNJ)/Robert Wood Johnson Medical School (RWJMS), 170 Frelinghuysen Road, Piscataway, NJ 08854

The results of several epidemiological studies suggest that acute and chronic exposures to particulates in ambient air are associated with increases in respiratory and cardiovascular morbidity and mortality. Due to certain features (large surface area, high lung deposition efficiency, etc.) and their prevalence and persistence in ambient air, nanoparticles may contribute to these adverse health effects. Although ambient concentration is what is measured and regulated, the ultimate biological response is dependent upon the target tissue dose. Nanoparticles deposit primarily by diffusion and are more likely to deposit in the distal airways where gas exchange occurs.

Inhalation dosimetry plays a key role in determining the link between environmental exposure to airborne contaminants and observed human health effects. Due to the limited availability of human experimental data and the complex nature of exposure-dose-response scenarios, mathematical modeling is an important tool in studying the mechanisms which dictate the inhaled "dose." Numerous factors affect the inhaled dose, and available computational models must include an extensive amount of information regarding physiological and anatomical parameters.

A Matlab-based inhalation module was developed at the Computational Chemodynamics Laboratory (CCL) as part of the Modeling ENvironment for TOtal Risk (MENTOR) system. A flexible design allows for adaptation to a variety of exposure conditions and scenarios. This semi-empirical model accounts for variation in inhalation rate and delivered dose across individuals and populations due to differences in factors such as age, gender, and level of physical activity. Capturing the inter- and intra-individual variability of input parameters is accomplished primarily through the interaction of the module with databases of anatomical and physiological parameters.

Predictions from existing models are compared to one another and to available experimental data. Results indicate that the variation in human airway anatomy and respiration physiology among individuals of different ages and genders affects the inhaled dose of particulates. Results also suggest that the level of physical activity is an important factor in nanoparticle deposition, and that available models often underpredict the effects of exercise.