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Computational analysis of deposition and translocation of inhaled nicotine and acrolein in the human body with e-cigarette puffing topographies

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journal contribution
posted on 27.03.2018 by Ahmadreza Haghnegahdar, Yu Feng, Xiaole Chen, Jiang Lin

Recently, toxicants such as formaldehyde and acrolein were detected in electronic cigarette (EC) aerosols. It is imperative to conduct research and provide sufficient quantitative evidence to address the associated potential health risks. However, it is still a lack of informative data, i.e., high-resolution local dosimetry of inhaled aerosols in lung airways and other systemic regions, due to the limited imaging resolutions, restricted operational flexibilities, and invasive nature of experimental and clinical studies. In this study, an experimentally validated multiscale numerical model, i.e., Computational Fluid-Particle Dynamics (CFPD) model combined with a Physiologically Based Toxicokinetic (PBTK) model is developed to predict the systemic translocation of nicotine and acrolein in the human body after the deposition in the respiratory system. In-silico parametric analysis is performed for puff topography influence on the deposition and translocation of nicotine and acrolein in human respiratory systems and the systemic region. Results indicate that the puff volume and holding time can contribute to the variations of the nicotine and acrolein plasma concentration due to enhanced aerosol deposition in the lung. The change in the holding time has resulted in significant difference in the chemical translocation which was neglected in a large group of experimental studies. The capability of simulating multiple puffs of the new CFPD-PBTK model paves the way to a valuable computational simulation tool for assessing the chronic health effects of inhaled EC toxicants.

Copyright © 2018 American Association for Aerosol Research

Funding

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institute of Health under Award Number P20GM103648. The authors also gratefully acknowledge the financial support of Dr. Jiang Lin from the National Natural Science Foundation of China (No. 51246002). The use of ANSYS software (Canonsburg, PA, USA) as part of the ANSYS-OSU academic partnership agreement is gratefully acknowledged (Dr. Thierry Marchal, Global Industry Director). Some of the computing for this project was performed at the OSU High Performance Computing Center at Oklahoma State University (Dr. Dana Brunson, Director and Dr. Evan Linde, Research Cyberinfrastructure Analyst).

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