Inhalation dosimetry modeling provides insights into regional respiratory tract toxicity of inhaled diacetyl.

Toxicology

Toxicology Program, Department of Pharmaceutical Sciences, School of Pharmacy, University of Connecticut, 69 N. Eagleville Road, Storrs, CT 06269, United States. Electronic address:

Published: August 2017


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Article Abstract

Vapor dosimetry models provide a means of assessing the role of delivered dose in determining the regional airway response to inspired vapors. A validated hybrid computational fluid dynamics physiologically based pharmacokinetic model for inhaled diacetyl has been developed to describe inhaled diacetyl dosimetry in both the rat and human respiratory tracts. Comparison of the distribution of respiratory tract injury with dosimetry estimates provides strong evidence that regional delivered dose rather than regional airway tissue sensitivity to diacetyl-induced injury is the critical determinant of the regional respiratory tract response to this water soluble reactive vapor. In the rat, inhalation exposure to diacetyl causes much lesser injury in the distal bronchiolar airways compared to nose and large tracheobronchial airways. The degree of injury correlates very strongly to model based estimates of local airway diacetyl concentrations. According to the model, regional dosimetry patterns of diacetyl in the human differ greatly from those in the rat with much greater penetration of diacetyl to the bronchiolar airways in the lightly exercising mouth breathing human compared to the rat, providing evidence that rat inhalation toxicity studies underpredict the risk of bronchiolar injury in the human. For example, repeated exposure of the rat to 200ppm diacetyl results in bronchiolar injury; the estimated bronchiolar tissue concentration in rats exposed to 200ppm diacetyl would occur in lightly exercising mouth breathing humans exposed to 12ppm. Consideration of airway dosimetry patterns of inspired diacetyl is critical to the proper evaluation of rodent toxicity data and its relevance for predicting human risk.

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http://dx.doi.org/10.1016/j.tox.2016.11.007DOI Listing

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