Physiologically Based Model Development and Parameter Estimation: Benzene Dosimetry in Humans and Respiratory Irritation Response in Rodents

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Title: Physiologically Based Model Development and Parameter Estimation: Benzene Dosimetry in Humans and Respiratory Irritation Response in Rodents
Author: Yokley, Karen Alyse
Advisors: Dr. Hien Tran, Committee Chair
Dr. Mette Olufsen, Committee Member
Dr. Paul Schlosser, Committee Member
Dr. James Selgrade, Committee Member
Abstract: One can form mathematical equations based on a combination of chemistry, physics, and biological information to represent a physiological system. Once a model is formulated based on the physiological system, we must make sure that the inputs or parameters to the model also faithfully represent the system. In this study, we adapt and combine existing mathematical models to describe different physiological systems. Benzene is myelotoxic and causes leukemia in humans when they are exposed to high doses by inhalation (<1 ppm) for extended periods; however, leukemia risks in humans at lower exposures are uncertain. Benzene occurs widely in the work environment and in outdoor air, although mostly at concentrations below 1 ppm. Hence, we recognize the importance of assessing the risk to humans when they are exposed to benzene at low concentrations. In Chapter 2, we describe a physiologically based pharmacokinetic (PBPK) model for the uptake and elimination of benzene in humans to relate the concentration of inhaled benzene to the tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone. To account for variability among humans, the mathematical model must be integrated into a statistical framework that acknowledges sources of variation in the data due to inherent intra- and inter-individual variation, measurement error, and other data collection issues. The main contribution of Chapter 2 is the estimation of population distributions of key PBPK model parameters. In particular, a Markov Chain Monte Carlo (MCMC) technique is employed to fit the mathematical model to two data sets, thereby updating the estimated parameter distributions. We first considered only variability in metabolic parameters, as observed in previous in vitro studies, but found that it was not sufficient to explain observed variability in benzene pharmacokinetics. Variability in physiological parameters, such as organ weights, must also be included to faithfully predict the observed human population variability. Inhaled gases can also cause respiratory depression by irritating (stimulating) nerves in the nasal cavity. In order to better understand how the nervous system responds to such chemicals, we have created a model to describe how the presence of irritants affects respiration in the rat. By combining and adapting two previous models, one that evaluates the relationship between inhaled acrylic acid vapor concentration and the tissue concentration in various regions of the nasal cavity and another model which describes the baroreflex-feedback mechanism regulating human blood pressure, we created a system of equations that models the sensory irritant response in rats. The adapted model in Chapter 3 focuses on the dosimetry of these reactive gases in the respiratory tract, with particular focus on the physiology of the upper respiratory tract, and on the neurological control of respiration rate due to signaling from the irritant-responsive nerves in the nasal cavity. Further, the model is evaluated and improved through optimization of particular parameters to describe formaldehyde-induced respiratory response data and through sensitivity analysis. The model in Chapter 3 describes this formaldehyde data well and is expected to translate well to other irritants.
Date: 2005-07-11
Degree: PhD
Discipline: Computational Mathematics
URI: http://www.lib.ncsu.edu/resolver/1840.16/5217


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