Author(s)

Carlos Gutierrez

Date Approved

7-23-2015

Document Type

Thesis

Degree Name

M.S. Chemical Engineering

Department

Chemical Engineering

College

Henry M. Rowan College of Engineering

First Advisor

Staehle, Mary

Subject(s)

Drug withdrawal symptoms;Alcohol--Physiological effect

Disciplines

Chemical Engineering

Abstract

The biochemical effects of ethanol on the human brain are manifested through many neurological pathways. Chronic exposure to the depressant has been shown to result in physical dependence. Subsequent cessation results in withdrawal symptoms such as seizures and both short- and long-term changes in neurological activity. One of the primary conduits implicated in the pathways of ethanol dependence and withdrawal is the detection of glutamate via N-methyl-D-aspartate (NMDA) receptors (NMDARs). Ethanol molecules inhibit these receptors, and consequent NMDA-induced glutamatergic changes can result in dependence on ethanol in order to sustain normal brain function. This study considers the relocation control of NMDARs in response to chronic alcoholism and withdrawal as a dynamic control system. Specifically, the system is modeled as a negative feedback control system with a dual-action relocation controller and an explicit set point. The model is used to investigate the effects of ethanol consumption frequency, duration, and magnitude as well as various withdrawal profiles on both the NMDAR population and withdrawal symptoms. The model results are consistent with published trends in NMDAR populations in response to ethanol. Simulated results suggest that withdrawal severity is independent of dependence dynamics, and that regulating the blood alcohol level throughout the progression of withdrawal can minimize withdrawal symptoms. Furthermore, the model suggests that the development of dependence is a function of the frequency of exposure, while the degree of dependence is related to the combination of duration and magnitude of intoxication. Finally, the model enables the possibility of capturing individualized patient neuroexcitatory states by adjusting controller parameters. The mathematical model of NMDAR dynamics provides a platform for analyzing alcohol dependence, predicting withdrawal severity, and designing treatments to minimize excitotoxic insult during alcohol withdrawal.

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