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- All Subjects: Computational model
- Creators: Wirkus, Stephen
Description
Pharmacokinetics describes the movement and processing of a drug in a body, while Pharmacodynamics describes the drug's effect on a given subject. Pharmacokinetic/Pharmacodynamic(Pk/Pd) models have become a fundamental tool when predicting bacterial behavior and drug development. In November of 2009, Katsube et al. published their paper detailing their Pk/Pd model for the drug Doripenem and the bacteria P. aeruginosa. In their paper, they determined that there is a dependent relationship between the drug's effectiveness and the dosing strategy of the drug. Therefore, this thesis has applied optimal control in order to optimize the drug's effectiveness, while not burdening the subject with the side effects of the drug. Optimal Control is a mathematical tool used to balance two competing factors. As a result, it has become a useful tool used to make decisions involving complex behavior. By using Optimal Control, the model will maximize the drug's effect on the bacterial population of P. aeruginosa, while minimizing the drug concentration of Doripenem. In doing so, our research will enable doctors and clinicians to maximize a drug's effectiveness on the body, while minimizing side effects.
ContributorsSawkins, Bryan Thomas (Author) / Camacho, Erika (Thesis director) / Wirkus, Stephen (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Acetylcholinesterase (AChE) inhibition by chemical toxicants such as organophosphates, nerve agents, and carbamates can lead to a series of adverse health outcomes including seizures, coma, and death. An adverse outcome pathway (AOP) is a framework that describes a series of biologically measurable key events (KEs) leading from some molecular initiating event (MIE) to an adverse outcome (AO) of regulatory significance, all developed and hosted in the AOP Wiki. A quantitative AOP (qAOP) is a mathematical model that predicts how perturbations in the MIE affect KEs based on the key event relationships (KERs) that define the AOP. The purpose of this thesis was to expand upon the KERs that define the AOP for AChE inhibition leading to neurodegeneration in order to better understand the effects of AChE inhibitors and the risks they pose to ecosystems, wildlife, and human health. In order to reduce the resources and time spent for chemical toxicity testing, a qAOP was developed based on the available quantitative data and models that supported the AOP. A literature review for the collection of qualitative evidence and quantitative data in support of the AOP was performed resulting in further expansion of the relationships between key events (KERs) through construction of additional KER description pages. A model evaluation was performed by comparing the qAOP model predictions with experimental data, with a subsequent sensitivity analysis of unknown parameters. The qAOP model simulates the MIE through its fifth KE (KE 5) and KE 7. Model predictions compared to experimentally measured data either under- or overpredicting multiple KEs warranting additional refinement such as a formal parameter optimization. Overall, more data amenable to qAOP model development are needed. To aid qAOP model development, the presentation of data in the AOPWiki may be improved by presenting the quantitative data in the AOP Wiki in a tabular format and allowing for the hosting of mathematical models or raw data. With these recommendations in mind, and through continued AOP construction in the AOP Wiki, new qAOP models will be developed, ultimately supporting chemical risk assessment and the mitigation of effects upon exposed individuals and wildlife populations.
ContributorsSinitsyn, Dennis (Author) / Watanabe, Karen (Thesis advisor) / Vinas, Natalia (Committee member) / Wirkus, Stephen (Committee member) / Arizona State University (Publisher)
Created2023