Description
TitleOptimal treatment strategies to improve medical outcomes for human diseases
Date Created2022
Other Date2022-05 (degree)
Extent189 pages : illustrations
DescriptionBiological systems are complex, with many interacting subsystems that operate at different functional levels. These different subsystems operate in an interconnected manner, with many complementary functions and redundancies in place to protect the organism from malfunction. Consequently, when a disease arises, it is typically a malfunction of multiple subsystems and thus is a complex phenomenon in its own right. As a result, the treatment of diseases should aim to address the multi-faceted challenges of the disease, rather than treating a single aspect of the disease’s cause. Furthermore, determining drug dosages is critical for achieving optimal therapeutic outcome and conducting clinical tests during the drug development process. By identifying the proper dosages and frequency of administration, dosage algorithms aim to optimize the onset, intensity, and duration of the therapeutic effects of the drugs while minimizing any adverse effects.For these reasons, the objectives of this research, which led to this doctoral dissertation, are proposing solutions to overcome the limitations of the conventional therapeutic approaches, developing mathematical and quantitative techniques to improve medical treatments for human diseases through systems biology, systems pharmacology and systems immunology approaches, as well as supporting public health decisions associated with viral pandemics through epidemiological modeling. In particular, these objectives will be achieved by:
• Presenting a comprehensive study on mathematical methods and network medicine techniques that model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance.
• Designing a method for determining an optimal dosage strategy to combat drug resistance in tumor progression, based on a dynamic model for the clonal evolution of cancerous cells, as well as the Pharmacokinetic/Pharmacodynamic (PKPD) model of combination therapy, through solving an optimization problem that minimizes the total number of cancerous cells.
• Postponing the emergence of resistance in heterogeneous tumors, by proposing a mathematical framework involving clonal evolution modeling of drug-sensitive and drug-resistant clones and solving an optimal control problem during control phase of therapy in a containment treatment approach.
• Proposing a model for the innate and adaptive immune systems in response to SARS-CoV-2 viral infection, modeling within-host dynamics of the novel corona virus and investigating the effectiveness of antiviral treatment on COVID-19.
• Modeling the dynamics of COVID-19 transmission through extending classic compartmental models to reflect empirical observations, while considering various vaccination strategies as well as the emergence of new variants.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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