Semester of Graduation
First Major Professor
Dr. Jonathan Mochel
Master of Science (MS)
Important to the world of pharmacology and drug discovery is the process of pharmacokinetic modeling. This part of the process gives developers and clinicians a basis of knowledge on how a drug moves through the body. This project focuses on three types of models: non-compartmental analysis, nonlinear mixed-effects models, and physiologically-based pharmacokinetic models. Each model differs in complexity and varies in the information it can provide. With an understanding of each type of model and an overview of the functions each serves, two drugs are examined in this project. The data collectors working on each project identified the level of modeling, i.e. the types of models they wanted for their drug.
The first is a drug called Tulathromycin. The data collectors on this project requested non-compartmental analysis. This individual project served largely as an introduction to the process of modeling and the programs that were required at each step. Following Tulathromycin, a second project was completed with a drug called Flunixin Meglumine. It was requested that nonlinear mixed-effects models be created for this drug. This second project expounded upon the knowledge gained in the Tulathromycin project, and went further to create a more complex model that gave the data collectors more freedom and knowledge about how the drug could be applied. The process of selecting a model and the criteria used by the programs for selection is reported and discussed in the section for Flunixin Meglumine.
Finally, this overall project examined a real-life application of pharmacokinetic models in order to provide perspective of their use. Neither model that was worked with hands-on used the observations for predictive models, so it is central that a common application like this was examined. Overall, models like these are central in allowing clinical observations to pave the way for knowledgeable dosing regimens in animals. These models are used by clinicians to predict the recommended administration amount and method, and allow them to take in covariates of the subject they are working with. The predictive knowledge informs confident and safe dosing strategies in the world of medicine and pharmacology.
Heinen, Jessica, "Process of Modeling Pharmacokinetics Using Nonlinear Mixed-Effect Models" (2020). Creative Components. 507.