abstract: Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic prostate cancer is most commonly treated with hormonal therapy. The idea behind hormonal therapy is to reduce androgen production, which prostate cancer cells require for growth. Recently, the exploration of the synergistic effects of the drugs used in hormonal therapy has begun. The aim was to build off of these recent advancements and further refine the synergistic drug model. The advancements I implement come by addressing biological shortcomings and improving the model’s internal mechanistic structure. The drug families being modeled, anti-androgens, and gonadotropin-releasing hormone analogs, interact with androgen production in a way that is not completely understood in the scientific community. Thus the models representing the drugs show progress through their ability to capture their effect on serum androgen. Prostate-specific antigen is the primary biomarker for prostate cancer and is generally how population models on the subject are validated. Fitting the model to clinical data and comparing it to other clinical models through the ability to fit and forecast prostate-specific antigen and serum androgen is how this improved model achieves validation. The improved model results further suggest that the drugs’ dynamics should be considered in adaptive therapy for prostate cancer. / Dissertation/Thesis / Masters Thesis Mathematics 2020