Fernandes, RúbenAlves, MarcoBaylina, PilarSantos, Inês Ribeiro da Silva de Lima2022-02-092024-12-092021-12-09http://hdl.handle.net/10400.22/19852Obesity and diabetes are two metabolic risk factos for cancer. However, there is a metabolic paradox in prostate cancer in which diabetes appears to protect the patient form this type of cancer. The current study aims to develop explanatory models of this contradiction utilizing prostate cancer cell lines, PC3 and LNCaP, in contrast to the metabolismo of normal prostate cells, using bioinformatics methods (HPEpiC). Two of the major routes of prostate metabolism, glycolysis and gluconeogensis, were mathematically manipulated in this study. This mathematical model offers unique and revolutionary implications in personalized medicine since it predicts the Effect, therapeutic dose, and efficacy of medications in varied conditions of the tumor microenvironment and the patient’s metabolismo. As na illustration od the model’s usefulness, a novel anti-tumor drug in the clinical trials phase, 3-bromopyruvate, which has the modeled metabolic pathways as a therapeutic target, was employed. The efficacy od 3-bromopyruvate was investigated, and the IC50 was found to be capable of significantly inhibiting tumor cell lines. When compared to basal metabolismo, its IC50 delayed glycolytic metabolismo by 12 minutes. As a result, the diabetic environment has a slowing Effect on glycolytic metabolismo. The obese environment had no significant diferences in this form os cancer as compared to the healthy environment. Tha value of mathematical modeling is clear, as the Effect of anew drug on metabolismo may be computer evaluated and used as a novel tool to provide a tailored approach to each patient.engBioinformaticsDiabetesModelingObesityProstate cancermaster thesis202929787