Planejamento e identificação “in silico” de novos candidatos a protótipos de fármacos antitumorais

• Ano de defesa: 2015
• Autor(a) principal: Silva, Arthur de Carvalho e
• Orientador(a): Andrade, Carolina Horta
• Banca de defesa: Andrade , Carolina Horta, Trossini , Gustavo Henrique Goulart, Cravo , Pedro Vítor Lemos, Lacerda , Elisângela de Paula Silveira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciências Farmacêuticas (FF)
• Departamento: Faculdade Farmácia - FF (RG)
• País: Brasil
• Palavras-chave em Português: QSAR; CADD; Planejamento de fármacos; Câncer; Bcl2
• Palavras-chave em Inglês: QSAR; CADD; Drug; Design; Cancer; Bcl2
• Área do conhecimento CNPq: CIENCIAS BIOLOGICAS::FARMACOLOGIA
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/5989

Cancer is a group of diseases characterized by uncontrolled cell proliferation as a result of epigenetic changes, genetic mutations and accumulated mutations over the time. Tumor cells can invade other tissues in the body in a process called metastasis, significantly worsening the patient's prognosis. In Brazil, for the biennium 2014/2015 are expected 576,000 new cases and around the world, according to WHO, 27 million new cancer cases are expected in 2030 and 17 million deaths from the disease. The antiapoptotic proteins, members of Bcl-2 family proteins, are essential for the survival of tumor cells, even when there are cell death stimuli. In this study were compiled, integrated and prepared the largest publicly available data sets containing biological activity data against the antiapoptotic protein Bcl-xL. Robust and predictive pharmacophore models and QSAR models in line with the OECD recommendations were generated. The pharmacophore models discriminated active and inactive structures with a rate of 0.68-0.92 of success and QSAR models discriminated active and inactive structures at a rate of 0.89-0.93 of success. NCI 2014 dataset was carefully prepared to be submitted to the virtual screening process in which the best pharmacophore model was used as molecular filter. Among the 280 thousand compounds in NCI dataset, 1407 compounds passed to the next stage in which the best consensus QSAR model was used to predict their activity. In the end, the top 50 compounds were selected for purchase and proceed to experimental evaluation as potential candidates for antiapoptotic protein Bcl-xL inhibitors.