Avaliação in silico do potencial de atividade de lignanas e neolignanas frente a doenças negligenciadas e neurodegenerativas

• Ano de defesa: 2021
• Autor(a) principal: Maia, Mayara dos Santos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Farmacologia; Programa de Pós-Graduação em Produtos Naturais e Sintéticos Bioativos; UFPB
• Palavras-chave em Português: Leishmania; Trypanossoma cruzi; Schistossoma mansoni; Alzheimer; Lignanas; Neolignanas; Modelos de predição; Docking molecular; Docking consenso; Simulações de dinâmica molecular; Cálculos de energia livre; Lignans; Neolignans; Prediction models; Molecular docking; Consensus docking; Molecular dynamics simulations; Free energy calculations; CNPQ::CIENCIAS BIOLOGICAS::FARMACOLOGIA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/22116

Natural products are considered potential sources of new therapeutic agents due to the diversity of structures and their properties. Among the natural products, lignans and neolignans stand out, which have a variety of biological activities, but due to their structural diversity, it is necessary to identify and investigate new sources of pharmacological effects. Neglected diseases threaten and affect millions of people around the world. Among them, leishmaniasis, Chagas disease and schistosomiasis, whose available chemotherapy treatments are highly toxic and hardly effective in the chronic phase of the disease. Degenerative diseases have also affected many people, with Alzheimer’s being the most common. Treatment is also limited as it does not prevent disease progression. Several computational approaches in Chemo and Bioinformatics can help, mediate, guide and identify new compounds for the treatment of various diseases. Therefore, the objective of this work is to evaluate the pharmacological potential of lignans and neolignans against neglected and neurodegenerative diseases with the help of several computational tools and approaches. In chapter 2, lignans were evaluated from the ChEMBL database and applied approaches such as pharmacokinetic profiling, combined analysis based on ligand and structure, homology modeling, resistance prediction and molecular dynamics simulations. Four of the lignans selected in the screening were isolated and tested against promastigote forms of Leishmania major and L. (Viannia) braziliensis. The results showed that the most active compound, (159) epipinoresinol-4-O-β-D-glucopyranoside, had an IC50 value of 5.39 µM for L. braziliensis and an IC50 value of 36.51 µM for L. major. In Chapter 3, we predicted the trypanocidal potential of 47 neolignans using predictive models, molecular docking, molecular dynamics simulations, and free energy calculations. Of the compounds analyzed, two were isolated and showed to inhibit the growth of epimastigote forms at concentrations of 9.64 and 8.72 µM, and trypomastigote forms at 4.88 and 2.73 µM. While in Chapter 4, in silico approaches using pharmacokinetic profile analysis, consensus docking, consensus predictive models, molecular dynamics simulations and free energy calculations were also used to select potential and selective lignans against an important target of Schistosoma mansoni. Four lignans had excellent results and we suggest that they are a therapeutic alternative in cases of resistance. In chapter 5, lignans were analyzed with the aim of identifying potential and multitarget compounds for the treatment of Alzheimer’s. A combined analysis, based on ligand and structure, followed by prediction of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties was performed. The results showed that the combined analysis was able to select 139 potentially active and multitarget lignans, providing treatment alternatives through neuroprotective and antioxidant activity. Chapter 6 is a review that describes various studies, approaches, and methods of consensus docking.