Abstract
Over decades, drug discovery has changed from non-systematic methods, based on serendipitous findings, to modern methodological strategies using different rational drug design approaches, which include Ligand Based Drug Design (LBDD) and Structure Based Drug Design (SBDD) approaches. One major example of this latter strategy is Virtual Screening (VS) of potential bioactive compounds (ligands). In the literature, it has been reported examples of successful applications of VS approach in order to identify potential drug candidates of several therapeutic classes, including antitumor agents and antituberculosis drugs (antiTB drugs), which are the subject of this PhD proposal. Cancer and tuberculosis are diseases of high social and economic impact, mostly in developing countries, and are responsible for millions of deaths/year worldwide. However, their pharmacological treatment remains still unsatisfactory. The main limitations of antitumor therapy include lack of cancer cells specificity, leading to severe adverse effects; development of drug resistance mechanisms; and, in some cases, lack of clinical efficacy. The failure in the tuberculosis pharmacotherapy, on the other hand, has been attributed to the following factors: emergence of multidrug-resistant strains of Mycobacterium tuberculosis; intolerance to treatment and toxicities; pharmacokinetic drug-drug interactions, particularly with antiretroviral drugs, leading to intolerance, loss of efficacy and toxicities; and lack of patient adherence due to its long duration. Considering these limitations, there is an urgent need for investment in the search for new antitumor agents and new antiTB drugs. In this PhD project, we intend to generate VS models in order to search for potential inhibitors (ligands) against, respectively, two proteins: (i) human ecto-5'-nucleotidase, recognized as a valid target to the design of compounds with potential antitumor properties, alone or in combination with other therapeutic strategies, and (ii) M. tuberculosis thioredoxin reductase (MtTrxR), which is considered an interesting target in the search for new antiTB drugs. From each 3D protein structure (available in PDB), we intend to generate the VS models applying a sequence of filters (e.g., pharmacophore, drug-like and docking) to commercially available databases (e.g., ZINC database). All procedures have been previously implemented in the group. In addition, enzymatic inhibition assays will be performed for each target-enzyme in order to validate the generated VS models. Compounds identified as MtTrxR inhibitors will be submitted to antimycobacterial in vitro assays. (AU)
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