Targeting the proteasome in anticancer therapy by a computational based drug discovery approach

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Publication . Guedes, Romina; Guedes, Rita Alexandra do Nascimento Cardoso; Salvador, Jorge António Ribeiro; Gavioli, Riccardo

The ubiquitin-proteasome system plays an important role in cellular homeostasis and also has a critical role in regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair and transcription. The proteasome is an essential multicatalytic protease that directs the majority of intracellular protein degradation in eukaryotic cells. Defects in these pathways have been implicated in several human pathologies, including cancer. This project relies on a chemical-based drug discovery campaign to develop alternative new, selective and more effective proteasome inhibitors. The efforts to discover new anticancer drugs proposed here combine computer-aided drug design, in vitro assays, preparation of derivatives and analysis of chemical patterns. A structure-based virtual screening campaign of commercial and in house databases led to the selection of 286 compounds for biological evaluation. In vitro assays comprised proteasome inhibition assays, as well as cell viability assays. Two potential hit compounds were selected: compound 6.8 and compound 6.15, both with IC50 values in the low micromolar range. Compound 6.15 was selected as a hit compound and a small set of derivatives were synthesized. Compound 7.2 showed to be the most promising derivative, being observed improvements in the inhibitory activity and cytotoxicity. The analysis of the ligand interactions in the three active sites revealed that the number of interactions with relevant residues decrease from the CT-L to the C-L and T-L active sites. The analysis of a curated dataset of 680 proteasome inhibitors was performed to assess how different chemical descriptors coupled with statistical tools can be used to extract activity patterns. Multiple instances of structure-activity relationship were observed. A decision tree was built and two meaningful decision rules were identified. The knowledge obtained with all the computational and in vitro results, as well as the chemical patterns identified, provide useful insight that can be applied in future drug discovery projects.

2019-12Tese de doutoramento

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Fundação para a Ciência e a Tecnologia

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SFRH/BD/104441/2014