Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/103278
Title: Design and optimisation of novel MEK inhibitors using the anti-neoplastic TAK-733 scaffold as a lead
Authors: Micallef, Roderick (2022)
Keywords: Protein kinases -- Inhibitors
Drugs -- Design -- Data processing
Computer-aided design
Issue Date: 2022
Citation: Micallef, R. (2022). Design and optimisation of novel MEK inhibitors using the anti-neoplastic TAK-733 scaffold as a lead (Master's dissertation).
Abstract: The mitogen-activated protein kinase signalling pathway is dysregulated in numerous human malignancies. Mutations exert their oncogenic activity through downstream proteins such as the MEK1/2 protein kinases. TAK-733 is a selective, orally administrable, allosteric MEK1 inhibitor that has had a demonstrable antineoplastic effect. The aim of this project was to develop a series of novel MEK inhibitors by using the MEK1 kinase inhibitor TAK-733 scaffold as the lead molecule. A virtual screening approach was adopted in the first phase of the study and a de novo design approach was adopted in the second phase of the study. In the virtual screening phase of the study, a consensus pharmacophore was generated based on the optimal conformers of the lead molecule TAK-733 and a second MEK1 kinase inhibitor G799. A series of lead-like molecules were identified, docked into the MEK1 protomol and ranked by affinity. In the de novo phase of the study, seed fragments were modelled, docked into the MEK1 bioactive site and allowed molecular growth within this space. The ligand binding affinity and ligand binding energy of the resultant structures were calculated. The structures with the greatest difference between ligand binding affinity and ligand binding energy were selected as the optimal structures from this molecular cohort. A total of 1121 lead-like molecules were identified through virtual screening and a total of 80 molecules were generated through de novo design. The two molecular cohorts were analysed and the optimal structures were identified on the basis of ligand binding affinity and pharmacokinetic properties. Molecule ZINC04293482 was identified as the optimal molecule from the virtual screening molecular cohort and Molecule S1R26 was identified as the optimal molecule from the de novo design molecular cohort.
Description: M.Pharm.(Melit.)
URI: https://www.um.edu.mt/library/oar/handle/123456789/103278
Appears in Collections:Dissertations - FacM&S - 2022
Dissertations - FacM&SPha - 2022

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