4.6 Article

Repositioning of Quinazolinedione-Based Compounds on Soluble Epoxide Hydrolase (sEH) through 3D Structure-Based Pharmacophore Model-Driven Investigation

期刊

MOLECULES
卷 27, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/molecules27123866

关键词

drug repositioning; soluble epoxide hydrolase; drug discovery; computational techniques; chemical synthesis; anti-inflammatory agents

资金

  1. AIRC [IG 2018-ID, 21397]

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The development of new bioactive compounds is a major focus in drug discovery. In order to identify new drug candidates against relevant biological targets, various tools and methodologies are used. In this study, a 3D structure-based pharmacophoric model was employed to re-evaluate a library of compounds, leading to the successful repositioning of certain molecules to a different biological target.
The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes.

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