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

Abstract

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.

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

“…To do so, we generated pharmacophore hypotheses for soluble epoxide hydrolase (sEH, UniProt ID: P34913) and compared them with those reported in our previous work. 22 Interestingly, the automatically generated hypotheses overlapped almost perfectly with the reported ones (Figure 8); the only difference was represented by an acceptor feature which, in the manually generated hypothesis (indicated with a red arrow), was inserted intentionally and its volume was considerably larger than the others. Despite this, the central part of the two hypotheses resulted highly similar, and this gave a preliminary indication of the efficacy of our approach.…”

“…Specifically, the time necessary to build all 1741 hypotheses using 20 CPUs on a machine equipped with AMD EPYC 7452 processors was nearly a week against the over 2 weeks required to manually develop seven hypotheses that were used in other works. 21,22 Then, to assess the predictive capabilities of the generated pharmacophores and the general applicability of this tool in drug design and repurposing studies, we decided to perform a pharmacophore screening using a selective binder for one of the available bromodomains and confirm that the binding profile was correctly reproduced. Moreover, this chosen molecule must not have been contained in any crystal structure deposited in the PDB, otherwise it would have been used to generate the pharmacophore hypotheses since, in this case, it can lead to false-positive results.…”

“…Comparison between the pharmacophores generated by PharmaCore and the one reported in our previous work. 22 The only different feature is indicated with a green arrow (PharmaCore hypothesis) and a red arrow (manually generated). Hydrogen bond acceptor features are represented as pink spheres, the hydrogen bond donors are cyan spheres, and the hydrophobic features are depicted as green spheres.…”

“…Recently, driven by the need to create a reliable filter to screen large compound libraries, our group presented a new strategy to extract the pharmacophore features from small molecules cocrystallized with their target. − Briefly, in the effort to develop new binders for the epigenetic reader bromodomain-containing protein 9 (BRD9) and soluble epoxide hydrolase (sEH), we decided to consider all of the available crystal structures containing a ligand for the development of a tailored pharmacophore that would trace the actual binding mode of the bioactive molecules. The benefits deriving from the integration between the intrinsic chemical features of the ligands and the way they are oriented inside the pocket were immediately visible, leading to the selection of very promising compounds. − However, the whole procedure was still laborious and required several days of work to develop a few hypotheses.…”

Screening and Identification of Novel Soluble Epoxide Hydrolase Inhibitors from Corn Gluten Peptides

“…Many sEH inhibitors have urea and amide groups [ 27 ]. Some natural compounds containing urea and amide were discovered to have excellent sEH inhibitory activities.…”