Veliflapon

Due to the importance of both prostaglandins (PGs) and leukotrienes (LTs) as pro-inflammatory mediators, and the potential for eicosanoid shunting in the presence of pathway target inhibitors, we have investigated an approach to inhibiting the formation of both PGs and LTs as part of a multi-targeted drug discovery effort.

We generated ligand-protein X-ray crystal structures of known inhibitors of microsomal prostaglandin E2 synthase-1 (mPGES-1) and the 5-Lipoxygenase Activating Protein (FLAP), with their respective proteins, to understand the overlapping pharmacophores. We subsequently used molecular modeling and structure-based drug design (SBDD) to identify hybrid structures intended to inhibit both targets.

This work enabled the preparation of compounds 4 and 5, which showed potent in vitro inhibition of both targets.

Our findings enhance the structural understanding of mPGES-1 and FLAP's unique ligand binding pockets and should accelerate the discovery of additional dual inhibitors for these two important integral membrane protein drug targets.

Inflammatory diseases can be treated by inhibiting 5‐lipo‐oxygenase activating protein (FLAP). In this study, a data set containing 2,112 FLAP inhibitors was collected. A total of 25 classification models were built by five machine learning algorithms with five different types of fingerprints. The best model, which was built by support vector machine algorithm with ECFP_4 fingerprint had an accuracy and a Matthews correlation coefficient of 0.862 and 0.722 on the test set, respectively. The predicted results were further evaluated by the application domain dSTD‐PRO (a distance between one compound to models). Each compound had a dSTD‐PRO value, which was calculated by the predicted probabilities obtained from all 25 models. The application domain results suggested that the reliability of predicted results depended mainly on the compounds themselves rather than algorithms or fingerprints. A group of customized 10‐bit fingerprint was manually defined for clustering the molecular structures of 2,112 FLAP inhibitors into eight subsets by K‐Means. According to the clustering results, most of inhibitors in two subsets (subsets 2 and 4) were highly active inhibitors. We found that aryl oxadiazole/oxazole alkanes, biaryl amino‐heteroarenes, two aromatic rings (often N‐containing) linked by a cyclobutene group, and 1,2,4‐triazole group were typical fragments in highly active inhibitors.