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    • It should be noted that one of

    2021-11-30

    It should be noted that one of the two phenyl groups of 15 is found in the main western pocket and the second phenyl group occupies the western secondary pocket (Ser53, Leu54, and Leu213). The nitrogen of the amide bond is involved in a hydrogen bonding interaction with Asp140. The alcoholic functionality of compound 14 does not receive any stabilizing interaction from the Asp140 residue, while the adamantyl substituent is deeply allocated into the main western pocket [63]. The same authors reported the synthesis of a series of HO-1/TK double inhibitors (Table 3, compounds 9a–j) with the portion of oxybutylimidazole connected in different positions on the ring of a benzamide nucleus variously substituted [56]. The position of the oxybutylimidazole (OR2) and the R1 groups greatly influence the potency and the dual activity of molecules (Table 3). In fact, when the oxybutylimidazole group is in position 2, the molecules (9a, 9d, and 9e) form a series of interactions with the internal hydrophobic pocket of the enzyme, particularly in the western region, and the flexible linker capped with imidazole points to the heme group, making possible the critical interaction with the Fe2+. Due to the steric impediment in the placement of the molecules inside the internal pockets, the interactions of the oxybutylimidazole group in position 4 with the Fe2+ (9g and 9i) are not allowed. The powerful compound 9g is stabilized by different interactions on the surface of the protein. Notably, the bromine HA-100 hydrochloride is well positioned in a pocket formed by Phe37, Leu147, and Ile150 and a hydrogen bond is formed with Gln38. Interestingly, the iodine atom, more voluminous, present in the less potent compound 9i cannot be located inside the same hydrophobic cavity. The molecule, flipping itself, does not get this set of interactions and this is, probably, the cause of the drop of activity. When the oxybutylimidazole group is at the 3-position (9j), the inhibitor is situated to the external portion of the western pocket, but the major part of the molecule is still inside the cavity. Another strategy lies in the drug design of competitive inhibitors. In this approach studied by Subashini et al. the competitive inhibitor binds to the catalytic site of the enzyme (i.e., the same site as the substrate binds) and increases the apparent Km for the substrate [64]. Compound 16 (Fig. 5) showed the best bond with the catalytic site thanks to the π-π interaction and the hydrogen bond with the enzyme Glu2 residue. Different ligand-based approaches have already been used for the identification of new molecules and scaffolds as HO-1 inhibitors. Amata et al. started the ligand-based era approach for HO-1 inhibitors with the building of a complete database HemeOxDB and employed the dataset of HO-1 inhibitors for the building of a 2D-QSAR model using the experimental IC50 values of the whole set of known HO-1 inhibitors [59]. The model was made using Coral as software and employing the Monte Carlo technique [77,78]; the statistical quality suggested that this model is robust and possesses desirable and predictive potential. The predictive capabilities of the 2D-QSAR model were then used to perform a VS of FDA approved drugs. The result of the VS allows the identification of different FDA approved drugs as possible HO-1 inhibitors. Particularly, the most potent molecules were Glimepiride (Fig. 6), Candoxatril (Fig. 6), Nateglinide, Mitiglinide, Hexalen, Gliclazide, Glyburide, Zafirlukast, and Ropinirole with a predicted pIC50 of 7.66, 7.34, 6.79, 6.75, 6.70, 6.54, 6.28, 6.26, and 6.06, respectively. An internal dataset of 222 molecules from the HemeOxDB was then used by Floresta et al. for the building of a 3D-QSAR model [79]. The 3D-QSAR model for the determination of HO-1 endpoints has been developed using the software Forge [80]. The final model shows both good predictive and descriptive capability with good r2 (0.83) and q2 (0.58) values for the training and the cross-validated training set, respectively, and an excellent r2 (0.75) for the external test set. The graphic visualization of the model's results allows the interpretation of the activity of the molecules by the ligand-based standpoint [79]. The HemeOxDB 3D-QSAR model was then used by the same authors for the evaluation of 2500 compounds derived from a scaffold-hopping of 1, 4 and 5 (Fig. 2, pIC50 = 5.55, 6.57 and 5.52, respectively), and the two most potent molecules of the training set, 6 (pIC50 = 7.22) and 20 (pIC50 = 6.96) (Table 9). The scaffold hopping was performed using Spark as software, and the overall results indicate that the bioisosteric replacement gives compounds with the appropriate chemical structure for the inhibition of HO-1 [80]. Interestingly, all the five groups of the virtually evaluated compounds resulted in a series of molecules more potent than the precursors; the most potent compound of each series is reported in Table 9, while for the full set of compounds and results we refer to the original paper [79].