Structure-Based Design and Synthesis of 3-Amino-1,5-dihydro-4H-pyrazolopyridin-4-one Derivatives as Tyrosine Kinase 2 Inhibitors.

We report herein the discovery and optimization of 3-amino-1,5-dihydro-4H-pyrazolopyridin-4-one TYK2 inhibitors. High-throughput screening against TYK2 and JAK1-3 provided aminoindazole derivative 1 as a hit compound. Scaffold hopping of the aminoindazole core led to the discovery of 3-amino-1,5-dihydro-4H-pyrazolopyridin-4-one derivative 3 as a novel chemotype of TYK2 inhibitors. Interestingly, initial SAR study suggested that this scaffold could have a vertically flipped binding mode, which prompted us to introduce a substituent at the 7-position as a moiety directed toward the solvent-exposed region. Introduction of a 1-methyl-3-pyrazolyl moiety at the 7-position resulted in a dramatic increase in TYK2 inhibitory activity, and further optimization led to the discovery of 20. Compound 20 inhibited IL-23-induced IL-22 production in a rat PD assay, as well as inhibited IL-23 signaling in human PBMC. Furthermore, 20 showed selectivity for IL-23 signaling inhibition against GM-CSF, demonstrating the unique cytokine selectivity of the novel TYK2 inhibitor.

Discovery of cannabinoid-1 receptor antagonists by virtual screening.

In this work, we tried to find a new scaffold for a CB1 receptor antagonist using virtual screening. We first analyzed structural features for the known cannabinoid-1 receptor antagonists and, then, we built pharmacophore models using the HipHop concept and carried out a docking study based on our homology CB1 receptor 3D structure. The most active compound, including thiazole-4-one moiety, showed an activity value of 125 nM IC(50), with a good PK profile.

Predictive models of Cannabinoid-1 receptor antagonists derived from diverse classes.

Chemical database design is an important consideration for screening processes in drug discovery. More specifically, classification of a diverse compound set deeply influences the validation and the predictive power of prediction model for the designing of novel compounds. In this work, we investigated the effect of the reasonable classification on the prediction model. We first collected the known Cannabinoid-1 receptor antagonists. Following this, we calculate the chemical descriptors in order to classify the collected compounds. Finally, we build two predictive models via the 3D-QSAR using different molecular alignment and the alignment independent Molecular Interaction Field models.

Identification of small molecules that inhibit GSK-3beta through virtual screening.

Glycogen synthase kinase-3beta (GSK-3beta) is involved in glycogen metabolism, neuronal cell development, osteoblast differentiation. Small molecule inhibitors of GSK-3beta have various therapeutic potential for the treatment of diabetes type II, bipolar disorders, stroke and chronic inflammatory disease. To identify GSK-3beta inhibitors with novel scaffold from chemical library, we primarily screened out putative inhibitors through computer modeling and subsequently evaluated the inhibitory activity of selected compounds against GSK-3beta by in vitro Z'-LYTEtrade mark assay. A series of compound KRMs strongly inhibited phosphorylation of its substrate with IC(50) value of approximately 0.5microM. Also, we demonstrated that KRM-189 and KRM-191 competed with ATP for GSK-3beta, leading to decreased Vmax and constant Km with increasing concentrations of ATP as determined from Lineweaver-Berk equation. Moreover, they showed the selectivity for GSK-3beta over other kinases with IC(50) values of 2 to 10microM or more Incubation of cells with KRM-191 with highly selective and potent inhibitory activity caused accumulation of beta-catenin, downstream of GSK-3beta signaling pathway, indicating that small molecule can prevent degradation of beta-catenin via GSK-3beta inhibition. Our results suggest that modeling in combination with in vitro assays can be used for the identification of selective and potent inhibitors.