Pyrazolo[1,5-a]pyrimidine TRPC6 antagonists for the treatment of gastric cancer.

Transient receptor potential canonical 6 (TRPC6) proteins form receptor-operated Ca2+-permeable channels, which have been thought to bring benefit to the treatment of diseases, including cancer. However, selective antagonists for TRPC channels are rare and none of them has been tested against gastric cancer. Compound 14a and analogs were synthesized by chemical elaboration of previously reported TRPC3/6/7 agonist 4o. 14a had very weak agonist activity at TRPC6 expressed in HEK293 cells but exhibited strong inhibition on both 4o-mediated and receptor-operated activation of TRPC6 with an IC50 of about 1 µM. When applied to the culture media, 14a suppressed proliferation of AGS and MKN45 cells with IC50 values of 17.1 ±â€¯0.3 and 18.5 ±â€¯1.0 µM, respectively, and inhibited tube formation and migration of cultured human endothelial cells. This anti-tumor effect on gastric cancer was further verified in xenograft models using nude mice. This study has found a new tool compound which shows excellent therapeutic potential against human gastric cancer most likely through targeting TRPC6 channels.

Predicting target-ligand interactions using protein ligand-binding site and ligand substructures.

BACKGROUND: Cell proliferation, differentiation, Gene expression, metabolism, immunization and signal transduction require the participation of ligands and targets. It is a great challenge to identify rules governing molecular recognition between chemical topological substructures of ligands and the binding sites of the targets. METHODS: We suppose that the ligand-target interactions are determined by ligand substructures as well as the physical-chemical properties of the binding sites. Therefore, we propose a fragment interaction model (FIM) to describe the interactions between ligands and targets, with the purpose of facilitating the chemical interpretation of ligand-target binding. First we extract target-ligand complexes from sc-PDB database, based on which, we get the target binding sites and the ligands. Then we represent each binding site as a fragment vector based on a target fragment dictionary that is composed of 199 clusters (denoted as fragements in this work) obtained by clustering 4200 trimers according to their physical-chemical properties. And then, we represent each ligand as a substructure vector based on a dictionary containing 747 substructures. Finally, we build the FIM by generating the interaction matrix M (representing the fragment interaction network), and the FIM can later be used for predicting unknown ligand-target interactions as well as providing the binding details of the interactions. RESULTS: The five-fold cross validation results show that the proposed model can get higher AUC score (92%) than three prevalence algorithms CS-PD (80%), BLM-NII (85%) and RF (85%), demonstrating the remarkable predictive ability of FIM. We also show that the ligand binding sites (local information) overweight the sequence similarities (global information) in ligand-target binding, and introducing too much global information would be harmful to the predictive ability. Moreover, The derived fragment interaction network can provide the chemical insights on the interactions. CONCLUSIONS: The target and ligand bindings are local events, and the local information dominate the binding ability. Though integrating of the global information can promote the predictive ability, the role is very limited. The fragment interaction network is helpful for understanding the mechanism of the ligand-target interaction.