Design, synthesis, and docking studies of novel benzimidazoles for the treatment of metabolic syndrome

In addition to lowering blood pressure, telmisartan, an angiotensin (AT(1)) receptor blocker, has recently been shown to exert pleiotropic effects as a partial agonist of nuclear peroxisome proliferator-activated receptor gamma (PPAR gamma). On the basis of these findings and docking pose similarity between telmisartan and rosiglitazone in PPAR gamma active site, two classes of benzimidazole derivatives were designed and synthesized as dual PPAR gamma agonist/angiotensin II antagonists for the possible treatment of metabolic syndrome. Compound 4, a bisbenzimidazole derivative showed the best affinity for the AT(1) receptor with a K(i) = 13.4 nM, but it was devoid of PPAR gamma activity. On the other hand 9, a monobenzimidazole derivative, showed the highest activity in PPAR gamma transactivation assay (69% activation) with no affinity for the AT(1) receptor. Docking studies lead to the designing of a molecule with dual activity, 10, with moderate PPARgamma activity (29%) and affinity for the AT(1) receptor (K(i) = 2.5 microM)

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered