G-quadruplex structures can be formed at the single-stranded overhang of telomeric DNA, and ligands able to stabilize this structure have recently been identified as potential anticancer drugs. Among the potential G-quadruplex binders, we have studied the binding ability of berberine and sanguinarine, two members of the alkaloid family, an important class of natural products long known for medicinal purpose. Our spectroscopic (CD, NMR, and fluorescence) studies and molecular modeling approaches revealed binding modes at ligand-complex stoichiometries >1:1 and ligand self-association induced by DNA for the interactions of the natural alkaloids berberine and sanguinarine with the human telomeric G-quadruplex DNA.
The aim of the study was to investigate the in vitro and in vivo pharmacological profile of cebranopadol, a novel agonist for opioid and nociceptin/orphanin FQ (N/OFQ) receptors (NOP). In vitro cebranopadol was assayed in calcium mobilization studies in cells coexpressing NOP or opioid receptors and chimeric G‐proteins and in a bioluminescence resonance energy transfer (BRET) assay for studying receptor interaction with G‐protein and β‐arrestin 2. The mouse tail withdrawal and formalin tests were used for investigating cebranopadol antinociceptive properties. In calcium mobilization studies cebranopadol showed the following rank order of potency NOP = mu > kappa ≥ delta. In BRET studies, cebranopadol promoted NOP and mu receptors interaction with G‐protein with similar high potency and efficacy. However, cebranopadol did not stimulated NOP–β‐arrestin 2 interactions and displayed reduced potency at mu/β‐arrestin 2. In vivo, cebranopadol exhibits highly potent and extremely long‐lasting antinociceptive effects. The effects of cebranopadol in the tail withdrawal assay were sensitive to both SB‐612111 and naloxone. Collectively the present results confirm and extend previous finding demonstrating that cebranopadol, by acting as mixed NOP/opioid receptor agonist, elicits robust analgesic effects in different pain models.
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