Pharmacological properties and predicted binding mode of arylmethylene quinuclidine-like derivatives at the α3β4 nicotinic acetylcholine receptor (nAChR)

Pharmacological profile of zacopride and new quaternarized fluorobenzamide analogues on mammalian α7 nicotinic acetylcholine receptor

From quaternarization of quinuclidine enantiomers of 2-fluoro benzamide LMA10203 in dichloromethane, the corresponding N-chloromethyl derivatives LMA10227 and LMA10228 were obtained. Here, we compared the agonist action of known zacopride and its 2-fluoro benzamide analogues, LMA10203, LMA10227 and LMA10228 against mammalian homomeric α7 nicotinic acetylcholine receptor expressed in Xenopus oocytes. We found that LMA10203 was a partial agonist of α7 receptor with a pEC50 value of 4.25 ± 0.06 μM whereas LMA10227 and LMA10228 were poorly active on α7 homomeric nicotinic receptor. LMA10227 and LMA10228 were identified as antagonists of acetylcholine-induced currents with IC50 values of 28.4 μM and 39.3 μM whereas LMA10203 and zacopride possessed IC50 values of 8.07 μM and 7.04 μM, respectively. Moreover, despite their IC50 values, LMA10227 was the most potent inhibitor of nicotine-induced current amplitudes (65.7 ± 2.1% inhibition). LMA10203 and LMA10228 had the same inhibitory effects (26.5 ± 7.5% and 33.2 ± 4.1%, respectively), whereas zacopride had no significant inhibitory effect (4.37 ± 4%) on nicotine-induced responses. Our results revealed different pharmacological properties between the four compounds on acetylcholine and nicotine currents. The mode of action of benzamide compounds may need to be reinterpreted with respect to the potential role of α7 receptor.

Comparative study on the use of docking and Bayesian categorization to predict ligand binding to nicotinic acetylcholine receptors (nAChRs) subtypes

We have carried out a comparative study between docking into homology models and Bayesian categorization, as applied to virtual screening of nicotinic ligands for binding at various nAChRs subtypes (human and rat α4β2, α7, α3β4, and α6β2β3). We found that although results vary with receptor subtype, Bayesian categorization exhibits higher accuracy and enrichment than unconstrained docking into homology models. However, docking accuracy is improved when one sets up a hydrogen-bond (HB) constraint between the cationic center of the ligand and the main-chain carbonyl group of the conserved Trp-149 or its homologue (a residue involved in cation-π interactions with the ligand basic nitrogen atom). This finding suggests that this HB is a hallmark of nicotinic ligands binding to nAChRs. Best predictions using either docking or Bayesian were obtained with the human α7 nAChR, when 100 nM was used as cutoff for biological activity. We also found that ligand-based Bayesian-derived enrichment factors and structure-based docking-derived enrichment factors highly correlate to each other. Moreover, they correlate with the mean molecular fractional polar surface area of actives ligands and the fractional hydrophobic/hydrophilic surface area of the binding site, respectively. This result is in agreement with the fact that hydrophobicity strongly contributes in promoting nicotinic ligands binding to their cognate nAChRs.