Literature DB >> 3494849

6-Methyl-6-azabicyclo[3.2.1]octan-3 alpha-ol 2,2-diphenylpropionate (azaprophen), a highly potent antimuscarinic agent.

F I Carroll, P Abraham, K Parham, R C Griffith, A Ahmad, M M Richard, F N Padilla, J M Witkin, P K Chiang.   

Abstract

The synthesis and antimuscarinic properties of 6-methyl-6-azabicyclo[3.2.1]octan-3 alpha-ol 2,2-diphenylpropionate (1, azaprophen) are described. Azaprophen is 50 times more potent than atropine as an antimuscarinic agent as measured by the inhibition of acetylcholine-induced contraction of guinea pig ileum and is more than 1000 times better than atropine in its ability to block alpha-amylase release from pancreatic acini cells induced by carbachol. In addition, azaprophen is 27 times more potent than atropine as an inhibitor of binding of [N-methyl-3H]scopolamine to muscarinic receptors, with human IMR-30 neuroblastoma cells. The potencies of azaprophen and atropine in altering operant behavior were similar. The structural features of 1 are compared to the standard anticholinergic drugs atropine and quinuclidinyl benzilate by using energy calculations and molecular modelling studies. A modification of the pharmacophore model hypothesis for cholinergic agents is suggested.

Entities:  

Mesh:

Substances:

Year:  1987        PMID: 3494849     DOI: 10.1021/jm00388a010

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  3 in total

1.  Conformational studies on the four stereoisomers of the novel anticholinergic 4-(dimethylamino)-2-phenyl-2-(2-pyridyl)pentanamide.

Authors:  H Oyasu; I Nakanishi; A Tanaka; K Murano; M Matsuo
Journal:  J Comput Aided Mol Des       Date:  1995-04       Impact factor: 3.686

2.  Semi-rigid analogues of the calcium antagonist verapamil: a molecular modelling study.

Authors:  M N Romanelli; S Dei; S Scapecchi; E Teodori; F Gualtieri; R Budriesi; R Mannhold
Journal:  J Comput Aided Mol Des       Date:  1994-04       Impact factor: 3.686

3.  Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

Authors:  Ann E Cleves; Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2018-06-22       Impact factor: 3.686

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.