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

Fi, Carroll; Abraham, P.; Parham, Karol; Griffith, RC; Ahmad, A; Mm, Richard; Fn, Padilla; Jm, Witkin; Pk, Chiang

doi:10.1021/jm00388a010

Cited by 47 publications

(6 citation statements)

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“…Similarly, the rank order of potency for the set of compounds against convulsions and hypersecretions does not correlate well with in vitro affinities utilizing crude muscarinic receptor preparations (Hulme et al, 1978;Broomfield et al, 1987;Carroll et al, 1987;Syvalahti et al, 1987Syvalahti et al, , 1988; Witkin et al, 1987;Beach et al, 1988). The emergence of selective muscarinic antagonists has identified several muscarinic receptor subtypes in the central nervous system (CNS) and periphery.…”

Section: Number Of Deaths Within 24 Hmentioning

confidence: 99%

Anticonvulsant Actions of Anticholinergic Drugs in Soman Poisoning

Capacio

Shih

1991

Epilepsia

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The acute effects of the organophosphorus cholinesterase inhibitor soman include hypersecretions, convulsions, and death. The purpose of this study was to evaluate the anticholinergic compounds aprophen, atropine sulfate, azaprophen, benactyzine, benztropine, biperiden, scopolamine HBr, and trihexyphenidyl for their efficacy in preventing soman-induced hypersecretions and convulsions. Male rats were injected with the oxime HI-6 (125 mg/kg, i.p.), to increase survival time, along with various intramuscular doses of the anticholinergics 30 min prior to a dose of soman (180 micrograms/kg, s.c.; equivalent to 1.6 x the median lethal dose) that produced 100% convulsions. Signs of intoxication as well as the time-to-onset of convulsions were observed. The calculated anticonvulsant median effective dose values were 0.18, 0.33, 0.36, 0.55, 2.17, 2.30, 2.45, and 31.09 mumol/kg for scopolamine HBr, biperiden, trihexyphenidyl, benactyzine, benztropine, azaprophen, aprophen, and atropine sulfate, respectively. The same rank order of potency for inhibition of hypersecretions among these compounds was observed. Parallel studies with quaternary analogs of atropine sulfate and scopolamine HBr demonstrated, however, that these charged compounds afford no protection against soman-induced hypersecretions and convulsions. The results indicate that tertiary anticholinergic compounds afford protection against soman-induced convulsions and hypersecretions and that the beneficial anticonvulsant effects are mediated through the central cholinergic system. Excitatory amino acid neurotransmitter systems may be involved in the effectiveness of these compounds.

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Section: Number Of Deaths Within 24 Hmentioning

confidence: 99%

Anticonvulsant Actions of Anticholinergic Drugs in Soman Poisoning

Capacio

Shih

1991

Epilepsia

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“…Compound 5, the other equatorial isomer, obviously shows the same distance of 5.1/~ and can be superimposed on 7 simply by rotating ~. So, by overlapping the lipophilic head of the molecules and suitably rotating "c, the axial nitrogen and the equatorial nitrogen are at a distance of 2.2 A from each other, the same range of distance as between the two ends of a 'bidentate' carboxylate anion [21].…”

Section: Resultsmentioning

confidence: 94%

Semi-rigid analogues of the calcium antagonist verapamil: A molecular modelling study

Romanelli

Dei

Scapecchi

et al. 1994

J Computer-Aided Mol Des

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In this work the rigid-analogue approach has been used to obtain information on the active conformation(s) of the calcium antagonist verapamil. A series of semi-rigid analogues of verapamil were synthesized and their biological activities evaluated on guinea-pig heart and aorta. These molecules were analysed by means of molecular modelling techniques. On the basis of the pharmacological profile and conformational analysis of these compounds, two different models for negative inotropic and negative chronotropic activity are proposed. The two actions seem to be due to conformations of the molecules which differ in the orientation of their phenylethylamino groups.

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“…Figure 17 shows four examples of the predicted in-model potent ChEMBL molecules. The first compound (ChEMBL-287868) was the most potent, but least interesting, as it was an obvious analog of QNB, atropine, and related older antimuscarinics [ 56 ]. The second (ChEMBL-264414) was published in 2008 [ 57 ].…”

Section: Resultsmentioning

confidence: 99%

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

Cleves

Jain

2018

J Comput Aided Mol Des

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We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.

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

Cited by 47 publications

References 0 publications

Anticonvulsant Actions of Anticholinergic Drugs in Soman Poisoning

Anticonvulsant Actions of Anticholinergic Drugs in Soman Poisoning

Semi-rigid analogues of the calcium antagonist verapamil: A molecular modelling study

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

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