Literature DB >> 27503678

Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes.

Alexander R Geanes1, Hykeyung P Cho2, Kellie D Nance3, Kevin M McGowan2, P Jeffrey Conn4, Carrie K Jones4, Jens Meiler5, Craig W Lindsley6.   

Abstract

This Letter describes a ligand-based virtual screening campaign utilizing SAR data around the M5 NAMs, ML375 and VU6000181. Both QSAR and shape scores were employed to virtually screen a 98,000-member compound library. Neither approach alone proved productive, but a consensus score of the two models identified a novel scaffold which proved to be a modestly selective, but weak inhibitor (VU0549108) of the M5 mAChR (M5 IC50=6.2μM, M1-4 IC50s>10μM) based on an unusual 8-((1,3,5-trimethyl-1H-pyrazol-4-yl)sulfonyl)-1-oxa-4-thia-8-azaspiro[4,5]decane scaffold. [(3)H]-NMS binding studies showed that VU0549108 interacts with the orthosteric site (Ki of 2.7μM), but it is not clear if this is negative cooperativity or orthosteric binding. Interestingly, analogs synthesized around VU0549108 proved weak, and SAR was very steep. However, this campaign validated the approach and warranted further expansion to identify additional novel chemotypes.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  M(5); Muscarinic acetylcholine receptor; Structure–activity relationship (SAR); Virtual screen

Mesh:

Substances:

Year:  2016        PMID: 27503678      PMCID: PMC4996684          DOI: 10.1016/j.bmcl.2016.07.071

Source DB:  PubMed          Journal:  Bioorg Med Chem Lett        ISSN: 0960-894X            Impact factor:   2.823


  15 in total

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Authors:  P Jeffrey Conn; Craig W Lindsley; Jens Meiler; Colleen M Niswender
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Authors:  Patrick R Gentry; Masaya Kokubo; Thomas M Bridges; Meredith J Noetzel; Hyekyung P Cho; Atin Lamsal; Emery Smith; Peter Chase; Peter S Hodder; Colleen M Niswender; J Scott Daniels; P Jeffrey Conn; Craig W Lindsley; Michael R Wood
Journal:  J Med Chem       Date:  2014-09-03       Impact factor: 7.446

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Review 4.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

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