Literature DB >> 16945012

High throughput screening identified a substituted imidazole as a novel RANK pathway-selective osteoclastogenesis inhibitor.

Taosheng Chen1, Anita C Knapp, Yang Wu, Jinwen Huang, Jean S Lynch, John K Dickson, R Michael Lawrence, Jean H M Feyen, Michele L Agler.   

Abstract

Receptor activator of nuclear factor-kappaB (NF-kappaB) (RANK) plays a key role in the differentiation, activation, and survival of osteoclasts. Upon activation of RANK with RANK ligand (RANKL), osteoclast precursor cells differentiate into tartrate-resistant acid phosphatase (TRAP)-positive, multinucleated osteoclasts. To identify compounds that block osteoclastogenesis, a cell-based assay was developed using RAW264.7 cells stably transfected with a TRAP promoter-dependent reporter gene as a surrogate readout for differentiation. Described herein is the strategy for high throughput screening and subsequent secondary biological assays for hit triage, which resulted in the identification of compound 1, a 4-nitroimidazole derivative, that specifically inhibited RANKL-induced TRAP gene and protein expression. Compound 1 did not affect the tumor necrosis factor-alpha- or lipopolysaccharide-induced TRAP-luciferase response, suggesting selective inhibition of the RANKL-induced pathway. Reverse transcription polymerase chain reaction analysis confirmed the inhibition of expression of osteoclast marker genes, such as TRAP, cathepsin K, and carbonic anhydrase type II. Compound 1 did not inhibit the RANKL-induced activation of a NF-kappaB reporter gene, or p38 kinase activity, suggesting a mechanism of action downstream of NF-kappaB. Together, these results suggest that we have identified a RANK pathway-specific inhibitor able to block the RANKL-induced osteoclast differentiation process. The hit identification strategy described here can be applied to other cell-based assays using an indirect surrogate readout to improve success rates.

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Year:  2006        PMID: 16945012     DOI: 10.1089/adt.2006.4.387

Source DB:  PubMed          Journal:  Assay Drug Dev Technol        ISSN: 1540-658X            Impact factor:   1.738


  1 in total

1.  Predicting cytotoxicity from heterogeneous data sources with Bayesian learning.

Authors:  Sarah R Langdon; Joanna Mulgrew; Gaia V Paolini; Willem P van Hoorn
Journal:  J Cheminform       Date:  2010-12-09       Impact factor: 5.514

  1 in total

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