Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response

Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response

Identifying ligands which activate the specific effectors driving particular in vivo drug effects remains challenging. Here, the authors apply unsupervised clustering of pharmacodynamic parameters to classify GPCR ligands into different categories with similar signaling profiles and shared frequency...

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Autores principales: Besma Benredjem, Jonathan Gallion, Dennis Pelletier, Paul Dallaire, Johanie Charbonneau, Darren Cawkill, Karim Nagi, Mark Gosink, Viktoryia Lukasheva, Stephen Jenkinson, Yong Ren, Christopher Somps, Brigitte Murat, Emma Van Der Westhuizen, Christian Le Gouill, Olivier Lichtarge, Anne Schmidt, Michel Bouvier, Graciela Pineyro
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Science
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Acceso en línea:https://doaj.org/article/c0db3f1cf110426bbd5f35d40fd3681b
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Sumario:Identifying ligands which activate the specific effectors driving particular in vivo drug effects remains challenging. Here, the authors apply unsupervised clustering of pharmacodynamic parameters to classify GPCR ligands into different categories with similar signaling profiles and shared frequency of report of side effects.

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