Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue SLAS TECHNOLOGY: Translating Life Sciences Innovation Année : 2018

Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays

(1) , (1) , (1, 2) , (3) , (2) , (1)
1
2
3

Résumé

Phenotypic cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs mainly because they allow for scanning a wide spectrum of possible targets at once. However, despite compelling methodological advances, posterior identification of a compound's mechanism of action (MOA) has remained difficult and highly refractory to automated analyses. Methods such as the cell painting assay and multiplexing fluorescent dyes to reveal broadly relevant cellular components were recently suggested for MOA prediction. We demonstrated that adding fluorescent dyes to a single assay has limited impact on MOA prediction accuracy, as monitoring only the nuclei stain could reach compelling levels of accuracy. This observation suggested that multiplexed measurements are highly correlated and nuclei stain could possibly reflect the general state of the cell. We then hypothesized that combining unrelated and possibly simple cell-based assays could bring a solution that would be biologically and technically more relevant to predict a drug target than using a single assay multiplexing dyes. We show that such a combination of past screen data could rationally be reused in screening facilities to train an ensemble classifier to predict drug targets and prioritize a possibly large list of unknown compound hits at once.

Dates et versions

hal-02425375 , version 1 (30-12-2019)

Identifiants

Citer

France Rose, Sreetama Basu, Elton Rexhepaj, Anne Chauchereau, Elaine del Nery, et al.. Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays. SLAS TECHNOLOGY: Translating Life Sciences Innovation, 2018, 23 (3), pp.243-251. ⟨10.1177/2472630317740831⟩. ⟨hal-02425375⟩
33 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More