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Cell Chem Biol DOI:10.1016/j.chembiol.2018.01.015

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Publication TypeJournal Article
Year of Publication2018
AuthorsSimm, J, Klambauer, G, Arany, A, Steijaert, M, Wegner, JKurt, Gustin, E, Chupakhin, V, Chong, YT, Vialard, J, Buijnsters, P, Velter, I, Vapirev, A, Singh, S, Carpenter, AE, Wuyts, R, Hochreiter, S, Moreau, Y, Ceulemans, H
JournalCell Chem Biol
Date Published2018 05 17
KeywordsAntineoplastic Agents, Cell Line, Tumor, Drug Repositioning, High-Throughput Screening Assays, Humans, Image Processing, Computer-Assisted, Machine Learning, Neoplasms, Neural Networks, Computer

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.


Alternate JournalCell Chem Biol
PubMed ID29503208
PubMed Central IDPMC6031326
Grant ListR35 GM122547 / GM / NIGMS NIH HHS / United States