Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Biochemistry & Molecular Biology, SMALL-MOLECULE LIBRARIES, MICROSCOPY, Bayesian matrix factorization, computational chemistry, deep learning, drug discovery, high-content imaging, high-throughput screening, machine learning, matrix factorization, Antineoplastic Agents, Cell Line, Tumor, Drug Repositioning, High-Throughput Screening Assays, Humans, Image Processing, Computer-Assisted, Machine Learning, Neoplasms, Neural Networks, Computer, MODEL, STADIUS-17-54

Abstract:

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.