1. High‐Content Image‐Based Screening and Deep Learning for the Detection of Anti‐Inflammatory Drug Leads
- Author
-
Lau, Tannia A, Mair, Elmar, Rabbitts, Beverley M, Lohith, Akshar, and Lokey, R Scott
- Subjects
Medicinal and Biomolecular Chemistry ,Chemical Sciences ,Biotechnology ,Networking and Information Technology R&D (NITRD) ,Machine Learning and Artificial Intelligence ,Aetiology ,Development of treatments and therapeutic interventions ,2.1 Biological and endogenous factors ,5.1 Pharmaceuticals ,Inflammatory and immune system ,Mice ,Animals ,NF-kappa B ,Lipopolysaccharides ,Deep Learning ,Anti-Inflammatory Agents ,Cytokines ,Nitric Oxide ,Biochemistry and Cell Biology ,Organic Chemistry ,Biochemistry and cell biology ,Medicinal and biomolecular chemistry - Abstract
We developed a high-content image-based screen that utilizes the pro-inflammatory stimulus lipopolysaccharide (LPS) and murine macrophages (RAW264.7) with the goal of enabling the identification of novel anti-inflammatory lead compounds. We screened 2,259 bioactive compounds with annotated mechanisms of action (MOA) to identify compounds that block the LPS-induced phenotype in macrophages. We utilized a set of seven fluorescence microscopy probes to generate images that were used to train and optimize a deep neural network classifier to distinguish between unstimulated and LPS-stimulated macrophages. The top hits from the deep learning classifier were validated using a linear classifier trained on individual cells and subsequently investigated in a multiplexed cytokine secretion assay. All 12 hits significantly modulated the expression of at least one cytokine upon LPS stimulation. Seven of these were allosteric inhibitors of the mitogen-activated protein kinase kinase (MEK1/2) and showed similar effects on cytokine expression. This deep learning morphological assay identified compounds that modulate the innate immune response to LPS and may aid in identifying new anti-inflammatory drug leads.
- Published
- 2024