Abstract

Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease

Nat Commun. 2021 Jul 12;12(1):4246. doi: 10.1038/s41467-021-24470-5.

Debashis Sahoo # 1 2 3, Lee Swanson # 4, Ibrahim M Sayed # 5 6, Gajanan D Katkar 4, Stella-Rita Ibeawuchi 6, Yash Mittal 7, Rama F Pranadinata 4, Courtney Tindle 4, Mackenzie Fuller 4, Dominik L Stec 4, John T Chang 7, William J Sandborn 7, Soumita Das 8, Pradipta Ghosh 9 10 11 12

 
     

Author information

  • 1Department of Pediatrics, University of California San Diego, San Diego, CA, USA. dsahoo@ucsd.edu.
  • 2Department of Computer Science and Engineering, Jacob's School of Engineering, University of California San Diego, San Diego, CA, USA. dsahoo@ucsd.edu.
  • 3Rebecca and John Moore Comprehensive Cancer Center, University of California San Diego, San Diego, CA, USA. dsahoo@ucsd.edu.
  • 4Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, CA, USA.
  • 5Department of Medical Microbiology and Immunology, Faculty of Medicine, Assiut University, Assiu, Egypt.
  • 6Department of Pathology, University of California San Diego, San Diego, CA, USA.
  • 7Department of Medicine, University of California San Diego, San Diego, CA, USA.
  • 8Department of Pathology, University of California San Diego, San Diego, CA, USA. sodas@ucsd.edu.
  • 9Rebecca and John Moore Comprehensive Cancer Center, University of California San Diego, San Diego, CA, USA. prghosh@ucsd.edu.
  • 10Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, CA, USA. prghosh@ucsd.edu.
  • 11Department of Medicine, University of California San Diego, San Diego, CA, USA. prghosh@ucsd.edu.
  • 12Veterans Affairs Medical Center, La Jolla, CA, USA. prghosh@ucsd.edu.

#Contributed equally.

Abstract

Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here we outline an unbiased AI-assisted approach for target identification and validation. A network was built in which clusters of genes are connected by directed edges that highlight asymmetric Boolean relationships. Using machine-learning, a path of continuum states was pinpointed, which most effectively predicted disease outcome. This path was enriched in gene-clusters that maintain the integrity of the gut epithelial barrier. We exploit this insight to prioritize one target, choose appropriate pre-clinical murine models for target validation and design patient-derived organoid models. Potential for treatment efficacy is confirmed in patient-derived organoids using multivariate analyses. This AI-assisted approach identifies a first-in-class gut barrier-protective agent in IBD and predicted Phase-III success of candidate agents.

 

 

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