W182 - Data-driven Positioning of Immune and Inflammation Related Drugs to Target Neuroinflammation Diseases

Anna Blazier – Sanofi; Samuel Lessard – Sanofi; Zhaohui Du – Sanofi; Zhipeng Liu – Sanofi; Heming Xing – Sanofi; Travis Ahn-Horst – Sanofi; Dongyu Liu – Sanofi; Franck Rapaport – Sanofi; Pablo Sardi – Sanofi; Emanuele De Rinaldis – Sanofi; Clement Chatelain – Sanofi; Shameer Khader – Sanofi; Dimitry Ofengeim – Sanofi

Abstract Text: Immune pathways drive diverse biological functions and disease phenotypes. Data-driven drug repurposing of existing portfolio of immune-disease related drugs to new indications can accelerate drug development cycle times to clinical trial by leveraging demonstrated product safety profile, efficacy in a related disease indication or mechanism of action. In this study we developed an end-to-end drug repurposing strategy leveraging compound signatures and multi-omics evidence from the Sanofi Disease Data Ecosystem to match existing immunology drugs and potentially new indications in neuroinflammatory diseases. To enable this, we build an Sanofi Therapeutic Signature database, with 5,900 compounds and >3,800 target perturbation profiles (RNAi/CRISPR, ligand, ovExp, Perturb-Seq) in >200 diseases & 190 cell model systems and integrated with immunology-related clinical trial data from Citeline's Pharmaprojects. For a subset of targets with compound signature available in immune cells, we used an artificial intelligence-based transcriptomic fingerprinting scoring system (AITxFP) to compute similarity of compound transcriptomic profile to prioritize compounds reversing disease signatures. We further integrated the signatures with multi-omics evidence including transcriptomics enrichment, genetic evidence, knowledge graph, protein-protein networks, and literature-based associations from PubMed. Further, an indication priority score was computed for all immunology targets in each selected neuroinflammatory disease (for example: Alzheimer’s disease). We validated two compounds from the prioritized ranked list in a disease relevant iPSC-derived neuro tri-culture model. In conclusion, our data-driven end-to-end drug indication positioning approach can expand the drug value and accelerate patient impact by reducing the cycle time of drug discovery, development, and positioning.