{
  "abstract": "Capturing the systemic effects of drugs in vivo remains a fundamental challenge for drug repurposing strategies. Here, we present an in vivo transcriptomics-based drug repurposing framework that profiles multi-organ RNA-seq from drug-treated rodents to model systemic drug responses and match tissue-specific drug signatures against a compendium of disease signatures for indication expansion. We applied this framework to semaglutide, a GLP-1 receptor agonist originally developed for diabetes and subsequently expanded for several other indications. By harmonizing more than 900 human disease transcriptomic signatures via agentic AI and using Z-transformed reversal of gene expression scoring to quantify drug-disease relationships, our approach successfully recovered established semaglutide indications while deprioritizing Alzheimers disease, an indication for which semaglutide was not effective. Top predictions for common diseases were supported by real-world electronic health record data from the Mount Sinai Health System and several predictions for rare indications are proposed for further investigation. This framework provides a scalable strategy for indication expansion across diverse therapeutic agents, especially investigational compounds for which real-world evidence remains sparse.",
  "abstract_shown": true,
  "affiliations": [
    "Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI, USA; Department of Epidemiology and Biostatistics, Michigan State University,"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "name": "Z. Palmer"
    },
    {
      "name": "S. Koirala"
    },
    {
      "name": "N. Talukdar"
    },
    {
      "name": "X. Zhang"
    },
    {
      "name": "H. Garver"
    },
    {
      "name": "M. Mahlmeister"
    },
    {
      "name": "L. Huang"
    },
    {
      "name": "D. Joseph"
    },
    {
      "name": "D. Leshchiner"
    },
    {
      "name": "R. Shankar"
    },
    {
      "name": "S. W. Watts"
    },
    {
      "name": "X. Li"
    },
    {
      "name": "B. S. Glicksberg"
    },
    {
      "name": "A. Lauver"
    },
    {
      "affs": [
        0
      ],
      "name": "B. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/209qdppnke1tg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.12.758524",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758524v1.full",
  "id": "209qdppnke1tg",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758524v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221822017",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758524v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/209qdppnke1tg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:46Z",
      "native_id": "10.64898/2026.10.12.758524",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758524v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion",
  "title_text": "In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:46Z",
  "url": "https://research.draconitic.com/paper/209qdppnke1tg",
  "venue": "bioRxiv",
  "versions": []
}