In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion
- 1Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI, USA; Department of Epidemiology and Biostatistics, Michigan State University,
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.
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BibTeX
@misc{palmer2026vivo,
title = {{In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion}},
author = {Z. Palmer and S. Koirala and N. Talukdar and X. Zhang and H. Garver and M. Mahlmeister and L. Huang and D. Joseph and D. Leshchiner and R. Shankar and S. W. Watts and X. Li and B. S. Glicksberg and A. Lauver and B. Chen},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758524},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758524v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion
AU - Z. Palmer
AU - S. Koirala
AU - N. Talukdar
AU - X. Zhang
AU - H. Garver
AU - M. Mahlmeister
AU - L. Huang
AU - D. Joseph
AU - D. Leshchiner
AU - R. Shankar
AU - S. W. Watts
AU - X. Li
AU - B. S. Glicksberg
AU - A. Lauver
AU - B. Chen
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758524
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758524v1
AB - 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.
ER -