A scalable natural language processing framework for drug repurposing in chemotherapy-induced adverse events from clinical narrative records
This study introduces a scalable NLP-epidemiology framework for non-invasive, real-world validation of drug repurposing candidates. The ARB finding provides human-level PoC evidence supporting prospective clinical testing, while the ramelteon signal warrants further exploration. Our approach demonstrates how EHR narratives can operationalize translational research, prioritizing safe, accessible agents for improving the tolerability of cancer treatment.
Authors
- Shungo Imai (ORCID: https://orcid.org/0000-0001-5706-613X)
- Shuntaro Yada (ORCID: https://orcid.org/0000-0002-6209-1054)
- Kiminori Shimamoto (ORCID: https://orcid.org/0000-0003-0838-7505)
- Tomohisa Seki
- Yoshimasa Kawazoe (ORCID: https://orcid.org/0000-0002-7277-0827)
- Emiko Shinohara
- Masami Tsuchiya (ORCID: https://orcid.org/0000-0003-3846-0435)
- Eiji Aramaki (ORCID: https://orcid.org/0000-0003-0201-3609)
- Hayato Kizaki (ORCID: https://orcid.org/0000-0002-4572-1333)
- Shoko Wakamiya (ORCID: https://orcid.org/0000-0002-9371-1340)
- Satoko Hori (ORCID: https://orcid.org/0000-0002-4596-5418)
- Mari Inoue
Institutions
- University of Tsukuba (JP)
- Keio University (JP)
- University of Tokyo Hospital (JP)
- The University of Tokyo (JP)
- Nara Institute of Science and Technology (JP)
Publication Details
- Journal
- European Journal of Cancer
- Published
- 2025-11-29
- DOI
- https://doi.org/10.1016/j.ejca.2025.116157
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Core Research for Evolutional Science and Technology