A scalable natural language processing framework for drug repurposing in chemotherapy-induced adverse events from clinical narrative records

  • Masami Tsuchiya
  • , Mari Inoue
  • , Yoshimasa Kawazoe
  • , Kiminori Shimamoto
  • , Tomohisa Seki
  • , Shungo Imai
  • , Hayato Kizaki
  • , Emiko Shinohara
  • , Shuntaro Yada
  • , Shoko Wakamiya
  • , Eiji Aramaki
  • , Satoko Hori

Research output: Contribution to journalArticlepeer-review

Abstract

Background Preventing chemotherapy-related adverse events (AEs) remains an unmet clinical challenge. Preclinical studies have suggested protective effects of several existing agents, but translation into human evidence has been limited. We aimed to establish proof of concept (PoC) for drug repurposing by applying a natural language processing (NLP)-based framework to electronic health record (EHR) narratives, thereby bridging preclinical findings with clinical validation. Methods We retrospectively analyzed 56,326 patients with cancer treated at the University of Tokyo Hospital (2004–2023). A transformer-based NLP model extracted symptomatic AEs from clinical notes. Candidate preventive drugs identified from preclinical evidence were assessed using propensity score matching and Cox proportional hazards models. We evaluated angiotensin II receptor blockers (ARBs) for fluoropyrimidine-induced oral mucositis and ramelteon for platinum-induced peripheral neuropathy, with laxatives serving as a negative control. Results NLP demonstrated high accuracy (precision 0.81–0.83; recall 0.95–0.97). After matching, ARB co-administration was significantly associated with reduced mucositis incidence (hazard ratio [HR] 0.58, 95 % confidence interval [CI] 0.44–0.77; P < 0.001), representing a clinical PoC consistent with mechanistic preclinical data. Ramelteon showed an exploratory protective signal against neuropathy (HR 0.60, 95 % CI:0.38–0.93; P = 0.024). No preventive association was observed for laxatives. Conclusions 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.

Original languageEnglish
Article number116157
JournalEuropean Journal of Cancer
Volume232
DOIs
Publication statusPublished - 2026 Jan

Keywords

  • Adverse events
  • Drug repurposing
  • Electronic health records
  • Natural language processing

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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