Scholarlink

Background <p>Drug development is lengthy and costly, making drug repurposing an attractive alternative. Identifying repurposing candidates from vast biomedical literature is challenging. Natural language processing (NLP) offers potential for literature-based discovery. We present and evaluate a novel, accessible NLP-based method using the Word2Vec algorithm to identify, test, and validate candidate medications for repurposing, demonstrated by seeking treatments for psychotic disorders.</p> Results <p>A Word2Vec model trained on 2.3 million PubMed abstracts (2000–2023) identified potential repurposing candidates based on cosine similarity to a known antipsychotic drug. We tested one candidate, a cephalosporin antibiotic, across independent datasets: MIMIC-IV, CRIS, and BRATECA. Cephalosporin antibiotics in MIMIC-IV demonstrated a reduced hazard ratio (aHR) for psychosis hospitalisation overall (0.94, 95% CI: 0.90–0.99) and more substantially for severe mental illness (0.52, 95% CI: 0.45–0.60). However, CRIS showed an increased risk (HR: 3.56, 95% CI: 2.66–4.77). BRATECA lacked suitable diagnostic data for analysis.</p> Conclusions <p>This methodological framework demonstrates the potential for machine learning approaches to systematically identify drug repurposing candidates, while highlighting population-specific variations in therapeutic effectiveness that warrant caution in translational applications. Our findings should not be interpreted as cephalosporins to be recommended for or against treating psychosis. Our findings merely offer validity to the application of our repurposing methodology, and should serve as a foundation for further investigation rather than direct clinical application.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An accessible, timely method for identifying medications for repurposing: hypothesis generating; testing and validation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月22日

查看全文
  • Maximin Lange,
  • Eoin Gogarty,
  • Meredith Martyn,
  • Philip Braude,
  • Feras Fayez,
  • Nikolaos Koutsouleris,
  • Oliver Howes,
  • Ricardo Twumasi,
  • Ben Carter

摘要

Background

Drug development is lengthy and costly, making drug repurposing an attractive alternative. Identifying repurposing candidates from vast biomedical literature is challenging. Natural language processing (NLP) offers potential for literature-based discovery. We present and evaluate a novel, accessible NLP-based method using the Word2Vec algorithm to identify, test, and validate candidate medications for repurposing, demonstrated by seeking treatments for psychotic disorders.

Results

A Word2Vec model trained on 2.3 million PubMed abstracts (2000–2023) identified potential repurposing candidates based on cosine similarity to a known antipsychotic drug. We tested one candidate, a cephalosporin antibiotic, across independent datasets: MIMIC-IV, CRIS, and BRATECA. Cephalosporin antibiotics in MIMIC-IV demonstrated a reduced hazard ratio (aHR) for psychosis hospitalisation overall (0.94, 95% CI: 0.90–0.99) and more substantially for severe mental illness (0.52, 95% CI: 0.45–0.60). However, CRIS showed an increased risk (HR: 3.56, 95% CI: 2.66–4.77). BRATECA lacked suitable diagnostic data for analysis.

Conclusions

This methodological framework demonstrates the potential for machine learning approaches to systematically identify drug repurposing candidates, while highlighting population-specific variations in therapeutic effectiveness that warrant caution in translational applications. Our findings should not be interpreted as cephalosporins to be recommended for or against treating psychosis. Our findings merely offer validity to the application of our repurposing methodology, and should serve as a foundation for further investigation rather than direct clinical application.