TheraMind: A Multi-LLM Agent for Accelerating Drug Repurposing in Lung Cancer via Case Report Mining

Published clinical case reports are a valuable yet underutilized source of evidence for drug repurposing. However, systematically identifying relevant reports remains a challenge due to the volume of literature and the diversity of candidate compounds. We present TheraMind, a multi-agent AI system that leverages large language models (LLMs) to automate the identification and analysis of case reports supporting potential drug repurposing for non-small cell lung cancer (NSCLC). Our system screened 10,023 PubMed-indexed case reports across 18 candidate drugs using coordinated data extraction and standardized four-question prompts assessing diagnosis, drug administration, discontinuation, and clinical outcomes. We employed three evaluation strategies—rule-based classifiers, single-model validators, and a majority-vote ensemble integrating GPT-4-turbo, Gemini-Pro, and LLaMA-3-8B. The ensemble approach achieved 92% recall and 99.7% specificity in detecting clinically relevant reports. Structured outputs included patient demographics, therapeutic responses, and case summaries. This LLM-driven framework offers a scalable approach to accelerate drug repurposing by mining real-world evidence from unstructured clinical literature.