Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment

TL;DR

This paper introduces the RAIN method, combining Graph SAGE and network meta-analysis, to identify effective drug combinations for gastric neoplasm, demonstrating improved efficacy of specific drug pairs and triplets supported by literature.

Contribution

The study presents a novel AI-driven approach integrating graph neural networks and meta-analysis to systematically identify optimal drug combinations for gastric cancer treatment.

Findings

Oxaliplatin, fluorouracil, and trastuzumab identified as effective.

Triple combination shows the lowest p-value, indicating highest efficacy.

Supports using AI and network analysis for drug repurposing and combination discovery.

Abstract

Background: Gastric neoplasm, primarily adenocarcinoma, is an aggressive cancer with high mortality, often diagnosed late, leading to complications like metastasis. Effective drug combinations are vital to address disease heterogeneity, enhance efficacy, reduce resistance, and improve patient outcomes. Methods: The RAIN method integrated Graph SAGE to propose drug combinations, using a graph model with p-value-weighted edges connecting drugs, genes, and proteins. NLP and systematic literature review (PubMed, Scopus, etc.) validated proposed drugs, followed by network meta-analysis to assess efficacy, implemented in Python. Results: Oxaliplatin, fluorouracil, and trastuzumab were identified as effective, supported by 61 studies. Fluorouracil alone had a p-value of 0.0229, improving to 0.0099 with trastuzumab, and 0.0069 for the triple combination, indicating superior efficacy. Conclusion: The RAIN method, combining AI and network meta-analysis, effectively identifies optimal drug combinations for gastric neoplasm, offering a promising strategy to enhance treatment outcomes and guide health policy.