  The objective of this research is to introduce a network specialized in
predicting drugs that can be repurposed by investigating real-world evidence
sources, such as clinical trials and biomedical literature. Specifically, it
aims to generate drug combination therapies for complex diseases (e.g., cancer,
Alzheimer's). We present a multilayered network medicine approach, empowered by
a highly configured ChatGPT prompt engineering system, which is constructed on
the fly to extract drug mentions in clinical trials. Additionally, we introduce
a novel algorithm that connects real-world evidence with disease-specific
signaling pathways (e.g., KEGG database). This sheds light on the
repurposability of drugs if they are found to bind with one or more protein
constituents of a signaling pathway. To demonstrate, we instantiated the
framework for breast cancer and found that, out of 46 breast cancer signaling
pathways, the framework identified 38 pathways that were covered by at least
two drugs. This evidence signals the potential for combining those drugs.
Specifically, the most covered signaling pathway, ID hsa:2064, was covered by
108 drugs, some of which can be combined. Conversely, the signaling pathway ID
hsa:1499 was covered by only two drugs, indicating a significant gap for
further research. Our network medicine framework, empowered by GenAI, shows
promise in identifying drug combinations with a high degree of specificity,
knowing the exact signaling pathways and proteins that serve as targets. It is
noteworthy that ChatGPT successfully accelerated the process of identifying
drug mentions in clinical trials, though further investigations are required to
determine the relationships among the drug mentions.
