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.

翻译：本研究旨在通过探究临床试验与生物医学文献等真实世界证据源，构建一个专门用于预测可再利用药物的网络。具体而言，该网络旨在为复杂疾病（如癌症、阿尔茨海默病）生成药物联合治疗方案。我们提出一种多层网络医学方法，该方法依托高度配置的ChatGPT提示工程系统动态构建，以实时提取临床试验中的药物提及信息。此外，我们引入一种创新算法，将真实世界证据与疾病特异性信号通路（如KEGG数据库）相关联。若发现药物能与信号通路中的一个或多个蛋白质成分结合，该算法可揭示其再利用潜力。为验证框架效能，我们以乳腺癌为例进行实例化分析。在46条乳腺癌相关信号通路中，该框架识别出38条通路至少被两种药物覆盖，这一证据提示了相关药物联合应用的潜力。具体而言，覆盖度最高的信号通路（ID hsa:2064）涉及108种药物，其中部分具备联合用药可能；而信号通路ID hsa:1499仅被两种药物覆盖，表明该领域存在显著的研究空白。本研究所构建的生成式人工智能增强型网络医学框架，通过精准定位作为靶点的信号通路与蛋白质，在特异性识别药物组合方面展现出显著潜力。值得注意的是，ChatGPT有效加速了临床试验中药物提及信息的识别过程，但药物提及间的关联机制仍需进一步探究。