Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Real-world data, such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework on a coronary artery disease cohort of millions of patients. We successfully identify drugs and drug combinations that substantially improve the coronary artery disease outcomes but haven’t been indicated for treating coronary artery disease, paving the way for drug repurposing.

药物再利用是确定现有药物新用途的有效策略，可以尽可能快地从实验室过渡到临床。真实世界的数据，例如电子健康记录和保险索赔，提供了许多药物的大量用户的信息。在这里，我们提出了一个高效且易于定制的框架，用于使用对现实世界数据的回顾性分析来生成和测试多个候选药物的重新利用。我们的框架建立在完善的因果推理和深度学习方法的基础上，模拟大规模医疗索赔数据库中存在的药物的随机临床试验。我们在数百万冠状动脉疾病患者队列中展示了我们的框架。我们成功地发现了可显着改善冠状动脉疾病结果但尚未被证明可用于治疗冠状动脉疾病的药物和药物组合，这为药物的重新利用铺平了道路。