The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

使用最初针对一种疾病开发的现有药物来治疗其他适应症的策略已在医学领域取得成功。这种药物再利用承诺可以更快地为患者提供药物，同时在漫长而艰难的药物开发过程中降低成本。然而，现有药物和疾病的数量，以及患者和疾病的异质性，尤其是癌症，可能会导致重新利用既耗时又效率低下。我们解决的关键问题是如何有效地重新利用现有药物来治疗给定的适应症。由于药物疗效仍然是整体成功的主要瓶颈，我们讨论了机器学习计算方法与特定表型研究以及机制研究相结合的需求，化学遗传学和组学分析成功预测疾病-药物对。对于面临异质性、复发性和转移性疾病并需要快速和个性化治疗的癌症患者来说，这样的管道可能特别重要。在这里，我们专注于针对结直肠癌的药物再利用，并描述已经为预防和/或治疗重新定位的选定疗法以及潜在的候选药物。我们认为这篇综述是对方法和方法的选择性汇编，并讨论了它们如何结合在一起，将药物再利用提升到一个新的水平。在这里，我们专注于针对结直肠癌的药物再利用，并描述已经为预防和/或治疗重新定位的选定疗法以及潜在的候选药物。我们认为这篇综述是对方法和方法的选择性汇编，并讨论了它们如何结合在一起，将药物再利用提升到一个新的水平。在这里，我们专注于针对结直肠癌的药物再利用，并描述已经为预防和/或治疗重新定位的选定疗法以及潜在的候选药物。我们认为这篇综述是对方法和方法的选择性汇编，并讨论了它们如何结合在一起，将药物再利用提升到一个新的水平。