Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer

Abstract

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.

Drug repurposing for breast cancer therapy: Old weapon for new battle

“…Also, the repositioned drug anyhow requires clinical re-assessment for optimizing its efficacy and cytotoxicity if the route of administration or drug dosage is different from the older indication; thereby increasing the overall cost of repositioning scheme. Nonetheless, the animal model used for drug testing does not represent the exact patient phenotype and hence is less predictive of efficacy in real [151]. Despite many benefits of drug repositioning, much attention is required to lower the drug dosage and toxicity without mitigating efficacy and resolve above discussed limitations for cost effective and more efficient drug development.…”

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

“…Here, we extended that study with a broadened default analysis, supplemented with Jupyter notebooks that implement repurposing based on prior knowledge in a reusable pipeline that can be applied in other studies ( Figure 6). As we found seveal hundred significantly regulated proteins when comparing lung tumor to non-cancerous appearing tissue a strategy for knowledgederived prioritization, such as text mining, disease and drug associations became necessary (Corsello et al, 2020;Nowak-Sliwinska et al, 2019;Pushpakom et al, 2018). The CKG combines these approaches by mining the graph to identify drug-target-disease triplets co-mentioned in the literature (3.3 million publications mentioning triplets), to enumerate side effects associated with drugs (72,000 associations), to find similar drugs based on side effects, indications, targets, and to connect drugs with functional pathways.…”

Drug repositioning in cancer: The current situation in Japan

“…One example of successful use of literature‐based DR in cancer is the Repurposing Drugs in Oncology (ReDO) project, which was designed to rapidly identify new and effective cancer treatments characterized by low toxicity and cost‐effectiveness . Researchers successfully applied a literature‐based approach using all forms of published data to identify compounds to repurpose …”