1 April 2019
Repurposing in oncology, Colorectal cancer, Drug repositioning, Mechanism of action, Signaling pathways, On/off-target effects, Polypharmacology, Side effects, Omics, Phenotypes, Computational approaches, A-II, angiotensin-II, Ab, antibody, ACF, aberrant crypt foci, ARD, adverse drug reactions, AMPK, adenosine monophosphate-activated protein kinase, AT1R, angiotensin II type 1 receptor, ATC, Anatomical Therapeutic Chemical classification, CaPP3, Cancer Prevention Project 3, CHAT, cancer hallmarks analytics tool, CMap, Connectivity Map, COX-2, cyclooxygenase-2, CRC, colorectal carcinoma, DCF, Diclofenac, EGFR, epidermal growth factor receptor, EMA, European Medicines Agency, FAP, familial adenomatous polyposis, FMCM, Functional Module Connectivity Map, FFN, function-function networks, GSToP, gene-selection-by-trend-of-progression procedure, GWAS, Genome-Wide Association Studies, HERV, human endogenous retrovirus, KEGG, Kyoto Encyclopedia of Genes and Genomes, LBD, literature-based discovery, LINCS, Library of Integrated Network-Based Cellular Signatures, MANTRA, Mode of Action by NeTwoRk Analysis, MRC, Medical Research Council, NSAID, non-steroidal anti-inflammatory drug, NTID, narrow therapeutic index drug, OS, overall survival, PFS, progression free survival, Pl3K, phosphatidylinositol 3-kinase, POG, Personalized OncoGenomic, PREDICT, PREdicting Drug IndiCaTions, RAR α, retinoic acid receptor alpha, ReDo, Repurposing Drugs in Oncology, RRM2, human ribonucleotide reductase 2, SEA, Similarity Ensemble Approach, sLA, sialyl Lewis-A antigen, SMILE, simplified molecular-input line-entry system, SVM, Support Vector Machine, TKI, tyrosine kinase inhibitors, TOP2A, Topisomarase 2-α, USPSTF, U.S. Preventive Services Task Force
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.