In past years, medical oncology has witnessed an unprecedented explosion in the understanding of cancer pathophysiology and pathogenesis. With the advancement of next-generation sequencing technologies such as single-cell RNA sequencing, we are better equipped to explore and model complex phenomena such as cancer heterogeneity, resistance, and etiologies at a granular level. Moreover, the establishment and expansion of multi-institutional and large-scale biospecimen collection and bioinformatics initiatives, such as the Cancer Genome Atlas (TCGA), have allowed for the aggregation, curation, and analysis of an unprecedented amount of patient-derived data that has led to the identification of novel therapeutic targets as well as the implication of well-known targets in new disease areas.
In spite of this extraordinary growth in the cancer biology field, progress in the area of drug discovery remains stagnant, still plagued with long development time to market and exorbitantly high costs despite the systematic implementation of high-throughput screening technologies. To date, bringing a drug to market takes about a decade with research and development (R&D) costs reaching approximately US $2.8 billion . Candidate drugs may fail at many points along the drug development pipeline due to numerous reasons such as poor pharmacokinetics, toxicity, or lack of clinical efficacy.
A promising solution to the considerable drug development challenges of novel compounds is the use of existing drugs for the treatment of new diseases.
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Naiem T. Issa, Vasileios Stathias, Stephan Schürer, Sivanesan Dakshanamurthy, Machine and deep learning approaches for cancer drug repurposing, Seminars in Cancer Biology, Volume 68, 2021, Pages 132-142, ISSN 1044-579X, https://doi.org/10.1016/j.semcancer.2019.12.011.