In silico approaches for drug repurposing in oncology: a scoping review

Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is...

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Bibliographic Details
Published in:	Frontiers in pharmacology Vol. 15; p. 1400029
Main Authors:	Cavalcante, Bruno Raphael Ribeiro, Freitas, Raíza Dias, Siquara da Rocha, Leonardo de Oliveira, Santos, Roberto de Souza Batista Dos, Souza, Bruno Solano de Freitas, Ramos, Pablo Ivan Pereira, Rocha, Gisele Vieira, Gurgel Rocha, Clarissa Araújo
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 2024
Subjects:	cancer drug repositioning drug repurposing in silico methods scoping review cancer drug repurposing drug repositioning in silico methods scoping review
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Summary:	Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show DR approaches and the gap between these strategies and their ultimate application in oncology. The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3
ISSN:	1663-9812 1663-9812
DOI:	10.3389/fphar.2024.1400029