Introduction

A variety of diseases have been part of human history, and although many of them have already been eradicated or have effective treatment, we still face the persistence of those that are still currently present. Medicines played a pivotal role in this process in which drug formulations were used to refrain disease progression at a molecular and physiological level. In this context, cancer, a set of diseases defined by an uncontrolled abnormal cell growth, defies new medicine and keeps demanding action as the lack of approved drugs continues to be a challenge and further requires the discovery of new therapeutic molecules.

It is noteworthy that the cost of new drug development has been increasing over the years. Indeed, the process of medicinal formulation from invention to the pharmacy shelves ranged from $1.1 billion in 2003 to $2.8 billion in 2013 [1]. Also, this is a time-consuming and high-risk process, as the research and development take about 10–15 years for a single new drug to be commercially available, with a success rate of developing a new molecular entity being only 2.01% [2, 3]. This current scenario has forced drug developers to become more innovative in finding new therapeutic applications for existing drugs that are outside the scope of their original medical indication, a strategy known as drug repurposing (also called drug repositioning, reprofiling or re‑tasking) [4]. Therefore, repurposing existing drugs for new therapeutic assignments is considered a better approach and serves as an important way to hasten drug discovery, especially in cancer therapy.

Identifying alternative purposes for known drugs is important for the pharmaceutical industry as well as for patients. However, this is not an easy journey: exploring the large collection of data commonly seen in the Big Data era requires additional efforts related to data extraction and, most importantly, data analyses. In this sense, the emergence of Computational methods has revolutionized life science studies, especially in terms of repositioning drugs, as they aim to identify the relationship network between target and drugs. In fact, many computational methods, including artificial intelligence techniques, have been proposed for predicting unknown associations between drugs and target proteins associated with diseases, in an attempt to tackle the traditional limitations of bulky information [3]. Succinctly, in silico approaches help in screening large compound libraries at once, can be applied to drug-target and toxicity predictions, and, most importantly, relieve the pressure concerning the costs of laboratory works and animal sacrifices [5].

In practical terms, the gap between what can be found through computational strategies and the lacking knowledge concerning drug repurposing in oncology is yet to be completely demonstrated. Although many reports show promising results, application of these strategies in drug repurposing does not always succeed, indicating a possible misleading and failed use in several cases so far, mostly due to the questionable chosen input data, poor data quality and debatable analysis methods. Besides, patent considerations, regulatory concerns and organizational hurdles contribute to delay to-be-repurposed drugs from reaching clinical practice [6]. Hence, we designed the present scoping review protocol to unravel how the in silico strategies can be implemented in drug repurposing in oncology, surpassing all current caveats.

Materials and methods

Search strategy

The PubMed/Medline search strategy was developed and adapted for the other databases (Embase, Scopus, and Web of Science). Furthermore, we will search for grey literature in Grey Literature in Europe (Open Grey). Our search strategy combines Medical Subject Headings (MeSH) terms, their relevant synonyms, and the Boolean operators “AND” and “OR”. Three concept clusters were included: (1) in silico approach, (2) drug repurposing, and (3) oncology. The description of the search strategies which will be used in each database can be found in Table 1.

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Table 1. Search strategies according to the electronic databases.

https://doi.org/10.1371/journal.pone.0271002.t001

We will perform the search on each database and export the results in Comma-Separated Values (CSV) format to Microsoft Excel. Subsequently, duplicates will be removed using their PubMed ID and title, and reviewers will proceed with the inclusion and exclusion of the studies.

Discussion

In silico methods have been widely used for drug repurposing in the biomedical field. However, their significance, efficiency and merit have yet to be fully appraised. In order to overcome the challenges in the research of drug repositioning, the results from this scoping review will be the first step on mapping how successful these methods have been since most of them are hypothesis-driven approaches that takes advantage of the use of Big data. This will be achieved by showing evidence on which strategies have reached a clinical application or are prone to be used in oncology research. Additionally, to circumvent the issues related to the efficacy/effectiveness and safety of drugs, the integration of multi-source information regarding drugs and their side effects and interactions of drugs and cancer will be addressed.

We intend to provide valuable information for researchers from diverse areas in understanding the favourable in silico methods used in oncology, and how they may be incorporated alone or in association with other methods in the process of drug repositioning. Thus, we aim to contribute to the future application of the in silico methods in drug repurposing based on the best evidence available.

Supporting information