Review of Drug Repositioning Approaches and Resources

Abstract

Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.

Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19

“…Many drugs have multiple protein targets and many diseases share overlapping molecular pathways (Hodos et al, 2016). In such cases, reusing drugs for more than one purpose and finding their new uses can significantly reduce the cost, time and risks of the drug development process using fast-growing computational approaches (Xue et al, 2018). This concept of drug repurposing has been used successfully for many years for known diseases.…”

Integrative Omics for Informed Drug Repurposing: Targeting CNS Disorders

“…Text mining has been used prolifically in the medical field and in conjunction with data-driven drug repurposing approaches [81]. A typical biological text mining effort involves four steps: 1) information retrieval, including parsing of relevant information from large data sources; 2) biological name entity recognition, with identification of valuable biological concepts using controlled vocabularies, and the last two steps; 3) biological information extraction; and finally, 4) biological knowledge discovery, which involves extracting useful biological information and constructing a knowledge graph, a compilation of interlinked descriptions of objects [82].…”

Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work

“…Then, with the help of bioinformatics tools as well as artificial intelligence, interaction networks between drug targets and drugs are identified [97,98]. There are various disease-drug knowledge databases such as ChemBank [99], DrugBank [100], KEGG [101], Pubmed [102], OMIA [103] and genomic databases such as PDB [104], GenBank [105], GEO [106], MIPS [107,108].…”

“…It is extensively used for extracting information and images which can be applied to drug repurposing. In this approach biological entity relationships are found out from datas in medical databases and a semantic network is built also based on existing ontology network and algorithms are developed to discover relationship in the network [108].…”