COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments

Abstract

Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

“…The work didn't depend on a prebuilt dataset; however, it obtained a knowledge graph from a huge amount of text documents about COVID-19, however the study focused only on one disease which COVID-19. In the work of [24] , a computational framework was designed for detecting drug combinations, by extracting drug names from biomedical publications and treatment sections of clinical trial records, a network model is constructed representing the drug names and their associations. The previous work was extended in [25] where an algorithm for constructing a knowledge graph from drug, gene, and disease mentions in the biomedical literature is presented with two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs.…”

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

“…A research was proposed in [41] where an Open Information Extraction system based on unsupervised learning without a prebuilt dataset obtains a knowledge graph from a vast amount of text documents about the disease COVID-19 and the dataset that was used to generate a knowledge graph focused only on COVID-19. A computational framework was designed in [42] for detecting drug combinations, by extracting drug names from biomedical publications and treatment sections of clinical trial records, and a network model is constructed representing the drug names and their associations. The previous work was extended in [43] through an algorithm for constructing a knowledge graph from drugs, genes, and diseases mentioned in the biomedical literature are presented with two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs.…”

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

“…Along with the technological advancement, bioinformatics approaches were shown to significantly benefit translational drug discovery research through the analysis of this vast body of knowledge (Wooller et al, 2017). Several computational approaches were reported to be implemented in SARS-CoV-2 drug repurposing studies, including network models (Li X. et al, 2021;Hamed et al, 2022;Howell et al, 2022;Siminea et al, 2022), text mining (Kuusisto et al, 2020;Tworowski et al, 2020;Muramatsu and Tanokura, 2021), molecular docking and molecular dynamics (MD) simulation (Wang, 2020;Egieyeh et al, 2021;Jalalvand et al, 2022), knowledge graph (KG) (Al-Saleem et al, 2021), weight regularization matrix factorization (WRMF) (Xu et al, 2022), and ensemble matrix completion model (Li W. et al, 2021). The application of artificial intelligence (AI) technologies was reported to hasten drug repurposing studies among the existing computational approaches Levin et al, 2020).…”