@article{nokey,

title = {COVID-19 Drug Repurposing: a Network-based Framework for Exploring BioMedical Literature and Clinical Trials for Possible Treatments},

author = {Ahmed Abdeen Hamed and Tamer E. Fandy and Karolina L. Tkaczuk and Byung Suk Lee

},

url = {https://doi.org/10.3390/pharmaceutics14030567

},

doi = {https://doi.org/10.3390/pharmaceutics14030567},

year  = {2022},

date = {2022-03-04},

urldate = {2022-03-04},

journal = {Pharmaceutics },

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. View Full-Text

},

keywords = {Clinical Data Science},

pubstate = {published},

tppubtype = {article}

}