Background Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments. Objective The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. Methods To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. Results From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride. Conclusions The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.
BACKGROUND Driven by the desperate time and the dire need racing to discover an antiviral drug to save our fellow humans, we explored the landscape of the SARS-CoV-2 Biomedical publications to satisfy the following objectives. OBJECTIVE (1) identify off-label drugs that may bring benefit for the COVID-19 pandemic, (2) present a ranking algorithm called CovidX, to recommend existing drugs for potential repurposing, and (3) validate the literature-based outcome with drug knowledge available in clinical trials. METHODS To achieve such objectives, we applied Natural Language Processing (NLP) techniques to identify drugs and linked entities (e.g., disease, gene, protein, chemical compounds, etc.). When such entities are linked, they form a map that can be further explored using Network Science tools. The ranking algorithm we present is based on a notion which we called "diversity". A diversity score for a given drug can is calculated by measuring how "diverse" a drug is connected to various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. RESULTS From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. CONCLUSIONS The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may be needed to be taken at the same time.
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