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Discovery of new drug indications for COVID-19: A drug repurposing approach.
Kumari, Priyanka; Pradhan, Bikram; Koromina, Maria; Patrinos, George P; Steen, Kristel Van.
  • Kumari P; GIGA-R Medical Genomics - BIO3 Systems Genomics, University of Liège, Liège, Belgium.
  • Pradhan B; Laboratory of Pharmaceutical Analytical Chemistry, CIRM, University of Liège, Liège, Belgium.
  • Koromina M; Indian Space Research Organisation (ISRO) Headquarters, Bengaluru, India.
  • Patrinos GP; University of Patras, School of Health Sciences, Department of Pharmacy, Patras, Greece.
  • Steen KV; University of Patras, School of Health Sciences, Department of Pharmacy, Patras, Greece.
PLoS One ; 17(5): e0267095, 2022.
Article in English | MEDLINE | ID: covidwho-1862262
ABSTRACT
MOTIVATION The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID CID0000005743), baricitinib(Pubchem ID CID44205240), remdesivir (PubchemID CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research.

METHOD:

Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs.

RESULT:

Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Repositioning / COVID-19 Drug Treatment Type of study: Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0267095

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Repositioning / COVID-19 Drug Treatment Type of study: Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0267095