Discovery of new drug indications for COVID-19: A drug repurposing approach
Priyanka Kumari, Bikram Pradhan, Maria Koromina, George P. Patrinos, Kristel Van Steen
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.
Introduction
Coronavirus (SARS-CoV-2) outbreaks, the first of which began in November/December 2019, have left only a few countries uninfected. However, the number of daily cases is constantly fluctuating at a global level. So far, there is no permanent cure nor a specific vaccine available that could potentially eliminate the adverse effects of SARS-CoV-2 infection. There is an urgent need to find effective and safe preventive medications which are easily accessible and inexpensive so that everyone can afford them.
Materials and method
Our study applied a unique drug similarity model to repurpose available drugs for SARS-CoV-2 by using three reference drugs based on their chemical-chemical and chemical-protein interactions. In search of new drug indications, we have used a two-tiered clustering approach, which is summarised in Fig 2. We have also applied this study pipeline toalternative drug sets, and our findings are given in S1 File.
Results
0.8 Chemical-chemical and chemical-protein interaction study
Chemical-chemical and chemical-protein interaction search for three reference drugs as an initial step for a potential anti-SARS-COV-2 drug search. There was no chemical interaction available for remdesivir in the STITCH database. Hence we started the data mining for candidate drugs with dexamethasone and baricitinib and included remdesivir in the drug similarity model to get the final list of proposed anti-SARS-COV-2 drugs. DIC contained 608 compounds, including two reference drugs(dexamethasone and baricitinib) and their interactive compounds (refer to S3 Table).
Discussion
Based on FDA approved drugs such as dexamethasone, baricitinib and remdesivir to treat patients infected with SARS-CoV-2, we have developed a drug similarity model to find other potential drug compounds for the treatment ofSARS-CoV-2 virus. Our study in section 0.5 resulted in 203 compounds, which had high determination values. Therefore, the 203 compounds acted as the initial sample for our analysis. Fifty features were selected based on molecular parameters of drug likeliness, physicochemical properties, and ADME of the filtered drug compounds mentioned in S2 Table.
Conclusion
This study proposed a unique method to discover potential candidate drugs for the SARS-CoV-2 virus. A list of small-molecule drugs was reported by investigating chemical-chemical and chemical-protein interactions followed by two-tier unsupervised clustering. These drugs may have the capability to treat SARS-CoV-2 infection. The potential of these candidate drugs was supported by an in-depth analysis of knowledge around the ten shortlisted compounds. Furthermore, interaction mechanisms of two filtered top drugs were tested and validated through docking studies. We hope that our approach may serve as an effective and reproducible aid for Computational driven drug repurposing.
Citation: Kumari P, Pradhan B, Koromina M, Patrinos GP, Steen KV (2022) Discovery of new drug indications for COVID-19: A drug repurposing approach. PLoS ONE 17(5): e0267095. https://doi.org/10.1371/journal.pone.0267095
Editor: Jinn-Moon Yang, National Chiao Tung University College of Biological Science and Technology, TAIWAN
Received: December 22, 2020; Accepted: April 3, 2022; Published: May 24, 2022
Copyright: © 2022 Kumari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: This work is supported in part by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement no. 860895 (TranSYS - h2020transys.eu). The funders had no role in study design, data collection and analysis, decision.
Competing interests: The authors have declared that no competing interests exist.
