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A novel integrated approach identifies drugs that inhibit Covid-19SARS-CoV-2 infection

Published on 25.1.2022
Tampere University
viitekuvassa piirretty koronavirusPhoto: Jonne Renvall
Researchers at Tampere University have identified and experimentally validated existing drugs that inhibit infection of the SARS-CoV-2 virus, both wild-type and Delta variant. By integrating multiple bioinformatics and cheminformatics approaches, they were able to rapidly screen a large compound library and prioritise promising drug candidates for the treatment of COVID-19.

The international team of researchers discovered that a combination of two compounds, 7-hydroxystaurosporine and bafetinib, inhibits infection by SARS-CoV-2 virus in vitro and also produces synergistic effects that enhance the efficacy of treatment. This combination of drugs was also found to prevent infection with the Delta variant.

The study was carried out at the Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), which develops alternatives for animal testing, among other things.

FHAIVE is also the Finnish national GLP reference laboratory for the validation of new alternative methods to animal testing and focuses on the development of robust integrated approaches for chemical safety assessment and drug development.

The study was a collaborative effort involving researchers at Tampere University, the University of Helsinki (Finland), Aarhus University (Denmark), the University of Queensland (Australia) and the Eotvos Lorand Research Network (Hungary).

What is particularly groundbreaking about the study is the novel combination of multiple bioinformatics and cheminformatics approaches.

The new integrated approach has extensive potential  

Computational methods have emerged as the subject of intense scientific interest as they hold promise for repurposing existing drugs and finding new ones.

“These, however, focus on specific aspects of the pathological mechanisms, or the interaction between the drugs and their molecular targets, or the effects of the compounds on the affected tissues, and have never been integrated to work in a unified framework”, says Dario Greco, professor of bioinformatics at Tampere University who led the COVID-19 drug discovery research.

“While the vaccination campaign continues globally, there is still an urgent need to perfectionate effective, cheap and sustainable treatments that can easily reach everyone worldwide.”

Traditionally, the early stages of drug discovery rely on screening of large libraries of compounds, which is laborious and time consuming. Hence, a reliable protocol to prioritise candidate compounds computationally, can significantly speed up drug discovery while boosting pharmaceutical innovation.

“The integrated strategy we developed in this study can also be applied to find new drugs for other diseases”, continues Greco.

From compound libraries to genetic effects

From the 8,000 or so drugs screened from the University of Alberta’s DrugBank database, the researchers selected 700 promising candidates for further investigation. They set out to strike a balance between the assumed efficacy of the drug candidates against COVID-19, their chemical substructures and a number of practical considerations, such as price, availability, shipping time and ease of storage. They pinpointed 23 cancer, antimicrobial and antiviral drugs on which to carry out in vitro testing to determine whether they are effective against COVID-19.

In addition, the FHAIVE researchers extracted the relevant chemical substructures of the selected drug candidates to provide a chemical vocabulary that may help in the design of new drugs.

The team of researchers used four complementary bioinformatics approaches to prioritise drug candidates.
For example, the researchers compared how the SARS-CoV-2 virus and the selected drug candidates affect gene expression in cells and tissues. Then they ranked the drug candidates based on the importance of their target  genes in the gene expression networks.

At the final stage, the researchers identified the most promising candidates based on the substructure fingerprints generated using bioinformatics and cheminformatics methods.

The research results were recently published in Briefings in Bioinformatics.

Angela Serra+, Michele Fratello+, Antonio Federico, Ravi Ojha, Riccardo Provenzani, Ervin Tasnadi, Luca Cattelani, Giusy del Giudice, Pia A. S. Kinaret, Laura A. Saarimäki, Alisa Pavel, Suvi Kuivanen, Vincenzo Cerullo, Olli Vapalahti, Peter Horvath, Antonio Di Lieto, Jari Yli-Kauhaluoma, Giuseppe Balistreri, Dario Greco*: Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation. Briefings in Bioinformatics, bbab507, 27 December 2021
+ equal contribution. https://doi.org/10.1093/bib/bbab507

Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)
 

Inquiries:
Professor of Bioinformatics Dario Greco,
dario.greco [at] tuni.fi

 

Dario Greco
What is particularly groundbreaking about the study is the integration of bioinformatics and cheminformatics approaches, Professor Dario Greco says. The integrated approach holds promise for repurposing existing drugs and finding new ones, while significantly speeding up pharmaceutical innovation and sustainability. Photo: Jonne Renvall