Science

Researchers develop AI system to identify drugs that can be repurposed to protect from Covid-19 severity

Prashasti Awasthi Mumbai | Updated on February 16, 2021 Published on February 16, 2021

Uhler and the team have developed a machine learning-based approach to identify drugs already on the market that could potentially be repurposed to fight Covid-19, particularly in the elderly

Researchers of the Massachusetts Institute of Technology have developed a system to identify drugs that can be repurposed to protect the elderly from coronavirus severity.

“Making new drugs takes forever. Really, the only expedient option is to repurpose existing drugs,” said Caroline Uhler, an associate member of the Broad Institute of MIT and Harvard.

Uhler and the team have developed a machine learning-based approach to identify drugs already on the market that could potentially be repurposed to fight Covid-19, particularly in the elderly. The system accounts for changes in gene expression in lung cells caused by both the disease and aging.

According to the researchers, this combination could allow medical experts to effectively seek drugs for clinical testing in elderly patients, who are more prone to severe symptoms. The researchers pinpointed the protein RIPK1 as a promising target for Covid-19 drugs, and they identified three approved drugs that act on the expression of RIPK1.

The researchers also added that the stiffening lung tissue in the elderly shows different patterns of gene expression than in younger people, even in response to the same signal. This makes them more vulnerable to any infection.

Methodology

To select approved drugs that might act on these pathways, the team turned to big data and artificial intelligence. The researchers focused on the most promising drug repurposing candidates in three broad steps. First, they generated a large list of possible drugs using a machine-learning technique called an autoencoder.

Secondly, they mapped the network of genes and proteins involved in both aging and SARS-CoV-2 infection. Finally, they used statistical algorithms to understand causality in that network, allowing them to pinpoint “upstream” genes that caused cascading effects throughout the network.

In principle, drugs targeting those upstream genes and proteins should be promising candidates for clinical trials. The autoencoder scoured the datasets to highlight drugs whose impacts on gene expression appeared to counteract the effects of SARS-CoV-2. The findings of the study were published in the journal Nature Communications.

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Published on February 16, 2021
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