[ad_1]
Scientists used machine learning to find drugs already on the market that could also fight COVID-19 in elderly patients.
“Making new drugs takes forever,” said Caroline Uhler, study co-author, computer biologist at MIT. “Really, the only appropriate option is to reuse existing drugs.”
The study team looked for potential treatments by analyzing changes in gene expressions in lung cells caused by both disease and aging.
Uhler said the combination could help medical experts find drugs to test on the elderly:
We need to look at aging with SARS-CoV-2 – what are the genes at the intersection of these two pathways?
The the researchers sought to answer this question through a three-step process
[Read: How Polestar is using blockchain to increase transparency]
First, they generated a list of drug candidates using an autoencoder, a type of neural network that finds unattended representations of data.
The autoencoder analyzed two sets of gene expression pattern data to identify drugs that appeared to counter the virus.
Then the researchers narrowed down the list by mapping the interactions of proteins involved in aging and routes of infection. They then identified the areas of overlap between the two maps.
This highlighted the gene expression network that a drug is expected to target to fight COVID-19 in older patients.
A causal framework
Finally, the team used statistical algorithms to analyze causality in the network. This allowed them to identify specific genes that a drug should target to minimize the impact of infection.
The system has highlighted the RIPK1 gene as a promising target for COVID-19 drugs. The researchers then found a list of approved drugs that act on RIPK1.
Some of them have been approved for cancer treatment, while others are already being tested in COVID-19 patients.
The researchers note that rigorous in vitro experiments and clinical trials are still needed to determine their effectiveness. But they also plan to expand their framework to other infections:
As we apply our computational platform in the context of SARS-CoV-2, our algorithms integrate data modalities available for many diseases, making them widely applicable.
You can read the study paper in Nature communications.
Published February 15, 2021 – 17:21 UTC
[ad_2]
Source link