Machine-learning software scours database of already available drugs that could treat COVID-19 infections
(2021/02/17)
- Reference: 1613563276
- News link: https://www.theregister.co.uk/2021/02/17/ai_algos_covid/
- Source link:
Machine-learning algorithms may help pharmaceutical companies identify old drugs that can be rehashed to treat the COVID-19 coronavirus in elderly patients, according to a study published in Nature Communications.
More than two million people worldwide have been killed by the human malware, and more than 100 million have caught the bio-nasty, [1]according to official statistics. As governments race to vaccinate billions of people to stop the spread of the SARS-CoV-2 coronavirus, a group of researchers from MIT, Harvard University, and ETH Zurich are looking to old-fashioned medicines to treat infected patients.
[2]
Want to let an AI-powered doctor loose on patients? Try slapping a food-label-like sticker on it, says Uncle Sam [3]READ MORE
“Making new drugs takes forever,” [4]said Caroline Uhler, lead author of the paper, and an assistant professor focused on computer science and biology at MIT. “Really, the only expedient option is to repurpose existing drugs.”
[5]
Here’s where AI systems come in handy. Searching through large databases of available drugs and inspecting their pharmacological effects to identify those that could treat a COVID-19 infection is no easy feat. Massive amounts of data have to be carefully analysed to home in on the right drug candidates that will effectively treat COVID-19.
The researchers decided to focus on the fact that older people tend to suffer most from the virus because their lung tissue becomes stiffer with age. They built software that walked back from proteins and genes linked to both aging and coronavirus infections to their genetic root cause, and then searched for available drugs that tackle the effects of those genes. The idea being that those drugs should also lessen the blow of the virus, and help a patient recover.
Here's how MIT described the team's work:
First, they generated a large list of possible drugs using a machine-learning technique called an autoencoder. Next, 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.
To generate an initial list of potential drugs, the team’s autoencoder relied on two key datasets of gene expression patterns. One dataset showed how expression in various cell types responded to a range of drugs already on the market, and the other showed how expression responded to infection with SARS-CoV-2. The autoencoder scoured the datasets to highlight drugs whose impacts on gene expression appeared to counteract the effects of SARS-CoV-2.
“By intersecting the resulting combined SARS-CoV-2 and aging interactome with the targets of the top-ranked FDA-approved drugs from the previous analysis, we identify serine/threonine and tyrosine kinases as potential drug targets for therapeutic interventions,” the group's [6]paper stated.
All of this led to the scientists homing in on RIPK1, a protein that has been linked to cell death. It's hoped that drugs can alleviate the effects of COVID-19 by altering the function of RIPK1. Choosing compounds that affect the enzymes involved in the function of the protein could prevent symptoms like inflammation.
[7]
Uhler said the team plans to share their results with pharmaceutical companies, and won’t know how effective their predictive model is until elderly patients are enrolled in real clinical trials. “Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs,” their paper added. ®
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[3] https://www.theregister.com/2021/01/13/ai_healthcare_regulation/
[4] https://news.mit.edu/2021/machine-learning-treatment-covid-19-0216
[5] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_emergent_tech/artificial_intelligence&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=3&c=33YC1Lr@ysHkOOOeHTv7u0KQAAAEE&t=ct%3Dns%26unitnum%3D3%26raptor%3Deagle%26pos%3Dmid%26test%3D0
[6] https://www.nature.com/articles/s41467-021-21056-z
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[8] https://whitepapers.theregister.com/
More than two million people worldwide have been killed by the human malware, and more than 100 million have caught the bio-nasty, [1]according to official statistics. As governments race to vaccinate billions of people to stop the spread of the SARS-CoV-2 coronavirus, a group of researchers from MIT, Harvard University, and ETH Zurich are looking to old-fashioned medicines to treat infected patients.
[2]
Want to let an AI-powered doctor loose on patients? Try slapping a food-label-like sticker on it, says Uncle Sam [3]READ MORE
“Making new drugs takes forever,” [4]said Caroline Uhler, lead author of the paper, and an assistant professor focused on computer science and biology at MIT. “Really, the only expedient option is to repurpose existing drugs.”
[5]
Here’s where AI systems come in handy. Searching through large databases of available drugs and inspecting their pharmacological effects to identify those that could treat a COVID-19 infection is no easy feat. Massive amounts of data have to be carefully analysed to home in on the right drug candidates that will effectively treat COVID-19.
The researchers decided to focus on the fact that older people tend to suffer most from the virus because their lung tissue becomes stiffer with age. They built software that walked back from proteins and genes linked to both aging and coronavirus infections to their genetic root cause, and then searched for available drugs that tackle the effects of those genes. The idea being that those drugs should also lessen the blow of the virus, and help a patient recover.
Here's how MIT described the team's work:
First, they generated a large list of possible drugs using a machine-learning technique called an autoencoder. Next, 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.
To generate an initial list of potential drugs, the team’s autoencoder relied on two key datasets of gene expression patterns. One dataset showed how expression in various cell types responded to a range of drugs already on the market, and the other showed how expression responded to infection with SARS-CoV-2. The autoencoder scoured the datasets to highlight drugs whose impacts on gene expression appeared to counteract the effects of SARS-CoV-2.
“By intersecting the resulting combined SARS-CoV-2 and aging interactome with the targets of the top-ranked FDA-approved drugs from the previous analysis, we identify serine/threonine and tyrosine kinases as potential drug targets for therapeutic interventions,” the group's [6]paper stated.
All of this led to the scientists homing in on RIPK1, a protein that has been linked to cell death. It's hoped that drugs can alleviate the effects of COVID-19 by altering the function of RIPK1. Choosing compounds that affect the enzymes involved in the function of the protein could prevent symptoms like inflammation.
[7]
Uhler said the team plans to share their results with pharmaceutical companies, and won’t know how effective their predictive model is until elderly patients are enrolled in real clinical trials. “Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs,” their paper added. ®
Get our [8]Tech Resources
[1] https://coronavirus.jhu.edu/map.html
[2] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_emergent_tech/artificial_intelligence&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=2&c=2YC1Lr@ysHkOOOeHTv7u0KQAAAEE&t=ct%3Dns%26unitnum%3D2%26raptor%3Dcondor%26pos%3Dtop%26test%3D0
[3] https://www.theregister.com/2021/01/13/ai_healthcare_regulation/
[4] https://news.mit.edu/2021/machine-learning-treatment-covid-19-0216
[5] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_emergent_tech/artificial_intelligence&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=3&c=33YC1Lr@ysHkOOOeHTv7u0KQAAAEE&t=ct%3Dns%26unitnum%3D3%26raptor%3Deagle%26pos%3Dmid%26test%3D0
[6] https://www.nature.com/articles/s41467-021-21056-z
[7] https://pubads.g.doubleclick.net/gampad/jump?co=1&iu=/6978/reg_emergent_tech/artificial_intelligence&sz=300x50%7C300x100%7C300x250%7C300x251%7C300x252%7C300x600%7C300x601&tile=4&c=44YC1Lr@ysHkOOOeHTv7u0KQAAAEE&t=ct%3Dns%26unitnum%3D4%26raptor%3Dfalcon%26pos%3Dmid%26test%3D0
[8] https://whitepapers.theregister.com/
Re: "repurpose existing drugs"
mevets
The who (https://www.who.int/news-room/q-a-detail/coronavirus-disease-covid-19-hydroxychloroquine) seems to disagree with you.
Maybe you should try redit's r/wallstreet if you want to hype up your investment.
Interesting work
Whitter
Interesting as a general approach, not just for Covid.
But how much resistance will there be from big pharma, who really don't like folks reusing old-working rather than paying for new-shiney. Much like any other industry.
Bleach and sunlight....
mevets
Did it come up with cut open your lungs under sunlight and pour bleach in? Or would that require an artificial stupidity?
"repurpose existing drugs"
Um, like Hydroxychloroquin ?
Which has been [1]largely demonstrated to help people recover from the respiratory sydrome ?
Sure, proper clinical trials are needed to establish facts with scientific rigor, but apparently millions have been treated with it and they're still alive to talk about it.
[1] https://pubmed.ncbi.nlm.nih.gov/32610879/