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New antibiotic discovered in AI could defeat dangerous superbugs

New antibiotic discovered in AI could defeat dangerous superbugs

 


Researchers say they have used artificial intelligence to discover a new type of antibiotic that works against particularly threatening drug-resistant bacteria. Related video above: Bacteria can change shape to survive antibiotics When antibiotics were tested on mouse skin, infections with this superbug controlled bacterial growth, and others It was suggested that this method could be used to create tailored antibiotics to combat drug-resistant pathogens in . The researchers also tested the antibiotic against 41 different strains of antibiotic-resistant Acinetobacter baumannii. The drug was effective against all pathogens, but needs to be further purified and tested in human clinical trials before it can be used in patients. Additionally, the compounds identified by the AI ​​were effective in a way that they stopped only the pathogen in question. It does not appear to kill many other beneficial bacterial species that live in the gut and skin, making it a rare narrowly targeted drug. The researchers said that if more antibiotics acted so precisely, they could prevent bacteria from becoming resistant. The study was published in the journal Nature Chemical Biology. “This is incredibly promising,” said Dr. Cesar de la Fuente, an assistant professor at the University of Pennsylvania’s Perlman School of Medicine. He also uses AI to make new discoveries. De la Fuente said this kind of approach to discovering new drugs is an emerging field that researchers have been experimenting with since around 2018. This greatly reduces the time it takes to sort thousands of promising compounds. “As we’ve seen, I think AI can be applied well in many areas, and I think drug discovery is sort of the next frontier.” focused. It inhabits hospitals and other medical settings, clinging to surfaces such as doorknobs and counters. Because we can take pieces of DNA from other organisms we come in contact with, we can integrate genes that resist the drugs that are best used by doctors to treat them. “This is what we call professional pathogens in the lab,” said John Stokes, one of the researchers and an assistant professor of biochemistry and biomedicine at McMaster University in Hamilton, Ontario. This species causes skin, blood, and respiratory infections that are difficult to treat. In 2019, the U.S. Centers for Disease Control and Prevention said new types of antibiotics were “most needed” to treat Acinetobacter baumannii infections. A recent study of hospitalized patients with Actinetobacter baumannii infections that were also resistant to strong carbapenem antibiotics found: One in four died within a month of diagnosis. For the new study, Stokes and his lab collaborated with researchers at MIT and Harvard’s Broad Institute. First, they grew Acinetobacter baumannii in laboratory dishes using a technique called high-throughput drug screening and spent weeks exposing these colonies to more than 7,500 drugs (drugs and drug active ingredients). spent. “They found 480 compounds that stopped the growth of bacteria. They put that information into a computer and used it to train an artificial intelligence algorithm.” is to start showing that model in a brand new state. “It’s a picture of a chemical you’ve never seen before, right? And based on what it learned during training, it predicts whether those molecules are antibacterial,” Stokes said. They then had the model screen his 6,000-plus molecules. Stokes said the AI ​​was able to do the search over several hours, narrowing it down to 240 chemicals that he tested in a lab. Laboratory tests allowed him to narrow the list of the best inhibitors against bacteria to nine. From there, they took a closer look at the structure of each antibiotic and ruled out those they thought could be dangerous or related to known antibiotics. What remained was one of his compounds called RS102895, which Stokes believes was originally developed as a potential treatment. Diabetes mellitus. It appears to work in an entirely new way by preventing the migration of bacterial components from inside the cell to the cell surface, he says. This is a rather intriguing mechanism, one that to my knowledge has not been observed with clinical antibiotics. What’s more, RS102895, which the researchers renamed Abaucin, works only on Actinetobacter baumannii, says Stokes. Most antibiotics are broad-spectrum agents, meaning they act on many types of bacteria. Broad-spectrum antibiotics put enormous selective pressure on many types of bacteria, causing them to rapidly evolve and share genes that help them resist drugs and survive. “For this molecule, it’s very potent only against Actinetobacter, so it doesn’t impose a universal selective pressure, so resistance won’t spread so quickly,” he said.

use artificial intelligenceresearchers say they have discovered a new type of antibiotic that works against particularly threatening drug-resistant bacteria.

Related video above: Bacteria can change shape to withstand antibiotics

When the antibiotic was tested on the skin of mice ly infected with superbugs, it inhibited bacterial growth, suggesting that the method could be used to create tailored antibiotics to combat other drug-resistant pathogens. suggested that it could be used.

The researchers also tested the antibiotic against 41 different strains of antibiotic-resistant Acinetobacter baumannii. The drug worked in all patients, but needs to be further refined and tested in human clinical trials before it can be used in patients.

Furthermore, the compound Identified by AI It worked in a way that stopped only the pathogen in question. It does not appear to kill many other species of beneficial bacteria that live in the gut or on the skin, making it a rare narrowly targeted drug.

The researchers said that more antibiotics working so precisely could prevent bacteria from becoming resistant in the first place.

the study It was published in Nature Chemical Biology.

“This is very promising,” said Dr. Cesar de la Fuente, an assistant professor at the University of Pennsylvania Perlman School of Medicine. Although he is using AI to find new treatments, he is not involved in any new research.

De la Fuente said this kind of approach to discovering new drugs is an emerging field that researchers have been experimenting with since around 2018. This greatly reduces the time it takes to screen thousands of promising compounds.

“As we have seen, I think AI can be applied well in many areas. I think drug discovery is kind of the next frontier.”

In this study, the researchers focused on the bacterium Actinetobacter baumannii. It inhabits hospitals and other medical settings, clinging to surfaces such as doorknobs and counters. It can grab a piece of DNA from any other organism it comes in contact with, so it can incorporate genes that help it resist the drugs doctors use to treat it, its best weapon.

“This is what we call professional pathogens in the lab,” said John Stokes, one of the researchers and an assistant professor of biochemistry and biomedical sciences at McMaster University in Hamilton, Ontario.

This species causes skin, blood and respiratory infections that are difficult to treat.U.S. Centers for Disease Control and Prevention said in 2019 Acinetobacter baumannii infection is “most in need” of new types of antibiotics for treatment, he said.

a Recent research One in four hospitalized patients with Actinetobacter baumannii infections that were also resistant to strong carbapenem antibiotics were found to have died within one month of diagnosis.

For the new research, Dr. Stokes and his lab collaborated with researchers at MIT and the Broad Institute at Harvard University. First, they grew Acinetobacter baumannii in laboratory dishes using a technique called high-throughput drug screening and spent weeks exposing these colonies to more than 7,500 drugs (drugs and drug active ingredients). spent. They are, bacterial growth.

They put that information into a computer and used it to train an artificial intelligence algorithm.

“Once you train a model, the next thing you can do is start showing a whole new picture of chemicals that the model has never seen before, right? Whether it’s antibacterial or not,” Stokes said.

They then had the model screen more than 6,000 molecules, which Stokes said the AI ​​was able to do over hours.

They narrowed their search to 240 chemicals and tested them in the lab. Laboratory tests allowed us to narrow down the list of the best inhibitors against bacteria to nine. From there, they took a closer look at each structure and ruled out those they thought could be dangerous or related to known antibiotics.

They were left with one compound called RS102895, which Stokes believes was originally developed as a potential treatment for diabetes.

He said it appears to work in a completely new way, preventing components of the bacterium from moving from inside the cell to the cell surface.

“It’s a pretty interesting mechanism, one that, to my knowledge, has never been observed with clinical antibiotics,” he said.

In addition, RS102895, which the researchers renamed Abaucin, works only on Actinetobacter baumannii, he said.

Stokes said most antibiotics are broad-spectrum drugs and work against many types of bacteria. Broad-spectrum antibiotics put tremendous selective pressure on many types of bacteria, causing them to rapidly evolve and share genes that help many bacteria resist and survive drugs.

“For this molecule, it’s only acting so strongly against Actinetobacter that we can’t impose a universal selective pressure, so resistance won’t spread as quickly,” he said.

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