2023-05-29 12:47:41
Using an artificial intelligence algorithm, researchers from MIT and McMaster University have identified a new antibiotic capable of killing a type of bacteria responsible for many drug-resistant infections.
If developed for use in patients, the drug could help fight Acinetobacter baumannii, a species of bacteria often found in hospitals that can cause pneumonia, meningitis and other serious infections. The microbe is also one of the main causes of infections in wounded soldiers in Iraq and Afghanistan.
“Acinetobacter can survive on doorknobs and hospital equipment for long periods of time, and it can pick up antibiotic resistance genes from its environment. It’s really common now to find A. baumannii isolates resistant to almost all antibiotics,” says Jonathan Stokes, a former MIT postdoc who is now an assistant professor of biochemistry and biomedical sciences at McMaster University.
The researchers identified the new drug from a library of nearly 7,000 potential drug compounds using a machine learning model they trained to assess whether a chemical compound inhibits the growth of A. baumannii.
“This finding further confirms the premise that AI can significantly accelerate and expand our search for new antibiotics,” says James Collins, Termeer Professor of Medical Engineering and Science at the Institute of Medical Engineering and Science (IMES) and in the Department of Biological Engineering at MIT. “I am delighted that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”
Collins and Stokes are the lead authors of the new study, which appears today in Nature Chemistry Biology. The principal authors of the article are McMaster University graduate students Gary Liu and Denise Catacutan and recent McMaster graduate Khushi Rathod.
drug discovery
In recent decades, many pathogenic bacteria have become increasingly resistant to existing antibiotics, while very few new antibiotics have been developed.
Several years ago, Collins, Stokes and MIT Professor Regina Barzilay (who is also the author of the new study), set out to tackle this growing problem using machine learning, a type of artificial intelligence. who can learn to recognize patterns in vast amounts of data. Collins and Barzilay, who co-lead MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, hoped this approach could be used to identify new antibiotics whose chemical structures are different from all existing drugs.
In their initial demonstration, the researchers trained a machine learning algorithm to identify chemical structures that might inhibit the growth of E. coli. In a screen of over 100 million compounds, this algorithm produced a molecule the researchers called halicin, after the fictional artificial intelligence system from “2001: A Space Odyssey.” This molecule, they showed, could not only kill E. coli but several other bacterial species resistant to treatment.
“After this article, when we showed that these machine learning approaches can work well for complex antibiotic discovery tasks, we turned our attention to what I perceive to be public enemy #1 for infections. multi-resistant bacteria, which is Acinetobacter“, it Stokes.
To obtain training data for their computer model, the researchers first exposed A. baumannii grown in a lab dish to about 7,500 different chemical compounds to see which ones might inhibit the growth of the microbe. Then they entered the structure of each molecule into the model. They also told the model whether or not each structure could inhibit bacterial growth. This allowed the algorithm to learn the chemical characteristics associated with growth inhibition.
Once the model was trained, the researchers used it to analyze a set of 6,680 compounds he had never seen before, which came from the Broad Institute’s Drug Repurposing Hub. This analysis, which lasted less than two hours, yielded a few hundred top hits. Of these, the researchers chose 240 to test experimentally in the lab, focusing on compounds with different structures than existing antibiotics or molecules from training data.
These tests yielded nine antibiotics, including one very potent. This compound, originally explored as a potential diabetes drug, has been shown to be extremely effective in killing A. baumannii but had no effect on other species of bacteria, including Pseudomonas aeruginosa, Staphylococcus aureusand resistant to carbapenems Enterobacteriaceae.
This “narrow-spectrum” killing ability is a desirable characteristic for antibiotics because it minimizes the risk that bacteria will quickly spread resistance against the drug. Another benefit is that the drug would likely spare the beneficial bacteria that live in the human gut and help suppress opportunistic infections such as Clostridium difficile.
“Antibiotics often have to be given systemically, and the last thing you want to do is cause significant dysbiosis and open up these already sick patients to secondary infections,” Stokes says.
A new mechanism
In mouse studies, the researchers showed that the drug, which they named abaucine, could treat wound infections caused by A. baumannii. They have also shown, in lab tests, that it works against a variety of resistant drugs. A. baumannii strains isolated from human patients.
Further experiments revealed that the drug kills cells by interfering with a process known as lipoprotein trafficking, which cells use to transport proteins from inside the cell to the cell envelope. Specifically, the drug appears to inhibit LolE, a protein involved in this process.
All Gram-negative bacteria express this enzyme, so the researchers were surprised to find that abaucin is so selective in targeting A. baumannii. They hypothesize that slight differences in how A. baumannii performs this task could explain the selectivity of the drug.
“We haven’t finalized the experimental data acquisition yet, but we think that’s because A. baumannii traffics lipoproteins somewhat differently than other Gram-negative species. We think that’s why we get this narrow-spectrum activity,” Stokes says.
Stokes’ lab is now working with other McMaster researchers to optimize the medicinal properties of the compound, with the hope of developing it for eventual use in patients.
The researchers also plan to use their modeling approach to identify potential antibiotics for other types of drug-resistant infections, including those caused by Staphylococcus aureus et Pseudomonas aeruginosa.
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