AI discovers new antibiotic to combat drug-resistant bacteria
Researchers at MIT and McMaster University have identified a new antibiotic that can combat bacteria responsible for drug-resistant infections. The drug has the potential to fight the bacterial strain Acinetobacter baumannii that is often found in hospitals and can survive on surfaces, acquiring antibiotic resistance genes from its environment. In a study published in Nature Chemistry Biology, researchers trained a machine learning algorithm to identify chemical compounds that could inhibit the growth of A. baumannii, and from a set of 6,680 compounds, selected 240 for experimental testing in the lab. Nine antibiotics were identified, including abaucin, a potent antibiotic that selectively killed A. baumannii without affecting beneficial gut bacteria. In studies with mice, the drug demonstrated its ability to treat wound infections caused by A. baumannii.
Abaucin’s mechanism of action
The researchers showed that abaucin works by interfering with a process known as lipoprotein trafficking, which cells use to transport proteins from inside the cell to the cell envelope. The drug inhibits LolE, a protein that plays a role in lipoprotein trafficking. Though all gram-negative bacteria express LolE, the selectivity of abaucin lies in the unique way A. baumannii performs this task, resulting in the drug’s narrow-spectrum killing ability. The researchers hypothesize that slight differences in A. baumannii’s lipoprotein trafficking process could explain the selectivity of the drug.
The potential for AI in antibiotic drug discovery
The discovery of abaucin demonstrates the potential for AI to accelerate antibiotic discovery and combat problematic pathogens like A. baumannii. Machine learning algorithms can recognize unique chemical patterns and identify new antibiotics whose chemical structures differ from existing drugs. They can also help optimize medicinal properties in drug development. In recent years, pathogenic bacteria have increasingly become resistant to existing antibiotics, while new antibiotics are slow to be developed. With such a pressing global health issue, AI’s potential in antibiotic drug discovery is vast and promising.
Additionally, AI has the potential to identify potential antibiotics for other types of drug-resistant infections, including those caused by Gram-positive bacteria like Staphylococcus aureus and Pseudomonas aeruginosa. AI’s ability to recognize patterns within massive amounts of data has great potential in drug discovery. However, AI dependency and lack of biological understanding remains a challenge that researchers must address to make the most of this burgeoning technology.
AI’s growing impact in medicine
Beyond drug discovery, AI has shown great potential in the medical field. It has been used to identify rare genetic disorders and predict patients’ survival rates in clinical trials. It has also been used to detect and diagnose skin cancer with remarkable accuracy. However, ethical concerns and the lack of transparency in AI decision-making still need to be addressed for AI’s full integration into healthcare.
Summary
Researchers at MIT and McMaster University have discovered a new antibiotic, abaucin, that can selectively kill the bacterial strain A. baumannii without affecting beneficial gut bacteria. Abaucin works by inhibiting LolE, a protein involved in lipoprotein trafficking, which cells use to transport proteins from inside the cell to the cell envelope. Machine learning algorithms identified potential inhibitors of A. baumannii’s growth from a set of 6,680 compounds. The researchers selected 240 compounds for lab testing, which resulted in the discovery of nine antibiotics, including abaucin. The discovery demonstrates the potential of AI in antibiotic drug discovery and has implications for future treatments of drug-resistant infections.
Additional piece
AI, the Potential Game-Changer in Antibiotic Resistance
The emergence of drug-resistant bacteria has been one of the most significant public health challenges in recent decades. Traditional antibiotic development methods have failed to keep up with the rapid evolution of bacteria, resulting in a rise of antibiotic-resistant infections that are difficult to treat. The increasing demand for new antibiotics and lack of novel antibiotics in the pipeline has driven researchers to look for new drug discovery methods. Enter, Artificial Intelligence (AI), a technology that has shown unprecedented potential for accelerating antibiotic discovery.
AI has revolutionized many areas of the biomedical field, from disease diagnosis to drug discovery. Machine learning algorithms have the ability to process large volumes of data and recognize patterns in hours or days, a process that would take months or years to do manually. With the right algorithms and training data, AI can reduce the time, cost, and failure rates of traditional drug discovery methods.
Researchers at MIT and McMaster University demonstrate the potential of AI in antibiotic discovery with the discovery of abaucin. The drug’s differential mechanism of action and potential selectivity for pathogenic bacteria has significant implications for the treatment of drug-resistant infections. However, AI’s potential in drug discovery goes beyond this discovery.
Machine learning algorithms can help identify chemical compounds that inhibit the growth of pathogenic bacteria using a hybrid approach, combining prediction of pharmacokinetic and pharmacodynamic properties, or property based on crystal structure. Deep learning can analyze huge data sets of genetic and molecular information to predict drug-protein interactions. Natural language processing technology can also help identify hypotheses for antibiotic discovery. With the right training data sets and machine learning algorithms, AI has the potential to revolutionize classical antibiotic development.
AI can also assist in clinical trial design and patient recruitment, increasing the efficiency of clinical trials and reducing the risks of trial failure. By reducing the time and cost of clinical trials, AI can help new antibiotics reach the market more quickly and cost-effectively.
AI can further assist in developing personalized antibiotic therapy. AI can help identify unique patient populations at risk of infections, predict their drug response, and accurately prescribe the appropriate dosage based on individual patient characteristics. This level of precision in antibiotic therapy can positively impact the course of the infection, limit the risk of side effects, and prevent the emergence of antibiotic-resistant bacteria.
Despite the tremendous potential of AI in antibiotic drug discovery and personalized therapy, researchers must address several challenges. The biological complexity of bacterial resistance, coupled with the non-linearity of AI models, requires deep understanding and a reliable data set to ensure the AI model does not fail due to noise. Furthermore, bias and transparency issues when introducing AI decision models into clinical practice must be addressed.
In conclusion, the discovery of abaucin is a testament to the tremendous potential of AI in antibiotic drug discovery and personalized therapy. AI has the potential to revolutionize traditional antibiotic development methods and drive innovation in healthcare. AI’s potential applications in healthcare are only scratching the surface, and with further innovation and research, AI will continue to drive transformative change in the field.
Summary
The rise of drug-resistant bacteria has resulted in a demand for novel antibiotics. AI has demonstrated unprecedented potential for accelerating antibiotic discovery, reducing the cost, and the time it takes for new antibiotics to reach the market. Machine learning algorithms have the unique ability to process large volumes of data and recognize patterns in hours or days, reducing the time, cost, and failure rates of traditional drug discovery methods. AI has the potential to revolutionize traditional antibiotic development methods, drive innovation in healthcare, and enable personalized antibiotic therapy. Despite tremendous potential, challenges such as bias and transparency issues must be addressed for AI to reach its full potential in antibiotic drug discovery and clinical practice.
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Using an artificial intelligence algorithm, researchers at MIT and McMaster University have identified a new antibiotic that can kill a type of bacteria that is responsible for many drug-resistant infections.
If developed for use in patients, the drug could help fight Acinetobacter baumannii, a species of bacteria that is often found in hospitals and can cause pneumonia, meningitis, and other serious infections. The microbe is also a leading cause of infections in wounded soldiers in Iraq and Afghanistan.
“Acinetobacter it can survive on hospital doorknobs and equipment for long periods of time, and it can acquire antibiotic resistance genes from its environment. Now it is very common to find A. baumannii isolates that are 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 will inhibit the growth of A. baumannii.
“This finding further supports the premise that AI can significantly accelerate and expand our search for new antibiotics,” says James Collins, Termeer Professor of Engineering and Medical Sciences at the Institute of Medical Engineering and Sciences (IMES) and the Department of Biological Engineering from MIT. “I am excited 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 paper’s lead authors are McMaster University graduate students Gary Liu and Denise Catacutan, and recent McMaster graduate Khushi Rathod.
drug discovery
During the last 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 an author on the new study), set out to combat this growing problem by using machine learning, a type of artificial intelligence that can learn to recognize patterns in a large amounts of data. Collins and Barzilay, who co-direct MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, hoped that this approach could be used to identify new antibiotics whose chemical structures are different from any existing drugs.
In their initial demonstration, the researchers trained a machine learning algorithm to identify chemical structures that could inhibit the growth of E.coli. In a screen of more than 100 million compounds, that algorithm produced a molecule the researchers named halicin, after the fictional artificial intelligence system from “2001: A Space Odyssey.” This molecule, they showed, could kill not only E.coli but several other bacterial species that are resistant to treatment.
“After that paper, when we demonstrated 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 number 1 for multi-resistant bacterial infections, which is Acinetobactersays Stokes.
To obtain training data for their computational model, the researchers first exposed A. baumannii grown in a laboratory dish to about 7,500 different chemical compounds to see which ones might inhibit the growth of the microbe. They then 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 chemical features associated with growth inhibition.
Once the model was trained, the researchers used it to analyze a set of 6,680 previously unseen compounds that came from the Broad Institute’s Center for Drug Reuse. This analysis, which took less than two hours, returned a few hundred top results. Of these, the researchers chose 240 for experimental testing in the lab, focusing on compounds with different structures than existing antibiotics or molecules from the training data.
Those tests turned up nine antibiotics, including one that was very potent. This compound, which was originally explored as a potential diabetes drug, turned out 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 enterobacteria.
This “narrow spectrum” killing ability is a desirable characteristic for antibiotics because it minimizes the risk of bacteria rapidly spreading resistance against the drug. Another advantage is that the drug would likely prevent beneficial bacteria that live in the human gut and help suppress opportunistic infections such as Clostridium difficile.
“Antibiotics often need to be given systemically, and the last thing you want to do is cause significant dysbiosis and expose these already sick patients to secondary infections,” says Stokes.
A novel mechanism
In studies in mice, the researchers showed that the drug, which they called abaucin, could treat wound infections caused by A. baumannii. They have also shown, in laboratory tests, that it works against a variety of resistant drugs. A. baumannii strains isolated from human patients.
Other 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 attacking A. baumannii. They hypothesize that slight differences in how A. baumannii performs this task could explain the selectivity of the drug.
“We haven’t finished the experimental data acquisition yet, but we think it’s because A. baumannii it does lipoprotein trafficking a bit differently than other Gram-negative species. We think that’s why we’re having this narrow-spectrum activity,” says Stokes.
Stokes’ lab is now working with other researchers at McMaster to optimize the compound’s medicinal properties, 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 and Pseudomonas aeruginosa.
https://www.sciencedaily.com/releases/2023/05/230525141523.htm
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