‘Public Enemy No. 1’ Bacteria Responsible for Pneumonia, Meningitis, and Other Deadly Illnesses May Finally Be Vulnerable to a New Antibiotic Discovery.
Acinetobacter, a type of bacteria commonly found in hospitals, has the ability to survive on various surfaces such as doorknobs and medical equipment for extended periods. Additionally, it possesses the capability to acquire antibiotic-resistance genes from its surroundings. Presently, it is alarmingly frequent to come across strains of A. baumannii that exhibit resistance to nearly all available antibiotics.
Scientists from the Massachusetts Institute of Technology (MIT) and McMaster University have made a significant discovery in the field of antibiotics. Their research has unveiled a novel antibiotic capable of effectively eliminating a specific strain of bacteria that causes drug-resistant infections.
The newly identified compound exhibits potential for combatting Acinetobacter baumannii, a bacterial species commonly encountered in hospital environments. This particular strain is known to cause severe infections such as pneumonia, meningitis, and other life-threatening conditions. Moreover, it represents a significant source of infections among wounded military personnel in Iraq and Afghanistan.
If further developed and approved for clinical use, this groundbreaking drug could offer a crucial weapon against the persistent threat posed by Acinetobacter baumannii. Its effectiveness in eradicating drug-resistant strains of this bacterium could potentially revolutionize the treatment and prevention of infections in healthcare settings and military contexts alike.
“Acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment. It’s really common now to find A. baumannii isolates that are resistant to nearly every antibiotic,” adds Jonathan Stokes of McMaster University.
The health researchers discovered the novel medication among a vast collection of approximately 7,000 potential drug compounds. They accomplished this by employing a machine-learning model that they extensively trained to assess the ability of a chemical compound to impede the growth of A. baumannii, a bacterium responsible for various infections.
Collins and Stokes serve as the principal investigators of the latest research, featured in today’s issue of Nature Chemical Biology. The study’s primary contributors include Gary Liu and Denise Catacutan, both esteemed graduate students at McMaster University, alongside Khushi Rathod, a recent graduate of McMaster.
Novel Approach in Drug Discovery Using Machine Learning to Combat Antibiotic Resistance
In recent decades, a concerning trend has emerged whereby numerous harmful bacteria have developed an increased resistance to existing antibiotics, while the development of new antibiotics has been limited.
Recognizing the urgency of this growing issue, Collins, Stokes, and esteemed MIT Professor Regina Barzilay embarked on a mission several years ago to address this challenge by harnessing the power of machine learning. Machine learning, a form of artificial intelligence capable of recognizing intricate patterns within vast datasets, held the potential to identify novel antibiotics with chemical structures distinct from any currently available drugs.
During their pioneering efforts, the researchers successfully trained a machine-learning algorithm to discern chemical structures capable of impeding the growth of E. coli, a commonly encountered bacterium. This algorithm was put to the test against a comprehensive library of over 100 million compounds, ultimately revealing a promising molecule christened as halicin, paying homage to the fictional artificial intelligence system depicted in “2001: A Space Odyssey.” Remarkably, the researchers demonstrated that halicin not only eradicated E. coli but also exhibited efficacy against several other bacterial strains that had developed resistance to conventional treatments.
“After that paper, 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 No. 1 for multidrug-resistant bacterial infections, which is Acinetobacter,” Stokes adds.
To gather training data for their computational model, the researchers initially exposed A. baumannii, a bacterium grown in a laboratory dish, to approximately 7,500 diverse chemical compounds. Their objective was to identify compounds capable of inhibiting the microbe’s growth. The researchers then input the molecular structure of each compound into the model, along with information indicating whether it could hinder bacterial growth. This enabled the algorithm to learn the chemical characteristics associated with growth inhibition.
After successfully training the model, the researchers employed it to analyze a distinct set of 6,680 compounds, obtained from the Drug Repurposing Hub at the Broad Institute, which had not been previously observed by the model. This analysis was completed in under two hours and generated a few hundred noteworthy findings. From this pool, the researchers carefully selected 240 compounds with distinctive structures, differing from those of existing antibiotics or molecules present in the training data, to subject them to experimental testing in the laboratory.
Subsequent laboratory tests revealed the efficacy of nine antibiotics, including one particularly potent compound. Surprisingly, this compound, initially explored for its potential as a diabetes drug, exhibited exceptional effectiveness in eradicating A. baumannii while displaying no impact on other bacterial species, such as Pseudomonas aeruginosa, Staphylococcus aureus, and carbapenem-resistant Enterobacteriaceae.
The ability to selectively target and eliminate specific bacteria while sparing others is a highly desirable characteristic known as “narrow spectrum” killing, as it reduces the risk of bacteria swiftly developing resistance to the drug. Additionally, this antibiotic is likely to preserve the beneficial bacteria residing in the human gut, which play a vital role in suppressing opportunistic infections like Clostridium difficile.
“Antibiotics often have to be administered systemically, and the last thing you want to do is cause significant dysbiosis and open up these already sick patients to secondary infections,” Stokes adds.
A new therapeutic mechanism
Researchers conducted studies on mice and discovered a promising drug called abaucin for treating wound infections caused by A. baumannii. Additionally, laboratory tests demonstrated its effectiveness against various drug-resistant strains of A. baumannii isolated from human patients.
Further investigations uncovered that abaucin functions by disrupting a cellular process called lipoprotein trafficking. This process enables cells to transport proteins from the interior of the cell to the cell envelope. Specifically, the drug inhibits the activity of LolE, a protein involved in this crucial process.
Interestingly, while all Gram-negative bacteria possess this enzyme, abaucin exhibits remarkable selectivity in targeting A. baumannii. The researchers propose that subtle variations in how A. baumannii performs this task might explain the drug’s specificity.
“We haven’t finalized the experimental data acquisition yet, but we think it’s because A. baumannii does lipoprotein trafficking a little bit differently than other Gram-negative species. We believe that’s why we’re getting this narrow spectrum activity,” Stokes adds.
Stokes’ laboratory is currently collaborating with researchers at McMaster University to enhance the therapeutic properties of the compound, with the aim of refining it for future application in medical treatments.
The researchers also intend to apply their modeling approach in order to identify potential antibiotics for various forms of drug-resistant infections, such as those caused by Staphylococcus aureus and Pseudomonas aeruginosa.
Source: 10.1038/s41589-023-01349-8
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