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Home » Artificial intelligence unveils breakthrough antibiotic ‘abaucin’ to combat drug-resistant superbug

Artificial intelligence unveils breakthrough antibiotic ‘abaucin’ to combat drug-resistant superbug

Researchers at McMaster University and the Massachusetts Institute of Technology have harnessed the power of artificial intelligence to uncover a novel antibiotic that holds promise in combating a dangerous drug-resistant pathogen prevalent among vulnerable hospital patients.

This groundbreaking process not only paves the way for an effective treatment against Acinetobacter baumannii, a bacteria classified by the World Health Organization as one of the most perilous antibiotic-resistant strains, but also accelerates the discovery of other antibiotics to tackle various challenging bacteria.

A. baumannii poses a formidable challenge due to its tenacity and resistance to eradication. It can cause life-threatening conditions such as pneumonia, meningitis, and wound infections, making it a significant threat to patients' lives.

Typically found in healthcare settings, A. baumannii can persist on surfaces for extended periods. Moreover, it has the capability to acquire genetic material from other bacterial species in its environment, including genes associated with antibiotic resistance.

In their recently published study in the journal Nature Chemical Biology, the scientists detail their utilization of an artificial intelligence algorithm to predict entirely new classes of antibacterial compounds. Through this approach, they successfully identified a fresh antibacterial molecule, named abaucin.

Conventional screening methods for discovering antibiotics against A. baumannii have proven to be challenging. These traditional techniques are time-consuming, expensive, and limited in their scope.

In contrast, modern algorithmic approaches can swiftly explore vast libraries comprising hundreds of millions, or even billions, of with potential antibacterial properties. This enables researchers to uncover promising candidates efficiently and effectively.

Lead author Jonathan Stokes, assistant professor in the Department of Biochemistry & Biomedical Science at McMaster University. Researchers identified a new antibacterial compound to treat the pathogen Acinetobacter baumannii. Credit: McMaster University

Jonathan Stokes, the lead author of the paper and an assistant professor in McMaster's Department of Biomedicine & Biochemistry, along with James J. Collins, a professor of medical engineering and science at MIT, and McMaster graduate students Gary Liu and Denise Catacutan, highlight the significant advantages of employing machine learning in the search for novel antibiotics.

Stokes, affiliated with McMaster's Global Nexus School for Pandemic Prevention and Response, emphasizes that artificial intelligence enables researchers to explore vast chemical spaces rapidly. This greatly enhances the likelihood of discovering entirely new antibacterial molecules.

Collins, the Life Sciences faculty lead at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health, affirms the efficacy of algorithmic models and anticipates their widespread adoption for more efficient and cost-effective antibiotic discovery.

The researchers note the exceptional potential of abaucin, as it specifically targets A. baumannii. This crucial characteristic reduces the likelihood of the pathogen rapidly developing resistance, offering the prospect of more precise and effective treatments.

Unlike most antibiotics with broad-spectrum activity that indiscriminately kill bacteria, disrupting the gut microbiome and leading to various infections like C. difficile, abaucin's targeted approach holds promise in minimizing such complications.

Stokes underscores the limitations of broad-spectrum antibiotics and the adaptability of pathogens to counter existing treatments. Leveraging AI methods significantly increases the rate of new antibiotic discovery while reducing costs. This avenue of exploration is deemed crucial for the development of novel antibiotic drugs.

Source: McMaster University


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