Researchers from MIT and McMaster University have harnessed the power of artificial intelligence (AI) to uncover a groundbreaking drug that shows potential in fighting drug-resistant infections. Through the implementation of a machine-learning algorithm, the team successfully identified an antibiotic capable of eradicating Acinetobacter baumannii, a bacterial strain responsible for numerous drug-resistant infections, including pneumonia, meningitis, and other severe ailments. This breakthrough could mark a significant turning point in the battle against antibiotic resistance.
Acinetobacter baumannii is a pervasive species of bacteria frequently found in hospital environments, posing a threat to both patients and healthcare workers. Additionally, it is a leading cause of infections among injured soldiers in conflict zones such as Iraq and Afghanistan. In recent years, the rise of antibiotic-resistant bacteria has outpaced the development of new antibiotics, intensifying the need for innovative solutions to combat these resilient pathogens.
MIT announced that their researchers employed a machine-learning model to analyze a catalog comprising approximately 7,000 potential drug compounds. The model was trained to evaluate each chemical compound’s efficacy in inhibiting the growth of Acinetobacter baumannii. To obtain training data for the model, the scientists exposed the bacteria to roughly 7,500 distinct chemical compounds in a laboratory setting, observing their effects on inhibiting microbial growth. The model was then fed the molecular structures of each compound, enabling it to distinguish between structures that could effectively suppress bacterial growth.
After training, the model was deployed to analyze a separate set of 6,680 compounds that it had not encountered previously. Through rigorous experimentation, the researchers narrowed down the candidates to 240 hits, focusing on compounds with unique structures, distinct from existing antibiotics and the training data molecules. Following further testing, nine antibiotics emerged, including one that exhibited exceptional potency.
Remarkably, the antibiotic compound that stood out was initially explored for its potential as a diabetes drug. Researchers named the compound abaucin, and subsequent studies conducted on mice showcased its efficacy in treating wound infections caused by Acinetobacter baumannii. Lab tests also demonstrated its effectiveness against various drug-resistant strains of the bacteria isolated from human patients. Furthermore, additional experiments revealed that abaucin works by disrupting the process of lipoprotein trafficking, which is vital for protein transportation from the cell’s interior to its envelope.
The narrow spectrum of abaucin’s killing ability is regarded as a desirable trait, as it reduces the risk of bacteria rapidly developing resistance to the drug. Moreover, it is expected to spare the beneficial bacteria residing in the human gut, which play a crucial role in suppressing opportunistic infections.
Currently, a laboratory at McMaster University is dedicated to optimizing the medicinal properties of abaucin, with the ultimate goal of developing it for clinical use. Additionally, the authors of the study intend to employ their modeling approach to identify potential antibiotics for combating other types of drug-resistant infections.
The remarkable findings of this research were published on Thursday in the journal “Nature Chemical Biology,” shedding light on the immense potential of artificial intelligence in revolutionizing the search for new antibiotics to combat drug-resistant bacteria.
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