Are scientists finally beating antimicrobial resistance?

Antimicrobial resistant infections kill millions every year. They have the potential to take us back to the dark ages, when common infections like urinary tract infections (UTIs) or pneumonia were lethal and untreatable.
Antimicrobial resistance (AMR) occurs when the germs that cause infections — bacteria, viruses, or fungi — develop ways to evade the drugs used to treat them.
Over-use of antibiotics in places like chicken farms and healthcare clinics has become a leading driver of AMR.
The good news is that a major scientific push is making significant progress in the fight against AMR.
“Antibiotic resistance is still far from solved, but there has been a lot of progress in both better understanding and better practices for discovering new antibiotics [which overcome antimicrobial resistance],” said Luis Pedro Coelho, a computational biologist at Queensland University of Technology in Australia.
Coelho led a new study published in the journal Cell in July, which presented a huge database of nearly one million potential antibiotic compounds. 
The study is proof we can be optimistic about AMR, said Sebastian Hiller, a structural biologist at the University of Basel in Switzerland, who was not involved in the research: “This is just one example of ongoing research showing our scientific capabilities to fight superbugs are huge,” Hiller told DW.
The study used machine learning to search for potential antibiotic agents in a huge database of microbes which live in environments such as soil, the ocean, and human and animal guts.
“Bacteria fight against each other constantly in these environments, using warfare tools called peptides which are shot against other bacteria to kill them. The researchers mined this space for antibiotic peptides and found some hidden gems,” Hiller said.
The algorithm sifted through billions of potential protein sequences and narrowed it down to the top candidates with predicted antimicrobial actions.
In total, 863,498 new antimicrobial peptides were predicted, more than 90% of which had never been described before.
Coelho said all the peptides had the same general mechanism of action for killing bacteria — by disrupting the cell membranes which protect bacteria from the environment.
“We also see that some peptides are more effective against certain bacterial strains than others, but we cannot yet predict exactly why, or [say] which peptide will work against which bacterium,” Coelho told DW.
To find out which of these peptides could be useful as antibiotics, the researchers synthesized 100 peptides and tested them against 11 disease-causing bacterial strains in laboratory dishes.
They found that 79 peptides disrupted bacterial membranes and 63 peptides specifically targeted antibiotic-resistant bacteria, such as Escherichia coli (E.coli) and Staphylococcus aureus.
The researchers also tested the compounds in mice with infected skin abscesses, but only three of the peptides showed antimicrobial effects in vivo (in a living organism).
“This indicates that their efficacy may be limited in vivo. Still, this is a remarkable result, and the compounds might circumvent severe toxicity side effects of last-resort antibiotics like polymyxins,” said Seyed Majed Modaresi at the University of Basel in Switzerland, who was also not involved in the study.
The authors published their dataset with open access, which allows other scientists to review the 863,498 peptides and develop antibiotic drugs with specific uses in mind.
For example, scientists could tailor antibiotic properties to minimize effects on “friendly” bacteria in the human gut. Many antibiotics in use are known to destroy beneficial gut microbiota, which can lead to health issues and potentially deadly pathogens to take over.
Scientist could also use the dataset to create antibiotics against which bacteria do not develop resistances, greatly helping in the long-term fight against AMR.
Modaresi said the new study shows that artificial intelligence has become instrumental in the scientific fight against AMR and that “the application of machine learning has accelerated the process of discovering new antibiotics.”
He added that the type of peptides discovered in this latest study were just one type of many antimicrobial agents out there, and that the same techniques could be used to discover many other types of antibiotics, including bacteriophages.
Hiller said that while there are reasons to be optimistic about the scientific fight against AMR, the next major challenge is creating new antibiotic agents which are commercially viable.  
“We only use new antibiotics when the old ones don’t work anymore. This is good as it prevents bacteria from developing resistances to them but means they’re not financially viable,” said Hiller.
Hiller said health organizations and governments were working on ways to make antibiotic commercialization more viable so that they can draw from the huge pools of potential antibiotics that scientists have discovered.
Edited by: Zulfikar Abbany

Sources:
Discovery of antimicrobial peptides in the global microbiome with machine learning, published in Cell (2024) by Santos-Júnior et al. https://doi.org/10.1016/j.cell.2024.05.013
Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Published in The Lancet (2022) https://doi.org/10.1016/S0140-6736(21)02724-0