When an antibiotic fails: MIT scientists are using AI to target “sleeper” bacteria
2021 MIT-Takeda fellow Jackie Valeri is the first author of a new paper published in this month’s issue of 'Cell Chemical Biology' that demonstrates how machine learning could help screen compounds that are lethal to bacteria resistant to traditional antibiotic treatment. The MIT-Takeda Programme is part of the MIT Jameel Clinic, where faculty life sciences lead, Jim Collins, and his colleagues used AI to discover a new class of antibiotics in 2023 that could yield potential drugs against Acinetobacter baumannii, an antibiotic-resistant bacterium.
Jim and his team were able to identify a compound called semapimod over a weekend, thanks to AI's ability to perform high-throughput screening. The discovery has the potential to expand the existing antibiotics available and help against the antimicrobial resistance crisis. “Resistance is happening more over time, and recurring infections are due to this dormancy,” says Jackie.
Since the 1970s, modern antibiotic discovery has been experiencing a lull. Now the World Health Organization has declared the antimicrobial resistance crisis as one of the top 10 global public health threats.
When an infection is treated repeatedly, clinicians run the risk of bacteria becoming resistant to the antibiotics. But why would an infection return after proper antibiotic treatment? One well-documented possibility is that the bacteria are becoming metabolically inert, escaping detection of traditional antibiotics that only respond to metabolic activity. When the danger has passed, the bacteria return to life and the infection reappears.
“Resistance is happening more over time, and recurring infections are due to this dormancy,” says Jackie Valeri, a former MIT-Takeda Fellow (centered within the MIT Abdul Latif Jameel Clinic for Machine Learning in Health) who recently earned her PhD in biological engineering from the Collins Lab. Valeri is the first author of a new paper published in this month’s print issue of Cell Chemical Biology that demonstrates how machine learning could help screen compounds that are lethal to dormant bacteria.
Tales of bacterial “sleeper-like” resilience are hardly news to the scientific community — ancient bacterial strains dating back to 100 million years ago have been discovered in recent years alive in an energy-saving state on the seafloor of the Pacific Ocean.