- MIT scientists employ AI to combat the antibiotic resistance crisis by targeting metabolically dormant bacteria.
- Bacterial “sleeper-like” resilience poses a significant challenge, countered by innovative AI-driven screening of compounds.
- Semapimod, initially an anti-inflammatory drug, emerges as a promising candidate against dormant bacteria, disrupting their membranes.
- The discovery holds promise for addressing the urgent need for effective antibiotics against Gram-negative bacteria.
Main AI News:
In recent decades, the pace of modern antibiotic discovery has stagnated, leading to a critical juncture where antimicrobial resistance is now recognized as one of the foremost global public health challenges by the World Health Organization. Repeated treatments for infections pose the peril of bacteria developing resistance to antibiotics. However, the resurgence of infections post-proper antibiotic treatment perplexes clinicians. One potential explanation is that bacteria enter a metabolically dormant state, evading detection by conventional antibiotics that target metabolic activity. Subsequently, when the threat subsides, these bacteria resurface, causing the infection to recur.
Jackie Valeri, a former MIT-Takeda Fellow affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health, who recently completed her PhD in biological engineering at the Collins Lab, underscores, “Resistance is proliferating over time, and recurrent infections stem from this dormancy.” Valeri spearheads a groundbreaking study, featured in this month’s edition of Cell Chemical Biology, showcasing the application of machine learning to screen compounds capable of eliminating dormant bacteria.
The concept of bacterial “sleeper-like” resilience is not novel; ancient bacterial strains, dating back millions of years, have been unearthed in an energy-conserving state on the Pacific Ocean’s seabed. Professor James J. Collins, leading the Life Sciences faculty at MIT Jameel Clinic, has garnered attention for his utilization of AI in unearthing a novel class of antibiotics, aligning with the clinic’s overarching mission of leveraging AI to revolutionize the landscape of existing antibiotics.
According to The Lancet, a staggering 1.27 million deaths in 2019 could have been averted had the infections been treatable with drugs. However, researchers grapple with myriad challenges, notably the identification of antibiotics capable of targeting metabolically dormant bacteria. In a paradigm-shifting move, researchers at the Collins Lab employed AI to expedite the exploration of antibiotic properties within known drug compounds. While this endeavor typically spans years owing to the vast array of molecules, researchers achieved a breakthrough over a single weekend by identifying semapimod, a compound exhibiting potent activity against dormant bacteria, propelled by AI-enabled high-throughput screening.
Originally an anti-inflammatory medication prescribed for Crohn’s disease, semapimod exhibited efficacy against stationary-phase Escherichia coli and Acinetobacter baumannii. Furthermore, researchers unveiled its disruptive effect on the membranes of “Gram-negative” bacteria, notorious for their innate resistance to antibiotics due to a denser outer membrane. Notable examples of Gram-negative bacteria include E. coli, A. baumannii, Salmonella, and Pseudomonis, posing significant challenges in the quest for novel antibiotics.
Valeri elucidates, “One of the pivotal insights into semapimod’s mechanism was its substantial molecular structure, reminiscent of compounds targeting the outer membrane.” By perturbing a constituent of the outer membrane, semapimod sensitizes Gram-negative bacteria to drugs primarily effective against Gram-positive bacteria, marking a significant stride in antibiotic development.
Reflecting on the urgency of the situation, Valeri quotes a 2013 paper from Trends in Biotechnology, emphasizing, “For Gram-positive infections, we necessitate superior drugs, whereas for Gram-negative infections, any efficacious drug is imperative.”
Conclusion:
The groundbreaking AI-driven approach by MIT scientists to combat antibiotic resistance, particularly against dormant bacteria, signals a paradigm shift in antibiotic discovery. This innovation not only addresses the pressing global health threat posed by antimicrobial resistance but also opens new avenues for the pharmaceutical market. Companies investing in AI-driven drug discovery technologies stand to gain a competitive edge in developing novel antibiotics to tackle the evolving challenges of bacterial infections.
