In recent years, the discovery of modern antibiotics has stagnated, leading to a growing concern over the rise of antimicrobial resistance. The World Health Organization has now declared this crisis as one of the top 10 global public health threats. But why do infections sometimes return even after proper antibiotic treatment?
One possible explanation lies in bacteria becoming metabolically inert, effectively escaping the detection of traditional antibiotics that only target active metabolic activity. These dormant bacteria can later become reactivated, leading to recurring infections. Identifying and tackling this dormancy is crucial in combating the antimicrobial resistance crisis.
Jackie Valeri, a former MIT-Takeda Fellow from the Collins Lab, sheds light on this issue. In her recent paper published in Cell Chemical Biology, Valeri proposes the use of machine learning to screen compounds that can effectively kill dormant bacteria. By leveraging the power of artificial intelligence, researchers aim to uncover potential antibiotics that can target these resistant dormant bacteria.
The resilience of bacteria in a dormant state is not a new phenomenon. Scientists have discovered ancient bacterial strains dating back 100 million years, still alive in an energy-saving state on the Pacific Ocean seafloor. Understanding the mechanisms behind bacterial dormancy is crucial in developing effective antibiotics.
MIT’s Jameel Clinic for Machine Learning in Health, under the leadership of James J. Collins, a prominent figure in the field of medical engineering and science, has been making strides in antibiotic discovery through the use of AI. Their research aims to expand the existing arsenal of antibiotics by utilizing machine learning algorithms to identify new classes of antimicrobial compounds.
According to a study published by The Lancet, millions of preventable deaths occurred in 2019 due to infections resistant to available drugs. Finding antibiotics that can effectively target metabolically dormant bacteria is one of the key challenges faced by researchers.
In the Collins Lab, researchers employed AI to expedite the process of screening known drug compounds for antibiotic properties. Traditionally, this process could take years due to the vast number of molecules to explore. However, thanks to the high-throughput screening capabilities of AI, researchers were able to identify a compound called semapimod in just a single weekend.
Surprisingly, semapimod, an anti-inflammatory drug typically used for Crohn’s disease, exhibited effectiveness against stationary-phase Escherichia coli and Acinetobacter baumannii. Additionally, it demonstrated the ability to disrupt the outer membranes of “Gram-negative” bacteria, which are notoriously resistant to antibiotics due to their unique structure. Examples of Gram-negative bacteria include E. coli, A. baumannii, Salmonella, and Pseudomonas.
By disrupting a component of the outer membrane, semapimod has the potential to sensitize Gram-negative bacteria to drugs that are usually active only against Gram-positive bacteria. This breakthrough opens up new possibilities for treating challenging infections caused by Gram-negative bacteria.
Valeri emphasizes the significance of semapimod’s unique structure in targeting the outer membrane, highlighting its potential as a valuable weapon against antibiotic resistance. As a research team, they firmly believe that finding new drugs for Gram-negative infections is just as vital as improving drugs for Gram-positive infections.
FAQ:
Q: Why are bacteria becoming resistant to traditional antibiotics?
A: Bacteria can develop resistance to antibiotics through repeated exposure. The misuse and overuse of antibiotics contribute to the selective pressure that drives the evolution of antibiotic-resistant strains.
Q: What is bacterial dormancy?
A: Bacterial dormancy refers to a state where bacteria enter a metabolically inactive state, making them less susceptible to antibiotics that rely on targeting active metabolic processes. Dormancy allows bacteria to survive adverse conditions and later reactivate, leading to recurring infections.
Q: How is AI being used in antibiotic discovery?
A: Artificial intelligence is revolutionizing the field of antibiotic discovery by speeding up the screening process for potential antibiotics. Machine learning algorithms can analyze vast amounts of data and identify promising compounds with higher efficiency, potentially leading to the discovery of new classes of antibiotics.
Q: What is the significance of targeting Gram-negative bacteria?
A: Gram-negative bacteria possess a unique outer membrane that makes them inherently resistant to many traditional antibiotics. Finding drugs that can effectively target Gram-negative bacteria is an important step in addressing the challenges posed by antibiotic-resistant infections caused by these bacteria.
Q: Why is the discovery of new antibiotics important?
A: The rise of antimicrobial resistance poses a significant threat to public health globally. Discovering new antibiotics is crucial in combating drug-resistant infections and ensuring effective treatment options are available to tackle emerging infectious diseases.
Source: The Lancet – URL: www.thelancet.com
In recent years, the discovery of modern antibiotics has stagnated, leading to a growing concern over the rise of antimicrobial resistance. The World Health Organization has now declared this crisis as one of the top 10 global public health threats. But why do infections sometimes return even after proper antibiotic treatment?
One possible explanation lies in bacteria becoming metabolically inert, effectively escaping the detection of traditional antibiotics that only target active metabolic activity. These dormant bacteria can later become reactivated, leading to recurring infections. Identifying and tackling this dormancy is crucial in combating the antimicrobial resistance crisis.
Jackie Valeri, a former MIT-Takeda Fellow from the Collins Lab, sheds light on this issue. In her recent paper published in Cell Chemical Biology, Valeri proposes the use of machine learning to screen compounds that can effectively kill dormant bacteria. By leveraging the power of artificial intelligence, researchers aim to uncover potential antibiotics that can target these resistant dormant bacteria.
The resilience of bacteria in a dormant state is not a new phenomenon. Scientists have discovered ancient bacterial strains dating back 100 million years, still alive in an energy-saving state on the Pacific Ocean seafloor. Understanding the mechanisms behind bacterial dormancy is crucial in developing effective antibiotics.
MIT’s Jameel Clinic for Machine Learning in Health, under the leadership of James J. Collins, a prominent figure in the field of medical engineering and science, has been making strides in antibiotic discovery through the use of AI. Their research aims to expand the existing arsenal of antibiotics by utilizing machine learning algorithms to identify new classes of antimicrobial compounds.
According to a study published by The Lancet, millions of preventable deaths occurred in 2019 due to infections resistant to available drugs. Finding antibiotics that can effectively target metabolically dormant bacteria is one of the key challenges faced by researchers.
In the Collins Lab, researchers employed AI to expedite the process of screening known drug compounds for antibiotic properties. Traditionally, this process could take years due to the vast number of molecules to explore. However, thanks to the high-throughput screening capabilities of AI, researchers were able to identify a compound called semapimod in just a single weekend.
Surprisingly, semapimod, an anti-inflammatory drug typically used for Crohn’s disease, exhibited effectiveness against stationary-phase Escherichia coli and Acinetobacter baumannii. Additionally, it demonstrated the ability to disrupt the outer membranes of “Gram-negative” bacteria, which are notoriously resistant to antibiotics due to their unique structure. Examples of Gram-negative bacteria include E. coli, A. baumannii, Salmonella, and Pseudomonas.
By disrupting a component of the outer membrane, semapimod has the potential to sensitize Gram-negative bacteria to drugs that are usually active only against Gram-positive bacteria. This breakthrough opens up new possibilities for treating challenging infections caused by Gram-negative bacteria.
Valeri emphasizes the significance of semapimod’s unique structure in targeting the outer membrane, highlighting its potential as a valuable weapon against antibiotic resistance. As a research team, they firmly believe that finding new drugs for Gram-negative infections is just as vital as improving drugs for Gram-positive infections.
FAQ:
Q: Why are bacteria becoming resistant to traditional antibiotics?
A: Bacteria can develop resistance to antibiotics through repeated exposure. The misuse and overuse of antibiotics contribute to the selective pressure that drives the evolution of antibiotic-resistant strains.
Q: What is bacterial dormancy?
A: Bacterial dormancy refers to a state where bacteria enter a metabolically inactive state, making them less susceptible to antibiotics that rely on targeting active metabolic processes. Dormancy allows bacteria to survive adverse conditions and later reactivate, leading to recurring infections.
Q: How is AI being used in antibiotic discovery?
A: Artificial intelligence is revolutionizing the field of antibiotic discovery by speeding up the screening process for potential antibiotics. Machine learning algorithms can analyze vast amounts of data and identify promising compounds with higher efficiency, potentially leading to the discovery of new classes of antibiotics.
Q: What is the significance of targeting Gram-negative bacteria?
A: Gram-negative bacteria possess a unique outer membrane that makes them inherently resistant to many traditional antibiotics. Finding drugs that can effectively target Gram-negative bacteria is an important step in addressing the challenges posed by antibiotic-resistant infections caused by these bacteria.
Q: Why is the discovery of new antibiotics important?
A: The rise of antimicrobial resistance poses a significant threat to public health globally. Discovering new antibiotics is crucial in combating drug-resistant infections and ensuring effective treatment options are available to tackle emerging infectious diseases.
Source: The Lancet – URL: www.thelancet.com
The source of the article is from the blog foodnext.nl