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Researchers have used machine-learning to identify a potential new antibiotic against a challenging species of disease-causing bacteria, they reported in a paper published in Nature Chemical Biology on May 25.
The finding is important because of the rise of antimicrobial resistance and the struggle to identify new classes of antibiotics. It also clarifies how machines can help speed up the identification, discovery, and testing of new antibiotics that the world desperately needs – and potentially reduce the cost of this laborious process.
What is antimicrobial resistance?
Antimicrobial resistance is one of the great crises of the 21st century that, like climate change, was brought on by human activities and affects the whole world, regardless of borders or points of origin. It refers to the ability of microbes to evolve to resist the compounds humans have developed to beat them.
As a result, many drugs, but especially antibiotics, have become less effective or ineffective against disease-causing bacteria, allowing the diseases to become more prevalent again.
The global cost of antimicrobial resistance is expected to be $300 billion to more than $1 trillion every year. India is a ‘hotspot’ of antimicrobial resistance thanks to the overuse of antibiotics, among people and animals, and the improper disposal of pharmaceutical waste.
Efforts to develop new antibiotics have been hamstrung by the fact that many existing compounds have been derived from a smaller group. This implies a higher cost and longer timelines to identify new drugs that can push back the tide of resistance.
One promising pathway here is to use machine-learning models that can be ‘taught’ to look for molecules with properties considered desirable to fight specific species of bacteria. Such models can also sift through large datasets in a short duration.
What is Acinetobacter baumannii?
In their study, the MIT researchers looked for a molecule to fight Acinetobacter baumannii bacteria. A. baumannii is a Gram-negative bacteria, which means it has a protective outer membrane that allows it to resist antibiotics. It has been associated with hospital-acquired infections in India.
A. baumannii was acknowledged even a decade ago to be a “red alert” pathogen “primarily because of its exceptional ability to develop resistance to all currently available antibiotics”. This remains the case today.
Recently, a Department of Biotechnology initiative launched a programme to find compounds that could fight A. baumannii, among five other pathogens.
In 2019, researchers from the Jawaharlal Nehru Centre for Advanced Scientific Research reported finding a new molecule that seemed to be potent against A. baumannii but left human cells alone. “Based on the in vitro studies, we feel this molecule has immense potential for being developed as a future therapeutic agent,” the lead author of the study, Jayanta Haldar, had told The Hindu at the time.
How did the MIT group find the compound?
First, the MIT group compiled a list of 7,684 molecules already known to inhibit the growth of A. baumannii in biomolecular studies in the lab. They used these molecules to train a machine-learning model. Specifically, the model ‘learnt’ the various relevant properties of each molecule and combined them into a single, complicated vector.
This vector was fed into a neural network – a system that learns information in a way inspired by the human brain – that optimised for each molecule’s antibacterial properties. Finally, they applied this system to a database of 6,680 molecules to look for those that could fight A. baumannii.
This step yielded a shortlist of 240 molecules after just a few hours. The researchers tested them for activity against A. baumannii and found that nine of them inhibited bacterial growth by 80% or more. They further pared the list down to remove molecules that had structures that the bacteria might be ‘familiar’ with.
They were left with abaucin.
“When we run wet-lab experiments based on model predictions, the model will inevitably make both correct predictions and incorrect predictions. We then take this wet-lab data and retrain the model,” Jon Stokes, an assistant professor of biochemistry at McMaster University, Ontario, and one of the people behind the study, told The Hindu. “Through this iterative retraining process, the model can improve its predictive performance.”
What is abaucin?
Abaucin is known to compromise the normal function of a protein called CCR2. One of the authors of the study told CNN it may have originally been developed to treat diabetes.
The researchers wrote in their paper that abaucin had “modest bactericidal activity against A. baumannii” in a medium containing other compounds that the bacteria resisted. They also observed that when they removed abaucin from the medium “after [six hours] of treatment”, the A. baumannii regrew.
“This experiment was conducted to verify that abaucin did not sterilise bacterial cultures in vitro,” Dr. Stokes said. “It was simply another method – in addition to the conventional bacterial cell-viability experiments – to determine the efficacy of abaucin at reducing the viability of bacterial cells.”
Abaucin appears to work by disrupting lipoprotein trafficking in A. baumannii. A lipoprotein is a molecular framework required to transport fat inside cells. Based on genetic studies, the researchers believe that abaucin could be preventing lipoprotein produced inside the bacteria from moving to the outer membrane.
Abaucin is also “species-selective”: it only disrupts the growth of A. baumannii, not other Gram-negative bacteria. The authors write that this could “at least in part” be because A. baumannii uses a slightly different lipoprotein transport system.
What next?
The team plans to improve the model. “There are always gaps in chemical training datasets since you can only explore a finite region of chemical space,” Dr. Stokes said. “We therefore have to focus on continually gathering more robust training data with which to train our models, as well as designing new types of models that can make robust predictions using less training data.”
The team members are also “designing and testing” compounds that are chemically similar to abaucin, to see if they could be more potent against A. baumannii and to “improve its medicinal properties”.
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Researchers have used machine-learning to identify a potential new antibiotic against a challenging species of disease-causing bacteria, they reported in a paper published in Nature Chemical Biology on May 25.
The finding is important because of the rise of antimicrobial resistance and the struggle to identify new classes of antibiotics. It also clarifies how machines can help speed up the identification, discovery, and testing of new antibiotics that the world desperately needs – and potentially reduce the cost of this laborious process.
What is antimicrobial resistance?
Antimicrobial resistance is one of the great crises of the 21st century that, like climate change, was brought on by human activities and affects the whole world, regardless of borders or points of origin. It refers to the ability of microbes to evolve to resist the compounds humans have developed to beat them.
As a result, many drugs, but especially antibiotics, have become less effective or ineffective against disease-causing bacteria, allowing the diseases to become more prevalent again.
The global cost of antimicrobial resistance is expected to be $300 billion to more than $1 trillion every year. India is a ‘hotspot’ of antimicrobial resistance thanks to the overuse of antibiotics, among people and animals, and the improper disposal of pharmaceutical waste.
Efforts to develop new antibiotics have been hamstrung by the fact that many existing compounds have been derived from a smaller group. This implies a higher cost and longer timelines to identify new drugs that can push back the tide of resistance.
One promising pathway here is to use machine-learning models that can be ‘taught’ to look for molecules with properties considered desirable to fight specific species of bacteria. Such models can also sift through large datasets in a short duration.
What is Acinetobacter baumannii?
In their study, the MIT researchers looked for a molecule to fight Acinetobacter baumannii bacteria. A. baumannii is a Gram-negative bacteria, which means it has a protective outer membrane that allows it to resist antibiotics. It has been associated with hospital-acquired infections in India.
A. baumannii was acknowledged even a decade ago to be a “red alert” pathogen “primarily because of its exceptional ability to develop resistance to all currently available antibiotics”. This remains the case today.
Recently, a Department of Biotechnology initiative launched a programme to find compounds that could fight A. baumannii, among five other pathogens.
In 2019, researchers from the Jawaharlal Nehru Centre for Advanced Scientific Research reported finding a new molecule that seemed to be potent against A. baumannii but left human cells alone. “Based on the in vitro studies, we feel this molecule has immense potential for being developed as a future therapeutic agent,” the lead author of the study, Jayanta Haldar, had told The Hindu at the time.
How did the MIT group find the compound?
First, the MIT group compiled a list of 7,684 molecules already known to inhibit the growth of A. baumannii in biomolecular studies in the lab. They used these molecules to train a machine-learning model. Specifically, the model ‘learnt’ the various relevant properties of each molecule and combined them into a single, complicated vector.
This vector was fed into a neural network – a system that learns information in a way inspired by the human brain – that optimised for each molecule’s antibacterial properties. Finally, they applied this system to a database of 6,680 molecules to look for those that could fight A. baumannii.
This step yielded a shortlist of 240 molecules after just a few hours. The researchers tested them for activity against A. baumannii and found that nine of them inhibited bacterial growth by 80% or more. They further pared the list down to remove molecules that had structures that the bacteria might be ‘familiar’ with.
They were left with abaucin.
“When we run wet-lab experiments based on model predictions, the model will inevitably make both correct predictions and incorrect predictions. We then take this wet-lab data and retrain the model,” Jon Stokes, an assistant professor of biochemistry at McMaster University, Ontario, and one of the people behind the study, told The Hindu. “Through this iterative retraining process, the model can improve its predictive performance.”
What is abaucin?
Abaucin is known to compromise the normal function of a protein called CCR2. One of the authors of the study told CNN it may have originally been developed to treat diabetes.
The researchers wrote in their paper that abaucin had “modest bactericidal activity against A. baumannii” in a medium containing other compounds that the bacteria resisted. They also observed that when they removed abaucin from the medium “after [six hours] of treatment”, the A. baumannii regrew.
“This experiment was conducted to verify that abaucin did not sterilise bacterial cultures in vitro,” Dr. Stokes said. “It was simply another method – in addition to the conventional bacterial cell-viability experiments – to determine the efficacy of abaucin at reducing the viability of bacterial cells.”
Abaucin appears to work by disrupting lipoprotein trafficking in A. baumannii. A lipoprotein is a molecular framework required to transport fat inside cells. Based on genetic studies, the researchers believe that abaucin could be preventing lipoprotein produced inside the bacteria from moving to the outer membrane.
Abaucin is also “species-selective”: it only disrupts the growth of A. baumannii, not other Gram-negative bacteria. The authors write that this could “at least in part” be because A. baumannii uses a slightly different lipoprotein transport system.
What next?
The team plans to improve the model. “There are always gaps in chemical training datasets since you can only explore a finite region of chemical space,” Dr. Stokes said. “We therefore have to focus on continually gathering more robust training data with which to train our models, as well as designing new types of models that can make robust predictions using less training data.”
The team members are also “designing and testing” compounds that are chemically similar to abaucin, to see if they could be more potent against A. baumannii and to “improve its medicinal properties”.
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