Scientists discover the first new antibiotics in over 60 years using AI
The use of artificial intelligence (AI) is proving to be a
game-changer when it comes to medicine with the technology now helping
scientists to unlock the first new antibiotics in 60 years.
The discovery of a new compound that can kill a
drug-resistant bacterium that kills thousands worldwide every year could prove
to be a turning point in the fight against antibiotic resistance.
"The insight here was that we could see what was being
learned by the models to make their predictions that certain molecules would
make for good antibiotics," James Collins, professor of Medical
Engineering and Science at the Massachusetts Institute of Technology (MIT) and
one of the study’s authors, said in a statement.
"Our work provides a framework that is time-efficient,
resource-efficient, and mechanistically insightful, from a chemical-structure
standpoint, in ways that we haven’t had to date".
The results were published today in Nature and co-authored
by a team of 21 researchers.
Study aimed to 'open the black box'
The team behind the project used a deep-learning model to
predict the activity and toxicity of the new compound.
Deep learning involves the use of artificial neural networks
to automatically learn and represent features from data without explicit
programming.
It is increasingly being applied in drug discovery to
accelerate the identification of potential drug candidates, predict their
properties, and optimise the drug development process.
In this case, researchers focused on methicillin-resistant
Staphylococcus aureus (MRSA).
Infections with MRSA can range from mild skin infections to
more severe and potentially life-threatening conditions such as pneumonia and
bloodstream infections.
Almost 150,000 MRSA infections occur every year in the
European Union while almost 35,000 people die annually in the bloc from
antimicrobial-resistant infections, according to the European Centre for
Disease Prevention and Control (ECDC).
The MIT team of researchers trained an extensively enlarged
deep learning model using expanded datasets.
To create the training data, approximately 39,000 compounds
were evaluated for their antibiotic activity against MRSA. Subsequently, both
the resulting data and details regarding the chemical structures of the
compounds were input into the model.
"What we set out to do in this study was to open the
black box. These models consist of very large numbers of calculations that
mimic neural connections, and no one really knows what's going on underneath
the hood," said Felix Wong, a postdoc at MIT and Harvard and one of the
study’s lead authors.
Discovering a new compound
To refine the selection of potential drugs, the researchers
employed three additional deep-learning models. These models were trained to
assess the toxicity of compounds on three distinct types of human cells.
By integrating these toxicity predictions with the
previously determined antimicrobial activity, the researchers pinpointed
compounds capable of effectively combating microbes with minimal harm to the
human body.
Using this set of models, approximately 12 million
commercially available compounds were screened.
The models identified compounds from five different classes,
categorised based on specific chemical substructures within the molecules, that
exhibited predicted activity against MRSA.
Subsequently, the researchers acquired around 280 of these
compounds and conducted tests against MRSA in a laboratory setting. This
approach led them to identify two promising antibiotic candidates from the same
class.
In experiments involving two mouse models - one for MRSA
skin infection and another for MRSA systemic infection - each of these
compounds reduced the MRSA population by a factor of 10.
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