Artificial intelligence helps scientists fight superbugs and breast cancer | Science and technology news
Using artificial intelligence, a new antibiotic has been identified that is able to kill a type of bacteria responsible for many drug-resistant infections.
The new antibiotic was identified by researchers at Massachusetts Institute of Technology and McMaster University from a library of nearly 7,000 potential drug compounds.
The researchers used a machine learning model they trained to assess whether a chemical compound would inhibit the growth of Acinetobacter baumannii.
James Collins of MIT’s Institute for Medical Engineering and Science and Department of Biological Engineering said the research supports the idea that “AI can significantly accelerate and amplify our search for novel antibiotics.”
“I am pleased that this work demonstrates that we can use AI to combat problematic pathogens such as Acinetobacter baumannii.”
Acinetobacter baumannii is common in hospitals and can cause pneumonia, meningitis, and other serious illnesses.
Jonathan Stokes, assistant professor of biochemistry and biomedical sciences at McMaster University, said Acinetobacter can survive on hospital doorknobs and appliances for long periods of time and pick up antibiotic resistance genes from its environment.
“Now it’s really common to find isolates of Acinetobacter baumannii that are resistant to almost every antibiotic.”
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The researchers plan to use their modeling to identify potential antibiotics for other types of drug-resistant infections and hope such compounds will be developed for use in patients.
Their research is published in Nature Chemical Biology.
Artificial intelligence is also being used in the fight against breast cancer, helping scientists develop a model that can predict whether an aggressive branch of the disease will spread.
The AI model detects changes in the lymph nodes of women with triple-negative breast cancer – one of the first places breast cancer often spreads is the lymph nodes under the arm on the same side, and in these cases patients are likely to need more intensive treatment.
dr Anita Grigoriadis, who led the research at the Breast Cancer Now Unit at King’s College London, said the development would give doctors “another tool in their arsenal to help prevent secondary breast cancer”.
She said: “By demonstrating that lymph node changes can predict whether triple-negative breast cancer is spreading, we have built on our growing knowledge of the important role that the immune response can play in understanding a patient’s prognosis.”
Researchers tested their AI model on more than 5,000 lymph nodes donated to biobanks by 345 patients, and the model was then able to determine the likelihood of breast cancer spreading by analyzing immune response.
Around 15% of all breast cancers in the UK are triple negative and account for around 25% of breast cancer deaths.