Research into some diseases is moving ahead more rapidly with the help of artificial intelligence.
Artificial intelligence has been used to identify a new antibiotic capable of killing 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 had trained to assess whether a chemical compound would inhibit the growth of acinetobacter baumannii.
James Collins, from 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 expand our search for novel antibiotics”.
“I’m excited that this work shows that we can use AI to help combat problematic pathogens such as acinetobacter baumannii.”
Acinetobacter baumannii is often found in hospitals and can lead to pneumonia, meningitis and other serious illnesses.
Jonathan Stokes, an assistant professor of biochemistry and biomedical sciences at McMaster University, said acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment.
“It’s really common now to find acinetobacter baumannii isolates that are resistant to nearly every antibiotic.”
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The researchers plan to use their modelling 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 which could 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 spread 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 for helping to prevent secondary breast cancer”.
She said: “By demonstrating that lymph node changes can predict if triple negative breast cancer will spread, we’ve built on our growing knowledge of the important role that immune response can play in understanding a patient’s prognosis.”
The researchers tested their AI model on more than 5,000 lymph nodes donated by 345 patients to biobanks, and the model was then able to establish the likelihood of breast cancer spreading by analysing the immune response.
Around 15%, of all breast cancers in the UK are triple negative, and it accounts for around 25% of breast cancer deaths.
