AI test could predict effective cancer drug combinations in less than two days
pharmafile | April 4, 2022 | News story | Sales and Marketing |
Scientists have created a prototype test that can predict which drug combinations are likely to work for cancer patients in as little as 24 to 48 hours. The first-of-its-kind test is more accurate than the current genetic approach for personalising treatment.
The cutting-edge technique uses artificial intelligence to analyse large-scale protein data from tumour samples, and is able to predict patients’ response to drugs more accurately than is currently possible.
“Our test provides proof of concept for using AI to analyse changes in the way information flows within cancer cells and make predictions about how tumours are likely to respond to combinations of drugs,” said Study leader Professor Udai Banerji, Professor of Molecular Cancer Pharmacology at The Institute of Cancer Research, London, and Consultant Medical Oncologist at The Royal Marsden NHS Foundation Trust.
“With a rapid turnaround time of less than two days, the test has the potential to guide doctors in their judgements on which treatments are most likely to benefit individual cancer patients. It is an important step to move forward from our current focus on using genetic mutations to predict response.”
Genetic analysis of tumours can reveal mutations that are fuelling cancer growth, but genomics alone cannot provide sufficiently accurate predictions to select drug combinations.
The study is published in Molecular Cancer Therapeutics, and was funded by the National Institute for Health Research (NIHR), Wellcome, Cancer Research UK, and The Institute of Cancer Research (ICR).
Professor Kristian Helin, Chief Executive of The Institute of Cancer Research, London, commented: “One of the greatest challenges we face in cancer research and treatment is the ability of cancer to adapt, evolve and become drug resistant. We expect that the future of cancer treatment will be in combining therapies to overcome resistance – but we need to get much better at predicting which drug combinations will work best for individual patients.”