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One of the greatest challenges in cancer treatment is maximising the impact for the patient from drug treatments. Conventional treatment strategies, which focus on killing as many cells as possible, are based on a ‘maximum tolerated dose’ (MTD) therapy, where the patient continually receives a high drug dose, with no breaks in treatment. However, these frequently fail against metastatic cancers due to the emergence of drug resistance.
Adaptive therapy strategies, which dynamically adjust treatment to suppress the growth of treatment-resistant populations, have emerged as a promising alternative. However, the lack of personalized approaches that account for patient variation limits their efficacy.
In a new study published in Cancer Research, researchers from the University of Oxford and Moffitt Cancer Center in Florida introduce a novel framework that applies deep reinforcement learning, DRL, (a form of AI) to create adaptive therapy schedules for individual prostate cancer patients. The results indicate that the new adaptive approach could potentially double the time to relapse compared to MTD or non-personalised treatment breaks.
First author Kit Gallagher, a DPhil student at Oxford’s Mathematical Institute, trained the DRL network on synthetic data from a mathematical model of prostate cancer, to replicate behaviour seen in previous clinical trials. The mathematical model was vital to generate sufficiently large quantities of ‘virtual patient’ data and allowed the researchers to evaluate treatment schedules that couldn’t easily be tested clinically.
Read the full story on the University of Oxford website.