Dynamic approaches to drug scheduling\, such as adaptive therapy\, enable individual-level personalisation of the dosing schedule to delay patient relapse. Promising clinical results in prostate cancer indicate the potent ial of these protocols\, but demonstrate broad heterogeneity in patient re sponse. This naturally leads to the question: why does this heterogeneity occur\, and is a ‘one-size-fits-all' protocol best for all patients?
Using deep reinforcement learning\, we obtain personalised and clinica lly feasible treatment protocols based on individual patient dynamics. We can subsequently rationalise these findings through a mathematical tumour model\, and propose new mathematical biomarkers that can identify the best responders from a clinical dataset after only the first treatment cycle. Overall\, I will highlight the importance of personalised treatment schedu les that explicitly account for patient heterogeneity\, and the power of m athematical models to capture\, analyse and facilitate this personalisatio n.
Kit Gallagher completed his PhD at the University of Oxford unde r the supervision of Prof. Philip Maini\, using population modelling\, sta tistical inference\, and deep learning to improve treatment scheduling and personalisation for metastatic prostate cancer. Throughout this\, he has worked with Prof. Alexander Anderson at the Moffitt Cancer Center (Tampa\, Florida)\, integrating clinical data into mathematical modelling and info rming experimental &\; clinical trial design. Currently\, he is startin g a postdoctoral fellowship in Prof. Ignacio Vazquez-Garcia’s lab at Mas s General Research Institute and Harvard Medical School\, applying time se ries modelling to longitudinal genomic datasets.
ONLINE BBB : Link ZIH-Colloquia (https://bbb.tu-dresden.de/b/har-oa6-col-lmy )
DTSTAMP:20260425T224646Z CREATED:20250910T053550Z LAST-MODIFIED:20251009T053554Z END:VEVENT END:VCALENDAR