FURI | Fall 2024

Efficient Identification of Adaptive Therapy Routines for Cancer using Evolutionary Search

In traditional chemotherapy, large doses of drugs are applied for fixed lengths of time, having the side effect of cells developing drug resistance. Adaptive therapy is an effective alternative that treats for stability by adjusting drugs, doses, and timing to maintain competition between drug-resistant and drug-sensitive cells. However, the sheer number of regimens and the costs of clinical trials make it prohibitively expensive to try every adaptive therapy. In this project, the research team uses genetic algorithms and CancerSim, a 3D tumor simulator, to efficiently explore large spaces of adaptive therapies, identifying promising candidates for further study in clinical trials.