Harnessing adaptive novelty for automated generation of cancer treatments

Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of
drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of
customization possibilities. However, it also leads to a rich design space which is difficult to investigate and
optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and
highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate
the design of potential therapies for such complex cases, we propose a computational model for fast, novelty based
machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable,
open-ended agent-based model, where the exploration of an initially small portion of the given state space
can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes
an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a
heterogeneous population of specialized groups of individuals optimized for tracking and killing different
phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be
used in real-world research and clinical practice.