The use of high-fidelity computational simulations promises to enable
high-throughput hypothesis testing and optimisation of cancer therapies.
However, increasing realism comes at the cost of increasing computational
requirements. This article explores the use of surrogate-assisted evolutionary
algorithms to optimise the targeted delivery of a therapeutic compound to
cancerous tumour cells with the multicellular simulator, PhysiCell. The use of
both Gaussian process models and multi-layer perceptron neural network
surrogate models are investigated. We find that evolutionary algorithms are
able to effectively explore the parameter space of biophysical properties
within the agent-based simulations, minimising the resulting number of
cancerous cells after a period of simulated treatment. Both model-assisted
algorithms are found to outperform a standard evolutionary algorithm,
demonstrating their ability to perform a more effective search within the very
small evaluation budget. This represents the first use of efficient
evolutionary algorithms within a high-throughput multicellular computing
approach to find therapeutic design optima that maximise tumour regression
