Healthcare companies must submit pharmaceutical
drugs or medical devices to regulatory bodies
before marketing new technology. Regulatory
bodies frequently require transparent and interpretable
computational modelling to justify a new
healthcare technology, but researchers may have
several competing models for a biological system
and too little data to discriminate between
the models. In design of experiments for model
discrimination, the goal is to design maximally
informative physical experiments in order to discriminate
between rival predictive models. Prior
work has focused either on analytical approaches,
which cannot manage all functions, or on datadriven
approaches, which may have computational
difficulties or lack interpretable marginal
predictive distributions. We develop a methodology
introducing Gaussian process surrogates
in lieu of the original mechanistic models. We
thereby extend existing design and model discrimination
methods developed for analytical models
to cases of non-analytical models in a computationally
efficient manner