Healthcare companies must submit pharmaceuti-
cal drugs or medical devices to regulatory bodies
before marketing new technology. Regulatory
bodies frequently require transparent and inter-
pretable computational modelling to justify a new
healthcare technology, but researchers may have
several competing models for a biological sys-
tem 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 dis-
criminate between rival predictive models. Prior
work has focused either on analytical approaches,
which cannot manage all functions, or on data-
driven approaches, which may have computa-
tional difficulties or lack interpretable marginal
predictive distributions. We develop a method-
ology introducing Gaussian process surrogates
in lieu of the original mechanistic models. We
thereby extend existing design and model discrim-
ination methods developed for analytical models
to cases of non-analytical models in a computa-
tionally efficient manner.