2 Nonparametric methods
All nonparametric methods are based on training an empirical function to replicate the input-output behaviour of the QSP system. The training involves generation of a large amount of pseudo-data from the full-order QSP model and then either fitting a user-defined empirical model (termed here Empirical Approximation) to the data or by a machine learning technique such as an artificial neural network (ANN). None of these methods retain mechanistic characteristics of the full-order model and therefore cannot be used for extrapolation to data that might arise from inputs beyond the range of the training inputs (e.g. to a dose that is not within the range of doses used to create the pseudo-data). These approaches are useful when parametric methods are not possible due to high model complexity or even when the structure of the model is unknown, e.g., models provided by third parties. The only requirement of nonparametric approaches is the ability to evaluate the original full-order model at a given set of input variables.