The design and analysis of parallel experiments to produce structurally identifiable models

Pharmacokinetic analysis in humans using compartmental models is restricted with respect to the estimation of parameter values. This is because the experimenter usually is only able to apply inputs and observations in a very small number of compartments in the system. This has implications for the structural identifiability of such systems and consequently limits the complexity and mechanistic relevance of the models that may be applied to such experiments. A number of strategies are presented whereby models are rendered globally identifiable by considering a series of experiments in parallel. Examples are taken from the pharmacokinetic literature and analysed using this parallel experiment methodology. It is concluded that considering a series of pharmacokinetic experiments where some, but not all, of the parameters may be shared across the experiments can improve the identifiability of some compartmental models.