Parallel design of pharmacodynamic experiments for the identification of antimicrobial-resistant bacterial population models

Abstract The use of detailed pharmacokinetic (PK) and pharmacodynamic (PD) models in order to investigate drug resistance and the susceptibility breakthrough by means of in-vivo or in-vitro trials is a widespread practice in the preliminary stages of drug development. However, complex PK-PD models are usually affected by identifiability issues typically related to their specific model structure and to the strong correlation among the model parameters. Model-based design of experiments (MBDoE) techniques can be successfully adopted to design multiple experiments to be executed simultaneously, detecting a proper set of experimental settings improving the identifiability of the model parameters. The preliminary results presented in this paper show that designing experiments in parallel, rather than sequentially, can substantially decrease the time and effort required by the model identification task for a microbial growth model.