The recent emergence of virtual comparative trials, in particular for virtual bioequivalence (VBE) assessment, implies a formalization of their analyses. In recent VBE assessments, pharmacokinetic models informed with in vitro data and verified with small clinical trials data were used to simulate otherwise unfeasibly large trials. Simulated VBE trials are assessed in a frequentist framework as if they were real, despite the unlimited number of virtual subjects they can use. This may control consumer risk adequately but imposes unnecessary risks to producers. We propose a fully Bayesian model-integrated VBE assessment framework to control consumer and minimize producer risk, and compare its performance with a data-based VBE workflow. We illustrate our approach with a case study on a hypothetical paliperidone palmitate generic long-acting injectable suspension formulation, using a validated population pharmacokinetic model published for the reference formulation. Our study shows that the fully Bayesian workflow is efficient and rewards data gathering and model-integration to make the best use of prior information. Safe space analyses in the two workflows differ because the accuracy of the second one is higher and gives a clearer estimate of the parameter region in which BE is expected.

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