Abstract <p>The usefulness of mathematical modeling of biological systems and their responses to exogenous products is now well recognized. However, this recognition is marred by problems of unreliability of representations of real populations and predictions of responses to treatments. To remedy this, the generation of virtual populations combined with quantitative systems pharmacology models is increasingly being adopted. However, the complexity of these models and the large number of parameters they involve, generally within a context of lack of information or data, raise the question of nonidentifiability as a potential source affecting the quality of model predictions. This article attempts to present a vision that confronts the management of nonidentifiability with the concerns linked to the classification of virtual populations and their corresponding parametric signatures, as a potential tool for the evaluation of therapeutic interventions.</p> Graphical abstract <p>Visual representation of the identification of virtual sub-populations through phenotype-guided filtering. See the end of Section “<InternalRef RefID="Sec15">Phenotype-guided filtering of virtual populations</InternalRef>” for more details</p>