Background
Quantitative systems pharmacology (QSP) models increasingly support clinical decision-making by integrating mechanistic knowledge with heterogeneous experimental data. Sparse population-level data and model non-identifiability often yield multiple plausible parameterizations, motivating the use of virtual populations (VPops) to represent uncertainty and inter-individual phenotypic variability [1]. Among VPop generation strategies, widely used approaches follow the Allen et al. framework [2], generating large plausible populations (PPops) and selecting VPops to match clinical distributions. In this two-stage workflow, candidate “plausible patients” are first screened to satisfy mechanistic/physiological constraints, and a subset is then selected (e.g., via acceptance–rejection/inclusion) so that the resulting VPop reproduces observed clinical statistics without relying on post-hoc weighting [2]. Recent surrogate/emulation strategies aim to reduce this burden by rapidly pre-screening parameter sets and prioritizing simulator evaluations in promising regions; however, comparative evidence across multiple QSP models and commonly reported VPop metrics remains limited [4]. As model complexity increases and VPop generation becomes more computationally demanding, we benchmarked classical optimization-based methods against surrogate-assisted alternatives across QSP models, focusing on total computational cost.
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