No One-Size-Fits-All: A Multi-Model Evaluation of Optimization-Based and Surrogate-Assisted Virtual Population Generation in QSP

Pub. type
Poster
Pub. date
April 8, 2026
Presented at
QSPC 2026 - Leiden
Authors
Righetti Elena
D’Agaro Niccolò
Bozza Marco
Simone Pezzuto
Reali Federico

Motivation and Background

  • Virtual populations (VPops) represent uncertainty and inter-
    individual phenotypic variability in QSP models, supporting clinical
    decision-making [1]
  • Widely used approaches following Allen et al. [2] generate large
    plausible populations (PPops) and select VPops to match target
    clinical distributions
  • Such methods are typically evaluated on single models,
    assuming transferability
  • As QSP model complexity increases, PPop generation becomes a
    major computational bottleneck

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