Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Abstract

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed Virtual Patients. In order to reproduce the statistics of a clinical population, Virtual Patients are often weighted to form a Virtual Population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate Virtual Patients and, from this ensemble, demonstrate how to select a Virtual Population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious Virtual Patients become over-represented in Virtual Populations.