Application of different approaches to generate virtual patient populations for QSP model of Erythropoiesis

Objectives: In the study we describe and compare four different techniques to generate virtual patient populations basing on experimentally measured statistics.Methods: QSP model of erythropoiesis was constructed to comprehensively describe cell dynamics from hematopoietic stem cell to circulating red cells. The model describes cell self-renewal, differentiation, proliferation, migration from bone marrow into circulation and cell death.Data describing time series of plasma reticulocyte count in response to single dose erythropoietin administered to 5 healthy subjects is used to find out final population of virtual patients (VP). Experimental data are given in the form of mean and standard deviation (SD). Four different approaches were applied to generate virtual patient populations (VPpop): (1) Monte-Carlo Markov Chain, (2) Model fitting to Monte-Carlo sample, (3) Population of clones, (4) Stochastically bounded selection. 39 parameters of the erythropoiesis model were chosen to be responsible for variability in observed clinical data. Conclusions: The approaches proposed are capable for reproducing distribution characteristics of plasma reticulocyte count observed in clinical trials. The approach 4 is the most universal, as it allows to describe any number of patients from clinical trials and it can be applied in case of complex models with large number of variable parameters.