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Comparison Of Different Approaches To Generate Virtual Patient Populations Of Different Sizes For QSP Model Of Erythropoiesis
EP39103
Poster Title: Comparison Of Different Approaches To Generate Virtual Patient Populations Of Different Sizes For QSP Model Of Erythropoiesis
Submitted on 03 Aug 2022
Author(s): Galina Kolesova, Oleg Demin, Alexander Stepanov
Affiliations: InSysBio
This poster was presented at ACoP12
Poster Views: 76
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Poster Information
Abstract: Objectives: In the study we compare four different approaches to generate virtual patient populations of a given size basing on experimentally measured statistics.

Methods: QSP model of erythropoiesis [1] 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. Binding of growth factors such as stem cell factor (SCF) and erythropoietin (EPO) to cell-surface receptors regulates cell dynamics modulated by interleukin-3 (IL-3). The model was calibrated across published in vitro/in vivo data. As a test dataset we use time series describing mean and standard deviation of plasma reticulocyte count in response to single dose of erythropoietin. The dataset describes the administration of this dose to 5 healthy subjects. Reticulocyte count was measured at 16 time points.
Four different approaches were applied to generate virtual patient populations: (1) Monte-Carlo Markov Chain, (2) Model fitting to Monte-Carlo sample, (3) Population of clones, (4) Stochastically bounded selection [2]. 39 parameters of the erythropoiesis model were chosen to be responsible for variability in observed clinical data. The comparison of approaches was done for the relatively small population size
(5 patients) and relatively big one (207 patients).

Results: In the case of 5 patients Approach 1 gives poor precision of mean and sd estimates. Better results were obtained in the Approach 2, which provides relatively accurate mean value estimates,
however sd values turned out to be too small. The last flaw is resolved in the results of application of the Approach 4. In this case even for extremely small sample size relatively good estimates of both mean
and sd values were obtained. Turning to the case of 207 patients, assessing the results visually all approaches appear to have relatively good accuracy of mean estimates at every moment of time.
However, the relatively precise estimates of sd are obtained only in the Approach 4. We also generate the Q-Q plots to compare the predicted in each approach distributions with the experimental ones.
Doing this, we show that the Approach 4 provides the largest number of relatively accurate time points. Since the experimental data are given in the form of series of mean 𝑚 = (𝑚1,… , 𝑚𝑇) and sd 𝑠𝑑 =(𝑠𝑑1, … , 𝑠𝑑𝑇), we use one sample t-test to assess the statistical significance. Approach 1 provides statistically significant results at three time points, Approach 2 - in two time points, Approach 4 - in four time points.

Conclusions: Comparison of the results of application of the four approaches to populations of two different sizes shows that the Approach 4 is the preferable one in both cases. Moreover, this approach
appears to have the smallest computational complexity and, therefore, it can be applied even to the high dimensional models with large number of variable parameters.
Summary: Comparison of the results of application of the four approaches to populations of two different sizes shows that the Approach 4 is the preferable one in both cases. Moreover, this approach
appears to have the smallest computational complexity and, therefore, it can be applied even to the high dimensional models with large number of variable parameters.
References: 1. ACoP11, "Stimulation of erythropoiesis with ESA or blood donation: QSP model", A. Stepanov, G.
Lebedeva
2. ACoP11, "Application of different approaches to generate virtual patient populations for QSP
model of Erythropoiesis",G. Kolesova, O. Demin, A. Stepanov
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