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EP34513
Abstract: Application of different approaches to generate virtual patient populations for QSP model of Erythropoiesis
Galina Kolesova1, Oleg Demin1, Alexander Stepanov1
1InSysBio, Moscow, Russia
Objectives: In the study we propose and compare four different techniques to generate virtual patient populations basing on experimentally measured mean data and 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.
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. 36 parameters of the erythropoiesis model were chosen to be
responsible for variability in observed clinical data. Initial VPpop was generated based on a priori distribution of the selected parameters. Number of VPs equal to that specified in clinical data and allowing to satisfactory describe both mean and SD of reticulocyte values measured clinically were chosen from initial VPpop on the basis of each of the approaches.
Results:
In addition to approaches (1), (2) and (3) presented earlier [2] we have developed an approach (4) entitled as “Stochastically bounded selection”. The approach includes (i) finding minimal (RETmin) and maximal (RETmax) values of reticulocyte count in particular time point among initial VPpop, (ii) building a truncated lognormal distribution for reticulocyte count, the distribution is concentrated at [RETmin, RETmax] and has mean and SD equal to those of experimental data, (iii) generation of S random values of the endpoint (in our case reticulocyte count and S=5 equal to number of patients in experimental data), (iv) finding 5 VPs in initial VPpop which reticulocyte values are closest to the 5 randomly selected values at step (iii). Fig 1 illustrates application of the approach (4). Similar results were obtained for other approaches. Summary: In the study we propose and compare four different techniques to generate virtual patient populations basing on experimentally measured mean data and statistics.
Galina Kolesova1, Oleg Demin1, Alexander Stepanov1
1InSysBio, Moscow, Russia
Objectives: In the study we propose and compare four different techniques to generate virtual patient populations basing on experimentally measured mean data and 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.
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. 36 parameters of the erythropoiesis model were chosen to be
responsible for variability in observed clinical data. Initial VPpop was generated based on a priori distribution of the selected parameters. Number of VPs equal to that specified in clinical data and allowing to satisfactory describe both mean and SD of reticulocyte values measured clinically were chosen from initial VPpop on the basis of each of the approaches.
Results:
In addition to approaches (1), (2) and (3) presented earlier [2] we have developed an approach (4) entitled as “Stochastically bounded selection”. The approach includes (i) finding minimal (RETmin) and maximal (RETmax) values of reticulocyte count in particular time point among initial VPpop, (ii) building a truncated lognormal distribution for reticulocyte count, the distribution is concentrated at [RETmin, RETmax] and has mean and SD equal to those of experimental data, (iii) generation of S random values of the endpoint (in our case reticulocyte count and S=5 equal to number of patients in experimental data), (iv) finding 5 VPs in initial VPpop which reticulocyte values are closest to the 5 randomly selected values at step (iii). Fig 1 illustrates application of the approach (4). Similar results were obtained for other approaches. Summary: In the study we propose and compare four different techniques to generate virtual patient populations basing on experimentally measured mean data and statistics.
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