Literature DB >> 23775141

A semi-mechanistic red blood cell survival model provides some insight into red blood cell destruction mechanisms.

Julia Korell1, Stephen B Duffull.   

Abstract

Most mathematical models developed for the survival of haematological cell populations, in particular red blood cells (RBCs), follow the principle of parsimony. They focus on the predominant destruction mechanism of age-related cell death (senescence) and do not account for within subject variability in the RBC lifespan. However, assessment of the underlying physiological destruction mechanisms can be of interest in pathological conditions that affect RBC survival, for example sickle cell anaemia or anaemia of chronic kidney disease. We have previously proposed a semi-mechanistic RBC survival model which accounts for four different types of RBC destruction mechanisms. In this work, it is shown that the proposed model in combination with informative RBC survival data is able to provide a deeper insight into RBC destruction mechanisms. The proposed model was applied in a non-linear mixed effect modelling framework to biotin derived RBC survival data available from literature. Three mechanisms were estimable based on the available data of twelve subjects, including random destruction, senescence and destruction due to delayed failure. It was possible to identify three subjects with a decreased RBC survival in the study population. These three subjects all showed differences in the contribution of the estimated destruction mechanisms: an increased random destruction, versus an accelerated senescence, versus a combination of both.

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Year:  2013        PMID: 23775141     DOI: 10.1007/s10928-013-9322-4

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  16 in total

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Journal:  J Pharmacokinet Biopharm       Date:  1999-10

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Journal:  Physiol Rev       Date:  1959-07       Impact factor: 37.312

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Authors:  F G EBAUGH; C P EMERSON; J F ROSS
Journal:  J Clin Invest       Date:  1953-12       Impact factor: 14.808

4.  Evaluation of red blood cell labelling methods based on a statistical model for red blood cell survival.

Authors:  Julia Korell; Carolyn V Coulter; Stephen B Duffull
Journal:  J Theor Biol       Date:  2011-09-21       Impact factor: 2.691

5.  A statistical model for red blood cell survival.

Authors:  Julia Korell; Carolyn V Coulter; Stephen B Duffull
Journal:  J Theor Biol       Date:  2010-10-13       Impact factor: 2.691

6.  Basic pharmacodynamic models for agents that alter the lifespan distribution of natural cells.

Authors:  Wojciech Krzyzanski; Juan Jose Perez-Ruixo; An Vermeulen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2008-06-13       Impact factor: 2.745

7.  Modeling of red blood cell life-spans in hematologically normal populations.

Authors:  Rocío Lledó-García; Robert M Kalicki; Dominik E Uehlinger; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-07-31       Impact factor: 2.745

8.  An ignored error in the mathematical formulation of erythrocyte survival curves.

Authors:  V Znojil
Journal:  J Theor Biol       Date:  1983-06-21       Impact factor: 2.691

9.  Pharmacodynamic analysis of time-variant cellular disposition: reticulocyte disposition changes in phlebotomized sheep.

Authors:  Kevin J Freise; John A Widness; Robert L Schmidt; Peter Veng-Pedersen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-05-22       Impact factor: 2.745

10.  Radioactive sodium chromate for the study of survival of red blood cells. I. The effect of radioactive sodium chromate on red cells.

Authors:  T F NECHELES; I M WEINSTEIN; G V LEROY
Journal:  J Lab Clin Med       Date:  1953-09
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  2 in total

Review 1.  Pharmacodynamic models of age-structured cell populations.

Authors:  Wojciech Krzyzanski
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-09-16       Impact factor: 2.745

2.  Mathematical modeling of erythrocyte chimerism informs genetic intervention strategies for sickle cell disease.

Authors:  Philipp M Altrock; Christian Brendel; Raffaele Renella; Stuart H Orkin; David A Williams; Franziska Michor
Journal:  Am J Hematol       Date:  2016-07-14       Impact factor: 10.047

  2 in total

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