Julia Korell (1,2), Stephen Duffull (1)
(1) School of Pharmacy, University of Otago, Dunedin, New Zealand, (2) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
Objectives: Modelling the survival time of various haematological cell populations, including red blood cells (RBCs), has recently gained interest within the area of pharmacometrics. Most of the proposed models follow the principle of parsimony. For RBCs, these models mainly focus on the predominant destruction mechanism of age-related cell death (senescence) and do not account for within subject variability in the RBC lifespan. 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 [1]. Here, it is shown that the proposed model in combination with informative RBC survival data obtained using the biotin labelling technique is able to provide a deeper insight into RBC destruction mechanisms.
Methods: The data used for this analysis was digitally extracted from Cohen et al. [2]. It had been obtained from six diabetic subjects and six healthy controls using age-independent random (i.e. population) labelling of RBCs with biotin. Non-linear mixed effect modelling was conducted in MATLAB R2012a using an implementation of the SAEM algorithm used in MONOLIX 1.1 [3].
Results: Three RBC destruction mechanisms were estimable based on the available data: random destruction, senescence and destruction due to delayed failure. Large between subject variability in the individual mean RBC lifespan was seen (50 to 90 days, median 76.7 days). It was possible to identify three subjects with a decreased RBC survival in the study population (mean RBC lifespan less than 60 days). 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. Diabetes mellitus was not a significant covariate on RBC survival.
Conclusions: The proposed RBC survival model is able to provide a deeper insight into the underlying RBC destruction mechanisms when it is used to analyse informative RBC survival data. In contrast to parsimonious RBC survival models, it allows for variability in the underlying RBC lifespan distribution and the mean RBC lifespan on the individual as well as the population level.
References:
[1] Korell J, et al. (2011). J. Theor. Biol. 268(1):39-49
[2] Cohen R, et al. (2008). Blood 112(10):4284-4291
[3] Lavielle M (2005) MONOLIX 1.1 User manual. Laboratoire de Mathematiques, Universite Paris-Sud, Orsay (France).
Reference: PAGE 22 () Abstr 2748 [www.page-meeting.org/?abstract=2748]
Poster: Other Modelling Applications