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Red blood cell survival and its influence on clinical biomarkers Julia Korell & Stephen Duffull School of Pharmacy, University of Otago, Dunedin, New Zealand Seoul, South Korea 5 th September 2012 Otago Pharmacometrics Group, School of

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Red blood cell survival and its influence on clinical biomarkers

Julia Korell & Stephen Duffull

School of Pharmacy, University of Otago, Dunedin, New Zealand Seoul, South Korea

5th September 2012

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Context

  • Glycated haemoglobin (HbA1c)

commonly used as marker for glycaemic control

– Extent of glycation depends on the blood glucose concentration AND the lifespan of red blood cells (RBCs) – Shortened RBC survival in patients with chronic kidney disease (CKD) results in lowered HbA1c concentrations  False assumption of an adequate diabetic control

Wild et al. (2004) Diabetes Care 279(5):1047-1053 Nathan (1993) N Engl J Med 328(23):1676-1685 Goldstein et al. (2004) Diabetes Care 27(7):1761-1773 Vos et al. (2011) Am. J. Kidney Dis. 58(4):591-598

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Overview

  • Background on RBCs:

– Physiological destruction mechanisms – Methods to estimate the lifespan of RBCs

  • Development of a semi-mechanistic model for RBC survival
  • Application of the model to clinical data
  • Discussion & Conclusions
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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Background – Red blood cells (RBCs)

  • RBC production in the bone marrow controlled by the

hormone erythropoietin (EPO)

  • RBCs die after a certain period of time = lifespan

– Common dogma: 120 days

EPO

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

  • Four general processes of RBC destruction:

– Early destruction of unviable RBCs – Constant random destruction and loss from circulation – Mid-term destruction of misshapen cells – Senescence (death due to old age)

 Do we really believe that all RBCs die at the same age?

Background – RBC destruction

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Berlin et al. (1959) Physiol Rev 39(3):577-616 Franco (2009) Am J Hematol 84(2):109-114

Background – Estimation of RBC survival

Method Mean RBC lifespan

  • No. of subjects

Year

Range Average Agglutination 110 – 135 days 117 days 87 1919

15N-glycine

109 – 127 days 118 days 3 1946

51Cr

108 – 120 days 113 days 37 1950 DF32P 124 days 10 1954 Biotin 103 days 7 1987

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Background – Estimation of RBC survival

  • Problems:

– Flawed labelling methods  Inaccurate estimation of RBC lifespan – Usually only a mean lifespan is reported  Restricted insight into physiological mechanisms of RBC destruction

  • Unanswered question:

– Which mechanism of RBC destruction is mostly affected in anaemic patients with CKD?

  • Increased random destruction
  • Accelerated senescence
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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Aim & Objectives

To obtain a better understanding of RBC survival and physiological destruction mechanisms.

Specific objectives: 1. To develop a semi-mechanistic model for RBC survival that is based on plausible physiological mechanisms of RBC destruction 2. To incorporate flaws associated with commonly used RBC labelling techniques into the model 3. To apply the developed model to clinical data obtained in an in vivo study of RBC survival

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Development of a semi-mechanistic RBC survival model

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Methodology

  • Based on principles of survival data analysis
  • Functions of survival time for a constant hazard model:

 Find a hazard function that can describe physiological mechanisms of RBC destruction

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Human mortality ≙ RBC mortality

Infant mortality ≙ early removal of unviable RBCs Constant risk of death ≙ random destruction Reduced life expectancy ≙ misshapen RBCs Death due to old age ≙ senescence

Bebbington et al. (2007) J Theor Biol. 245(3):528 - 538

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Human lifespan ≙ RBC lifespan

Death due to old age ≙ senescence Reduced life expectancy ≙ misshapen RBCs Constant risk of death ≙ random destruction Infant mortality ≙ early removal

  • f unviable RBCs

Bebbington et al. (2007) J Theor Biol. 245(3):528 - 538

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

RBC lifespan distribution

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

RBC lifespan distribution

s1 & s2 c r1 & r2

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Proposed RBC survival model

  • Simulations based on survival function:

with 0    t N(t) = number of RBCs present at day t p() = production rate at day  S(t-) = survival of a RBC cohort born on day 

  • Implemented in MATLAB

Korell et al. (2011) J Theor Biol 291(0):88-98

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Simulation – Ideal random labelling

Dornhorst (1951) Blood.6:1284-1292 Korell et al. (2011) J Theor Biol 268(1):39-49

Prediction assuming a normal distribution of RBC lifespans (1951) Prediction from our model

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Extension of the model to incorporate flaws of existing labelling methods

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

  • 51Cr = radioactive chromium
  • Most commonly used labelling method for RBCs
  • Random labelling method:

– Labels RBCs of all ages present at one point in time – % label left in the circulation measured over time

 Similar to catch-and-release studies on animals

Random labelling with 51Cr

Brown Kiwi Royal Albatross Kakapo

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Random labelling with 51Cr

Radioactive decay Elution Vesiculation

intracellular extracellular

51CrVIO4 2-

Hb51CrIII Hb + 51CrIII (Hb +) 51V

51CrIII

Hb51CrIII Hb51CrIII

Vesicle

Korell et al. (2011) J Theor Biol 291(0):88-98

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Simulation – Random labelling with 51Cr

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Application to clinical data

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

The data

  • In vivo RBC survival study conducted at Dunedin Hospital in

2010:

– 14 patients with CKD 14 age & sex matched controls – Using 51Cr as random label – 10 - 13 blood samples per individual taken between day 1 and day 40 (50) after labelling

 Difference in RBC survival between the two groups?  Possible mechanism?

Vos et al. (2011) Am J Kidney Dis 58(4):591-598

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

The data

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Data & Model prediction

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Data analysis

  • Population analysis using MONOLIX 1.1
  • Model contains six fixed effect parameters:
  • 51Cr data not informative enough to estimate all six

parameters

Korell et al. (2011) J Theor Biol 291(0):88-98

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Data analysis

  • Estimation focussed on parameters of highest interest only:

– s2: main parameter controlling senescence – c: controls random destruction  Which provides the better fit to the data?

  • CKD tested as covariate on the estimated parameter

– e.g. for c:

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Results

  • Estimating random

destruction (c) provided the better fit

 Mechanism of greater importance

  • CKD was a significant

covariate for RBC survival

 Mean RBC lifespan decreased by ~20% in CKD patients compared to healthy controls

Estimates Controls CKD Population mean c [days-1] 0.0106 0.0170* Mean RBC lifespan [days] 69.4 56.2 Between subject variability [CV%] 26.9% Proportional error [CV%] 2.56% Additive error [%label] 1.43

Loge et al. (1958) Am J Med 24:4-18 Korell et al. (2011) J Pharmacokinet Pharmacodyn 38(6):787-801

* value includes the covariate effect of CKD

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Results

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Discussion & Conclusions

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Discussion & Conclusions

  • Survival analysis methodology was successfully applied to

develop a semi-mechanistic RBC survival model

  • Flaws associated with commonly used RBC labelling

techniques can be incorporated:

– e.g. random labelling with 51Cr

  • The model can be used to analyse clinical data of RBC

survival

Korell et al. (2011) J Theor Biol 268(1):39-49 Korell et al. (2011) J Theor Biol 291(0):88-98 Korell et al. (2011) J Pharmacokinet Pharmacodyn 38(6):787-801

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Discussion & Conclusions

  • Random labelling with 51Cr is not informative enough to

support full parameter estimation in the model

 Better labelling methods are required in the future to obtain a deeper insight into RBC destruction mechanisms

  • A better understanding of RBC destruction & survival and how

these are affected by pathological conditions such as chronic kidney disease would ultimately improve the clinical use of RBC derived biomarkers

– e.g. HbA1c as marker for glycaemic control

Korell et al. (2011) J Theor Biol 291(0):88-98

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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Acknowledgments

  • WCoP Organisation

Committee

  • School of Pharmacy
  • University of Otago
  • Friends of the Otago

Pharmacometrics Group Collaborators: Renal Research Group, Department

  • f Medicine, University of Otago,

Dunedin, New Zealand:

  • Dr Frederiek Vos
  • Dr Carolyn Coulter
  • Dr John Schollum
  • Prof. Robert Walker
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Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Thank you!