Red blood cell survival and its influence on clinical biomarkers - PowerPoint PPT Presentation

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 Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Context • Glycated haemoglobin (HbA 1c ) 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 HbA 1c 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 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 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Background – Red blood cells (RBCs) EPO • 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 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Background – RBC destruction • 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? Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Background – Estimation of RBC survival Mean RBC lifespan Method No. of subjects Year Range Average 110 – 135 days Agglutination 117 days 87 1919 109 – 127 days 15 N-glycine 118 days 3 1946 108 – 120 days 51 Cr 113 days 37 1950 DF 32 P 124 days 10 1954 Biotin 103 days 7 1987 Berlin et al. (1959) Physiol Rev 39(3):577-616 Franco (2009) Am J Hematol 84(2):109-114 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 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 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Development of a semi-mechanistic RBC survival model 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 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Human mortality ≙ RBC mortality Infant mortality ≙ early removal of Death due to old age ≙ senescence unviable RBCs Reduced life expectancy ≙ misshapen RBCs Constant risk of death ≙ random destruction Bebbington et al. (2007) J Theor Biol. 245(3):528 - 538 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Human lifespan ≙ RBC lifespan Death due to old age ≙ senescence Infant mortality ≙ early removal of unviable RBCs Reduced life expectancy ≙ misshapen RBCs Constant risk of death ≙ random destruction Bebbington et al. (2007) J Theor Biol. 245(3):528 - 538 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

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

RBC lifespan distribution s 1 & s 2 r 1 & r 2 c 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 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Simulation – Ideal random labelling Prediction assuming a normal Prediction from our model distribution of RBC lifespans (1951) Dornhorst (1951) Blood.6:1284-1292 Korell et al . (2011) J Theor Biol 268(1):39-49 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Extension of the model to incorporate flaws of existing labelling methods Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Random labelling with 51 Cr • 51 Cr = 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 Brown Kiwi Kakapo Royal Albatross Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Random labelling with 51 Cr intracellular extracellular Hb  51 Cr III 51 Cr VI O 4 2- Radioactive decay Elution (Hb +) 51 V Hb + 51 Cr III 51 Cr III  Vesiculation Hb  51 Cr III Hb  51 Cr III Vesicle Korell et al. (2011) J Theor Biol 291(0):88-98 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Simulation – Random labelling with 51 Cr Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Application to clinical data 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 51 Cr 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 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

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

Data & Model prediction 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: • 51 Cr data not informative enough to estimate all six parameters Korell et al. (2011) J Theor Biol 291(0):88-98 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Data analysis • Estimation focussed on parameters of highest interest only: – s 2 : 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 : Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

Results • Estimating random Estimates Controls CKD destruction ( c ) provided the Population mean  c better fit 0.0106 0.0170* [days -1 ]  Mechanism of greater Mean RBC lifespan importance 69.4 56.2 [days] • CKD was a significant Between subject 26.9% variability [CV%] covariate for RBC survival Proportional error  Mean RBC lifespan 2.56% [CV%] decreased by ~20% in CKD Additive error patients compared to 1.43 [%label] healthy controls * value includes the covariate effect of CKD Loge et al. (1958) Am J Med 24:4-18 Korell et al. (2011) J Pharmacokinet Pharmacodyn 38(6):787-801 Otago Pharmacometrics Group, School of Pharmacy, University of Otago ~ www.pharmacometrics.co.nz

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