1. A FULLY IDENTIFIABLE PHYSIOLOGICAL MODEL OF INSULIN KINETICS FOR CLINICAL APPLICATIONS T Lotz 1, J G Chase 1, S Andreassen 2, C E Hann 1, J Lin 1, J Wong 1 and K A McAuley 3 1 Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand 2 Centre for Model-based Medical Decision Support, Aalborg University, Denmark 3 Edgar National Centre for Diabetes Research, University of Otago, Dunedin, New Zealand

2. Why model insulin kinetics? Glycaemic control for critically ill and Diabetes –Tight glycaemic control in ICU reduces mortality by up to 45% (Van den Berghe et al 2001) Diagnosis of insulin resistance –Requires knowledge of insulin kinetics Current models not physiological or difficult to identify!

3. Goals 1.Physiological accuracy Include key dynamics Parameters within physiological range 2.Useful in clinical applications Simple identification without the need of additional tests If possible population model

4. 2-compartment insulin kinetics model + glucose pharmacodynamics PLASMA INTERSTITIAL FLUID KIDNEYS LIVER diffusion CELLS PANCREAS nCnC nKnK nLnL nInI u en u ex GLUCOSE

5. Similarities with C-peptide PLASMA V P INTERSTITIAL FLUID V Q KIDNEYS PANCREAS nKnK nInI u en PLASMA V P INTERSTITIAL FLUID V Q KIDNEYS LIVER CELLS PANCREAS nCnC nKnK nLnL nInI u en Additional losses C-peptide (Van Cauter et al 1992) Insulin

6. A priori parameter identification Similar molecular size and equimolar secretion of insulin and C-peptide Distribution volumes (V P, V Q ), transcapillary diffusion (n I ), kidney clearance (n K ) assumed to match values for C-peptide Parameters from well validated population model for C-peptide kinetics (Van Cauter et. al. 1992) Saturation of hepatic clearance (α I ) fixed from published literature Clearance by the cells fixed to achieve ss-concentration gradient between the compartments (I ss /Q ss =5/3) (Sjostrand et al 2005) 1 key insulin parameter to be estimated, liver clearance n L

7. Validation on clamps Euglycaemic clamp trials (N=146) Fitting errors within measurement noise: e G =5.9±6.6% SD e I =6.2±6.4% SD Correlation of clamp ISI with model S I : r=0.98 All parameters well within physiological range!

8. Validation on dose response test 10g glucose, 0.5U insulin Population insulin parameters Endogenous insulin estimated through C-peptide kinetics Hepatic clearance n L estimated from fasting information No need for additional insulin parameter estimation!

9. Conclusions Physiological insulin kinetics model Easy a-priori identification with C-peptide population model Additional fitting of key parameters (1 for insulin, 2 for glucose) Fits within measurement noise Great potential for use in clinical applications

10. Acknowledgements – Questions? Geoff ChaseGeoff Shaw Dominic Lee Steen Andreassen Jim Mann Kirsten McAuley Jessica LinChris HannJason Wong