Journal of Pharmacokinetics and Pharmacodynamics

, Volume 44, Issue 5, pp 477–489 | Cite as

Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart

  • Erin J. Mansell
  • Signe Schmidt
  • Paul D. Docherty
  • Kirsten Nørgaard
  • John B. Jørgensen
  • Henrik Madsen
Original Paper


Effective mathematical modelling of continuous subcutaneous infusion pharmacokinetics should aid understanding and control in insulin therapy. Thorough analysis of candidate model performance is important for selecting the appropriate models. Eight candidate models for insulin pharmacokinetics included a range of modelled behaviours, parameters and complexity. The models were compared using clinical data from subjects with type 1 diabetes with continuous subcutaneous insulin infusion. Performance of the models was compared through several analyses: R2 for goodness of fit; the Akaike Information Criterion; a bootstrap analysis for practical identifiability; a simulation exercise for predictability. The simplest model fit poorly to the data (R2 = 0.53), had the highest Akaike score, and worst prediction. Goodness of fit improved with increasing model complexity (R2 = 0.85–0.92) but Akaike scores were similar for these models. Complexity increased practical non-identifiability, where small changes in the dataset caused large variation (CV > 10%) in identified parameters in the most complex models. Best prediction was achieved in a relatively simple model. Some model complexity was necessary to achieve good data fit but further complexity introduced practical non-identifiability and worsened prediction capability. The best model used two linear subcutaneous compartments, an interstitial and plasma compartment, and two identified variables for interstitial clearance and subcutaneous transfer rate. This model had optimal performance trade-off with reasonable fit (R2 = 0.85) and parameterisation, and best prediction and practical identifiability (CV < 2%).


Pharmacokinetic modelling Continuous subcutaneous insulin infusion Type 1 diabetes Parameter identification Goodness of fit Practical identifiability 



Clinical data collection was funded by the Danish Council for Strategic Research (NABIIT Project 2106-07-0034).”


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Centre for BioengineeringUniversity of CanterburyChristchurchNew Zealand
  2. 2.Department of EndocrinologyHvidovre University HospitalHvidovreDenmark
  3. 3.Danish Diabetes AcademyOdense CDenmark
  4. 4.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKgs. LyngbyDenmark

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