Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart

• Published in: Journal of pharmacokinetics and pharmacodynamics Vol. 44; no. 5; pp. 477 - 489
• Main Authors: Mansell, Erin J., Schmidt, Signe, Docherty, Paul D., Nørgaard, Kirsten, Jørgensen, John B., Madsen, Henrik
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-10-2017; Springer Nature B.V
• Subjects: Adult; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Compartments; Complexity; Computer simulation; Diabetes mellitus; Diabetes Mellitus, Type 1 - blood; Female; Humans; Infusions, Subcutaneous; Insulin; Insulin Aspart - administration & dosage; Insulin Aspart - blood; Insulin Aspart - pharmacokinetics; Male; Mathematical models; Middle Aged; Models, Cardiovascular; Original Paper; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Veterinary Medicine/Veterinary Science; Young Adult; Pharmacokinetic modelling; Parameter identification; Type 1 diabetes; Practical identifiability; Continuous subcutaneous insulin infusion; Goodness of fit
• ISSN: 1567-567X; 1573-8744
• DOI: 10.1007/s10928-017-9535-z

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: R
2
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 (R
2
 = 0.53), had the highest Akaike score, and worst prediction. Goodness of fit improved with increasing model complexity (R
2
 = 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 (R
2
 = 0.85) and parameterisation, and best prediction and practical identifiability (CV < 2%).