Journal article
Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation using Data from Patients with Type 1 Diabetes
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Copenhagen University Hospital Herlev and Gentofte3
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Zealand Pharma A/S5
Copenhagen Center for Health Technology, Centers, Technical University of Denmark6
Center for Energy Resources Engineering, Centers, Technical University of Denmark7
Background: Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon.
Methods: Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods.
Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). Results: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects.
Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. Conclusions: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.
Language: | English |
---|---|
Publisher: | SAGE Publications |
Year: | 2017 |
Pages: | 1101-1111 |
ISSN: | 19322968 |
Types: | Journal article |
DOI: | 10.1177/1932296817693254 |
ORCIDs: | Wendt, Sabrina Lyngbye , Møller, Jan Kloppenborg , Madsen, Henrik , Jørgensen, John Bagterp and 0000-0001-6853-3805 |
Cross-validation Glucagon Glucoregulatory model Parameter Estimation SDG 3 - Good Health and Well-being Simulation model Type 1 diabetes
Adult Biomarkers Blood Glucose Computer Simulation Diabetes Mellitus, Type 1 Drug Dosage Calculations Female Humans Hypoglycemia Hypoglycemic Agents Injections, Subcutaneous Insulin Male Middle Aged Models, Biological Reproducibility of Results Treatment Outcome Young Adult cross-validation glucagon glucoregulatory model parameter estimation simulation model type 1 diabetes