A Model of Glucose-Insulin-Pramlintide Pharmacokinetics and Pharmacodynamics in Type I Diabetes

• Published in: Journal of diabetes science and technology 2014-05, Vol.8 (3), p.529-542
• Main Authors: Ramkissoon, Charrise M., Aufderheide, Brian, Bequette, B. Wayne, Palerm, Cesar C.
• Format: Article
• Language: English
• Subjects: Administration, Intravenous; Biomarkers - blood; Blood Glucose - drug effects; Blood Glucose - metabolism; Computer Simulation; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - diagnosis; Diabetes Mellitus, Type 1 - drug therapy; Eating; Humans; Hypoglycemic Agents - administration & dosage; Hypoglycemic Agents - pharmacokinetics; Injections, Subcutaneous; Insulin Lispro - administration & dosage; Insulin Lispro - pharmacokinetics; Islet Amyloid Polypeptide - administration & dosage; Islet Amyloid Polypeptide - pharmacokinetics; Models, Biological; Nonlinear Dynamics; Original; Reproducibility of Results; Treatment Outcome
• ISSN: 1932-2968; 1932-2968; 1932-3107
• DOI: 10.1177/1932296813517323

Background: Type 1 diabetes mellitus (T1DM) complications are significantly reduced when normoglycemic levels are maintained via intensive therapy. The artificial pancreas is designed for intensive glycemic control; however, large postprandial excursions after a meal result in poor glucose regulation. Pramlintide, a synthetic analog of the hormone amylin, reduces the severity of postprandial excursions by reducing appetite, suppressing glucagon release, and slowing the rate of gastric emptying. The goal of this study is to create a glucose-insulin-pramlintide physiological model that can be employed into a controller to improve current control approaches used in the artificial pancreas. Methods: A model of subcutaneous (SC) pramlintide pharmacokinetics (PK) was developed by revising an intravenous (IV) pramlintide PK model and adapting SC insulin PK from a glucose-insulin model. Gray-box modeling and least squares optimization were used to obtain parameter estimates. Pharmacodynamics (PD) were obtained by choosing parameters most applicable to pramlintide mechanisms and then testing using a proportional PD effect using least squares optimization. Results: The model was fit and validated using 27 data sets, which included placebo, PK, and PD data. SC pramlintide PK root mean square error values range from 1.98 to 10.66 pmol/L. Pramlintide PD RMSE values range from 10.48 to 42.76 mg/dL. Conclusions: A new in silico model of the glucose-insulin-pramlintide regulatory system is presented. This model can be used as a platform to optimize dosing of both pramlintide and insulin as a combined therapy for glycemic regulation, and in the development of an artificial pancreas as the kernel for a model-based controller.