Abstract

The aim of this study was to develop a dynamic model-based approach to separately quantify the exogenous and endogenous contributions to total plasma insulin concentration and to apply it to assess the effects of inhaled-insulin administration on endogenous insulin secretion during a meal test. A three-step dynamic in-silico modeling approach was developed to estimate the two insulin contributions of total plasma insulin in a group of 21 healthy subjects who underwent two equivalent standardized meal tests on separate days, one of which preceded by inhalation of a Technosphere® Insulin dose (22U or 20U). In the 30–120 min test interval, the calculated endogenous insulin component showed a divergence in the time course between the test with and without inhaled insulin. Moreover, the supra-basal area-under-the-curve of endogenous insulin in the test with inhaled insulin was significantly lower than that in the test without (2.1 ± 1.7 × 104 pmol·min/L vs 4.2 ± 1.8 × 104 pmol·min/L, p < 0.01). The percentage of exogenous insulin reaching the plasma, relative to the inhaled dose, was 42 ± 21%. The proposed in-silico approach separates exogenous and endogenous insulin contributions to total plasma insulin, provides individual bioavailability estimates, and can be used to assess the effect of inhaled insulin on endogenous insulin secretion during a meal.

Details

Title
An in-silico modeling approach to separate exogenous and endogenous plasma insulin appearance, with application to inhaled insulin
Author
Piersanti, Agnese 1 ; Pacini, Giovanni 2 ; Tura, Andrea 3 ; D’Argenio, David Z. 4 ; Morettini, Micaela 1 

 Università Politecnica Delle Marche, Department of Information Engineering, Ancona, Italy (GRID:grid.7010.6) (ISNI:0000 0001 1017 3210) 
 Padua, Italy (GRID:grid.7010.6) 
 CNR Institute of Neuroscience, Padua, Italy (GRID:grid.418879.b) (ISNI:0000 0004 1758 9800) 
 University of Southern California, Department of Biomedical Engineering, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853) 
Pages
10936
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3054306458
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.