Linking 4GI Glucose Homeostasis and Hall Body Composition Models to study GLP-1R Agonist effects on glucose and body weight
Rolien Bosch
LAP&P Consultants B.V.
Objectives: Our previously published 4GI systems pharmacology model describes the interrelationships between glucose, glucagon-like peptide-1 (GLP-1), glucagon, glucose-dependent insulinotropic peptide (GIP) and insulin (4GI) dynamics after food intake and drug administration in type 2 diabetes mellitus (T2DM) and healthy volunteers (HV) [1]. Besides improving glycaemic control, glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1R agonist) and dual GLP-1/glucagon receptor agonists show significant body weight reduction [2,3]. Weight loss, especially from fat reduction, can improve insulin sensitivity, allowing the body to better use insulin and lower blood glucose levels [4,5]. The Kevin Hall Body Composition Model is a mathematical model that estimates body composition, specifically body fat percentage and lean body mass, based on body weight and metabolic rate data model [6]. This investigation aimed to link the 4GI glucose homeostasis model with the Hall body composition model to quantify the effect of GLP-1 receptor (GLP-1R) agonists on body weight, and body weight effects on glucose homeostasis.
Methods: A selection of literature clinical data were collected that included both diet and GLP-1R agonistic effects on body weight. These data were combined with the original 4GI literature dataset. The Hall body composition model code was translated from MATLAB to NONMEM code, extended for GLP-1R agonists effects using in-vitro EC50 normalized free drug concentration as driver of the effect, and linked with the 4GI model. The GLP-1R agonistic and diet effects on the Hall model energy intake, and the 4GI model meal related parameters, such as the glucose absorption rate constant, were estimated. All other system specific parameters were kept fixed to their original values. To assess the weight loss effect on insulin sensitivity the model-predicted absolute change from baseline body weight was used as a continuous covariate on the insulin dependent glucose clearance, which reflects insulin sensitivity in the 4GI model.
Results: The diet effect on body weight was captured by an initial reduction in food intake that diminishes over time. The GLP-1R agonistic effects of liraglutide and semaglutide on energy intake in the Hall model were best described using an Emax model with a tolerance effect on the EC50, resulting in a reduced effect over time. The developed model was able to adequately describe diet and GLP-1R agonist induced weight loss. Including weight loss effects on insulin sensitivity in the 4GI model resulted in a significantly lower objective function value and an improved description of postprandial glucose levels. The model was also able to describe the observed trends in underlying biomarkers such as total energy expenditure and carbohydrate oxidation.
Conclusions: The Hall body composition model was successfully extended for GLP-1R agonistic effects on body weight by assuming an inhibiting effect on energy intake.
It is anticipated that the GLP-1R agonist extended Hall model can also be applied for new compounds due to the use of in-vitro EC50 normalized free drug concentration as driver of the GLP-1 effect.
Combining the Hall model with the 4GI model showed that weight loss has a positive effect on insulin sensitivity.
The combined model was also able to describe trends in underlying biomarkers. This could potentially increase mechanistic insight and answer mechanism of action related questions of GLP-1R agonist and related compounds, such as dual GLP-1/glucagon and GLP-1/GIP dual agonists, on interactions between glycaemic control and body composition.
References:
[1] Bosch R, Petrone M, Arends R, Vicini P, Sijbrands EJG, Hoefman S, et al. A novel integrated QSP model of in vivo human glucose regulation to support the development of a glucagon/GLP-1 dual agonist. CPT Pharmacometrics Syst Pharmacol [Internet]. 2022 [cited 2022 Jan 13];11:302–17. Available from: https://onlinelibrary.wiley.com/doi/10.1002/psp4.12752
[2] O’Neil PM, Birkenfeld AL, McGowan B, Mosenzon O, Pedersen SD, Wharton S, et al. Efficacy and safety of semaglutide compared with liraglutide and placebo for weight loss in patients with obesity: a randomised, double-blind, placebo and active controlled, dose-ranging, phase 2 trial. Lancet. Elsevier Ltd; 2018;392:637–49.
[3] Parker VER, Robertson D, Wang T, Hornigold DC, Petrone M, Cooper AT, et al. Efficacy, Safety, and Mechanistic Insights of Cotadutide, a Dual Receptor Glucagon-Like Peptide-1 and Glucagon Agonist. J Clin Endocrinol Metab [Internet]. Endocrine Society; 2020 [cited 2020 Aug 13];105. Available from: https://pubmed.ncbi.nlm.nih.gov/31608926/
[4] Goodpaster BH, Kelley DE, Wing RR, Meier A, Thaete FL. Effects of weight loss on regional fat distribution and insulin sensitivity in obesity. Diabetes. 1999;48:839–47.
[5] Ross R, Janssen I, Dawson J, Kungl A-M, Kuk JL, Wong SL, et al. Exercise-Induced Reduction in Obesity and Insulin Resistance in Women: a Randomized Controlled Trial. Obes Res [Internet]. 2004;12:789–98. Available from: http://doi.wiley.com/10.1038/oby.2004.95
[6] Hall KD. Predicting metabolic adaptation, body weight change, and energy intake in humans. Am J Physiol - Endocrinol Metab. 2010;298.
