Adaptation of the Glucose, Glucagon, Gastric-Inhibitory Peptide, Glucagon-like Peptide-1, and Insulin (4GI) Model for Simulation of Acute Meal Challenges with Infused Antagonists

Objectives: Agonists of glucagon-like peptide-1 (GLP-1) are potent therapies for both type 2 diabetes and obesity. Recently, there has been interest in examining other incretins, such as gastric-inhibitory peptide (GIP) as a complement to GLP-1. Interestingly, GIP as a target has been tested clinically both as a purported agonist (1) and as an antibody (2). While most of the literature examines the effect of agonizing GLP-1 and GIP, we wished to develop a model to better understand quantitatively the physiological effects of antagonism, particularly in acute challenges. Methods: We adapted a published model of glucose-insulin-incretin dynamics (4GI model) as the base model for our analysis (3). This model was selected because it has been validated for glycemic efficacy against several GLP-1 agonists and thus contained sufficient biological scope. To adapt the 4GI model to simulate acute challenges of incretin antagonists, we tested the model against a series of challenges published by Gasbjerg et al. (4, 5). In these acute challenges antagonists of GIP, GLP-1, or the combination thereof was infused along with either a meal challenge or nutrient infusion. Outcomes of interest for evaluating the performance of the model included changes in glucose, insulin, glucagon, and incretin hormones. Results: We calibrated the baseline model to represent the healthy volunteers of the studies. Global sensitivity was performed to obtain a list of the most sensitive parameters at baseline and following meal challenges, allowing us to reduce the number of fitted parameters. With these parameters, we adequately captured the means and variability for all the observables. To correctly account for the loss of GIP signaling, we extended the model to account for the convergence of both GLP-1 and GIP on cAMP in beta cells (6). The updated model then adequately captured the response to the acute challenges with both antagonists. Conclusions: With updates to GLP-1 and GIP signaling, the 4GI model captures the expected antagonist response in healthy individuals following a meal or infusion challenge. Future work can extend to patients with type 2 diabetes and challenges following chronic treatment.Citations: 1. A. M. Jastreboff et al., N Engl J Med 387, 205-216 (2022).2. M. M. Veniant et al., Nat Metab 6, 290-303 (2024).3. R. Bosch et al., CPT Pharmacometrics Syst Pharmacol 11, 302-317 (2022).4. L. S. Gasbjerg et al., Diabetes 68, 906-917 (2019).

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Publication Details

Published
2025-02-18
DOI
https://doi.org/10.70534/rwpo1208
Primary Topic
Hyperglycemia and glycemic control in critically ill and hospitalized patients
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article

Adaptation of the Glucose, Glucagon, Gastric-Inhibitory Peptide, Glucagon-like Peptide-1, and Insulin (4GI) Model for Simulation of Acute Meal Challenges with Infused Antagonists

Theodore R. Rieger
Hyperglycemia and glycemic control in critically ill and hospitalized patients
article

Adaptation of the Glucose, Glucagon, Gastric-Inhibitory Peptide, Glucagon-like Peptide-1, and Insulin (4GI) Model for Simulation of Acute Meal Challenges with Infused Antagonists

Theodore R. Rieger
article en

Abstract

Objectives: Agonists of glucagon-like peptide-1 (GLP-1) are potent therapies for both type 2 diabetes and obesity. Recently, there has been interest in examining other incretins, such as gastric-inhibitory peptide (GIP) as a complement to GLP-1. Interestingly, GIP as a target has been tested clinically both as a purported agonist (1) and as an antibody (2). While most of the literature examines the effect of agonizing GLP-1 and GIP, we wished to develop a model to better understand quantitatively the physiological effects of antagonism, particularly in acute challenges. Methods: We adapted a published model of glucose-insulin-incretin dynamics (4GI model) as the base model for our analysis (3). This model was selected because it has been validated for glycemic efficacy against several GLP-1 agonists and thus contained sufficient biological scope. To adapt the 4GI model to simulate acute challenges of incretin antagonists, we tested the model against a series of challenges published by Gasbjerg et al. (4, 5). In these acute challenges antagonists of GIP, GLP-1, or the combination thereof was infused along with either a meal challenge or nutrient infusion. Outcomes of interest for evaluating the performance of the model included changes in glucose, insulin, glucagon, and incretin hormones. Results: We calibrated the baseline model to represent the healthy volunteers of the studies. Global sensitivity was performed to obtain a list of the most sensitive parameters at baseline and following meal challenges, allowing us to reduce the number of fitted parameters. With these parameters, we adequately captured the means and variability for all the observables. To correctly account for the loss of GIP signaling, we extended the model to account for the convergence of both GLP-1 and GIP on cAMP in beta cells (6). The updated model then adequately captured the response to the acute challenges with both antagonists. Conclusions: With updates to GLP-1 and GIP signaling, the 4GI model captures the expected antagonist response in healthy individuals following a meal or infusion challenge. Future work can extend to patients with type 2 diabetes and challenges following chronic treatment.Citations: 1. A. M. Jastreboff et al., N Engl J Med 387, 205-216 (2022).2. M. M. Veniant et al., Nat Metab 6, 290-303 (2024).3. R. Bosch et al., CPT Pharmacometrics Syst Pharmacol 11, 302-317 (2022).4. L. S. Gasbjerg et al., Diabetes 68, 906-917 (2019).

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Hyperglycemia and glycemic control in critically ill and hospitalized patients
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