A Semi-mechanistic Model for the Effects of a Novel Glucagon Receptor Antagonist on Glucagon and the Interaction Between Glucose, Glucagon, and Insulin Applied to Adaptive Phase II Design

Peng, Joanna; Denney, William S.; Musser, Bret J.; R, Liu; Tsai, Kuenhi; Fang, Lanyan; Reitman, Marc L.; Troyer, Matthew D.; Engel, Samuel S.; Liu, Xu; Stoch, Aubrey; Stone, Julie A.; Kowalski, Kenneth G.

doi:10.1208/s12248-014-9648-x

Cited by 17 publications

(4 citation statements)

References 25 publications

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“…13 The ability of insulin to suppress the glycogenolytic response to glucagon at high insulin concentration is not reflected in previously published models of glucose-glucagon dynamics. [18][19][20] A comparative study found that a multiplicative relationship was needed to describe insulin's inhibitory effect and glucagon's stimulating effect on glycogenolysis with insulin overriding the effect of glucagon at high concentrations of both hormones. 21 Recently, we extended the multiplicative model by incorporating the interaction between insulin and glucagon on glycogenolysis.…”

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confidence: 99%

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Wendt

Ranjan

Møller

et al. 2017

J Diabetes Sci Technol

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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.

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confidence: 99%

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Wendt

Ranjan

Møller

et al. 2017

J Diabetes Sci Technol

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“…The blocking effect of REGN1193 was evident as early as 6 h post‐dose and persisted for at least 3 weeks. Previous work with the small molecule MK‐0893 demonstrated predictable glucose‐lowering in subjects with diabetes based on the degree of inhibition of glucagon‐mediated hyperglycaemia; however, it is essential that the effects of REGN1193 be tested in subjects with type 2 diabetes to estimate clinical effects and allow comparison with the ≥1% glycated haemoglobin (HbA1c) reductions observed with the small molecule antagonists and antisense molecules…”

Section: Summary Of Glucagon Receptor Blocking Drugs Evaluated In Humansmentioning

confidence: 99%

Glucagon receptor as a drug target: A witches' brew of eye of newt (peptides) and toe of frog (receptors)

Nunez

D’Alessio

2017

Diabetes Obesity Metabolism

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Glucagon has a noble history in the annals of metabolic disease, 1 even though, to a layperson, insulin is its more famous counterregulatory partner. For decades medical students have been taught that glucagon raises blood glucose by increasing hepatic glucose output and that alleviation of hypoglycaemia is its primary function. 2 Thus, inhibition of glucagon secretion 3 or action 4 are logical approaches to the development of therapeutics that improve glycaemic control in both type 2 and type 1 diabetes mellitus; indeed, this strategy has been pursued for nearly 4 decades.The situation, however, is complex. The preproglucagon gene product can be cleaved at various points by specific prohormone convertases to yield several bioactive peptides, including glucagon, in a tissue-selective manner. 1 Prohormone convertase 2 is expressed in pancreatic islets to generate mainly glucagon in α cells. By contrast, prohormone convertase 1/3 processes proglucagon in the gut and brain to glucagon-like peptide-1 (GLP-1), glucagon-like peptide-2 (GLP-2), glicentin and oxyntomodulin. These boundaries are not absolute, however, and the relative abundance of these peptides in a tissue, and therefore its secretory potential, can vary depending on the differential expression of the processing enzymes. 5 There is evidence that GLP-1 is expressed in pancreatic islets, 6,7 while glucagon may be secreted by enteroendocrine cells in the gut. 8 Enteroendocrine cells also express other peptides such as peptide-tyrosinetyrosine (PYY), 9,10 and it now appears that secretion can be modulated by feedback inhibition by homotypic (GLP-1 reducing GLP-1 secretion) as well as heterotypic mechanisms (GLP-1 reducing PYY secretion). 11,12Glucagon receptor pharmacology is also multifaceted. (Table 1) have uncovered good, bad and ugly aspects in humans (Table 2) 25 ; therefore, the essential question for drug development is whether these features can be separated sufficiently to open a wide therapeutic index, and allow safe long-term use. Small molecules targeting the glucagon receptor have both common structural elements and molecule-specific motifs that can confound the interpretation of adverse event profiles. Comparing the results from totally different molecular classes, small molecule-, antibody-and antisense-based approaches, therefore, allows some parsing of effects that can reasonably be attributed to mechanism-of-action vs those that may be related to moleculespecific "toxicity." Moreover, preclinical models such as glucagon

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“…Peng et al [9] proposed a detailed model for the glucagon challenge test intended to study the effect of a glucagon receptor antagonist drug on glucose homeostasis in healthy and T2DM subjects. Their focus was to incorporate the PK characteristics of the drug into the model.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of the glucagon challenge test

Saeed

Dongen

Álvarez-Jiménez

et al. 2019

J Pharmacokinet Pharmacodyn

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A model for the homeostasis of glucose through the regulating hormones glucagon and insulin is described. It contains a subsystem that models the internalization of the glucagon receptor. Internalization is a mechanism in cell signaling, through which G-protein coupled receptors are taken from the surface of the cell to the endosome. The model is used to interpret data from a glucagon challenge test in which subjects have been under treatment with a novel glucagon receptor anti-sense drug which is aimed at reducing the number of receptors in the liver. It is shown how the receptor internalization results in tolerance of the blood glucose concentration to glucagon-induced hyperglycemia. We quantify the reduction of the number of receptors using the model and the data before and after treatment.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-019-09655-2) contains supplementary material, which is available to authorized users.

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A Semi-mechanistic Model for the Effects of a Novel Glucagon Receptor Antagonist on Glucagon and the Interaction Between Glucose, Glucagon, and Insulin Applied to Adaptive Phase II Design

Cited by 17 publications

References 25 publications

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Glucagon receptor as a drug target: A witches' brew of eye of newt (peptides) and toe of frog (receptors)

Mathematical modeling of the glucagon challenge test

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