A novel integrated QSP model of in vivo human glucose regulation to support development of a glucagon/GLP-1 dual agonist.
Rolien Bosch 1*, Marcella Petrone 2*, Paolo Vicini 2*, Nelleke Snelder 1*
1 LAP&P Consultants BV, Leiden, The Netherlands 2 MedImmune, Cambridge, United Kingdom
Objectives: MEDI0382 is a synthetic peptide with both glucagon-like peptide-1 (GLP-1) and glucagon receptor co-agonist activity. The combination of GLP-1 and glucagon activity has been shown to improve glycaemic control and lipid profiles, and cause significant weight loss in preclinical studies [1]. These effects are hypothesized to be mediated through stimulation of glucose-mediated insulin release (the incretin effect), delayed gastric emptying, and increased fatty acid oxidation. Mechanistic PKPD models are available to quantify drug effects on glucose, insulin and GLP-1 dynamics in humans [2-4]. However, the integrated interrelationship between glucagon and the GLP-1 and glucagon effects on gastric emptying has not been captured in these models. Hence, the aim of this research is to develop a quantitative systems pharmacology (QSP) model that characterises the interrelationship between glucose, insulin GLP-1, glucagon and glucose-dependent insulinotropic peptide (GIP), which can be used to support development of drugs modulating glucose regulation pathways.
Methods: A QSP model describing glucose, glucagon, GLP-1, GIP and insulin (4GI model) levels was developed using literature clinical data [2-10]. These data included various, non-pharmacological challenges to glucose regulated pathways (e.g. intravenous glucose, meals, glucagon and incretins). Mean data from three clinical studies (LEAD-3, LEAD-6 and AWARD-6), in which the effect of liraglutide (a GLP-1 agonist) on glucose and insulin was investigated, were added to the dataset to describe the effects of GLP-1 agonism [11-13]. The integrated glucose-insulin (IGI) model by Silber et al [2,3], in combination with knowledge from the Landersdorfer model [4], was used as a starting point, and glucose and insulin disposition parameters were fixed to the published values. The model was adjusted and extended to describe glucagon, GLP-1 and GIP dynamics. Liraglutide pharmacokinetics [14] was included to model the effects of liraglutide on glucose concentrations. The model was externally validated by predicting the effects of another GLP-1 agonist, dulaglutide, on glucose. For this, the 4GI model was combined with a published dulaglutide PK model [15], and used to predict the effects of dulaglutide on fasting and postprandial plasma glucose levels from the AWARD-6 study [13].
Results: The developed QSP model was shown to adequately describe glucose, insulin, GLP-1, GIP and glucagon dynamics. Important known feedback mechanisms could be identified with good precision (parameter CV < 50 %), and included glucose stimulation of insulin, glucose inhibition of glucagon, insulin stimulation of glucose clearance, GLP-1- and GIP stimulation of glucose-dependent insulin secretion, GLP-1 inhibition of glucose uptake, GLP-1 inhibition of glucagon, glucagon stimulation of glucose. Liraglutide effects on fasting and postprandial glucose levels were adequately described. External validation showed that the model can predict fasting and postprandial glucose levels after dulaglutide administration.
Conclusions: A novel integrated QSP model characterizing important known feedback mechanisms between glucose, insulin, glucagon, GLP and GIP (4GI) after food intake and/or drug administration was developed and externally validated using literature data. The 4GI model can be proposed as a quantitative decision making tool to support progression of novel molecules modulating these pathways. Future 4GI model features may include e.g. integrating mechanisms of energy expenditure and the effect of weight and lipid changes.
References:
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