From In Vitro Efficacy to Long-term HbA1c Response for GLP-1R/GlucagonR Agonism using the 4GI-HbA1c Systems Model
Rolien Bosch (1,6), Marcella Petrone (2), Rosalin Arends (3,4), Eric J.G. Sijbrands (5), Sven Hoefman (1), Nelleke Snelder (1)
(1) LAP&P Consultants Leiden, The Netherlands, (2) Clinical Pharmacology and Safety Sciences, AstraZeneca, Cambridge, United Kingdom, (3) AstraZeneca, Gaithersburg, USA, (5) Department of Internal Medicine, Erasmus MC, University Medical Centre, Rotterdam, The Netherlands
Background: Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder characterised by elevated blood glucose levels. Besides glucose, haemoglobin A1c (HbA1c) is a critical biomarker in diabetes management [1]. It is pivotal in assessing long-term glycaemic control in individuals with T2DM. HbA1c reflects the average blood glucose levels over the preceding two to three months, providing a valuable indicator of a patient's overall glucose regulation. One of the primary objectives in T2DM management is to achieve optimal glycaemic control, which requires not only the stabilisation of blood glucose levels but also the promotion of sustainable weight loss. The American Diabetes Association recommends high-efficacy approaches such as Glucagon-like peptide 1 (GLP-1)-based therapies to reach these goals [2]. The prediction of clinical outcome of these therapies on glucose levels as well as HbA1c using early available pharmacokinetic (PK) and in vitro efficacy information can be a valuable tool for compound selection and supporting drug development. Previously, the integrated glucose red blood cell-HbA1c (IGRH) model [5] has been used in combination with the integrated glucose/insulin (IGI) model to predict the late-phase outcome for a glucokinase activator [6]. The IGRH model takes daily average glucose concentrations (Cglc,av) as an input to predict HbA1c through the glycation of haemoglobin in red blood cells (RBCs)
We combined our previously developed 4GI systems model [3,4] with the IGRH model to predict the effects of GLP-1R and GLP-1R/glucagon-R receptor agonists on daily average glucose and HbA1c using PK and early in vitro efficacy information.
Methods: The 4GI systems model was calibrated on continuous glucose monitoring (CGM) data from the cotadutide Phase 2a study (D5670C00011, NCT03244800)[5]. System parameters remained fixed, while only parameters related to dietary and lifestyle changes were estimated. Model predictions on Cglc,av were compared with the calculated Cglc,av from the observed CGM data. Subsequently, the predicted Cglc,av served as input for the IGRH model to predict the impact of cotadutide on HbA1c. External validation involved predicting the in vivo effect, based on in vitro potency information, of both cotadutide and liraglutide on fasting plasma glucose and HbA1c in the cotadutide Phase 2b study (D5670C00004, NCT03235050)[6].
Results: Minimal 4GI model calibration to short-term cotadutide Ph2a CGM data enabled adequate daily average glucose concentration predictions. The resulting Cglc,av served as input for the IGRH model to predict the long-term effects on HbA1c. The combined 4GI-IGRH systems model effectively predicted the impact of cotadutide and liraglutide on fasting plasma glucose (FPG) (RMSPE 5.7%) and change from baseline HbA1c (RMSPE 13%) within a Phase 2b setting.
Conclusion: The 4GI-IGRH systems model was used in cotadutide's clinical development by providing predictive insights into the Phase 2b study prior to its initiation. The model accurately anticipated the effects of cotadutide and liraglutide on FPG and HbA1c based on in vitro efficacy information. Furthermore, it effectively depicted the dose-dependent impact of cotadutide on daily glucose data derived from CGM. This analysis shows the model's potential as a valuable tool in supporting the clinical development of cotadutide and existing and newly developed GLP-1R agonists.
References:
[1] Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c test in diagnosis and prognosis of diabetic patients. Biomark Insights [Internet]. Libertas Academica Ltd.; 2016 [cited 2023 Oct 12];11:95–104. Available from: https://www.idf.org/membership/mena/saudi-arabia.
[2] Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019. Results. Institute for Health Metrics and Evaluation. 2020 [Internet]. Available from: https://vizhub.healthdata.org/gbd-results/
[3] 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
[4] Bosch R, Petrone M, Arends R, Vicini P, Sijbrands EJG, Hoefman S, et al. Characterisation of cotadutide’s dual GLP‐1/glucagon receptor agonistic effects on glycaemic control using an in vivo human glucose regulation quantitative systems pharmacology model. Br J Pharmacol 2024; Available from: https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.16336
[5] Ambery PD, Klammt S, Posch MG, Petrone M, Pu W, Rondinone C, et al. MEDI0382, a GLP-1/glucagon receptor dual agonist, meets safety and tolerability endpoints in a single-dose, healthy-subject, randomized, Phase 1 study. Br J Clin Pharmacol. Blackwell Publishing Ltd; 2018;84:2325–35.
[6] Nahra R, Wang T, Gadde KM, Oscarsson J, Stumvoll M, Jermutus L, et al. Effects of cotadutide on metabolic and hepatic parameters in adults with overweight or obesity and type 2 diabetes: A 54-week randomized phase 2b study. Diabetes Care. 2021;44:1433–42.
