Arjun Kolappurath Madathil1, Shivaani E2, Amay Sareen1, BC Narmada1, Narendra Dixit2, Rukmini Kumar1
1Vantage Research Inc, 2Indian Institute of Sciences
Background & Motivation: Incretin-based therapies targeting hormones such as glucagon-like peptide-1 (GLP-1) have been effective in managing type 2 diabetes (T2D). These hormones play a crucial role in metabolic regulation by stimulating glucose-dependent insulin secretion, inhibiting glucagon release, slowing gastric emptying, and enhancing satiety. Recently GLP-1 receptor agonists (GLP1-RA) have emerged as exciting therapies for weight loss with additional positive effects on metabolic and overall health. Published workby Bosch et al. has shown the short and long-term effects of caloric intake on both body weight (1) and Glucose metabolism (2,3). Bosch’s 4GI model focuses on the interactions between glucose, glucagon, GLP-1, Gastric Inhibitory Polypeptide (GIP), and Insulin but does not account for energy intake, expenditure, or body weight. Conversely, Bosch’s adaptation of Hall’s body composition model incorporates weight loss predictions for GLP-1RAs but lacks key glucose regulatory elements like insulin and glucagon. These limitations hinder the ability to capture the full bidirectional relationship between weight loss and glycemic improvements, reducing their utility in assessing combination therapies. Objectives: To support drug development decisions, in this space, we propose a QSP model that integrates physiology of meal intake, energy expenditure, by integrating independent published models simulating weight gain/loss and glycemic control, and calibrating it to Semaglutide trial data in Type 2 Diabetics. This integrated model will be able to predict weight loss and glycemic control with caloric intake as input. Additionally, we would include the indirect yet important effect of weight loss-induced improvement in insulin sensitivity (7). We calibrate the model with a standard of care GLP-1 agonist Semaglutide as a proof of concept for both weight loss as well as glycemic control (5). Methods: Our first goal was to integrate the weight model and the 4GI energy models to simultaneously capture the effect of food intake on the key outcomes (reduction in glycated hemoglobin (HbA1c) and body weight loss) and their interactions in diabetic obese patients. In particular, to capture glucose-insulin dynamics, incretin effects (changes in fasting plasma glucose, fasting plasma insulin, fasting plasma glucagon), energy balance, and body weight regulation thereby the effects of GLP-1 RA for predicting their efficacy to optimize therapeutic outcomes. Then, we developed a 2-compartment model of GLP1-RA (with semaglutide) pharmacokinetics (PK). We modeled pharmacodynamic (PD) effects as being driven by plasma GLP-1 concentrations capturing their dose-dependent impact on food intake, gastric emptying, and insulin secretion, in line with observations reported in primary literature (4). These primary effects further result in the key efficacy outcome of body weight loss which was calibrated to fit clinical data at various doses (5,6). Additionally, we implemented an indirect effect of weight loss on HbA1c levels (via improvement in insulin sensitivity) by modifying the function of HbA1c in our model. This effect is well evidenced in literature wherein diet and exercise-induced weight loss has a significant impact on HbA1c aided by improved insulin sensitivity in these patients (7). The data showed that for every 1kg loss in body weight, there was a 0.1% reduction in HbA1c level. Results: The integrated model was tested with once weekly 1mg sub-cutaneously administered Semaglutide in diabetic obese patients. As seen in Heise et al 2022 (5) patient baseline characteristics, we generated a single virtual patient with baseline weight (92.38 kg), HbA1c (7.71%), fasting plasma glucose (128 mg/dl), fasting plasma insulin (52.4 pmol/L) and fasting plasma glucagon (12.3 pmol/L). The model efficiently captures the drug PK using the 2-compartment model, reduction in HbA1c to 6% over 28 weeks, and a 6.9kg decrease in body weight percentage over time with Semaglutide. We were also able to recapitulate the post therapy fasting plasma glucose, insulin and glucagon as reported by Heise (5). Our model thus captures drug PK, PD, and downstream effects on key clinical readouts weight loss, and HbA1c for Semaglutide. Conclusion: The model is able to capture PK and more importantly both glycemia-related PD outcomes as well as body weight loss in obese T2D patients treated with Semaglutide. In order to improve the utility of the model in drug development, several additional features are being added. In particular, a population approach that enables simulations of clinical trials and virtual populations of non-diabetic obese and diabetic obese people is under development to capture varied responses in specific populations. Further, other drugs in this class including GLP/GIP combinations (e.g. Tirzepatide), are being tested to improve model reliability and enhance the scope of the model application.
Hall KD. Predicting metabolic adaptation, body weight change, and energy intake in humans. Am J Physiol Endocrinol Metab. 2010 Mar;298(3):E449-66. doi: 10.1152/ajpendo.00559.2009. Epub 2009 Nov 24. PMID: 19934407; PMCID: PMC2838532. Silber, H. E., Jauslin, P. M., Frey, N., Gieschke, R., Simonsson, U. S. H., & Karlsson, M. O. (2007). An integrated model for glucose and insulin regulation in healthy volunteers and type 2 diabetic patients following intravenous glucose provocations. Journal of Clinical Pharmacology, 47(9), 1159–1171. https://doi.org/10.1177/0091270007304457 Bosch, R., Petrone, M., Arends, R., Vicini, P., Sijbrands, E. J. G., Hoefman, S., & Snelder, N. (2022). A novel integrated QSP model of in vivo human glucose regulation to support the development of a glucagon/GLP-1 dual agonist. CPT: Pharmacometrics & Systems Pharmacology, 11(3), 302–317. https://doi.org/10.1002/psp4.12752 Blundell J, Finlayson G, Axelsen M, Flint A, Gibbons C, Kvist T, Hjerpsted JB. Effects of once-weekly semaglutide on appetite, energy intake, control of eating, food preference and body weight in subjects with obesity. Diabetes Obes Metab. 2017 Sep;19(9):1242-1251. doi: 10.1111/dom.12932. Epub 2017 May 5. PMID: 28266779; PMCID: PMC5573908. Heise T, Mari A, DeVries JH, Urva S, Li J, Pratt EJ, Coskun T, Thomas MK, Mather KJ, Haupt A, Milicevic Z. Effects of subcutaneous tirzepatide versus placebo or semaglutide on pancreatic islet function and insulin sensitivity in adults with type 2 diabetes: a multicentre, randomised, double-blind, parallel-arm, phase 1 clinical trial. Lancet Diabetes Endocrinol. 2022 Jun;10(6):418-429. doi: 10.1016/S2213-8587(22)00085-7. Epub 2022 Apr 22. PMID: 35468322. Davies M, Færch L, Jeppesen OK, Pakseresht A, Pedersen SD, Perreault L, Rosenstock J, Shimomura I, Viljoen A, Wadden TA, Lingvay I; STEP 2 Study Group. Semaglutide 2·4 mg once a week in adults with overweight or obesity, and type 2 diabetes (STEP 2): a randomised, double-blind, double-dummy, placebo-controlled, phase 3 trial. Lancet. 2021 Mar 13;397(10278):971-984. doi: 10.1016/S0140-6736(21)00213-0. Epub 2021 Mar 2. PMID: 33667417. Gummesson A, Nyman E, Knutsson M, Karpefors M. Effect of weight reduction on glycated haemoglobin in weight loss trials in patients with type 2 diabetes. Diabetes Obes Metab. 2017 Sep;19(9):1295-1305. doi: 10.1111/dom.12971. Epub 2017 May 22. PMID: 28417575.
Reference: PAGE 33 (2025) Abstr 11585 [www.page-meeting.org/?abstract=11585]
Poster: Drug/Disease Modelling - Other Topics