Nils Bundgaard1, Malin Andersson1, Niels Rode Kristensen1
1Novo Nordisk A/S
Introduction: Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has emerged as a significant advancement in the treatment of type 2 diabetes (T2D). Semaglutide works by mimicking the incretin hormones, which increase insulin secretion in response to meals, thereby improving glycaemic control. Semaglutide is available in both subcutaneous and oral formulations, offering flexibility in administration. Semaglutide has shown significant effects on various biomarkers in patients with T2D, reflecting its multifaceted benefits beyond glycaemic control. One of the notable effects of semaglutide is the significant reduction of body weight. Other biomarkers, such as LDL-cholesterol, triglycerides, and blood pressure, which are commonly associated with outcomes such as cardiovascular disease (CVD), are also positively affected by treatment with semaglutide. Long-term simulations of biomarkers may help predict disease outcomes such as CVD. Here, we describe the framework to estimate the long-term treatment effects of semaglutide on various biomarkers in patients with T2D. Method: Indirect response models have been used successfully to describe pharmacodynamic responses [1]. Single biomarker models were developed with the following model development strategy on the SUSTAIN 1-6 trials: First, an indirect response model was developed on the placebo data to allow for the estimation of disease progression. Next, a full pharmacodynamic model, including covariate selection, was developed on the full data set, using the average concentration of semaglutide over one week to inhibit the production of the respective biomarker. Notably, biomarkers exhibited high variability in observations (i.e., HbA1C, blood pressure). To facilitate model estimation, an individual epsilon was estimated for each patient to accommodate the observed high variability. Results: Indirect response models were developed for multiple single biomarkers for T2D patients treated with once-weekly semaglutide (0.5 mg or 1 mg). The estimation of individual epsilon error was superior to the estimation of a population-based error model across all biomarkers. The individual biomarker models were validated using visual predictive checks, which exhibited an adequate fit across all trials and treatment groups. Conclusion: Single biomarker models were developed to describe the long-term effects of semaglutide treatment in T2D patients. The indirect response models were adequate to describe the long-term effect of semaglutide treatment up to 104 weeks post-treatment. Longer simulations must be evaluated with care, as disease progression may not be fully captured based on limited data availability. These models may help predict the long-term benefit in preventing severe disease outcomes such as CVD in T2D patients treated with Semaglutide. The simulation of these long-term effects may be enhanced by joint estimation of biomarkers.
[1] Dayneka, N.L., Garg, V. & Jusko, W.J. Comparison of four basic models of indirect pharmacodynamic responses. Journal of Pharmacokinetics and Biopharmaceutics 21, 457–478 (1993). https://doi.org/10.1007/BF01061691
Reference: PAGE 33 (2025) Abstr 11357 [www.page-meeting.org/?abstract=11357]
Poster: Drug/Disease Modelling - Endocrine