Ricardo Diaz De Leon Ortega1, Dannielle Ravenhill1, Zoe Kane1, Kevser Sevim1
1Quotient Sciences
Introduction: Rybelsus® (semaglutide) is an oral medication used to manage type 2 diabetes by improving blood sugar levels. It is an immediate release tablet formulated with sodium salcaprozate (SNAC). Semaglutide is a GLP-1 receptor agonist and one of its pharmacological effects is to slow gastric transit [1]. SNAC is a permeability enhancer which increases the absorption of poorly permeable compounds [2]. There are no CYP450 enzyme or transporter interactions reported for semaglutide [3]. SNAC has been found to inhibit OAT1 and OAT3 transporters, but these interactions have been reported as low risk [4]. Repeated co-administration of Rybelsus® with metformin had no impact on metformin’s Cmax (DDI ratio= 0.98, 90% confidence interval (CI) = 0.90 – 1.06) but led to an increase in AUC (1.32, 1.23 – 1.43) [5]. In contrast, when co-dosed with furosemide, furosemide’s Cmax decreased (0.66, 0.53 – 0.82) while AUC increased (1.28, 1.16 – 1.42) [6]. Physiologically based pharmacokinetic (PBPK) modelling can be used to mechanistically explore these interactions, and to predict the impact of co-administration of Rybelsus® on other compounds. Objectives: To use PBPK modelling to evaluate the effect of increased gastric transit time, caused by semaglutide, on the Cmax and AUC of metformin and furosemide. To use PBPK modelling to evaluate the effect of changes in permeability, caused by SNAC (permeability enhancer in Rybelsus® formulation), on the Cmax and AUC of metformin and furosemide. To use PBPK modelling to predict the effect of co-administering Rybelsus® on dipyridamole, phenytoin, ribociclib, atazanavir, dolugratevir, efavirenz, midazolam and warfarin. Methods: All the modelling and simulation work was performed using GastroPlus® X (Simulations Plus, Research Triangle Park, NC, USA). Published metformin [7,8] and furosemide [9] models were used to simulate the clinically observed DDI with Rybelsus® by varying gastric transit time (GTT) and effective jejunal permeability (Peff). Using published models for dipyridamole [10], phenytoin [11], ribociclib [12], and a subset of the models included in the GastroPlus® X DDI database (atazanavir, dolutegravir, efavirenz, midazolam and warfarin) the potential permeability and GTT induced interactions by Rybelsus® were predicted. Virtual populations were utilised for the DDI predictions: n=50 subjects, default parameter variability and distributions were applied throughout, except for GTT and Peff which were specified as following a log-normal distribution with a %CV of 30%. 90% CIs were calculated in Phoenix WinNonLin (Certara, UK). Results: The published models of metformin and furosemide predicted the observed PK parameters within ± 30% deviation (Cmax = –17%, AUCt = 0.1%, and Cmax = –20%, AUCt = 29%, respectively). GTT was investigated in a range of 0.25 to 2.75 h. Increasing GTT resulted in a decreased Cmax but there were no changes in AUC. Increasing Peff increased exposure. With a GTT of 2 h and a Peff of 140% of the original value, the DDI ratios and CIs were predicted for metformin: Cmax = 0.95 (0.84 – 1.07), AUCt = 1.22 (1.08 – 1.38), and for furosemide: Cmax = 0.74 (0.68 – 0.81), AUCt = 1.24 (1.13 – 1.35). Investigating the impact of increased GTT alone, the upper 90% CI for the Cmax DDI ratio decreased below 0.82 for dipyridamole, atazanavir, ribociclib, and midazolam. The AUCt DDI ratio decreased for atazanavir (0.83, 0.73 – 0.95) and midazolam (0.43, 0.35 – 0.53), and increased for efavirenz (1.15, 1.01 – 1.30) and dolutegravir (1.20, 1.08 –1.33). The combined effect of increased GTT and an increased Peff, resulted in the same trends, but with a slightly higher Cmax ratio than with just the GTT effect. This increment was only significant for Efavirenz (1.30, 1.16 – 1.45). No significant changes were predicted for phenytoin and warfarin. Conclusion: PBPK modelling is a useful tool to risk assess DDIs caused by changes in GTT and permeability that result from repeated dosing of Rybelsus®, highlighting drug combinations that may benefit from clinical investigation. Further work is needed to establish the magnitude of changes in GTT and intestinal permeability caused by semaglutide and SNAC.
[1] https://www.medicines.org.uk/emc/product/11507/smpc#gref, section 4.5 [2] Twarog C, et al. Pharmaceutics (2019) 11, 78 [3] https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/209637Orig1s000MedR.pdf [4] https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/213051Orig1s000PharmR.pdf [5] Jordy AB, et al. Clin Pharmacokinet. 2021 Sep;60(9):1171-1185. [6] Bækdal TA, et al. Clin Pharmacokinet. 2019 Sep;58(9):1193-1203. [7] Almukainzi, M. et al. Journal of Diabetes & Metabolism. 2014, 5:3. [8] Dahan A, et al. Pharmaceutics. 2021 Nov 5;13(11):1873 [9] Markovic M, et al. Pharmaceutics. 2020 Dec 2;12(12):1175. [10] Patel S, et al. J Pharm Sci. 2019 Jan;108(1):574-583 [11] Rodriguez-Vera L, et al. Pharmaceutics. 2023 Oct 18;15(10):2486. [12] Laisney M, et al. J Pharm Sci. 2022 Jan;111(1):274-284.
Reference: PAGE 33 (2025) Abstr 11408 [www.page-meeting.org/?abstract=11408]
Poster: Drug/Disease Modelling - Absorption & PBPK