ИСТИНА

Introduction: Dapagliflozin is a highly potent, sodium-glucose cotransporter 2 (SGLT2) selective inhibitor approved for use in patients with type 2 (T2DM) and type 1 diabetes mellitus to improve glycemic control. Inhibition of renal glucose reabsorption via SGLT2 increases urinary glucose excretion (UGE) and results in lowering of plasma glucose level and subsequent glycated haemoglobin (HbA1c) reduction. While dapagliflozin’s mechanism of action is independent of insulin, plasma glucose lowering by dapagliflozin would affect both endogenous insulin concentration and UGE. In patients with T2DM the regulation is complicated with the impairment of feedback control between glucose and insulin due to insulin resistance. Objectives: The aim of this work was to quantify the dynamic interplay of dapagliflozin, glucose, and insulin, understand how patient-specific characteristics determine response to dapagliflozin treatment and predict the long-term HbA1c response in patients with T2DM using quantitative systems pharmacology (QSP) mechanistic model. Methods: A QSP model of HbA1c response in patients with T2DM model was built through the integration of three previously published models: the integrated glucose-insulin (IGI) [1], the integrated glucose-red blood cell-HbA1c (IGRH) [2] and the renal glucose reabsorption models [3]. The IGI model was modified by including diurnal variation in endogenous glucose production to model plasma glucose dynamics during overnight periods. Parameters involved in glucose-insulin relationships like glucose (GSS) and insulin (ISS) steady states, slope of incretin effect and insulin-dependent glucose clearance as well as dapagliflozin PK were estimated. Subject-level plasma glucose, insulin, dapagliflozin concentrations and UGE data from phase IIa clinical trial of dapagliflozin treatment in patients with T2DM (NCT00162305 [4], n = 47, placebo or 5, 25, 100 mg QD for 14 days) were used for model development. Data on HbA1c profiles following 24 weeks of dapagliflozin treatment from four phase III studies (NCT00528879, n = 915, NCT00683878, n = 972, NCT00680745, n = 597 and NCT00673231, n = 1240, placebo or 2.5, 5, 10 mg) were used for model validation. The adequacy of developed models was evaluated by relative standard errors for parameter estimates, improvements in likelihood-based objective functions and visual inspection of goodness of fit plots. The effect of patient-level characteristics was evaluated through covariate search procedures. The Monolix software (version 2019R1) was used for non-linear mixed effects modeling. Data visualization and forward simulations were performed in R software (version 3.5.1). Results: A robust mechanistic integrated glucose-insulin-dapagliflozin (IGID) model was developed to describe the relationship between glucose-insulin dynamics and dapagliflozin treatment effects and predict the HbA1c response of dapagliflozin treatment in patients with T2DM. The model is able to describe daily variations in plasma insulin and glucose as well as UGE under the variety of dapagliflozin doses and meal scenarios. The integration of IGRH with IGI and renal glucose reabsorption models into a single quantitative framework allowed to seamlessly predict dynamic response in long-term glycemic control to dapagliflozin treatment as a function of haemoglobin and average daily glucose. Based on the model predictions, both higher GSS or lower ISS parameters (9.8 vs. 6.5 mmol/L or 4.57 vs. 9.14 mU/L respectively) were associated with greater HbA1c reduction (0.22 or 0.16 vs. 0.1%). HbA1c reduction under dapagliflozin treatment was shown to be interrelated with the rate of insulin-dependent glucose clearance to the tissues: for patients with 50% of the steady-state clearance corresponding to the 6.1 mU/L of ISS, the HbA1c the reduction was 0.41%, while for patients with 150% of the clearance rate, corresponding HbA1c response was 0.19%. Conclusion. In this work, we have proposed application of the QSP modeling approach to describe complex relationship between glucose-insulin dynamic and dapagliflozin treatment in patients with T2DM. The integrated model allows to predict both short-term glucose-insulin variations and long-term marker of glycemic control (HbA1c) following dapagliflozin administration. The results suggest that dapagliflozin treatment is beneficial in patients with inadequate glycemic control from insulin alone and this benefit increases as insulin control diminishes. References: [1] Jauslin PM, Silber HE, Frey N, et al. An integrated glucose-insulin model to describe oral glucose tolerance test data in type 2 diabetics. J Clin Pharmacol 2007;47(10):1244-1255. [2] Lledó-García R, Mazer NA, Karlsson MO. A semi-mechanistic model of the relationship between average glucose and HbA1c in healthy and diabetic subjects. J Pharmacokinet Pharmacodyn 2013;40(2):129-142. [3] Yakovleva T, Sokolov V, Chu L, et al. Comparison of the urinary glucose excretion contributions of SGLT2 and SGLT1: a quantitative systems pharmacology analysis in healthy individuals and patients with type 2 diabetes treated with SGLT2 inhibitors. Diabetes Obes Metab 2019;21(12):2684-2693. [4] Komoroski B, Vachharajani N, Feng Y, Li L, Kornhauser D, Pfister M. Dapagliflozin, a novel, selective SGLT2 inhibitor, improved glycemic control over 2 weeks in patients with type 2 diabetes mellitus. Clin Pharmacol Ther 2009;85(5):513-519.