Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus

Masako Saito; Atsunori Kaibara; Takeshi Kadokura; Junko Toyoshima; Satoshi Yoshida; Kenichi Kazuta; Eiji Ueyama

doi:10.1007/s13300-020-00785-2

Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus

Diabetes Ther. 2020 Apr;11(4):951-964. doi: 10.1007/s13300-020-00785-2. Epub 2020 Mar 12.

Authors

Masako Saito¹, Atsunori Kaibara², Takeshi Kadokura², Junko Toyoshima², Satoshi Yoshida², Kenichi Kazuta², Eiji Ueyama²

Affiliations

¹ Astellas Pharma Inc., Tokyo, Japan. masako.saito@astellas.com.
² Astellas Pharma Inc., Tokyo, Japan.

Abstract

Introduction: Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term effects of ipragliflozin on clinical outcomes in patients with T2DM.

Methods: The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the maximum drug effect (E_max) model and disease progression model. As a predictive factor of drug effect, estimated ΔUGE was introduced into the E_max model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category.

Results: The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant effects on the glucose-lowering effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study.

Conclusions: Empirical models established based on the effect of UGE well predicted the renal function-dependent long-term glucose-lowering effects of ipragliflozin in patients with T2DM.

Keywords: Antidiabetic effect; Disease progression; Exposure–response; Ipragliflozin; SGLT2 inhibitor; Suglat; Type 2 diabetes mellitus.