Physiologically based pharmacokinetic model of sodium-glucose cotransporter 2 inhibitors predicted pharmacokinetics and pharmacodynamics to explore dosage regimen for patients with type 2 diabetes mellitus and renal insufficiency.

Journal: Frontiers In Pharmacology
Published:
Abstract

This study aimed to compare the hypoglycemic effects of four SGLT2 inhibitors (dapagliflozin, canagliflozin, empagliflozin, and ipragliflozin), simulate the 24-h urinary glucose excretion (UGE) of these inhibitors in T2DM patients with renal insufficiency, and investigate optimal dosage regimen for the SGLT2 inhibitor in these patients. We established a physiologically based pharmacokinetic (PBPK) model of SGLT2 inhibitors using the PK-Sim software, and the renal physiological tissue structure was expanded to include renal tubules using the MoBi software. The PBPK/PD (pharmacodynamics) model of SGLT2 inhibitors was validated following comparison of the observed plasma concentration and pharmacokinetic parameters. The model simulation results showed that 71.4% of the predicted pharmacokinetic parameters AUC (area under the curve) and Cmax (peak concentration) closely matched the observed values within 0.8-1.3 folds accuracy. Further, 83.9% of the predicted concentration-time curves and 84.65% of the predicted 24-h urinary glucose excretion aligned with the observed data points within 0.5-2 folds accuracy. The MPE, AFE and AAFE values for all concentration-time data points were 0.90, 1.07 and 1.08, indicating that the predictive performance of the PBPK/PD model was robust and reliable. It was predicted that optimal hypoglycemic effects would be achieved in T2DM patients with mild, moderate, and severe renal insufficiency, when treated with ipragliflozin 50 mg qd, dapagliflozin 10 mg qd or canagliflozin 100 mg qd, empagliflozin 10 mg, respectively. This study provided a scientific basis for optimizing the dosage regimen in T2DM patients with renal insufficiency.

Authors
Guimu Guo, Meng Ke, Jianwen Xu, Wanhong Wu, Jiarui Chen, Chengjie Ke, Pinfang Huang, Cuihong Lin
Relevant Conditions

Type 2 Diabetes (T2D)