Dataset Information

Pharmacokinetic and pharmacodynamic modelling for renal function dependent urinary glucose excretion effect of ipragliflozin, a selective sodium-glucose cotransporter 2 inhibitor, both in healthy subjects and patients with type 2 diabetes mellitus.

ABSTRACT:

Aims

To provide a model-based prediction of individual urinary glucose excretion (UGE) effect of ipragliflozin, we constructed a pharmacokinetic/pharmacodynamic (PK/PD) model and a population PK model using pooled data of clinical studies.

Methods

A PK/PD model for the change from baseline in UGE for 24 hours (?UGE_24h ) with area under the concentration-time curve from time of dosing to 24 h after administration (AUC_24h ) of ipragliflozin was described by a maximum effect model. A population PK model was also constructed using rich PK sampling data obtained from 2 clinical pharmacology studies and sparse data from 4 late-phase studies by the NONMEM $PRIOR subroutine. Finally, we simulated how the PK/PD of ipragliflozin changes in response to dose regime as well as patients' renal function using the developed model.

Results

The estimated individual maximum effect were dependent on fasting plasma glucose and renal function, except in patients who had significant UGE before treatment. The PK of ipragliflozin in type 2 diabetes mellitus (T2DM) patients was accurately described by a 2-compartment model with first order absorption. The population mean oral clearance was 9.47 L/h and was increased in patients with higher glomerular filtration rates and body surface area. Simulation suggested that medians (95% prediction intervals) of AUC_24h and ?UGE_24h were 5417 (3229-8775) ng·h/mL and 85 (51-145) g, respectively. The simulation also suggested a 1.17-fold increase in AUC_24h of ipragliflozin and a 0.76-fold in ?UGE_24h in T2DM patients with moderate renal impairment compared to those with normal renal function.

Conclusions

The developed models described the clinical data well, and the simulation suggested mechanism-based weaker antidiabetic effect in T2DM patients with renal impairment.

SUBMITTER: Saito M

PROVIDER: S-EPMC6624389 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pharmacokinetic and pharmacodynamic modelling for renal function dependent urinary glucose excretion effect of ipragliflozin, a selective sodium-glucose cotransporter 2 inhibitor, both in healthy subjects and patients with type 2 diabetes mellitus.

Saito Masako M Kaibara Atsunori A Kadokura Takeshi T Toyoshima Junko J Yoshida Satoshi S Kazuta Kenichi K Ueyama Eiji E

British journal of clinical pharmacology 20190620 8

<h4>Aims</h4>To provide a model-based prediction of individual urinary glucose excretion (UGE) effect of ipragliflozin, we constructed a pharmacokinetic/pharmacodynamic (PK/PD) model and a population PK model using pooled data of clinical studies.<h4>Methods</h4>A PK/PD model for the change from baseline in UGE for 24 hours (ΔUGE<sub>24h</sub> ) with area under the concentration-time curve from time of dosing to 24 h after administration (AUC<sub>24h</sub> ) of ipragliflozin was described by a m ...[more]

PMID: 31026084

Similar Datasets

Project description:IntroductionSodium-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.MethodsThe 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 (Emax) model and disease progression model. As a predictive factor of drug effect, estimated ΔUGE was introduced into the Emax 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.ResultsThe 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.ConclusionsEmpirical 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.

Dataset Information

Pharmacokinetic and pharmacodynamic modelling for renal function dependent urinary glucose excretion effect of ipragliflozin, a selective sodium-glucose cotransporter 2 inhibitor, both in healthy subjects and patients with type 2 diabetes mellitus.

Aims

Methods

Results

Conclusions

Publications

Pharmacokinetic and pharmacodynamic modelling for renal function dependent urinary glucose excretion effect of ipragliflozin, a selective sodium-glucose cotransporter 2 inhibitor, both in healthy subjects and patients with type 2 diabetes mellitus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets