首页> 外文期刊>British Journal of Clinical Pharmacology >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
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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

机译:肾功能依赖性尿葡萄糖排泄效应,一种选择性钠 - 葡萄糖基因力2抑制剂,两者在健康受试者和2型糖尿病患者中的肾功能依赖性尿葡萄糖排泄效果

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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 (ΔUGE 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 ΔUGE 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 ΔUGE 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.
机译:目的是提供一种基于模型的Ipragliflozin的尿葡萄糖排泄(UGE)效应的模型预测,我们构建了使用临​​床研究的汇集数据的药代动力学/药物动力学(PK / PD)模型和群体PK模型。方法通过最大效果模型描述了从给药后给药(AUC 24H)后的浓度 - 时间曲线中的24小时(ΔUGE24H)的34小时(ΔUGE24H)的PK / PD模型。 。还使用从2个临床药理学研究中获得的丰富的PK采样数据和来自NONMEM $之前的子程序的4个后期研究获得的富PK采样数据构建了人口PK模型。最后,我们模拟了使用开发模型的响应剂量制度的PK / PD的PK / PD如何变化。结果估计的个体最大效果依赖于禁食血浆葡萄糖和肾功能,但在治疗前具有显着uge的患者外。通过一级吸收的2室模型精确地描述了2型糖尿病患者(T2DM)患者的IPragliflozin的PK。人口平均清除是9.47?L / h,肾小球过滤率较高的患者患者增加。模拟表明,AUC 24H和ΔUGE24H的中位数(95%预测间隔)分别为5417(3229-875)ng·h / ml和85(51-145)g。仿真还提出了IPragliflozin的AUC 24H的117倍,T2DM患者Δuge24h中的0.76倍,与具有正常肾功能的肾功能相比,肾功能较高。结论开发模型描述了临床资料良好,并仿真提出了基于机制的肾脏损伤患者T2DM患者的弱化抗糖尿病效应。

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