Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption.
Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption.
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DOI：
10.1002/psp4.12553
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Isoherranen N
中科院分区：
文献类型：
--
作者：
Huang W;Isoherranen N
慢性肾脏疾病（CKD）对药物的肾脏清除率（CLr）有显著影响。基于生理的药代动力学（PBPK）模型已被用于预测CKD对转运蛋白介导的肾活性分泌和亲水性非渗透性化合物的CLr的影响。然而，没有研究显示PBPK系统模拟CKD中疏水渗透药物的肾脏被动重吸收或CLr。本研究的目的是扩展我们之前开发和验证的机械肾脏模型，以建立一个通用模型来预测CKD中通透性和非通透性药物的CLr变化，该模型解释了CKD对通透性药物肾脏被动重吸收的巨大非线性影响。开发的模型结合了CKD中每个剩余功能肾元的水重吸收减少/管状流速增加的生理基础管状变化。最终的适应性肾脏模型成功地（绝对折叠误差（AFE）均< 2）预测了20种可渗透和不可渗透试验化合物在CKD各阶段的肾脏被动重吸收和CLr。相比之下，使用比例肾小球滤过率降低方法而不考虑CKD中的小管适应过程来预测CLr，对严重CKD中渗透性化合物的CLr预测结果是不可接受的（AFE = 2.61-7.35）。最后，适应性肾脏模型准确预测了CKD中对氨基马尿酸和美金刚这两种分泌化合物的CLr，这表明该模型成功地将主动分泌与被动重吸收结合在一起。总之，开发的适应性肾脏模型可以通过CKD进展来预测体内CLr的机制，而无需任何经验比例因子，并且可以在评估肾脏损害的药物处置之前用于CLr预测。
Chronic kidney disease (CKD) has significant effects on renal clearance (CLr) of drugs. Physiologically‐based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter‐mediated renal active secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CLr in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically‐based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CLr for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CLr generated unacceptable CLr predictions (AFE = 2.61–7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of para‐amino‐hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CLr through CKD progression without any empirical scaling factors and can be used for CLr predictions prior to assessment of drug disposition in renal impairment.
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