Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance

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• 作者：Daniel Scotcher^a ; Christopher Jones^b ; Amin Rostami-Hodjegan^a ; ^c ; Aleksandra Galetin^a ; ^{Aleksandra.Galetin@manchester.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：AUC ; area under the plasma concentration-time profile ; BCRP ; breast cancer resistance protein ; CD ; collecting duct ; Cp ; plasma concentration ; CLR ; renal excretion clearance ; CLR ; filt ; renal filtration clearance ; CLR ; int ; reab ; intrinsic permeability clearance in renal tubule ; CLR ; sec ; renal secretion clearance ; DT ; distal tubule ; fu ; p ; fraction of drug unbound in plasma ; Freab ; fraction of the drug reabsorbed in the renal tubule ; Freab&rsquo ; intermediate model parameter ; representing the frac
• 刊名：European Journal of Pharmaceutical Sciences
• 出版年：2016
• 出版时间：30 October 2016
• 年：2016
• 卷：94
• 期：Complete
• 页码：59-71
• 全文大小：973 K

文摘

Develop a minimal mechanistic model based on in vitro–in vivo extrapolation (IVIVE) principles to predict extent of passive tubular reabsorption. Assess the ability of the model developed to predict extent of passive tubular reabsorption (F_reab) and renal excretion clearance (CL_R) from in vitro permeability data and tubular physiological parameters.

Methods

Model system parameters were informed by physiological data collated following extensive literature analysis. A database of clinical CL_R was collated for 157 drugs. A subset of 45 drugs was selected for model validation; for those, Caco-2 permeability (P_app) data were measured under pH 6.5–7.4 gradient conditions and used to predict F_reab and subsequently CL_R. An empirical calibration approach was proposed to account for the effect of inter-assay/laboratory variation in P_app on the IVIVE of F_reab.

Results

The 5-compartmental model accounted for regional differences in tubular surface area and flow rates and successfully predicted the extent of tubular reabsorption of 45 drugs for which filtration and reabsorption were contributing to renal excretion. Subsequently, predicted CL_R was within 3-fold of the observed values for 87% of drugs in this dataset, with an overall gmfe of 1.96. Consideration of the empirical calibration method improved overall prediction of CL_R (gmfe = 1.73 for 34 drugs in the internal validation dataset), in particular for basic drugs and drugs with low extent of tubular reabsorption.

Conclusions

The novel 5-compartment model represents an important addition to the IVIVE toolbox for physiologically-based prediction of renal tubular reabsorption and CL_R. Physiological basis of the model proposed allows its application in future mechanistic kidney models in preclinical species and human.