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

Abstract

Purpose: 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 pH6.5-7.4 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.

Keywords: In vitro–in vivo extrapolation; Renal excretion clearance; Tubular reabsorption.

Graphical abstract

Fig. 1

Schematic diagram of the minimal physiologically-based model for tubular reabsorption of drugs in the kidney. The nephron is represented by a glomerulus compartment in addition to four compartments representing different regions of the nephron tubule (proximal tubule (PT), loop of Henle (LoH), distal tubule (DT) and collecting duct (CD) in descending anatomical order). Physiological parameters, tubular flow rate (TFR_i) and tubular surface area (TSA_i), for each individual tubular region are indicated. Tubular filtrate flow (Q) is represented by grey arrows connecting tubular compartments and used to calculate TFR_i (average midpoint flow rates). The intrinsic reabsorption clearance of each individual region (CL_R,int,reab,i) is calculated using the corresponding TSA_i. Total renal excretion clearance (CL_R) is obtained from the filtration clearance (CL_R,filt) and the overall fraction reabsorbed (F_reab), by rearrangement of Eq. (9).

Fig. 2

Comparison of predicted and observed CL_R using CL_R,filt alone to predict CL_R. Neutral (), basic (), acidic (), zwitterion () and amphoteric () drugs are indicated. Solid and dashed lines represent line of unity and 3-fold error, respectively.

Fig. 3

Comparison of observed and predicted F_reab using the mechanistic tubular reabsorption model and P_app data obtained in Caco-2 cells. Panel A: Predicted relationship between F_reab and P_app is shown by the solid black line, whereas observed data are shown by symbols; Panel B: Predicted/observed F_reab are plotted as symbols, whereas solid and dashed black lines indicate Predicted/observed = 1 and 3-fold error, respectively. Symbols indicate neutral (), basic (), acidic (), zwitterion () and amphoteric () drugs. Drugs with negative values for observed F_reab are plotted as F_reab = 0 in Panel A, or predicted/observed F_reab = 100 in Panel B, as described in the Materials and methods.

Fig. 4

Comparison between observed and predicted CL_R by the mechanistic tubular reabsorption model. Symbols indicate neutral (), basic (), acidic (), zwitterion () and amphoteric () drugs respectively. Solid and dashed lines represent line of unity and 3-fold error, respectively. The inset shows the data for lower CL_R values for clarity.

Fig. 5

Best-fit curve and 90% confidence interval of the Hill equation to the P_app and F_reab′ data for 45 drugs (solid and dashed lines respectively). Symbols indicate neutral (), basic (), acidic (), zwitterion () and amphoteric () drugs. Drugs with negative values for observed F_reab′ are plotted as F_reab′ = 0, as described in the text.

Fig. 6

Prediction of CL_R using the minimal model following calibration of P_app data using reference drugs (n = 45 drugs). Symbols indicate neutral (), basic (), acidic (), zwitterion () and amphoteric () drugs in the internal ‘validation’ dataset and drugs in the reference dataset (●; n = 11). Solid and dashed lines represent line of unity and 3-fold error, respectively. The inset shows the data for lower CL_R values for clarity.