Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance

Abstract

In vitro-in vivo extrapolation (IVIVE) of renal excretory clearance (CL R ) using the physiologically based kidney models can provide mechanistic insight into the interplay of multiple processes occurring in the renal tubule; however, the ability of these models to capture quantitatively the impact of perturbed conditions (e.g., urine flow, urine pH changes) on CL R has not been fully evaluated. In this work, we aimed to assess the predictability of the effect of urine flow and urine pH on CL R and tubular drug concentrations (selected examples). Passive diffusion clearance across the nephron tubule membrane was scaled from in vitro human epithelial cell line Caco-2 permeability data by nephron tubular surface area to predict the fraction reabsorbed and the CL R of caffeine, chloramphenicol, creatinine, dextroamphetamine, nicotine, sulfamethoxazole, and theophylline. CL R values predicted using mechanistic kidney model at a urinary pH of 6.2 and 7.4 resulted in prediction bias of 2.87-and 3.62-fold, respectively. Model simulations captured urine flow-dependent CL R , albeit with minor underprediction of the observed magnitude of change. The relationship between drug solubility, urine flow, and urine pH, illustrated in simulated intratubular concentrations of acyclovir and sulfamethoxazole, agreed with clinical data on tubular precipitation and crystal-induced acute kidney injury. This study represents the first systematic evaluation of the ability of the mechanistic kidney model to capture the impact of urine flow and urine pH on CL R and drug tubular concentrations with the aim of facilitating refinement of IVIVE-based mechanistic prediction of renal excretion.

Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine

“…Conversely, our model accurately recapitulated the renal excretion of methamphetamine and amphetamine under acidic (urine pH = 5.0), uncontrolled (urine pH = 6.5), and alkaline (urine pH = 8.0) urine conditions ( Figure 6), and was previously shown to capture the varying renal clearance of salicylic acid and memantine as a function of urine pH (Huang and Isoherranen, 2018). We confirmed the importance of the stepwise gradient via an in-house head-to-head comparison of the urinary methamphetamine excretion using our stepwise pH gradients and the previously published (Matsuzaki et al, 2019) constant pH value. The constant pH value approach failed to recapitulate the observed data under uncontrolled (AAFE = 2.2 or 6.5) and alkaline urine condition (AAFE = 13.6) ( Supplementary Figure 5) while the stepwise pH gradient approach successfully (AAFE < 2) simulated methamphetamine urinary excretion ( Supplementary Figure 4).…”

“…This better performance could be due to our strategy to use a stepwise gradient for renal tubular filtrate pH to account for the naturally continuous acidification process of tubular filtrate, although other differences such as microvilli consideration and a larger number (11 vs 7) of tubular compartments in our model may also contribute to our better performance. Further, the previous study (Matsuzaki et al, 2019) showed a relatively insensitive response of simulated renal clearance to urine pH changes. For example, their simulated amphetamine renal clearance did not change with urine pH between 5 and 8, which is inconsistent with the observed dramatic changes (Beckett and Rowland, 1965a;Beckett et al, 1969).…”

“…Recently, the MechKiM model embedded in the Simcyp platform was used to predict renal clearance and urine pH effects on renal clearance (Matsuzaki et al, 2019). In that study, seven compounds were used as the test set to examine model performance, and the simulations were conducted assuming uniform renal tubular filtrate pH throughout all renal segments.…”

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

“…This trend was evident also in the static Nakada model, which required a fraction re‐absorbed of 34% to recover creatinine‐trimethoprim interaction data; value that was much higher than < 10% predicted by available mechanistic re‐absorption models 40 . So far, limited evidence supports possibility of saturable tubular reabsorption of creatinine, 41 whereas reports of urine flow dependent CL R or creatinine‐to‐inulin clearance ratio have inconsistent findings 42–44 . One potential candidate for creatinine tubular re‐absorption is OAT4 expressed on the apical membrane of the proximal tubule, 45 although relevant in vitro data to support this are equivocal 10,15 .…”

A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations

“…3-6. 27 Membrane permeability (P mem ; cm/second × 10 −6 ) was calculated from the P app after assuming resistance across the cell is sum of resistances across each membrane and that apical and basolateral membranes had equal permeability. P mem and…”