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.