RT Journal Article
SR Electronic
T1 Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance
JF Journal of Pharmacology and Experimental Therapeutics
JO J Pharmacol Exp Ther
FD American Society for Pharmacology and Experimental Therapeutics
SP jpet.118.251413
DO 10.1124/jpet.118.251413
A1 Takanobu Matsuzaki
A1 Daniel Scotcher
A1 Adam Darwich
A1 Aleksandra Galetin
A1 Amin Rostami-Hodjegan
YR 2018
UL http://jpet.aspetjournals.org/content/early/2018/11/09/jpet.118.251413.abstract
AB In vitro-in vivo extrapolation (IVIVE) of renal excretory clearance (CLR) 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 quantitatively capture the impact of perturbed conditions (e.g., urine flow/ urine pH changes) on CLR has not been fully evaluated. The current work aimed to assess the predictability of the effect of urine flow and urine pH on CLR and tubular drug concentrations (selected examples). Passive diffusion clearance across the nephron tubule membrane was scaled from in vitro Caco-2 permeability data by nephron tubular surface area to predict fraction reabsorbed and CLR of caffeine, chloramphenicol, creatinine, dextroamphetamine, nicotine, sulfamethoxazole and theophylline. CLR values predicted using mechanistic kidney model at 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 CLR, albeit with minor under-prediction of the observed magnitude of change. The relationship between drug solubility, urine flow and urine pH, illustrated in simulated intra-tubular concentrations of acyclovir and sulfamethoxazole, was in agreement 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 CLR and drug tubular concentrations with the aim to facilitate refinement of IVIVE-based mechanistic prediction of renal excretion.