Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance

Abstract

Renal clearance is usually predicted via empirical approaches including quantitative structure activity relationship and allometric scaling. Recently, mechanistic prediction approaches using in silico kidney models have been proposed. However, empirical scaling factors are typically used to adjust for either passive diffusion or active secretion, to acceptably predict renal clearances. The goal of this study was to establish a renal clearance simulation tool that allows prediction of renal clearance (filtration and pH‐dependent passive reabsorption) from in vitro permeability data. A 35‐compartment physiologically based mechanistic kidney model was developed based on human physiology. The model was verified using 46 test compounds, including neutrals, acids, bases, and zwitterions. The feasibility of incorporating active secretion and pH‐dependent bidirectional passive diffusion into the model was demonstrated using para‐aminohippuric acid (PAH), cimetidine, memantine, and salicylic acid. The developed model enables simulation of renal clearance from in vitro permeability data, with predicted renal clearance within twofold of observed for 87% of the test drugs.

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

“…Colon-derived Caco-2 and other in vitro cell lines differ from heterogeneous epithelial cells constituting the nephron tubule in terms of tight junctions (affecting para-cellular drug permeability), transporter expression, and presence of microvilli. To address the latter, one study used an empirical surface-area scaling factor to recapitulate CL R from in vitro permeability data using a 35-compartment model (Huang and Isoherranen, 2018). No empirical scaling factor was applied in the current study; instead, the IVIVE approach relied on physiologic assumptions, although verification of each of the specific parameter values has not yet been achieved.…”

“…In addition, some parameters may exhibit biologic variability that is not controlled or monitored in a typical clinical study; for example, urinary pH can range from 4.5 to 8, but it is generally slightly acidic (i.e., 5.5-7.0) because of metabolic activity (Simerville et al, 2005). Another challenge with such complex models is ensuring the identifiability of parameters as plasma concentration-time data may not always be informative for all model parameters (Hsu et al, 2014;Huang and Isoherranen, 2018), as discussed previously (Tsamandouras et al, 2015;Scotcher et al, 2017;Guo et al, 2018). All the preceding challenges are also applicable in the case of renal elimination, especially when attempting to separate quantitatively the roles of active transport and passive permeability to overall secretion and/or reabsorption (e.g., salicylic acid, creatinine).…”

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

“…In this study, we hypothesized that modeling techniques could be leveraged to understand and predict urine pH effect on drug and metabolite disposition. To test this hypothesis, a recently developed and verified dynamic physiologically-based mechanistic kidney model (Huang and Isoherranen, 2018) was integrated into a parent-metabolite full body physiologically based pharmacokinetic (PBPK) model (Huang and Isoherranen, 2020) to simulate urine pH dependent parent-metabolite systemic disposition and urinary excretion using methamphetamine and amphetamine as model compounds.…”

Emerging Role of Organ‐on‐a‐Chip Technologies in Quantitative Clinical Pharmacology Evaluation

“…At present, there are no good static systems to predict renal clearance due to the complex physiology of the kidney and the generation of concentration gradients and pH gradients in the kidneys during passive reabsorption of drugs and water from tubular lumen. Passive permeability measures from Caco‐2 or Manine‐Darby canine kidney (MDCK) cells are generally used to estimate and predict reabsorption clearance in the kidneys and can be used to populate PBPK models . This approach has many weaknesses, and, therefore, better in vitro models that closely mimic the processes observed in the human kidneys are needed to improve the validity of kidney PBPK models and predictions of renal clearance and renal transporter contribution to renal clearance.…”

“…Passive permeability measures from Caco-2 or Manine-Darby canine kidney (MDCK) cells are generally used to estimate and predict reabsorption clearance in the kidneys and can be used to populate PBPK models. 61,62 This approach has many weaknesses, and, therefore, better in vitro models that closely mimic the processes observed in the human kidneys are needed to improve the validity of kidney PBPK models and predictions of renal clearance and renal transporter contribution to renal clearance. The kidney-on-a-chip system that incorporates two fluid flow compartments that mimic the blood and tubular lumen in the kidneys separated by the layer of tubular cells may be the best experimental model to generate appropriate in vitro values for predicting and modeling renal clearance.…”