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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.

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