Amplifying the impact of kidney microphysiological systems: predicting renal drug clearance using mechanistic modelling based on reconstructed drug secretion

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Pedro Caetano-Pinto
Pär Nordell
Tom Nieskens
Katie Haughan
Katherine S. Fenner
Simone H. Stahl

Abstract

Accurate prediction of pharmacokinetic parameters such as renal clearance is fundamental to the development of effective and safe new treatments for patients. However, conventional renal models have a limited ability to predict renal drug secretion, a process that is dependent on transporters in the proximal tubule. Improvements in microphysiological systems (MPS) have extended our in vitro capabilities to predict pharmacokinetic parameters. In this study a kidney-MPS model was developed that successfully recreated renal drug secretion. Human proximal tubule cells grown in the kidney-MPS, resembling an in vivo phenotype, actively secreted organic cation drug metformin and organic anion drug cidofovir, in contrast to cells cultured in conventional culture formats. Metformin and cidofovir renal secretory clearance were predicted from kidney-MPS data within 3.3- and 1.3-fold, respectively, of clinically reported values employing a semi-mechanistic drug distribution model, using kidney-MPS drug transport parameters together with in vitro to in vivo extrapolation. This approach introduces an effective application of a kidney-MPS model coupled with pharmacokinetic modelling tools to evaluate and predict renal drug clearance in humans. Kidney-MPS renal clearance predictions can potentially complement pharmacokinetic animal studies and contribute to the reduction of pre-clinical species use during pre-clinical drug development.

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How to Cite
Caetano-Pinto, P., Nordell, P. ., Nieskens, T., Haughan, K., Fenner, K. S. and Stahl, S. H. (2022) “Amplifying the impact of kidney microphysiological systems: predicting renal drug clearance using mechanistic modelling based on reconstructed drug secretion”, ALTEX - Alternatives to animal experimentation. doi: 10.14573/altex.2204011.
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